US20090098543A1 - Gene methylation in lung cancer diagnosis - Google Patents

Gene methylation in lung cancer diagnosis Download PDF

Info

Publication number
US20090098543A1
US20090098543A1 US12/024,701 US2470108A US2009098543A1 US 20090098543 A1 US20090098543 A1 US 20090098543A1 US 2470108 A US2470108 A US 2470108A US 2009098543 A1 US2009098543 A1 US 2009098543A1
Authority
US
United States
Prior art keywords
methylation
dna
ha1p
threshold value
cancer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US12/024,701
Inventor
Muhammad A. Budiman
Jeffrey A. Jeddeloh
Rebecca Maloney
Yulia Korshunova
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Orion Genomics LLC
Original Assignee
Orion Genomics LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Orion Genomics LLC filed Critical Orion Genomics LLC
Priority to US12/024,701 priority Critical patent/US20090098543A1/en
Assigned to ORION GENOMICS LLC reassignment ORION GENOMICS LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: JEDDELOH, JEFFREY A., BUDIMAN, MOHAMMAD A., KORSHUNOVA, YULIA, MALONEY, REBECCA
Publication of US20090098543A1 publication Critical patent/US20090098543A1/en
Abandoned legal-status Critical Current

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • Human cancer cells typically contain somatically altered genomes, characterized by mutation, amplification, or deletion of critical genes.
  • the DNA template from human cancer cells often displays somatic changes in DNA methylation. See, e.g., E. R. Fearon, et al, Cell 61:759 (1990); P. A. Jones, et al., Cancer Res. 46:461 (1986); R. Holliday, Science 238:163 (1987); A. De Bustros, et al., Proc. Natl. Acad. Sci. USA 85:5693 (1988); P. A. Jones, et al., Adv. Cancer Res. 54:1 (1990); S. B.
  • DNA methylases transfer methyl groups from the universal methyl donor S-adenosyl methionine to specific sites on the DNA.
  • Several biological functions have been attributed to the methylated bases in DNA. The most established biological function is the protection of the DNA from digestion by cognate restriction enzymes. This restriction modification phenomenon has, so far, been observed only in bacteria.
  • Mammalian cells possess different methylases that exclusively methylate cytosine residues on the DNA that are 5′ neighbors of guanine (CpG). This methylation has been shown by several lines of evidence to play a role in gene activity, cell differentiation, tumorigenesis, X-chromosome inactivation, genomic imprinting and other major biological processes (Razin, A., H., and Riggs, R. D. eds. in DNA Methylation Biochemistry and Biological Significance , Springer-Verlag, N.Y., 1984).
  • methylation of cytosine residues that are immediately 5′ to a guanosine occurs predominantly in CpG poor loci (Bird, A., Nature 321:209 (1986)).
  • CpG islands discrete regions of CG dinucleotides called CpG islands (CGi) remain unmethylated in normal cells, except during X-chromosome inactivation and parental specific imprinting (Li, et al., Nature 366:362 (1993)) where methylation of 5′ regulatory regions can lead to transcriptional repression.
  • de novo methylation of the Rb gene has been demonstrated in a small fraction of retinoblastomas (Sakai, et al., Am. J.
  • VHL gene shows aberrant methylation in a subset of sporadic renal cell carcinomas (Herman, et al., Proc. Natl. Acad. Sci. U.S.A., 91:9700 (1994)).
  • Expression of a tumor suppressor gene can also be abolished by de novo DNA methylation of a normally unmethylated 5′ CpG island. See, e.g., Issa, et al., Nature Genet. 7:536 (1994); Merlo, et al., Nature Med.
  • the present invention provides methods for determining the methylation status of an individual.
  • the methods comprise:
  • the methods comprise determining the presence or absence of cancer, including but not limited to, bladder, breast, cervical, colon, endometrial, esophageal, head and neck, liver, lung, melanoma, ovarian, prostate, renal, and thyroid cancer, in an individual.
  • cancer including but not limited to, bladder, breast, cervical, colon, endometrial, esophageal, head and neck, liver, lung, melanoma, ovarian, prostate, renal, and thyroid cancer, in an individual.
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the methods comprise:
  • the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker, wherein the biomarker is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346
  • the determining step comprises determining the methylation status of the DNA region corresponding to a biomarker.
  • the sample can be from any body fluid.
  • the sample is selected from blood serum, blood plasma, fine needle aspirate of the breast, biopsy of the breast, ductal fluid, ductal lavage, feces, urine, sputum, saliva, semen, lavages, or tissue biopsy, such as biopsy of the lung, bronchial lavage or bronchial brushings in the case of lung cancer.
  • the sample is from a tumor or polyp.
  • the sample is a biopsy from lung, kidney, liver, ovarian, head, neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate or skin tissue.
  • the sample is from cell scrapes, washings, or resected tissues.
  • the methylation status of at least one cytosine is compared to the methylation status of a control locus.
  • the control locus is an endogenous control. In some embodiments, the control locus is an exogenous control.
  • the determining step comprises determining the methylation status of at least one cytosine in at least two of the DNA regions.
  • the invention provides computer implemented methods for determining the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in an individual.
  • the methods comprise:
  • the receiving step comprises receiving at least two methylation values, the two methylation values representing the methylation status of at least one cytosine biomarkers from two different DNA regions;
  • the invention provides computer program products for determining the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in an individual.
  • the computer readable products comprise:
  • kits for determining the methylation status of at least one biomarker comprise:
  • the pair of polynucleotides are capable of specifically amplifying a biomarker selected from the group consisting of one or more of SEQ ID NOs: 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373,
  • kits comprise at least two pairs of polynucleotides, wherein each pair is capable of specifically amplifying at least a portion of a different DNA region.
  • kits further comprise a detectably labeled polynucleotide probe that specifically detects the amplified biomarker in a real time amplification reaction.
  • kits for determining the methylation status of at least one biomarker comprise:
  • kits for determining the methylation status of at least one biomarker comprise:
  • kits for determining the methylation status of at least one biomarker comprise:
  • kits for determining the methylation status of at least one biomarker comprise:
  • Methods refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine or other types of nucleic acid methylation.
  • In vitro amplified DNA is unmethylated because in vitro DNA amplification methods do not retain the methylation pattern of the amplification template.
  • unmethylated DNA or “methylated DNA” can also refer to amplified DNA whose original template was methylated or methylated, respectively.
  • a “methylation profile” refers to a set of data representing the methylation states of one or more loci within a molecule of DNA from e.g., the genome of an individual or cells or tissues from an individual.
  • the profile can indicate the methylation state of every base in an individual, can comprise information regarding a subset of the base pairs (e.g., the methylation state of specific restriction enzyme recognition sequence) in a genome, or can comprise information regarding regional methylation density of each locus.
  • “Methylation status” refers to the presence, absence and/or quantity of methylation at a particular nucleotide, or nucleotides within a portion of DNA.
  • the methylation status of a particular DNA sequence e.g., a DNA biomarker or DNA region as described herein
  • the methylation status can optionally be represented or indicated by a “methylation value.”
  • a methylation value can be generated, for example, by quantifying the amount of intact DNA present following restriction digestion with a methylation dependent restriction enzyme.
  • a value i.e., a methylation value, for example from the above described example, represents the methylation status and can thus be used as a quantitative indicator of methylation status. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold value.
  • a “methylation-dependent restriction enzyme” refers to a restriction enzyme that cleaves or digests DNA at or in proximity to a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is not methylated.
  • Methylation-dependent restriction enzymes include those that cut at a methylated recognition sequence (e.g., DpnI) and enzymes that cut at a sequence near but not at the recognition sequence (e.g., McrBC).
  • McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where “R” is a purine and “mC” is a methylated cytosine and “N40-3000” indicates the distance between the two RmC half sites for which a restriction event has been observed.
  • McrBC generally cuts close to one half-site or the other, but cleavage positions are typically distributed over several base pairs, approximately 30 base pairs from the methylated base. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of both half sites, and sometimes between the two sites.
  • Exemplary methylation-dependent restriction enzymes include, e.g., McrBC (see, e.g., U.S. Pat. No.
  • a “methylation-sensitive restriction enzyme” refers to a restriction enzyme that cleaves DNA at or in proximity to an unmethylated recognition sequence but does not cleave at or in proximity to the same sequence when the recognition sequence is methylated.
  • Exemplary methylation-sensitive restriction enzymes are described in, e.g., McClelland et al., Nucleic Acids Res. 22(17):3640-59 (1994) and http://rebase.neb.com.
  • Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when a cytosine within the recognition sequence is methylated at position C 5 include, e.g., Aat II, Aci I, Acl I, Age I, Alu I, Asc I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II, BssK I, BstB I, BstN I, BstU I, Cla I, Eae L, Eag L, Fau I, Fse I, Hha I, HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II
  • Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N 6 include, e.g., Mbo I.
  • any methylation-sensitive restriction enzyme including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention.
  • a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence.
  • a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence.
  • Sau3AI is sensitive (i.e., fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near its recognition sequence.
  • methylation-sensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.
  • a “threshold value that distinguishes between individuals with and without” a particular disease refers to a value or range of values of a particular measurement that can be used to distinguish between samples from individuals with the disease and samples without the disease.
  • a threshold value or values that absolutely distinguishes between the two groups i.e., values from the diseased group are always on one side (e.g., higher) of the threshold value and values from the healthy, non-diseased group are on the other side (e.g., lower) of the threshold value.
  • threshold values do not absolutely distinguish between diseased and non-diseased samples (for example, when there is some overlap of values generated from diseased and non-diseased samples).
  • corresponding to a nucleotide in a biomarker refers to a nucleotide in a DNA region that aligns with the same nucleotide (e.g. a cytosine) in a biomarker sequence.
  • biomarker sequences are subsequences of (i.e., have 100% identity with) the DNA regions. Sequence comparisons can be performed using any BLAST including BLAST 2.2 algorithm with default parameters, described in Altschul et al., Nuc. Acids Res. 25:3389 3402 (1977) and Altschul et al., J. Mol. Biol. 215:403 410 (1990), respectively.
  • “Sensitivity” of a given biomarker refers to the percentage of tumor samples that report a DNA methylation value above a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:
  • Sensitivity [ ( the ⁇ ⁇ number ⁇ ⁇ of ⁇ ⁇ tumor ⁇ ⁇ samples above ⁇ ⁇ the ⁇ ⁇ threshold ) ( the ⁇ ⁇ total ⁇ ⁇ number ⁇ ⁇ of ⁇ ⁇ tumor ⁇ ⁇ samples ⁇ ⁇ tested ) ] ⁇ 100
  • Sensitivity [ ( the ⁇ ⁇ number ⁇ ⁇ of ⁇ ⁇ true ⁇ ⁇ positive ⁇ ⁇ samples ) ( the ⁇ ⁇ number ⁇ ⁇ of ⁇ ⁇ true ⁇ ⁇ positive ⁇ ⁇ samples ) + ( the ⁇ ⁇ number ⁇ ⁇ of ⁇ ⁇ false ⁇ ⁇ negative ⁇ ⁇ samples ) ] ⁇ 100
  • true positive is defined as a histology-confirmed tumor sample that reports a DNA methylation value above the threshold value (i.e. the range associated with disease)
  • false negative is defined as a histology-confirmed tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease).
  • the value of sensitivity therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known diseased sample will be in the range of disease-associated measurements.
  • the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given biomarker would detect the presence of a clinical condition when applied to a patient with that condition.
  • Specificity of a given biomarker refers to the percentage of non-tumor samples that report a DNA methylation value below a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:
  • true negative is defined as a histology-confirmed non-tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease)
  • false positive is defined as a histology-confirmed non-tumor sample that reports DNA methylation value above the threshold value (i.e. the range associated with disease).
  • the value of specificity therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known non-diseased sample will be in the range of non-disease associated measurements.
  • the clinical relevance of the calculated specificity value represents an estimation of the probability that a given biomarker would detect the absence of a clinical condition when applied to a patient without that condition.
  • Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always ⁇ 0).
  • M forward score for a pair of matching residues; always >0
  • N penalty score for mismatching residues; always ⁇ 0.
  • a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached.
  • the BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment.
  • W wordlength
  • E expectation
  • a nucleic acid, polynucleotide or oligonucleotide can comprise the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil) and/or bases other than the five biologically occurring bases.
  • a polynucleotide of the invention can contain one or more modified, non-standard, or derivatized base moieties, including, but not limited to, N 6 -methyl-adenine, N 6 -tert-butyl-benzyl-adenine, imidazole, substituted imidazoles, 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxymethyl)uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N 6 -isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, 5-
  • modified, non-standard, or derivatized base moieties may be found in U.S. Pat. Nos. 6,001,611; 5,955,589; 5,844,106; 5,789,562; 5,750,343; 5,728,525; and 5,679,785.
  • nucleic acid, polynucleotide or oligonucleotide can comprise one or more modified sugar moieties including, but not limited to, arabinose, 2-fluoroarabinose, xylulose, and a hexose.
  • the present invention is based, in part, on the discovery that sequences in certain DNA regions are methylated in cancer cells, but not normal cells. Specifically, the inventors have found that methylation of biomarkers within the DNA regions described herein are associated with various types of cancer.
  • the inventors have recognized that methods for detecting the biomarker sequences and DNA regions comprising the biomarker sequences as well as sequences adjacent to the biomarkers that contain a significant amount of CpG subsequences, methylation of the DNA regions, and/or expression of the genes regulated by the DNA regions can be used to detect cancer cells. Detecting cancer cells allows for diagnostic tests that detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor disease, and/or aid in the selection of treatment for a person with disease.
  • the presence or absence or quantity of methylation of the chromosomal DNA within a DNA region or portion thereof is detected.
  • a DNA region or portion thereof e.g., at least one cytosine selected from SEQ ID Nos: 389-485
  • Portions of the DNA regions described herein will comprise at least one potential methylation site (i.e., a cytosine) and can in some embodiments generally comprise 2, 3, 4, 5, 10, or more potential methylation sites.
  • the methylation status of all cytosines within at least 20, 50, 100, 200, 500 or more contiguous base pairs of the DNA region are determined.
  • methylation can be detected for the purposes described herein to detect the methylation status of at least one cytosine in a sequence from:
  • the methylation of a DNA region or portion thereof is determined and then normalized (e.g., compared) to the methylation of a control locus.
  • the control locus will have a known, relatively constant, methylation status.
  • the control sequence can be previously determined to have no, some or a high amount of methylation, thereby providing a relative constant value to control for error in detection methods, etc., unrelated to the presence or absence of cancer.
  • the control locus is endogenous, i.e., is part of the genome of the individual sampled.
  • testes-specific histone 2B gene (hTH2B in human) gene is known to be methylated in all somatic tissues except testes.
  • control locus can be an exogenous locus, i.e., a DNA sequence spiked into the sample in a known quantity and having a known methylation status.
  • the wingspan of the one or more DNA regions is about 0.5 kb, 0.75 kb, 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in both 3′ and 5′ directions relative to the sequence represented by the microarray feature.
  • the methylation sites in a DNA region can reside in non-coding transcriptional control sequences (e.g. promoters, enhancers, etc.) or in coding sequences, including introns and exons of the designated genes listed in Tables 1 and 2 and in section “SEQUENCE LISTING.”
  • the methods comprise detecting the methylation status in the promoter regions (e.g., comprising the nucleic acid sequence that is about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5′ from the transcriptional start site through to the transcriptional start site) of one or more of the genes identified in Tables 1 and 2 and in section “SEQUENCE LISTING.”
  • the DNA regions of the invention also include naturally occurring variants, including for example, variants occurring in different subject populations and variants arising from single nucleotide polymorphisms (SNPs).
  • SNPs encompasses insertions and deletions of varying size and simple sequence repeats, such as dinucleotides and trinucleotide repeats.
  • Variants include nucleic acid sequences from the same DNA region (e.g. as set forth in Tables 1 and 2 and in section “SEQUENCE LISTING”) sharing at least 90%, 95%, 98%, 99% sequence identity, i.e., having one or more deletions, additions, substitutions, inverted sequences, etc., relative to the DNA regions described herein.
  • Any method for detecting DNA methylation can be used in the methods of the present invention.
  • methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and/or analyzing the cut or uncut DNA.
  • Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See, e.g., U.S. Pat. No. 7,186,512.
  • the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby only amplifying DNA that was not cleaved by the restriction enzyme in the area amplified. See, e.g., U.S. patent application Ser. Nos.
  • the methods comprise quantifying the average methylation density in a target sequence within a population of genomic DNA.
  • the method comprises contacting genomic DNA with a methylation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantity of amplified product to a control value representing the quantity of methylation of control DNA, thereby quantifying the average methylation density in the locus compared to the methylation density of the control DNA.
  • the quantity of methylation of a locus of DNA can be determined by providing a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest.
  • the amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA.
  • the amount of methylation in a DNA locus can be determined by comparing the quantity of intact DNA or cut DNA to a control value representing the quantity of intact DNA or cut DNA in a similarly-treated DNA sample.
  • the control value can represent a known or predicted number of methylated nucleotides.
  • the control value can represent the quantity of intact or cut DNA from the same locus in another (e.g., normal, non-diseased) cell or a second locus.
  • methylation-sensitive or methylation-dependent restriction enzyme By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample.
  • a methylation-dependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample.
  • assays are disclosed in, e.g., U.S. patent application Ser. No. 10/971,986.
  • Kits for the above methods can include, e.g., one or more of methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, amplification (e.g., PCR) reagents, probes and/or primers.
  • amplification e.g., PCR
  • Quantitative amplification methods can be used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion.
  • Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996); DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, et al., Mol Biotechnol. 20(2):163-79 (2002). Amplifications may be monitored in “real time.”
  • Additional methods for detecting DNA methylation can involve genomic sequencing before and after treatment of the DNA with bisulfite. See, e.g., Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). When sodium bisulfite is contacted to DNA, unmethylated cytosine is converted to uracil, while methylated cytosine is not modified.
  • restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA is used to detect DNA methylation. See, e.g., Sadri & Hornsby, Nucl. Acids Res. 24:5058-5059 (1996); Xiong & Laird, Nucleic Acids Res. 25:2532-2534 (1997).
  • a MethyLight assay is used alone or in combination with other methods to detect DNA methylation (see, Eads et al., Cancer Res. 59:2302-2306 (1999)). Briefly, in the MethyLight process genomic DNA is converted in a sodium bisulfite reaction (the bisulfite process converts unmethylated cytosine residues to uracil). Amplification of a DNA sequence of interest is then performed using PCR primers that hybridize to CpG dinucleotides.
  • amplification can indicate methylation status of sequences where the primers hybridize.
  • the amplification product can be detected with a probe that specifically binds to a sequence resulting from bisulfite treatment of a unmethylated (or methylated) DNA. If desired, both primers and probes can be used to detect methylation status.
  • kits for use with MethyLight can include sodium bisulfite as well as primers or detectably-labeled probes (including but not limited to Taqman or molecular beacon probes) that distinguish between methylated and unmethylated DNA that have been treated with bisulfite.
  • kit components can include, e.g., reagents necessary for amplification of DNA including but not limited to, PCR buffers, deoxynucleotides; and a thermostable polymerase.
  • a Ms-SNuPE Metal-sensitive Single Nucleotide Primer Extension reaction
  • the Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo & Jones, supra). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest.
  • Typical reagents for Ms-SNuPE analysis can include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPE reaction); and detectably-labeled nucleotides.
  • bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.
  • a methylation-specific PCR (“MSP”) reaction is used alone or in combination with other methods to detect DNA methylation.
  • An MSP assay entails initial modification of DNA by sodium bisulfite, converting all unmethylated, but not methylated, cytosines to uracil, and subsequent amplification with primers specific for methylated versus unmethylated DNA. See, Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, (1996); U.S. Pat. No. 5,786,146.
  • Additional methylation detection methods include, but are not limited to, methylated CpG island amplification (see, Toyota et al., Cancer Res. 59:2307-12 (1999)) and those described in, e.g., U.S. Patent Publication 2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No. WO 00/70090.
  • the methods include the step of correlating the methylation status of at least one cytosine in a DNA region with the expression of nearby coding sequences, as described in Tables 1 and 2 and in section “SEQUENCE LISTING.” For example, expression of gene sequences within about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in either the 3′ or 5′ direction from the cytosine of interest in the DNA region can be detected. Methods for measuring transcription and/or translation of a particular gene sequence are well known in the art.
  • the gene or protein expression of a gene in Tables 1 and 2 and in section “SEQUENCE LISTING” is compared to a control, for example, the methylation status in the DNA region and/or the expression of a nearby gene sequence from a sample from an individual known to be negative for cancer or known to be positive for cancer, or to an expression level that distinguishes between cancer and noncancer states.
  • a control for example, the methylation status in the DNA region and/or the expression of a nearby gene sequence from a sample from an individual known to be negative for cancer or known to be positive for cancer, or to an expression level that distinguishes between cancer and noncancer states.
  • Such methods like the methods of detecting methylation described herein, are useful in providing diagnosis, prognosis, etc., of cancer.
  • the present biomarkers and methods can be used in the diagnosis, prognosis, classification, prediction of disease risk, detection of recurrence of disease, and selection of treatment of a number of types of cancers.
  • a cancer at any stage of progression can be detected, such as primary, metastatic, and recurrent cancers.
  • Information regarding numerous types of cancer can be found, e.g., from the American Cancer Society (available on the worldwide web at cancer.org), or from, e.g., Harrison's Principles of Internal Medicine , Kaspar, et al., eds., 16th Edition, 2005, McGraw-Hill, Inc.
  • Exemplary cancers that can be detected include lung, breast, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
  • the present invention provides methods for determining whether or not a mammal (e.g., a human) has cancer, whether or not a biological sample taken from a mammal contains cancerous cells, estimating the risk or likelihood of a mammal developing cancer, classifying cancer types and stages, monitoring the efficacy of anti-cancer treatment, or selecting the appropriate anti-cancer treatment in a mammal with cancer.
  • a mammal e.g., a human
  • a biological sample taken from a mammal contains cancerous cells
  • estimating the risk or likelihood of a mammal developing cancer classifying cancer types and stages
  • monitoring the efficacy of anti-cancer treatment or selecting the appropriate anti-cancer treatment in a mammal with cancer.
  • Such methods are based on the discovery that cancer cells have a different methylation status than normal cells in the DNA regions described in the invention. Accordingly, by determining whether or not a cell contains differentially methylated sequences in the DNA regions as described herein, it is possible to determine whether
  • the presence of methylated nucleotides in the diagnostic biomarker sequences of the invention is detected in a biological sample, thereby detecting the presence or absence of cancerous cells in the biological sample.
  • the biological sample comprises a tissue sample from a tissue suspected of containing cancerous cells.
  • tissue suspected of containing cancerous cells For example, in an individual suspected of having cancer, breast tissue, lymph tissue, lung tissue, brain tissue, or blood can be evaluated.
  • the tissue or cells can be obtained by any method known in the art including, e.g., by surgery, biopsy, phlebotomy, swab, nipple discharge, stool, etc.
  • a tissue sample known to contain cancerous cells e.g., from a tumor
  • the methods will be used in conjunction with additional diagnostic methods, e.g., detection of other cancer biomarkers, etc.
  • Genomic DNA samples can be obtained by any means known in the art. In cases where a particular phenotype or disease is to be detected, DNA samples should be prepared from a tissue of interest, or as appropriate, from blood. For example, DNA can be prepared from biopsy tissue to detect the methylation state of a particular locus associated with cancer.
  • the nucleic acid-containing specimen used for detection of methylated loci may be from any source and may be extracted by a variety of techniques such as those described by Ausubel et al., Current Protocols in Molecular Biology (1995) or Sambrook et al., Molecular Cloning, A Laboratory Manual (3rd ed. 2001).
  • the methods of the invention can be used to evaluate individuals known or suspected to have cancer or as a routine clinical test, i.e., in an individual not necessarily suspected to have cancer. Further diagnostic assays can be performed to confirm the status of cancer in the individual.
  • the present methods may be used to assess the efficacy of a course of treatment.
  • the efficacy of an anti-cancer treatment can be assessed by monitoring DNA methylation of the biomarker sequences described herein over time in a mammal having cancer.
  • a reduction or absence of methylation in any of the diagnostic biomarkers of the invention in a biological sample taken from a mammal following a treatment, compared to a level in a sample taken from the mammal before, or earlier in, the treatment indicates efficacious treatment.
  • the methods detecting cancer can comprise the detection of one or more other cancer-associated polynucleotide or polypeptides sequences. Accordingly, detection of methylation of any one or more of the diagnostic biomarkers of the invention can be used either alone, or in combination with other biomarkers, for the diagnosis or prognosis of cancer.
  • the methods of the present invention can be used to determine the optimal course of treatment in a mammal with cancer.
  • the presence of methylated DNA within any of the diagnostic biomarkers of the invention or an increased quantity of methylation within any of the diagnostic biomarkers of the invention can indicate a reduced survival expectancy of a mammal with cancer, thereby indicating a more aggressive treatment for the mammal.
  • a correlation can be readily established between the presence, absence or quantity of methylation at a diagnostic biomarker, as described herein, and the relative efficacy of one or another anti-cancer agent.
  • Such analyses can be performed, e.g., retrospectively, i.e., by detecting methylation in one or more of the diagnostic genes in samples taken previously from mammals that have subsequently undergone one or more types of anti-cancer therapy, and correlating the known efficacy of the treatment with the presence, absence or levels of methylation of one or more of the diagnostic biomarkers.
  • the quantity of methylation may be compared to a threshold value that distinguishes between one diagnosis, prognosis, risk assessment, classification, etc., and another.
  • a threshold value can represent the degree of methylation found at a particular DNA region that adequately distinguishes between cancer samples and normal samples with a desired level of sensitivity and specificity.
  • a threshold value will likely vary depending on the assays used to measure methylation, but it is also understood that it is a relatively simple matter to determine a threshold value or range by measuring methylation of a DNA sequence in cancer samples and normal samples using the particular desired assay and then determining a value that distinguishes at least a majority of the cancer samples from a majority of non-cancer samples. If methylation of two or more DNA regions is detected, two or more different threshold values (one for each DNA region) will often, but not always, be used. Comparisons between a quantity of methylation of a sequence in a sample and a threshold value can be performed in any way known in the art.
  • a manual comparison can be made or a computer can compare and analyze the values to detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor, or aid in the selection of treatment for a person with disease.
  • threshold values provide at least a specified sensitivity and specificity for detection of a particular cancer type. In some embodiments, the threshold value allows for at least a 50%, 60%, 70%, or 80% sensitivity and specificity for detection of a specific cancer, e.g., breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
  • a specific cancer e.g., breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
  • the threshold value is set such that there is at least 10, 20, 30, 40, 50, 60, 70, 80% or more sensitivity and at least 70% specificity with regard to detecting cancer.
  • the methods comprise recording a diagnosis, prognosis, risk assessment or classification, based on the methylation status determined from an individual. Any type of recordation is contemplated, including electronic recordation, e.g., by a computer.
  • This invention also provides kits for the detection and/or quantification of the diagnostic biomarkers of the invention, or expression or methylation thereof using the methods described herein.
  • kits for detection of methylation can comprise at least one polynucleotide that hybridizes to at least one of the diagnostic biomarker sequences of the invention and at least one reagent for detection of gene methylation.
  • Reagents for detection of methylation include, e.g., sodium bisulfite, polynucleotides designed to hybridize to sequence that is the product of a biomarker sequence of the invention if the biomarker sequence is not methylated (e.g., containing at least one C ⁇ U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme.
  • the kits can provide solid supports in the form of an assay apparatus that is adapted to use in the assay.
  • kits may further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit.
  • detectable labels optionally linked to a polynucleotide, e.g., a probe, in the kit.
  • Other materials useful in the performance of the assays can also be included in the kits, including test tubes, transfer pipettes, and the like.
  • the kits can also include written instructions for the use of one or more of these reagents in any of the assays described herein.
  • kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably-labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof.
  • the kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.
  • kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably-labeled polynucleotoides) to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region that is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435
  • kits comprise a methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and/or a methylation-sensitive restriction enzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445
  • kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460,
  • a methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine.
  • a molecule e.g., a polypeptide
  • restriction enzymes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).
  • the calculations for the methods described herein can involve computer-based calculations and tools. For example, a methylation value for a DNA region or portion thereof can be compared by a computer to a threshold value, as described herein.
  • the tools are advantageously provided in the form of computer programs that are executable by a general purpose computer system (referred to herein as a “host computer”) of conventional design.
  • the host computer may be configured with many different hardware components and can be made in many dimensions and styles (e.g., desktop PC, laptop, tablet PC, handheld computer, server, workstation, mainframe). Standard components, such as monitors, keyboards, disk drives, CD and/or DVD drives, and the like, may be included.
  • the connections may be provided via any suitable transport media (e.g., wired, optical, and/or wireless media) and any suitable communication protocol (e.g., TCP/IP); the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card).
  • suitable transport media e.g., wired, optical, and/or wireless media
  • TCP/IP any suitable communication protocol
  • the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card).
  • the host computer may implement any of a variety of operating systems, including UNIX, Linux, Microsoft Windows, MacOS, or any other operating system.
  • Computer code for implementing aspects of the present invention may be written in a variety of languages, including PERL, C, C++, Java, JavaScript, VBScript, AWK, or any other scripting or programming language that can be executed on the host computer or that can be compiled to execute on the host computer. Code may also be written or distributed in low level languages such as assembler languages or machine languages.
  • the host computer system advantageously provides an interface via which the user controls operation of the tools.
  • software tools are implemented as scripts (e.g., using PERL), execution of which can be initiated by a user from a standard command line interface of an operating system such as Linux or UNIX.
  • commands can be adapted to the operating system as appropriate.
  • a graphical user interface may be provided, allowing the user to control operations using a pointing device.
  • the present invention is not limited to any particular user interface.
  • Scripts or programs incorporating various features of the present invention may be encoded on various computer readable media for storage and/or transmission.
  • suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet.
  • Loci that are differentially methylated in tumors relative to matched adjacent histologically normal tissue were identified using a DNA microarray-based technology platform that utilizes the methylation-dependent restriction enzyme McrBC. See, e.g. U.S. Pat. No. 7,186,512.
  • McrBC methylation-dependent restriction enzyme
  • Gels were visualized with long-wave UV, and gel slices including DNA within the modal size range of the untreated fraction (approximately 1-4 kb) were excised with a clean razor blade. DNA was extracted from gel slices using gel extraction kits (Qiagen).
  • Untreated and Treated portions were resolved by agarose gel electrophoresis, and DNA within the modal size range of the Untreated portions were excised and gel extracted, the Untreated portions represent the entire fragmented genome of the sample while the Treated portions are depleted of DNA fragments including Pu m C. Fractions were analyzed using a duplicated dye swap microarray hybridization paradigm. For example, equal mass (200 ng) of Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a microarray (described below).
  • Equal mass (200 ng) of the same Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a second microarray (these two hybridizations represent a dye swap of Untreated 1/Treated 1 fractions).
  • Equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a third microarray.
  • equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a fourth microarray (the final two hybridizations represent a technical replicate of the first dye swap). All DNA samples (e.g., tumor samples and adjacent normal samples) were analyzed in this way.
  • the microarray described in this Example consists of 380,727 features. Each 50mer oligonucleotide feature is represented by three replicates per microarray slide, yielding a total of 124,877 unique feature probes, and 2412 control probes. Each probe was selected to represent a 1 Kb interval of the human genome. Because of the natural intersection of epigenetically interesting loci (i.e. promoters, CpG Islands, etc) there are multiple probes per genomic interval providing the capacity of supporting measurements with adjacent feature's data. The genomic content represented by the features represents the majority of ENSEMBL recognized human transcriptional start sites (TSS) with two probes per TSS (>55,000 probes).
  • TSS human transcriptional start sites
  • loci that were predicted to be differentially methylated in at least 70% of tumors relative to normal tissues were identified.
  • differential DNA methylation of a collection of loci identified by a microarray discovery experiment was verified within the discovery panel of tumor and normal samples, as well as validated in larger panels of independent cancer tissue DNA, normal DNA tissue samples, and normal peripheral blood samples.
  • Tables 1 and 2 and the section “SEQUENCE LISTING” list the unique microarray feature identifier (Feature name) for each of these loci.
  • the genomic region in which a given microarray feature can report DNA methylation status is dependent upon the molecular size of the DNA fragments that were labeled for the microarray hybridizations.
  • DNA in the size range of 1 to 4 kb was purified by agarose gel extraction and used as template for cyanogen dye labeling. Therefore, a conservative estimate for the genomic region interrogated by each microarray feature is 1 kb (i.e., 500 bp upstream and 500 bp downstream of the sequence represented by the microarray feature).
  • Some features represent loci in which there is no Ensembl gene ID and no annotated transcribed gene within this 1 kb “wingspan” (e.g., CHR01P063154999, CHR03P027740753, CHR10P118975684, CHR11P021861414, CHR14P093230340, ha1p — 12601 — 150, ha1p — 42350 — 150, and ha1p — 44897 — 150) and some features have Ensembl gene IDs but no gene description (e.g., CHR01P043164342, CHR01P225608458, CHR02P223364582, CHR03P052525960, CHR16P000373719, CHR19P018622408).
  • wingspan e.g., CHR01P063154999, CHR03P027740753, CHR10P118975684, CHR11P021861414, CHR14P093230340, ha
  • CDK4I Cyclin-dependent kinase 4 inhibitor A
  • p16-INK4a Multiple tumor suppressor 1
  • MTS1 Multiple tumor suppressor 1
  • AQP-1 Aquaporin-1
  • Amporin-CHIP Water channel protein for red blood cells and kidney proximal tubule
  • PCR primers that interrogated the loci predicted to be differentially methylated between tumor and histologically normal tissue were designed. Due to the functional properties of the enzyme, DNA methylation-dependent depletion of DNA fragments by McrBC is capable of monitoring the DNA methylation status of sequences neighboring the genomic sequences represented by the features on the microarray described in Example 1 (wingspan). Since the size of DNA fragments analyzed as described in Example 1 was approximately 1-4 kb, we selected a 1 kb region spanning the sequence represented by the microarray feature as a conservative estimate of the predicted region of differential methylation.
  • PCR primers were selected within this approximately 1 kb region flanking the genomic sequence represented on the DNA microarray (approximately 500 bp upstream and 500 bp downstream). Selection of primer sequences was guided by uniqueness of the primer sequence across the genome, as well as the distribution of purine-CG sequences within the 1 kb region. PCR primer pairs were selected to amplify an approximately 400-600 bp sequence within each 1 kb region. Optimal PCR cycling conditions for the primer pairs were empirically determined, and amplification of a specific PCR amplicon of the correct size was verified. The sequences of the microarray features, primer pairs and amplicons are indicated in Table 2, and in section “SEQUENCE LISTING.”
  • DNA methylation state of the loci was independently assayed in 10 ovarian carcinoma samples and the 10 histologically normal samples described above (i.e. the discovery tissue panel used for microarray experiments). DNA methylation was assayed by a quantitative PCR approach utilizing digestion by the McrBC restriction enzyme to monitor DNA methylation status. Genomic DNA purified from each sample was split into two equal portions of 9.6 ⁇ g.
  • One 9.6 ⁇ g portion was digested with McrBC in a total volume of 120 ⁇ L including 1 ⁇ NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 80 units of McrBC enzyme (New England Biolabs).
  • the second 9.6 ⁇ g portion was treated exactly the same as the Treated Portion, except that 8 ⁇ L of sterile 50% glycerol was added instead of McrBC enzyme. Reactions were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. for 20 minutes to inactivate McrBC.
  • McrBC cleavage at each locus was monitored by quantitative real-time PCR (qPCR). For each assayed locus, qPCR was performed using 20 ng of the Untreated Portion DNA as template and, separately, using 20 ng of the Treated Portion DNA as template. Each reaction was performed in 10 ⁇ L total volume including 1 ⁇ LightCycler 480 SYBR Green I Master mix (Roche) and 625 nM of each primer. Reactions were run in a Roche LightCycler 480 instrument. Optimal annealing temperatures varied depending on the primer pair. Primer sequences (Left Primer; Right Primer) and appropriate annealing temperatures (Annealing Temp.) are shown in Table 2. Cycling conditions were: 95° C.
  • the differential DNA methylation status of the loci was further validated by analyzing an independent panel of 26 ovarian carcinoma samples (17 Stage 1 and 9 Stage II), 27 normal ovarian tissue samples, and 23 normal blood samples.
  • the normal ovarian tissues included in this panel were obtained from mastectomies unrelated to ovarian cancer. Each sample was split into two equal portions of 4 ⁇ g. One portion was digested with McrBC (Treated Portion) in a total volume of 200 ⁇ L including 1 ⁇ NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs).
  • the second portion was mock treated under identical conditions, except that 3.2 ⁇ L sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in Example 3.
  • Gain biomarkers are biomarkers that show more methylation in tumor samples than normal samples and loss biomarkers show conversely.
  • sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus.
  • loss biomarkers sensitivity reflects the frequency of scoring a known tumor sample as negative for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as positive for DNA methylation at each locus.
  • an average delta Ct>1.0 (Treated Portion—Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population).
  • Percent sensitivity of gain biomarkers was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates>1 cycle) ⁇ 100.
  • Percent specificity of gain biomarkers was calculated as (1 ⁇ (the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus)) ⁇ 100.
  • the loci have sensitivities>8% and specificities relative to normal ovarian samples>40%. Notably, at least 9 of the loci have 100% specificity relative to normal ovarian and relative to normal blood samples. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.
  • the differential DNA methylation status of the 49 loci was validated by analyzing an independent panel of 4 lung non-small adenocarcinoma samples (1 Stage I, 1 Stage II, 1 Stage III, 1 Stage 1V) and 4 matched adjacent histologically normal as well as in 23 samples of peripheral blood of normal individuals. Each sample was split into two equal portions of 4 ⁇ g. One portion was digested with McrBC (Treated Portion) in a total volume of 200 ⁇ L including 1 ⁇ NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs).
  • the second portion was mock treated under identical conditions, except that 3.2 ⁇ L sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in these Examples.
  • Gain biomarkers are biomarkers that show more methylation in tumor samples than normal samples and loss biomarkers show conversely.
  • sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus.
  • loss biomarkers sensitivity reflects the frequency of scoring a known tumor sample as negative for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as positive for DNA methylation at each locus.
  • an average delta Ct>1.0 (Treated Portion—Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population).
  • Percent sensitivity of gain biomarkers was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates>1 cycle) ⁇ 100.
  • Percent specificity of gain biomarkers was calculated as (1 ⁇ (the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus)) ⁇ 100.
  • the 49 loci have sensitivities>32% up to 100% and specificities in range 8-100% in tissues and in range 0-100% specificity in peripheral blood. Notably, 33 of the 49 loci have 100% specificity in tissues, and 19 of the 49 loci have 100% specificity in blood. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.
  • a panel of 37 loci were selected for further validation in a panel of 25 additional lung carcinoma samples as well as 25 additional matched adjacent histologically normal samples, bringing the total number of tumor and normal samples analyzed to 38.
  • the panel also included 22 lung samples from individuals who died from reasons other than cancer (i.e., benign samples). Samples were treated and analyzed as described in these Examples. As shown in Table 5, these loci display greater than 19% sensitivity, and all of them showed greater than 70% specificity relative to normal lung tissue, and 30 showed greater than 90% specificity relative to normal peripheral blood.
  • ROC Receiver Operating Characteristic
  • sensitivity refers to the percentage of tumor samples that report a value above (for a gain of DNA methylation event in tumor) or below (for a loss of DNA methylation event in tumor) a threshold value determined by ROC analysis.
  • Specificity refers to the percentage of normal samples that report a value below (for a gain of DNA methylation event in tumor) or above (for a loss of DNA methylation event in tumor) a threshold value determined by ROC analysis.
  • the models developed on the training set were validated on a test dataset of twenty-six tumor samples and twenty-seven normal samples. Error rates of 0% and 1.92% were achieved when classifying tumor vs. normal using each of the two models (see Table 9 and 10).
  • the loci identified as differentially methylated were originally discovered based on DNA methylation-dependent microarray analyses.
  • the sequences of the microarray features reporting this differential methylation are indicated in Table 2 and in section “SEQUENCE LISTING.”
  • the “wingspan” of genomic interrogation by each array feature is proportional to the size of the sheared target at the beginning of the experiment (e.g., 1 to 4 Kbp), therefore regions of the genome comprising the probe participated the interrogation for differential methylation. Because the DNA was randomly sheared the effective genomic region scanned is roughly twice the size of the average molecular weight. The smallest fragments in the molecular population were 1 Kb, this suggests the minimum region size.
  • PCR primers that amplify an amplicon within a 1 kb region surrounding the sequence represented by each microarray feature were selected and used for independent verification and validation experiments. Primer sequences and amplicon sequences are indicated in Table 2 and in section “SEQUENCE LISTING.” To optimize successful PCR amplification, these amplicons were designed to be less than the entire 1 kb region represented by the wingspan of the microarray feature. However, it should be noted that differential methylation may be detectable anywhere within this 1-8 Kb sequence window adjacent to the probe.
  • CG density was expressed as the ratio of CG dinucleotides per kb.
  • a region anywhere within the ⁇ 4 kb peak of CG density associated with the promoter region of the gene could be monitored for DNA methylation and could be important in development of a clinical diagnostic assay.
  • the sequences indicated in Table 2 (DNA Region sequences) and in section “SEQUENCE LISTING” were selected using an 8 Kb criteria.
  • CHR01P043164342 is a DNA methylation loss marker, and this sequence is less methylated in tumor sample relative to the normal sample.
  • two other loci were analyzed by bisulfite genomic sequencing as described above. Between 10 and 24 independent clones were sequenced per amplicon per sample. The sequencing results were in line with the qPCR results (see Table 11). Note that the CG Position column in Table 11 refers to the CG position in the amplicons used for bisulfite sequencing.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) benign normal samples.
  • Neg. of Total Number of negative benign normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e. methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e. methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Sensitivity % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
  • Pos. of Total Number of positive tumors relative to the total number of tumors analyzed.
  • Specificity % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
  • Neg. of Total Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.

Abstract

The present invention provides DNA biomarker sequences that are differentially methylated in samples from normal individuals and individuals with lung cancer. The invention further provides methods of identifying differentially methylated DNA biomarker sequences and their use the detection and diagnosis of lung cancer.

Description

    CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
  • The present patent application claims benefit of priority to U.S. Provisional Patent Application No. 60/899,218, filed Feb. 2, 2007; U.S. Provisional Patent Application No. 60/970,322, filed Sep. 6, 2007; U.S. Provisional Patent Application No. 61/021,840, filed Jan. 17, 2008; U.S. Provisional Patent Application No. 60/899,137, filed Feb. 2, 2007; and U.S. Provisional Patent Application No. 60/968,690, filed Aug. 29, 2007, each of which are incorporated by reference.
    • BACKGROUND OF THE INVENTION
  • Human cancer cells typically contain somatically altered genomes, characterized by mutation, amplification, or deletion of critical genes. In addition, the DNA template from human cancer cells often displays somatic changes in DNA methylation. See, e.g., E. R. Fearon, et al, Cell 61:759 (1990); P. A. Jones, et al., Cancer Res. 46:461 (1986); R. Holliday, Science 238:163 (1987); A. De Bustros, et al., Proc. Natl. Acad. Sci. USA 85:5693 (1988); P. A. Jones, et al., Adv. Cancer Res. 54:1 (1990); S. B. Baylin, et al., Cancer Cells 3:383 (1991); M. Makos, et al., Proc. Natl. Acad. Sci. USA 89:1929 (1992); N. Ohtani-Fujita, et al., Oncogene 8:1063 (1993).
  • DNA methylases transfer methyl groups from the universal methyl donor S-adenosyl methionine to specific sites on the DNA. Several biological functions have been attributed to the methylated bases in DNA. The most established biological function is the protection of the DNA from digestion by cognate restriction enzymes. This restriction modification phenomenon has, so far, been observed only in bacteria.
  • Mammalian cells, however, possess different methylases that exclusively methylate cytosine residues on the DNA that are 5′ neighbors of guanine (CpG). This methylation has been shown by several lines of evidence to play a role in gene activity, cell differentiation, tumorigenesis, X-chromosome inactivation, genomic imprinting and other major biological processes (Razin, A., H., and Riggs, R. D. eds. in DNA Methylation Biochemistry and Biological Significance, Springer-Verlag, N.Y., 1984).
  • In eukaryotic cells, methylation of cytosine residues that are immediately 5′ to a guanosine, occurs predominantly in CpG poor loci (Bird, A., Nature 321:209 (1986)). In contrast, discrete regions of CG dinucleotides called CpG islands (CGi) remain unmethylated in normal cells, except during X-chromosome inactivation and parental specific imprinting (Li, et al., Nature 366:362 (1993)) where methylation of 5′ regulatory regions can lead to transcriptional repression. For example, de novo methylation of the Rb gene has been demonstrated in a small fraction of retinoblastomas (Sakai, et al., Am. J. Hum. Genet., 48:880 (1991)), and a more detailed analysis of the VHL gene showed aberrant methylation in a subset of sporadic renal cell carcinomas (Herman, et al., Proc. Natl. Acad. Sci. U.S.A., 91:9700 (1994)). Expression of a tumor suppressor gene can also be abolished by de novo DNA methylation of a normally unmethylated 5′ CpG island. See, e.g., Issa, et al., Nature Genet. 7:536 (1994); Merlo, et al., Nature Med. 1:686 (1995); Herman, et al., Cancer Res., 56:722 (1996); Graff, et al., Cancer Res., 55:5195 (1995); Herman, et al., Cancer Res. 55:4525 (1995).
  • Identification of the earliest genetic and epigenetic changes in tumorigenesis is a major focus in molecular cancer research. Diagnostic approaches based on identification of these changes can allow implementation of early detection strategies, tumor staging and novel therapeutic approaches targeting these early changes, leading to more effective cancer treatment. The present invention addresses these and other problems.
    • BRIEF SUMMARY OF THE INVENTION
  • The present invention provides methods for determining the methylation status of an individual. In one aspect, the methods comprise:
      • obtaining a biological sample from an individual; and
      • determining the methylation status of at least one cytosine within a DNA region in a sample from an individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO.: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In a further aspect, the methods comprise determining the presence or absence of cancer, including but not limited to, bladder, breast, cervical, colon, endometrial, esophageal, head and neck, liver, lung, melanoma, ovarian, prostate, renal, and thyroid cancer, in an individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without bladder cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of bladder cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without breast cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of breast cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without cervical cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cervical cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without colon cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of colon cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without endometrial cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of endometrial cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without esophageal cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of esophageal cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without head and neck cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of head and neck cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without liver cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of liver cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without lung cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of lung cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without melanoma, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of melanoma in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without ovarian cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of ovarian cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without prostate cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of prostate cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without renal cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of renal cancer in the individual.
  • In some embodiments, the methods comprise:
      • a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
      • b) comparing the methylation status of the at least one cytosine to a threshold value for the biomarker, wherein the threshold value distinguishes between individuals with and without thyroid cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of thyroid cancer in the individual.
  • With regard to the embodiments, in some embodiments, the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker, wherein the biomarker is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, and 388.
  • In some embodiments, the determining step comprises determining the methylation status of the DNA region corresponding to a biomarker.
  • The sample can be from any body fluid. In some embodiments, the sample is selected from blood serum, blood plasma, fine needle aspirate of the breast, biopsy of the breast, ductal fluid, ductal lavage, feces, urine, sputum, saliva, semen, lavages, or tissue biopsy, such as biopsy of the lung, bronchial lavage or bronchial brushings in the case of lung cancer. In some embodiments, the sample is from a tumor or polyp. In some embodiments, the sample is a biopsy from lung, kidney, liver, ovarian, head, neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate or skin tissue. In some embodiments, the sample is from cell scrapes, washings, or resected tissues.
  • In some embodiments, the methylation status of at least one cytosine is compared to the methylation status of a control locus. In some embodiments, the control locus is an endogenous control. In some embodiments, the control locus is an exogenous control.
  • In some embodiments, the determining step comprises determining the methylation status of at least one cytosine in at least two of the DNA regions.
  • In a further aspect, the invention provides computer implemented methods for determining the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in an individual. In some embodiments, the methods comprise:
      • receiving, at a host computer, a methylation value representing the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence is selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; and
      • comparing, in the host computer, the methylation value to a threshold value, wherein the threshold value distinguishes between individuals with and without cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma), wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in the individual.
  • In some embodiments, the receiving step comprises receiving at least two methylation values, the two methylation values representing the methylation status of at least one cytosine biomarkers from two different DNA regions; and
      • the comparing step comprises comparing the methylation values to one or more threshold value(s) wherein the threshold value distinguishes between individuals with and without cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma), wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in the individual.
  • In another aspect, the invention provides computer program products for determining the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in an individual. In some embodiments, the computer readable products comprise:
      • a computer readable medium encoded with program code, the program code including:
      • program code for receiving a methylation value representing the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; and
      • program code for comparing the methylation value to a threshold value, wherein the threshold value distinguishes between individuals with and without cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma), wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of cancer (including but not limited to cancers of the bladder, breast, cervix, colon, endometrium, esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, and thyroid, and melanoma) in the individual.
  • In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:
      • a pair of polynucleotides capable of specifically amplifying at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; and
      • a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.
  • In some embodiments, the pair of polynucleotides are capable of specifically amplifying a biomarker selected from the group consisting of one or more of SEQ ID NOs: 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, and 388.
  • In some embodiments, the kits comprise at least two pairs of polynucleotides, wherein each pair is capable of specifically amplifying at least a portion of a different DNA region.
  • In some embodiments, the kits further comprise a detectably labeled polynucleotide probe that specifically detects the amplified biomarker in a real time amplification reaction.
  • In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:
      • sodium bisulfite and polynucleotides to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region that is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:
      • sodium bisulfite, primers and adapters for whole genome amplification, and polynucleotides to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region that is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In another aspect, the methods provide kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:
      • a methylation sensing restriction enzymes, primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In a further aspect, the invention provides kits for determining the methylation status of at least one biomarker. In some embodiments, the kits comprise:
      • a methylation sensing binding moiety and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to, or comprises, a sequence selected from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
    DEFINITIONS
  • “Methylation” refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine or other types of nucleic acid methylation. In vitro amplified DNA is unmethylated because in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was methylated or methylated, respectively.
  • A “methylation profile” refers to a set of data representing the methylation states of one or more loci within a molecule of DNA from e.g., the genome of an individual or cells or tissues from an individual. The profile can indicate the methylation state of every base in an individual, can comprise information regarding a subset of the base pairs (e.g., the methylation state of specific restriction enzyme recognition sequence) in a genome, or can comprise information regarding regional methylation density of each locus.
  • “Methylation status” refers to the presence, absence and/or quantity of methylation at a particular nucleotide, or nucleotides within a portion of DNA. The methylation status of a particular DNA sequence (e.g., a DNA biomarker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the base pairs (e.g., of cytosines or the methylation state of one or more specific restriction enzyme recognition sequences) within the sequence, or can indicate information regarding regional methylation density within the sequence without providing precise information of where in the sequence the methylation occurs. The methylation status can optionally be represented or indicated by a “methylation value.” A methylation value can be generated, for example, by quantifying the amount of intact DNA present following restriction digestion with a methylation dependent restriction enzyme. In this example, if a particular sequence in the DNA is quantified using quantitative PCR, an amount of template DNA approximately equal to a mock treated control indicates the sequence is not highly methylated whereas an amount of template substantially less than occurs in the mock treated sample indicates the presence of methylated DNA at the sequence. Accordingly, a value, i.e., a methylation value, for example from the above described example, represents the methylation status and can thus be used as a quantitative indicator of methylation status. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold value.
  • A “methylation-dependent restriction enzyme” refers to a restriction enzyme that cleaves or digests DNA at or in proximity to a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is not methylated. Methylation-dependent restriction enzymes include those that cut at a methylated recognition sequence (e.g., DpnI) and enzymes that cut at a sequence near but not at the recognition sequence (e.g., McrBC). For example, McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where “R” is a purine and “mC” is a methylated cytosine and “N40-3000” indicates the distance between the two RmC half sites for which a restriction event has been observed. McrBC generally cuts close to one half-site or the other, but cleavage positions are typically distributed over several base pairs, approximately 30 base pairs from the methylated base. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of both half sites, and sometimes between the two sites. Exemplary methylation-dependent restriction enzymes include, e.g., McrBC (see, e.g., U.S. Pat. No. 5,405,760), McrA, MrrA, BisI, GlaI and DpnI. One of skill in the art will appreciate that any methylation-dependent restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention.
  • A “methylation-sensitive restriction enzyme” refers to a restriction enzyme that cleaves DNA at or in proximity to an unmethylated recognition sequence but does not cleave at or in proximity to the same sequence when the recognition sequence is methylated. Exemplary methylation-sensitive restriction enzymes are described in, e.g., McClelland et al., Nucleic Acids Res. 22(17):3640-59 (1994) and http://rebase.neb.com. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when a cytosine within the recognition sequence is methylated at position C5 include, e.g., Aat II, Aci I, Acl I, Age I, Alu I, Asc I, Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II, BssK I, BstB I, BstN I, BstU I, Cla I, Eae L, Eag L, Fau I, Fse I, Hha I, HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N6 include, e.g., Mbo I. One of skill in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention. One of skill in the art will further appreciate that a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence. Likewise, a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence. For example, Sau3AI is sensitive (i.e., fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near its recognition sequence. One of skill in the art will also appreciate that some methylation-sensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.
  • A “threshold value that distinguishes between individuals with and without” a particular disease refers to a value or range of values of a particular measurement that can be used to distinguish between samples from individuals with the disease and samples without the disease. Ideally, there is a threshold value or values that absolutely distinguishes between the two groups (i.e., values from the diseased group are always on one side (e.g., higher) of the threshold value and values from the healthy, non-diseased group are on the other side (e.g., lower) of the threshold value). However, in many instances, threshold values do not absolutely distinguish between diseased and non-diseased samples (for example, when there is some overlap of values generated from diseased and non-diseased samples).
  • The phrase “corresponding to a nucleotide in a biomarker” refers to a nucleotide in a DNA region that aligns with the same nucleotide (e.g. a cytosine) in a biomarker sequence. Generally, as described herein, biomarker sequences are subsequences of (i.e., have 100% identity with) the DNA regions. Sequence comparisons can be performed using any BLAST including BLAST 2.2 algorithm with default parameters, described in Altschul et al., Nuc. Acids Res. 25:3389 3402 (1977) and Altschul et al., J. Mol. Biol. 215:403 410 (1990), respectively.
  • “Sensitivity” of a given biomarker refers to the percentage of tumor samples that report a DNA methylation value above a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:
  • Sensitivity = [ ( the number of tumor samples above the threshold ) ( the total number of tumor samples tested ) ] × 100
  • The equation may also be stated as follows:
  • Sensitivity = [ ( the number of true positive samples ) ( the number of true positive samples ) + ( the number of false negative samples ) ] × 100
  • where true positive is defined as a histology-confirmed tumor sample that reports a DNA methylation value above the threshold value (i.e. the range associated with disease), and false negative is defined as a histology-confirmed tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given biomarker would detect the presence of a clinical condition when applied to a patient with that condition.
  • “Specificity” of a given biomarker refers to the percentage of non-tumor samples that report a DNA methylation value below a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:
  • Specificity =   [ ( the number of non - tumor samples below the threshold ) ( the total number of non - tumor samples tested ) ] × 100
  • The equation may also be stated as follows:
  • Specificity = [ ( the number of true negative samples ) ( the number of true negative samples ) + ( the number of false positive samples ) ] × 100
  • where true negative is defined as a histology-confirmed non-tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease), and false positive is defined as a histology-confirmed non-tumor sample that reports DNA methylation value above the threshold value (i.e. the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known non-diseased sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given biomarker would detect the absence of a clinical condition when applied to a patient without that condition.
  • Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as defaults a wordlength (W) of 11, an expectation (E) of 10, M=5, N=−4 and a comparison of both strands. For amino acid sequences, the BLASTP program uses as defaults a wordlength of 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989)) alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparison of both strands.
  • As used herein, the terms “nucleic acid,” “polynucleotide” and “oligonucleotide” refer to nucleic acid regions, nucleic acid segments, primers, probes, amplicons and oligomer fragments. The terms are not limited by length and are generic to linear polymers of polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose), and any other N-glycoside of a purine or pyrimidine base, or modified purine or pyrimidine bases. These terms include double- and single-stranded DNA, as well as double- and single-stranded RNA.
  • A nucleic acid, polynucleotide or oligonucleotide can comprise, for example, phosphodiester linkages or modified linkages including, but not limited to phosphotriester, phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate, carbamate, thioether, bridged phosphoramidate, bridged methylene phosphonate, phosphorothioate, methylphosphonate, phosphorodithioate, bridged phosphorothioate or sulfone linkages, and combinations of such linkages.
  • A nucleic acid, polynucleotide or oligonucleotide can comprise the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil) and/or bases other than the five biologically occurring bases. For example, a polynucleotide of the invention can contain one or more modified, non-standard, or derivatized base moieties, including, but not limited to, N6-methyl-adenine, N6-tert-butyl-benzyl-adenine, imidazole, substituted imidazoles, 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxymethyl)uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N6-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D mannosylqueosine, 5′-methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, uracil-5-oxyacetic acidmethylester, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w, 2,6-diaminopurine, and 5-propynyl pyrimidine. Other examples of modified, non-standard, or derivatized base moieties may be found in U.S. Pat. Nos. 6,001,611; 5,955,589; 5,844,106; 5,789,562; 5,750,343; 5,728,525; and 5,679,785.
  • Furthermore, a nucleic acid, polynucleotide or oligonucleotide can comprise one or more modified sugar moieties including, but not limited to, arabinose, 2-fluoroarabinose, xylulose, and a hexose.
    • DETAILED DESCRIPTION OF THE INVENTION I. Introduction
  • The present invention is based, in part, on the discovery that sequences in certain DNA regions are methylated in cancer cells, but not normal cells. Specifically, the inventors have found that methylation of biomarkers within the DNA regions described herein are associated with various types of cancer.
  • In view of this discovery, the inventors have recognized that methods for detecting the biomarker sequences and DNA regions comprising the biomarker sequences as well as sequences adjacent to the biomarkers that contain a significant amount of CpG subsequences, methylation of the DNA regions, and/or expression of the genes regulated by the DNA regions can be used to detect cancer cells. Detecting cancer cells allows for diagnostic tests that detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor disease, and/or aid in the selection of treatment for a person with disease.
    • II. Methylation Biomarkers
  • In some embodiments, the presence or absence or quantity of methylation of the chromosomal DNA within a DNA region or portion thereof (e.g., at least one cytosine) selected from SEQ ID Nos: 389-485 is detected. Portions of the DNA regions described herein will comprise at least one potential methylation site (i.e., a cytosine) and can in some embodiments generally comprise 2, 3, 4, 5, 10, or more potential methylation sites. In some embodiments, the methylation status of all cytosines within at least 20, 50, 100, 200, 500 or more contiguous base pairs of the DNA region are determined.
  • Some of the DNA regions overlap with each other, indicating that methylation can be detected in a larger chromosomal region as defined by the boundaries of the overlapping sequences. For example, SEQ ID NO:402 overlaps with SEQ ID NO:403; SEQ ID NOs: 407, 408 and 409 overlap with each other; SEQ ID NO:425 overlaps with SEQ ID NO:426; SEQ ID NOs:411, 427, 428, 442, 443, 444 overlap with each other; and SEQ ID NO:429 overlaps with SEQ ID NO:445. Thus, for example, methylation can be detected for the purposes described herein to detect the methylation status of at least one cytosine in a sequence from:
  • either SEQ ID NO: 402 or 403;
    • any of SEQ ID NO:403; SEQ ID NOs: 407, 408 or 409;
  • either SEQ ID NO:425 or SEQ ID NO:426;
    • any of SEQ ID NOs:411, 427, 428, 442, 443, 444;
  • either SEQ ID NO:429 or SEQ ID NO:445.
  • In some embodiments, the methylation of more than one DNA region (or portion thereof) is detected. In some embodiments, the methylation status of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97 of the DNA regions is determined.
  • In some embodiments of the invention, the methylation of a DNA region or portion thereof is determined and then normalized (e.g., compared) to the methylation of a control locus. Typically the control locus will have a known, relatively constant, methylation status. For example, the control sequence can be previously determined to have no, some or a high amount of methylation, thereby providing a relative constant value to control for error in detection methods, etc., unrelated to the presence or absence of cancer. In some embodiments, the control locus is endogenous, i.e., is part of the genome of the individual sampled. For example, in mammalian cells, the testes-specific histone 2B gene (hTH2B in human) gene is known to be methylated in all somatic tissues except testes. Alternatively, the control locus can be an exogenous locus, i.e., a DNA sequence spiked into the sample in a known quantity and having a known methylation status.
  • A DNA region comprises a nucleic acid including one or more methylation sites of interest (e.g., a cytosine, a “microarray feature,” or an amplicon amplified from select primers) and flanking nucleic acid sequences (i.e., “wingspan”) of up to 4 kilobases (kb) in either or both of the 3′ or 5′ direction from the amplicon. This range corresponds to the lengths of DNA fragments obtained by randomly fragmenting the DNA before screening for differential methylation between DNA in two or more samples (e.g. carrying out methods used to initially identify differentially methylated sequences as described in the Examples, below). In some embodiments, the wingspan of the one or more DNA regions is about 0.5 kb, 0.75 kb, 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in both 3′ and 5′ directions relative to the sequence represented by the microarray feature.
  • The methylation sites in a DNA region can reside in non-coding transcriptional control sequences (e.g. promoters, enhancers, etc.) or in coding sequences, including introns and exons of the designated genes listed in Tables 1 and 2 and in section “SEQUENCE LISTING.” In some embodiments, the methods comprise detecting the methylation status in the promoter regions (e.g., comprising the nucleic acid sequence that is about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5′ from the transcriptional start site through to the transcriptional start site) of one or more of the genes identified in Tables 1 and 2 and in section “SEQUENCE LISTING.”
  • The DNA regions of the invention also include naturally occurring variants, including for example, variants occurring in different subject populations and variants arising from single nucleotide polymorphisms (SNPs). SNPs encompasses insertions and deletions of varying size and simple sequence repeats, such as dinucleotides and trinucleotide repeats. Variants include nucleic acid sequences from the same DNA region (e.g. as set forth in Tables 1 and 2 and in section “SEQUENCE LISTING”) sharing at least 90%, 95%, 98%, 99% sequence identity, i.e., having one or more deletions, additions, substitutions, inverted sequences, etc., relative to the DNA regions described herein.
    • III. Methods for Determining Methylation
  • Any method for detecting DNA methylation can be used in the methods of the present invention.
  • In some embodiments, methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and/or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See, e.g., U.S. Pat. No. 7,186,512. Alternatively, the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby only amplifying DNA that was not cleaved by the restriction enzyme in the area amplified. See, e.g., U.S. patent application Ser. Nos. 10/971,986; 11/071,013; and 10/971,339. In some embodiments, amplification can be performed using primers that are gene specific. Alternatively, adaptors can be added to the ends of the randomly fragmented DNA, the DNA can be digested with a methylation-dependent or methylation-sensitive restriction enzyme, intact DNA can be amplified using primers that hybridize to the adaptor sequences. In this case, a second step can be performed to determine the presence, absence or quantity of a particular gene in an amplified pool of DNA. In some embodiments, the DNA is amplified using real-time, quantitative PCR.
  • In some embodiments, the methods comprise quantifying the average methylation density in a target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting genomic DNA with a methylation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantity of amplified product to a control value representing the quantity of methylation of control DNA, thereby quantifying the average methylation density in the locus compared to the methylation density of the control DNA.
  • The quantity of methylation of a locus of DNA can be determined by providing a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest. The amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA. The amount of methylation in a DNA locus can be determined by comparing the quantity of intact DNA or cut DNA to a control value representing the quantity of intact DNA or cut DNA in a similarly-treated DNA sample. The control value can represent a known or predicted number of methylated nucleotides. Alternatively, the control value can represent the quantity of intact or cut DNA from the same locus in another (e.g., normal, non-diseased) cell or a second locus.
  • By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Similarly, if a methylation-dependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Such assays are disclosed in, e.g., U.S. patent application Ser. No. 10/971,986.
  • Kits for the above methods can include, e.g., one or more of methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, amplification (e.g., PCR) reagents, probes and/or primers.
  • Quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) can be used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion. Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996); DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, et al., Mol Biotechnol. 20(2):163-79 (2002). Amplifications may be monitored in “real time.”
  • Additional methods for detecting DNA methylation can involve genomic sequencing before and after treatment of the DNA with bisulfite. See, e.g., Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). When sodium bisulfite is contacted to DNA, unmethylated cytosine is converted to uracil, while methylated cytosine is not modified.
  • In some embodiments, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA is used to detect DNA methylation. See, e.g., Sadri & Hornsby, Nucl. Acids Res. 24:5058-5059 (1996); Xiong & Laird, Nucleic Acids Res. 25:2532-2534 (1997).
  • In some embodiments, a MethyLight assay is used alone or in combination with other methods to detect DNA methylation (see, Eads et al., Cancer Res. 59:2302-2306 (1999)). Briefly, in the MethyLight process genomic DNA is converted in a sodium bisulfite reaction (the bisulfite process converts unmethylated cytosine residues to uracil). Amplification of a DNA sequence of interest is then performed using PCR primers that hybridize to CpG dinucleotides. By using primers that hybridize only to sequences resulting from bisulfite conversion of unmethylated DNA, (or alternatively to methylated sequences that are not converted) amplification can indicate methylation status of sequences where the primers hybridize. Similarly, the amplification product can be detected with a probe that specifically binds to a sequence resulting from bisulfite treatment of a unmethylated (or methylated) DNA. If desired, both primers and probes can be used to detect methylation status. Thus, kits for use with MethyLight can include sodium bisulfite as well as primers or detectably-labeled probes (including but not limited to Taqman or molecular beacon probes) that distinguish between methylated and unmethylated DNA that have been treated with bisulfite. Other kit components can include, e.g., reagents necessary for amplification of DNA including but not limited to, PCR buffers, deoxynucleotides; and a thermostable polymerase.
  • In some embodiments, a Ms-SNuPE (Methylation-sensitive Single Nucleotide Primer Extension) reaction is used alone or in combination with other methods to detect DNA methylation (see, Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531 (1997)). The Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo & Jones, supra). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest.
  • Typical reagents (e.g., as might be found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysis can include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPE reaction); and detectably-labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.
  • In some embodiments, a methylation-specific PCR (“MSP”) reaction is used alone or in combination with other methods to detect DNA methylation. An MSP assay entails initial modification of DNA by sodium bisulfite, converting all unmethylated, but not methylated, cytosines to uracil, and subsequent amplification with primers specific for methylated versus unmethylated DNA. See, Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, (1996); U.S. Pat. No. 5,786,146.
  • Additional methylation detection methods include, but are not limited to, methylated CpG island amplification (see, Toyota et al., Cancer Res. 59:2307-12 (1999)) and those described in, e.g., U.S. Patent Publication 2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No. WO 00/70090.
  • It is well known that methylation of genomic DNA can affect expression (transcription and/or translation) of nearby gene sequences. Therefore, in some embodiments, the methods include the step of correlating the methylation status of at least one cytosine in a DNA region with the expression of nearby coding sequences, as described in Tables 1 and 2 and in section “SEQUENCE LISTING.” For example, expression of gene sequences within about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in either the 3′ or 5′ direction from the cytosine of interest in the DNA region can be detected. Methods for measuring transcription and/or translation of a particular gene sequence are well known in the art. See, for example, Ausubel, Current Protocols in Molecular Biology, 1987-2006, John Wiley & Sons; and Sambrook and Russell, Molecular Cloning: A Laboratory Manual, 3rd Edition, 2000, Cold Spring Harbor Laboratory Press. In some embodiments, the gene or protein expression of a gene in Tables 1 and 2 and in section “SEQUENCE LISTING” is compared to a control, for example, the methylation status in the DNA region and/or the expression of a nearby gene sequence from a sample from an individual known to be negative for cancer or known to be positive for cancer, or to an expression level that distinguishes between cancer and noncancer states. Such methods, like the methods of detecting methylation described herein, are useful in providing diagnosis, prognosis, etc., of cancer.
  • In some embodiments, the methods further comprise the step of correlating the methylation status and expression of one or more of the gene regions identified in Tables 1 and 2 and in section “SEQUENCE LISTING.”
    • IV. Cancer Detection
  • The present biomarkers and methods can be used in the diagnosis, prognosis, classification, prediction of disease risk, detection of recurrence of disease, and selection of treatment of a number of types of cancers. A cancer at any stage of progression can be detected, such as primary, metastatic, and recurrent cancers. Information regarding numerous types of cancer can be found, e.g., from the American Cancer Society (available on the worldwide web at cancer.org), or from, e.g., Harrison's Principles of Internal Medicine, Kaspar, et al., eds., 16th Edition, 2005, McGraw-Hill, Inc. Exemplary cancers that can be detected include lung, breast, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
  • The present invention provides methods for determining whether or not a mammal (e.g., a human) has cancer, whether or not a biological sample taken from a mammal contains cancerous cells, estimating the risk or likelihood of a mammal developing cancer, classifying cancer types and stages, monitoring the efficacy of anti-cancer treatment, or selecting the appropriate anti-cancer treatment in a mammal with cancer. Such methods are based on the discovery that cancer cells have a different methylation status than normal cells in the DNA regions described in the invention. Accordingly, by determining whether or not a cell contains differentially methylated sequences in the DNA regions as described herein, it is possible to determine whether or not the cell is cancerous.
  • In numerous embodiments of the present invention, the presence of methylated nucleotides in the diagnostic biomarker sequences of the invention is detected in a biological sample, thereby detecting the presence or absence of cancerous cells in the biological sample.
  • In some embodiments, the biological sample comprises a tissue sample from a tissue suspected of containing cancerous cells. For example, in an individual suspected of having cancer, breast tissue, lymph tissue, lung tissue, brain tissue, or blood can be evaluated. Alternatively, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate, or skin tissue can be evaluated. The tissue or cells can be obtained by any method known in the art including, e.g., by surgery, biopsy, phlebotomy, swab, nipple discharge, stool, etc. In other embodiments, a tissue sample known to contain cancerous cells, e.g., from a tumor, will be analyzed for the presence or quantity of methylation at one or more of the diagnostic biomarkers of the invention to determine information about the cancer, e.g., the efficacy of certain treatments, the survival expectancy of the individual, etc. In some embodiments, the methods will be used in conjunction with additional diagnostic methods, e.g., detection of other cancer biomarkers, etc.
  • Genomic DNA samples can be obtained by any means known in the art. In cases where a particular phenotype or disease is to be detected, DNA samples should be prepared from a tissue of interest, or as appropriate, from blood. For example, DNA can be prepared from biopsy tissue to detect the methylation state of a particular locus associated with cancer. The nucleic acid-containing specimen used for detection of methylated loci (see, e.g. Ausubel et al., Current Protocols in Molecular Biology (1995 supplement)) may be from any source and may be extracted by a variety of techniques such as those described by Ausubel et al., Current Protocols in Molecular Biology (1995) or Sambrook et al., Molecular Cloning, A Laboratory Manual (3rd ed. 2001).
  • The methods of the invention can be used to evaluate individuals known or suspected to have cancer or as a routine clinical test, i.e., in an individual not necessarily suspected to have cancer. Further diagnostic assays can be performed to confirm the status of cancer in the individual.
  • Further, the present methods may be used to assess the efficacy of a course of treatment. For example, the efficacy of an anti-cancer treatment can be assessed by monitoring DNA methylation of the biomarker sequences described herein over time in a mammal having cancer. For example, a reduction or absence of methylation in any of the diagnostic biomarkers of the invention in a biological sample taken from a mammal following a treatment, compared to a level in a sample taken from the mammal before, or earlier in, the treatment, indicates efficacious treatment.
  • The methods detecting cancer can comprise the detection of one or more other cancer-associated polynucleotide or polypeptides sequences. Accordingly, detection of methylation of any one or more of the diagnostic biomarkers of the invention can be used either alone, or in combination with other biomarkers, for the diagnosis or prognosis of cancer.
  • The methods of the present invention can be used to determine the optimal course of treatment in a mammal with cancer. For example, the presence of methylated DNA within any of the diagnostic biomarkers of the invention or an increased quantity of methylation within any of the diagnostic biomarkers of the invention can indicate a reduced survival expectancy of a mammal with cancer, thereby indicating a more aggressive treatment for the mammal. In addition, a correlation can be readily established between the presence, absence or quantity of methylation at a diagnostic biomarker, as described herein, and the relative efficacy of one or another anti-cancer agent. Such analyses can be performed, e.g., retrospectively, i.e., by detecting methylation in one or more of the diagnostic genes in samples taken previously from mammals that have subsequently undergone one or more types of anti-cancer therapy, and correlating the known efficacy of the treatment with the presence, absence or levels of methylation of one or more of the diagnostic biomarkers.
  • In making a diagnosis, prognosis, risk assessment, classification, detection of recurrence or selection of therapy based on the presence or absence of methylation in at least one of the diagnostic biomarkers, the quantity of methylation may be compared to a threshold value that distinguishes between one diagnosis, prognosis, risk assessment, classification, etc., and another. For example, a threshold value can represent the degree of methylation found at a particular DNA region that adequately distinguishes between cancer samples and normal samples with a desired level of sensitivity and specificity. It is understood that a threshold value will likely vary depending on the assays used to measure methylation, but it is also understood that it is a relatively simple matter to determine a threshold value or range by measuring methylation of a DNA sequence in cancer samples and normal samples using the particular desired assay and then determining a value that distinguishes at least a majority of the cancer samples from a majority of non-cancer samples. If methylation of two or more DNA regions is detected, two or more different threshold values (one for each DNA region) will often, but not always, be used. Comparisons between a quantity of methylation of a sequence in a sample and a threshold value can be performed in any way known in the art. For example, a manual comparison can be made or a computer can compare and analyze the values to detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor, or aid in the selection of treatment for a person with disease.
  • In some embodiments, threshold values provide at least a specified sensitivity and specificity for detection of a particular cancer type. In some embodiments, the threshold value allows for at least a 50%, 60%, 70%, or 80% sensitivity and specificity for detection of a specific cancer, e.g., breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
  • In embodiments involving prognosis of cancer (including, for example, the prediction of progression of non-malignant lesions to invasive carcinoma, prediction of metastasis, prediction of disease recurrance or prediction of a response to a particular treatment), in some embodiments, the threshold value is set such that there is at least 10, 20, 30, 40, 50, 60, 70, 80% or more sensitivity and at least 70% specificity with regard to detecting cancer.
  • In some embodiments, the methods comprise recording a diagnosis, prognosis, risk assessment or classification, based on the methylation status determined from an individual. Any type of recordation is contemplated, including electronic recordation, e.g., by a computer.
    • V. Kits
  • This invention also provides kits for the detection and/or quantification of the diagnostic biomarkers of the invention, or expression or methylation thereof using the methods described herein.
  • For kits for detection of methylation, the kits of the invention can comprise at least one polynucleotide that hybridizes to at least one of the diagnostic biomarker sequences of the invention and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfite, polynucleotides designed to hybridize to sequence that is the product of a biomarker sequence of the invention if the biomarker sequence is not methylated (e.g., containing at least one C→U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme. The kits can provide solid supports in the form of an assay apparatus that is adapted to use in the assay. The kits may further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit. Other materials useful in the performance of the assays can also be included in the kits, including test tubes, transfer pipettes, and the like. The kits can also include written instructions for the use of one or more of these reagents in any of the assays described herein.
  • In some embodiments, the kits of the invention comprise one or more (e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g., primers and/or probes) capable of specifically amplifying at least a portion of a DNA region where the DNA region is a sequence selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485. Optionally, one or more detectably-labeled polypeptide capable of hybridizing to the amplified portion can also be included in the kit. In some embodiments, the kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably-labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof. The kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.
  • In some embodiments, the kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably-labeled polynucleotoides) to quantify the presence of the converted methylated and or the converted unmethylated sequence of at least one cytosine from a DNA region that is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In some embodiments, the kits comprise a methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and/or a methylation-sensitive restriction enzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
  • In some embodiments, the kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485. A methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine. Examples include restriction enzymes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).
    • VI. Computer-Based Methods
  • The calculations for the methods described herein can involve computer-based calculations and tools. For example, a methylation value for a DNA region or portion thereof can be compared by a computer to a threshold value, as described herein. The tools are advantageously provided in the form of computer programs that are executable by a general purpose computer system (referred to herein as a “host computer”) of conventional design. The host computer may be configured with many different hardware components and can be made in many dimensions and styles (e.g., desktop PC, laptop, tablet PC, handheld computer, server, workstation, mainframe). Standard components, such as monitors, keyboards, disk drives, CD and/or DVD drives, and the like, may be included. Where the host computer is attached to a network, the connections may be provided via any suitable transport media (e.g., wired, optical, and/or wireless media) and any suitable communication protocol (e.g., TCP/IP); the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card). The host computer may implement any of a variety of operating systems, including UNIX, Linux, Microsoft Windows, MacOS, or any other operating system.
  • Computer code for implementing aspects of the present invention may be written in a variety of languages, including PERL, C, C++, Java, JavaScript, VBScript, AWK, or any other scripting or programming language that can be executed on the host computer or that can be compiled to execute on the host computer. Code may also be written or distributed in low level languages such as assembler languages or machine languages.
  • The host computer system advantageously provides an interface via which the user controls operation of the tools. In the examples described herein, software tools are implemented as scripts (e.g., using PERL), execution of which can be initiated by a user from a standard command line interface of an operating system such as Linux or UNIX. Those skilled in the art will appreciate that commands can be adapted to the operating system as appropriate. In other embodiments, a graphical user interface may be provided, allowing the user to control operations using a pointing device. Thus, the present invention is not limited to any particular user interface.
  • Scripts or programs incorporating various features of the present invention may be encoded on various computer readable media for storage and/or transmission. Examples of suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet.
    • EXAMPLES Example 1 Identification of Cancer DNA Methylation Biomarkers
  • Loci that are differentially methylated in tumors relative to matched adjacent histologically normal tissue were identified using a DNA microarray-based technology platform that utilizes the methylation-dependent restriction enzyme McrBC. See, e.g. U.S. Pat. No. 7,186,512. In the discovery phase, cancer tissue and normal tissue samples were analyzed. Purified genomic DNA from each sample (60 μg) was randomly sheared to a range of 1 to 4 kb. The sheared DNA of each sample was then split into four equal portions of 15 μg each. Two portions were digested with McrBC under the following conditions: 15 μg sheared genomic DNA, 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 120 units of McrBC enzyme (New England Biolabs) in a total volume of 600 μL at 37° C. for approximately 12 hours. These two portions represent a technical replicate of McrBC digestion (Treated 1 and Treated 2). The remaining two 15 μg portions were mock treated under identical conditions with the exception that 12 μL of sterile 50% glycerol were added instead of McrBC enzyme. These two portions represent a technical replicate of mock treatment (Untreated 1 and Untreated 2). All reactions were treated with 5 μL proteinase K (50 mg/mL) for 1 hour at 50° C., and precipitated with EtOH under standard conditions. Pellets were washed twice with 70% EtOH, dried and resuspended in 30 μL H2O. Samples were then resolved on a 1% low melting point SeaPlaque GTG Agarose gel (Cambridge Bio Sciences). Untreated 1 and Treated 1 portions were resolved side-by-side, as were Untreated 2 and Treated 2 portions. 1 kb DNA sizing ladder was resolved adjacent to each untreated/treated pair to guide accurate gel slice excision. Gels were visualized with long-wave UV, and gel slices including DNA within the modal size range of the untreated fraction (approximately 1-4 kb) were excised with a clean razor blade. DNA was extracted from gel slices using gel extraction kits (Qiagen).
  • McrBC recognizes a pair of methylated cytosine residues in the context 5′-PumC (N40-2000) PumC-3′ (where Pu=A or G, mC=5-methylcytosine, and N=any nucleotide), and cleaves within approximately 30 base-pairs from one of the methylated cytosine residues. Therefore, loci that include high local densities of Pu mC will be cleaved to a greater extent than loci that include low local densities of Pu mC. Since Untreated and Treated portions were resolved by agarose gel electrophoresis, and DNA within the modal size range of the Untreated portions were excised and gel extracted, the Untreated portions represent the entire fragmented genome of the sample while the Treated portions are depleted of DNA fragments including Pu mC. Fractions were analyzed using a duplicated dye swap microarray hybridization paradigm. For example, equal mass (200 ng) of Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a microarray (described below). Equal mass (200 ng) of the same Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a second microarray (these two hybridizations represent a dye swap of Untreated 1/Treated 1 fractions). Equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy3 and Cy5, respectively, and hybridized to a third microarray. Finally, equal mass (200 ng) of Untreated 2 and Treated 2 fraction DNA were used as template for labeling with Cy5 and Cy3, respectively, and hybridized to a fourth microarray (the final two hybridizations represent a technical replicate of the first dye swap). All DNA samples (e.g., tumor samples and adjacent normal samples) were analyzed in this way.
  • The microarray described in this Example consists of 380,727 features. Each 50mer oligonucleotide feature is represented by three replicates per microarray slide, yielding a total of 124,877 unique feature probes, and 2412 control probes. Each probe was selected to represent a 1 Kb interval of the human genome. Because of the natural intersection of epigenetically interesting loci (i.e. promoters, CpG Islands, etc) there are multiple probes per genomic interval providing the capacity of supporting measurements with adjacent feature's data. The genomic content represented by the features represents the majority of ENSEMBL recognized human transcriptional start sites (TSS) with two probes per TSS (>55,000 probes). In addition there are more than 35,000 probes designed to informatically identified CpG Islands (see, Takai and Jones, Proc Natl. Acad. Sci. U.S.A. 99(6):3740-3745 (2002)). In addition, more than 7000 probes are dedicated to tiling consensus cancer genes at 1 probe kb of genomic sequence. There are high, low and middle repetitious copy number controls (HERV, line and sine) and the design included tiles of the mitochondrial genome and a consensus rDNA gene.
  • Following statistical analysis of these datasets, loci that were predicted to be differentially methylated in at least 70% of tumors relative to normal tissues were identified. As described in the Examples below, differential DNA methylation of a collection of loci identified by a microarray discovery experiment was verified within the discovery panel of tumor and normal samples, as well as validated in larger panels of independent cancer tissue DNA, normal DNA tissue samples, and normal peripheral blood samples. Tables 1 and 2 and the section “SEQUENCE LISTING” list the unique microarray feature identifier (Feature name) for each of these loci.
  • The genomic region in which a given microarray feature can report DNA methylation status is dependent upon the molecular size of the DNA fragments that were labeled for the microarray hybridizations. As described above, DNA in the size range of 1 to 4 kb was purified by agarose gel extraction and used as template for cyanogen dye labeling. Therefore, a conservative estimate for the genomic region interrogated by each microarray feature is 1 kb (i.e., 500 bp upstream and 500 bp downstream of the sequence represented by the microarray feature). Note that some features represent loci in which there is no Ensembl gene ID and no annotated transcribed gene within this 1 kb “wingspan” (e.g., CHR01P063154999, CHR03P027740753, CHR10P118975684, CHR11P021861414, CHR14P093230340, ha1p—12601—150, ha1p—42350—150, and ha1p—44897—150) and some features have Ensembl gene IDs but no gene description (e.g., CHR01P043164342, CHR01P225608458, CHR02P223364582, CHR03P052525960, CHR16P000373719, CHR19P018622408). Also note that some features represent loci in which more than one Ensembl gene IDs within wingspan (e.g., CHR01P204123050, CHR02P223364582, and CHR16P066389027). DNA methylation at these loci may potentially affect the regulation of any of these neighboring genes.
  • TABLE 1
    Microarray Features Reporting Differential Methylation and Identity of Annotated Genes within 1 kb of Each Feature
    Locus Number Feature Name Ensembl Gene ID Annotations
    1 CHR01P001976799 ENSG00000067606 Protein kinase C, zeta type (EC 2.7.1.37) (nPKC-zeta).
    [Source: Uniprot/SWISSPROT; Acc: Q05513]
    2 CHR01P026794862 ENSG00000175793 14-3-3 protein sigma (Stratifin) (Epithelial cell marker protein
    1). [Source: Uniprot/SWISSPROT; Acc: P31947]
    3 CHR01P043164342 ENSG00000184157 no desc
    4 CHR01P063154999 N/A N/A
    5 CHR01P204123050 ENSG00000162891 Interleukin-20 precursor (IL-20) (Four alpha helix cytokine
    Zcyto10). [Source: Uniprot/SWISSPROT; Acc: Q9NYY1]
    ENSG00000162896 Polymeric-immunoglobulin receptor precursor (Poly-Ig
    receptor) (PIGR) [Contains: Secretory component].
    [Source: Uniprot/SWISSPROT; Acc: P01833]
    6 CHR01P206905110 ENSG00000196878 Laminin beta-3 chain precursor (Laminin 5 beta 3) (Laminin
    B1k chain) (Kalinin B1 chain).
    [Source: Uniprot/SWISSPROT; Acc: Q13751]
    7 CHR01P225608458 ENSG00000198504 no desc
    8 CHR02P005061785 ENSG00000171853 Tetratricopeptide repeat protein 15 (TPR repeat protein 15).
    [Source: Uniprot/SWISSPROT; Acc: Q8WVT3]
    9 CHR02P042255672 ENSG00000162878 no desc
    10 CHR02P223364582 ENSG00000135903 Paired box protein Pax-3 (HUP2).
    [Source: Uniprot/SWISSPROT; Acc: P23760]
    ENSG00000163081 no desc
    11 CHR03P027740753 N/A N/A
    12 CHR03P052525960 ENSG00000168268 no desc
    13 CHR03P069745999 ENSG00000187098 Microphthalmia-associated transcription factor.
    [Source: Uniprot/SWISSPROT; Acc: O75030]
    14 CHR05P059799713 ENSG00000152931 Prostate-specific and androgen regulated protein PART-1.
    [Source: Uniprot/SWISSPROT; Acc: Q9NPD0]
    15 CHR05P059799813 ENSG00000152931 Prostate-specific and androgen regulated protein PART-1.
    [Source: Uniprot/SWISSPROT; Acc: Q9NPD0]
    16 CHR05P177842690 ENSG00000050767 collagen, type XXIII, alpha 1
    [Source: RefSeq_peptide; Acc: NP_775736]
    17 CHR06P010694062 ENSG00000111846 N-acetyllactosaminide beta-1,6-N-acetylglucosaminyl-
    transferase (EC 2.4.1.150) (N-acetylglucosaminyltransferase)
    (I-branching enzyme) (IGNT).
    [Source: Uniprot/SWISSPROT; Acc: Q06430]
    18 CHR06P026333318 ENSG00000196966 Histone H3.1 (H3/a) (H3/c) (H3/d) (H3/f) (H3/h) (H3/i) (H3/j)
    (H3/k) (H3/l). [Source: Uniprot/SWISSPROT; Acc: P68431]
    19 CHR08P102460854 ENSG00000083307 transcription factor CP2-like 3
    [Source: RefSeq_peptide; Acc: NP_079191]
    20 CHR08P102461254 ENSG00000083307 transcription factor CP2-like 3
    [Source: RefSeq_peptide; Acc: NP_079191]
    21 CHR08P102461554 ENSG00000083307 transcription factor CP2-like 3
    [Source: RefSeq_peptide; Acc: NP_079191]
    22 CHR09P000107988 ENSG00000184492 Forkhead box protein D4 (Forkhead-related protein FKHL9)
    (Forkhead-related transcription factor 5) (FREAC-5)
    (Myeloid factor-alpha).
    [Source: Uniprot/SWISSPROT; Acc: Q12950]
    23 CHR09P021958839 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    24 CHR09P131048752 ENSG00000165699 Hamartin (Tuberous sclerosis 1 protein).
    [Source: Uniprot/SWISSPROT; Acc: Q92574]
    25 CHR10P118975684 N/A N/A
    26 CHR11P021861414 N/A N/A
    27 CHR12P004359362 ENSG00000118972 Fibroblast growth factor 23 precursor (FGF-23) (Tumor-
    derived hypophosphatemia-inducing factor).
    [Source: Uniprot/SWISSPROT; Acc: Q9GZV9]
    28 CHR12P016001231 ENSG00000023697 Putative deoxyribose-phosphate aldolase (EC 4.1.2.4)
    (Phosphodeoxyriboaldolase) (Deoxyriboaldolase) (DERA).
    [Source: Uniprot/SWISSPROT; Acc: Q9Y315]
    29 CHR14P018893344 ENSG00000185271 PREDICTED: similar to RIKEN cDNA C530050O22
    [Source: RefSeq_peptide_predicted; Acc: XP_063481]
    30 CHR14P093230340 N/A N/A
    31 CHR16P000373719 ENSG00000198098 no desc
    32 CHR16P066389027 ENSG00000089505 Chemokine-like factor (C32).
    [Source: Uniprot/SWISSPROT; Acc: Q9UBR5]
    ENSG00000140932 Chemokine-like factor super family member 2.
    [Source: Uniprot/SWISSPROT; Acc: Q8TAZ6]
    33 CHR16P083319654 ENSG00000140945 Cadherin-13 precursor (Truncated-cadherin) (T-cadherin) (T-
    cad) (Heart-cadherin) (H-cadherin) (P105).
    [Source: Uniprot/SWISSPROT; Acc: P55290]
    34 CHR18P019705147 ENSG00000053747 Laminin alpha-3 chain precursor (Epiligrin 170 kDa subunit)
    (E170) (Nicein alpha subunit).
    [Source: Uniprot/SWISSPROT; Acc: Q16787]
    35 CHR19P018622408 ENSG00000167487 no desc
    36 CHR19P051892823 ENSG00000105287 Protein kinase C, D2 type (EC 2.7.1.—) (nPKC-D2) (Protein
    kinase D2). [Source: Uniprot/SWISSPROT; Acc: Q9BZL6]
    37 CHRXP013196410 ENSG00000046653 Neuronal membrane glycoprotein M6-b (M6b).
    [Source: Uniprot/SWISSPROT; Acc: Q13491]
    38 CHRXP013196870 ENSG00000046653 Neuronal membrane glycoprotein M6-b (M6b).
    [Source: Uniprot/SWISSPROT; Acc: Q13491]
    39 ha1p16_00179_l50 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    40 ha1p16_00182_l50 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    41 ha1p16_00257_l50 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    42 ha1p_12601_l50 N/A N/A
    43 ha1p_17147_l50 ENSG00000072201 Ubiquitin ligase LNX (EC 6.3.2.—) (Numb-binding protein 1)
    (Ligand of Numb-protein X 1).
    [Source: Uniprot/SWISSPROT; Acc: Q8TBB1]
    44 ha1p_42350_l50 N/A N/A
    45 ha1p_44897_l50 N/A N/A
    46 ha1p_61253_l50 ENSG00000168767 Glutathione S-transferase Mu 2 (EC 2.5.1.18) (GSTM2-2)
    (GST class-mu 2).
    [Source: Uniprot/SWISSPROT; Acc: P28161]
    47 chr01p001005050 N/A N/A
    48 chr16p001157479 ENSG00000196557 Voltage-dependent T-type calcium channel alpha-1H subunit
    (Voltage-gated calcium channel alpha subunit Cav3.2).
    [Source: Uniprot/SWISSPROT; Acc: O95180]
    49 ha1g_00681 ENSG00000105997 Homeobox protein Hox-A3 (Hox-1E).
    [Source: Uniprot/SWISSPROT; Acc: O43365]
    50 ha1g_01966 N/A N/A
    51 ha1g_02153 N/A N/A
    52 ha1g_02319 ENSG00000135638 Homeobox protein EMX1 (Empty spiracles homolog 1)
    (Empty spiracles-like protein 1).
    [Source: Uniprot/SWISSPROT; Acc: Q04741]
    53 ha1g_02335 ENSG00000106006 Homeobox protein Hox-A6 (Hox-1B).
    [Source: Uniprot/SWISSPROT; Acc: P31267]
    54 ha1p16_00182 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    55 ha1p16_00185 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    56 ha1p16_00193 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    57 ha1p16_00259 ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)
    (p16-INK4a) (Multiple tumor suppressor 1) (MTS1).
    [Source: Uniprot/SWISSPROT; Acc: P42771]
    58 ha1p_02799 N/A N/A
    59 ha1p_03567 ENSG00000165678 Growth hormone inducible transmembrane protein (Dermal
    papilla derived protein 2) (Transmembrane BAX inhibitor
    motif containing protein 5).
    [Source: Uniprot/SWISSPROT; Acc: Q9H3K2]
    60 ha1p_03671 ENSG00000158195 Wiskott-Aldrich syndrome protein family member 2 (WASP-
    family protein member 2) (WAVE-2 protein) (Verprolin
    homology domain-containing protein 2).
    [Source: Uniprot/SWISSPROT; Acc: Q9Y6W5]
    61 ha1p_05803 N/A N/A
    62 ha1p_07131 N/A N/A
    63 ha1p_07989 ENSG00000066032 Alpha-2 catenin (Alpha-catenin-related protein) (Alpha N-
    catenin). [Source: Uniprot/SWISSPROT; Acc: P26232]
    ENSG00000181987 no desc
    64 ha1p_08588 N/A N/A
    65 ha1p_09700 ENSG00000171243 Sclerostin domain containing protein 1 precursor (Ectodermal
    BMP inhibitor) (Ectodin) (Uterine sensitization-associated
    gene 1 protein) (USAG-1).
    [Source: Uniprot/SWISSPROT; Acc: Q6X4U4]
    66 ha1p_104458 N/A N/A
    67 ha1p_105287 ENSG00000089356 FXYD domain-containing ion transport regulator 3 precursor
    (Chloride conductance inducer protein Mat-8) (Mammary
    tumor 8 kDa protein) (Phospholemman-like).
    [Source: Uniprot/SWISSPROT; Acc: Q14802]
    68 ha1p_10702 ENSG00000105996 Homeobox protein Hox-A2.
    [Source: Uniprot/SWISSPROT; Acc: O43364]
    69 ha1p_108469 ENSG00000099337 Potassium channel subfamily K member 6 (Inward rectifying
    potassium channel protein TWIK-2) (TWIK-originated
    similarity sequence).
    [Source: Uniprot/SWISSPROT; Acc: Q9Y257]
    70 ha1p_108849 ENSG00000083844 Zinc finger protein 264.
    [Source: Uniprot/SWISSPROT; Acc: O43296]
    71 ha1p_11016 ENSG00000106125 Aquaporin-1 (AQP-1) (Aquaporin-CHIP) (Water channel
    protein for red blood cells and kidney proximal tubule) (Urine
    water channel). [Source: Uniprot/SWISSPROT; Acc: P29972]
    72 ha1p_11023 ENSG00000154978 EGFR-coamplified and overexpressed protein
    [Source: RefSeq_peptide; Acc: NP_110423]
    73 ha1p_12974 ENSG00000154277 Ubiquitin carboxyl-terminal hydrolase isozyme L1 (EC
    3.4.19.12) (EC 6.—.—.—) (UCH-L1) (Ubiquitin thiolesterase L1)
    (Neuron cytoplasmic protein 9.5) (PGP 9.5) (PGP9.5).
    [Source: Uniprot/SWISSPROT; Acc: P09936]
    74 ha1p_16027 ENSG00000170178 Homeobox protein Hox-D12 (Hox-4H).
    [Source: Uniprot/SWISSPROT; Acc: P35452]
    75 ha1p_16066 ENSG00000128709 Homeobox protein Hox-D9 (Hox-4C) (Hox-5.2).
    [Source: Uniprot/SWISSPROT; Acc: P28356]
    76 ha1p_18911 ENSG00000115306 Spectrin beta chain, brain 1 (Spectrin, non-erythroid beta
    chain 1) (Beta-II spectrin) (Fodrin beta chain).
    [Source: Uniprot/SWISSPROT; Acc: Q01082]
    77 ha1p_19254 ENSG00000149571 Kin of IRRE-like protein 3 precursor (Kin of irregular chiasm-
    like protein 3) (Nephrin-like 2).
    [Source: Uniprot/SWISSPROT; Acc: Q8IZU9]
    78 ha1p_19853 ENSG00000186960 Full-length cDNA clone CS0DF012YF04 of Fetal brain of
    Homo sapiens (human) (Fragment).
    [Source: Uniprot/SPTREMBL; Acc: Q86U37]
    79 ha1p_22257 ENSG00000001626 Cystic fibrosis transmembrane conductance regulator (CFTR)
    (cAMP-dependent chloride channel).
    [Source: Uniprot/SWISSPROT; Acc: P13569]
    80 ha1p_22519 N/A N/A
    81 ha1p_31800 N/A N/A
    82 ha1p_33290 ENSG00000147408 Chondroitin beta-1,4-N-acetylgalactosaminyltransferase 1 (EC
    2.4.1.174) (beta4GalNAcT-1).
    [Source: Uniprot/SWISSPROT; Acc: Q8TDX6]
    83 ha1p_37635 ENSG00000066405 Claudin-18. [Source: Uniprot/SWISSPROT; Acc: P56856]
    84 ha1p_39189 ENSG00000121853 Growth hormone secretagogue receptor type 1 (GHS-R) (GH-
    releasing peptide receptor) (GHRP) (Ghrelin receptor).
    [Source: Uniprot/SWISSPROT; Acc: Q92847]
    85 ha1p_39511 ENSG00000164035 Endomucin precursor (Endomucin-2) (Gastric cancer antigen
    Ga34). [Source: Uniprot/SWISSPROT; Acc: Q9ULC0]
    86 ha1p_39752 ENSG00000169836 Neuromedin K receptor (NKR) (Neurokinin B receptor) (NK-
    3 receptor) (NK-3R) (Tachykinin receptor 3).
    [Source: Uniprot/SWISSPROT; Acc: P29371]
    87 ha1p_60945 ENSG00000070814 Treacle protein (Treacher Collins syndrome protein).
    [Source: Uniprot/SWISSPROT; Acc: Q13428]
    88 ha1p_62183 N/A N/A
    89 ha1p_69418 ENSG00000180667 no desc
    90 ha1p_71224 ENSG00000113205 Protocadherin beta 3 precursor (PCDH-beta3).
    [Source: Uniprot/SWISSPROT; Acc: Q9Y5E6]
    91 ha1p_74221 ENSG00000125895 no desc
    92 ha1p_76289 ENSG00000145888 Glycine receptor alpha-1 chain precursor (Glycine receptor 48 kDa
    subunit) (Strychnine binding subunit).
    [Source: Uniprot/SWISSPROT; Acc: P23415]
    93 ha1p_81050 ENSG00000187529 PREDICTED: similar to 60S ribosomal protein L7
    [Source: RefSeq_peptide_predicted; Acc: XP_018432]
    94 ha1p_81674 ENSG00000174197 MGA protein (Fragment).
    [Source: Uniprot/SPTREMBL; Acc: Q81WI9]
    95 ha1p_86355 ENSG00000171878 Ferritin light chain (Fragment).
    [Source: Uniprot/SPTREMBL; Acc: Q6DMM8]
    96 ha1p_98491 N/A N/A
    97 ha1p_99426 ENSG00000198028 zinc finger protein 560
    [Source: RefSeq_peptide; Acc: NP_689689]
    • Example 2 Design of Independent DNA Methylation Verification and Validation Assays
  • PCR primers that interrogated the loci predicted to be differentially methylated between tumor and histologically normal tissue were designed. Due to the functional properties of the enzyme, DNA methylation-dependent depletion of DNA fragments by McrBC is capable of monitoring the DNA methylation status of sequences neighboring the genomic sequences represented by the features on the microarray described in Example 1 (wingspan). Since the size of DNA fragments analyzed as described in Example 1 was approximately 1-4 kb, we selected a 1 kb region spanning the sequence represented by the microarray feature as a conservative estimate of the predicted region of differential methylation. For each locus, PCR primers were selected within this approximately 1 kb region flanking the genomic sequence represented on the DNA microarray (approximately 500 bp upstream and 500 bp downstream). Selection of primer sequences was guided by uniqueness of the primer sequence across the genome, as well as the distribution of purine-CG sequences within the 1 kb region. PCR primer pairs were selected to amplify an approximately 400-600 bp sequence within each 1 kb region. Optimal PCR cycling conditions for the primer pairs were empirically determined, and amplification of a specific PCR amplicon of the correct size was verified. The sequences of the microarray features, primer pairs and amplicons are indicated in Table 2, and in section “SEQUENCE LISTING.”
  • TABLE 2
    Sequence identification numbers for all sequences described in the application.
    See, section “SEQUENCE LISTING” for actual sequences as listed by
    number in the table.
    Locus Left Right Amplicon DNA Region
    Number Primer Primer Seq. Seq.
    (SEQ (SEQ ID (SEQ ID Annealing (SEQ ID (SEQ ID
    Feature Name ID NO:) NO:) NO:) Temp. NO:) NO:)
    CHR01P001976799 1 98 195 66 C. 292 389
    CHR01P026794862 2 99 196 62 C. 293 390
    CHR01P043164342 3 100 197 66 C. 294 391
    CHR01P063154999 4 101 198 66 C. 295 392
    CHR01P204123050 5 102 199 62 C. 296 393
    CHR01P206905110 6 103 200 66 C. 297 394
    CHR01P225608458 7 104 201 66 C. 298 395
    CHR02P005061785 8 105 202 72 C. 299 396
    CHR02P042255672 9 106 203 66 C. 300 397
    CHR02P223364582 10 107 204 66 C. 301 398
    CHR03P027740753 11 108 205 66 C. 302 399
    CHR03P052525960 12 109 206 66 C. 303 400
    CHR03P069745999 13 110 207 66 C. 304 401
    CHR05P059799713 14 111 208 66 C. 305 402
    CHR05P059799813 15 112 209 66 C. 306 403
    CHR05P177842690 16 113 210 62 C. 307 404
    CHR06P010694062 17 114 211 66 C. 308 405
    CHR06P026333318 18 115 212 66 C. 309 406
    CHR08P102460854 19 116 213 66 C. 310 407
    CHR08P102461254 20 117 214 66 C. 311 408
    CHR08P102461554 21 118 215 66 C. 312 409
    CHR09P000107988 22 119 216 66 C. 313 410
    CHR09P021958839 23 120 217 66 C. 314 411
    CHR09P131048752 24 121 218 66 C. 315 412
    CHR10P118975684 25 122 219 66 C. 316 413
    CHR11P021861414 26 123 220 66 C. 317 414
    CHR12P004359362 27 124 221 66 C. 318 415
    CHR12P016001231 28 125 222 66 C. 319 416
    CHR14P018893344 29 126 223 66 C. 320 417
    CHR14P093230340 30 127 224 66 C. 321 418
    CHR16P000373719 31 128 225 66 C. 322 419
    CHR16P066389027 32 129 226 66 C. 323 420
    CHR16P083319654 33 130 227 66 C. 324 421
    CHR18P019705147 34 131 228 66 C. 325 422
    CHR19P018622408 35 132 229 66 C. 326 423
    CHR19P051892823 36 133 230 66 C. 327 424
    CHRXP013196410 37 134 231 66 C. 328 425
    CHRXP013196870 38 135 232 66 C. 329 426
    ha1p16_00179_l50 39 136 233 66 C. 330 427
    ha1p16_00182_l50 40 137 234 66 C. 331 428
    ha1p16_00257_l50 41 138 235 66 C. 332 429
    ha1p_12601_l50 42 139 236 66 C. 333 430
    ha1p_17147_l50 43 140 237 66 C. 334 431
    ha1p_42350_l50 44 141 238 66 C. 335 432
    ha1p_44897_l50 45 142 239 66 C. 336 433
    ha1p_61253_l50 46 143 240 72 C. 337 434
    CHR01P001005050 47 144 241 72 C. 338 435
    CHR16P001157479 48 145 242 72 C. 339 436
    ha1g_00681 49 146 243 65 C. 340 437
    ha1g_01966 50 147 244 65 C. 341 438
    ha1g_02153 51 148 245 65 C. 342 439
    ha1g_02319 52 149 246 65 C. 343 440
    ha1g_02335 53 150 247 65 C. 344 441
    ha1p16_00182 54 151 248 65 C. 345 442
    ha1p16_00185 55 152 249 65 C. 346 443
    ha1p16_00193 56 153 250 65 C. 347 444
    ha1p16_00259 57 154 251 65 C. 348 445
    ha1p_02799 58 155 252 65 C. 349 446
    ha1p_03567 59 156 253 65 C. 350 447
    ha1p_03671 60 157 254 65 C. 351 448
    ha1p_05803 61 158 255 65 C. 352 449
    ha1p_07131 62 159 256 65 C. 353 450
    ha1p_07989 63 160 257 65 C. 354 451
    ha1p_08588 64 161 258 65 C. 355 452
    ha1p_09700 65 162 259 65 C. 356 453
    ha1p_104458 66 163 260 65 C. 357 454
    ha1p_105287 67 164 261 65 C. 358 455
    ha1p_10702 68 165 262 65 C. 359 456
    ha1p_108469 69 166 263 65 C. 360 457
    ha1p_108849 70 167 264 65 C. 361 458
    ha1p_11016 71 168 265 65 C. 362 459
    ha1p_11023 72 169 266 65 C. 363 460
    ha1p_12974 73 170 267 65 C. 364 461
    ha1p_16027 74 171 268 65 C. 365 462
    ha1p_16066 75 172 269 65 C. 366 463
    ha1p_18911 76 173 270 65 C. 367 464
    ha1p_19254 77 174 271 65 C. 368 465
    ha1p_19853 78 175 272 65 C. 369 466
    ha1p_22257 79 176 273 65 C. 370 467
    ha1p_22519 80 177 274 65 C. 371 468
    ha1p_31800 81 178 275 65 C. 372 469
    ha1p_33290 82 179 276 65 C. 373 470
    ha1p_37635 83 180 277 65 C. 374 471
    ha1p_39189 84 181 278 65 C. 375 472
    ha1p_39511 85 182 279 65 C. 376 473
    ha1p_39752 86 183 280 65 C. 377 474
    ha1p_60945 87 184 281 65 C. 378 475
    ha1p_62183 88 185 282 65 C. 379 476
    ha1p_69418 89 186 283 65 C. 380 477
    ha1p_71224 90 187 284 65 C. 381 478
    ha1p_74221 91 188 285 65 C. 382 479
    ha1p_76289 92 189 286 65 C. 383 480
    ha1p_81050 93 190 287 65 C. 384 481
    ha1p_81674 94 191 288 65 C. 385 482
    ha1p_86355 95 192 289 65 C. 386 483
    ha1p_98491 96 193 290 65 C. 387 484
    ha1p_99426 97 194 291 65 C. 388 485
    • Example 3 Verification of Microarray DNA Methylation Predictions
  • Initially, the DNA methylation state of the loci was independently assayed in 10 ovarian carcinoma samples and the 10 histologically normal samples described above (i.e. the discovery tissue panel used for microarray experiments). DNA methylation was assayed by a quantitative PCR approach utilizing digestion by the McrBC restriction enzyme to monitor DNA methylation status. Genomic DNA purified from each sample was split into two equal portions of 9.6 μg. One 9.6 μg portion (Treated Portion) was digested with McrBC in a total volume of 120 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 80 units of McrBC enzyme (New England Biolabs). The second 9.6 μg portion (Untreated Portion) was treated exactly the same as the Treated Portion, except that 8 μL of sterile 50% glycerol was added instead of McrBC enzyme. Reactions were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. for 20 minutes to inactivate McrBC.
  • The extent of McrBC cleavage at each locus was monitored by quantitative real-time PCR (qPCR). For each assayed locus, qPCR was performed using 20 ng of the Untreated Portion DNA as template and, separately, using 20 ng of the Treated Portion DNA as template. Each reaction was performed in 10 μL total volume including 1× LightCycler 480 SYBR Green I Master mix (Roche) and 625 nM of each primer. Reactions were run in a Roche LightCycler 480 instrument. Optimal annealing temperatures varied depending on the primer pair. Primer sequences (Left Primer; Right Primer) and appropriate annealing temperatures (Annealing Temp.) are shown in Table 2. Cycling conditions were: 95° C. for 5 min.; 45 cycles of 95° C. for 1 min., [annealing temperature, see Table 2] for 30 sec., 72° C. for 1 min., 83° C. for 2 sec. followed by a plate read. Melting curves were calculated under the following conditions: 95° C. for 5 sec., 65° C. for 1 min., 65° C. to 95° C. at 2.5° C./sec. ramp rate with continuous plate reads. Each Untreated/Treated qPCR reaction pair was performed in duplicate. The difference in the cycle number at which amplification crossed threshold (delta Ct) was calculated for each Untreated/Treated qPCR reaction pair by subtracting the Ct of the Untreated Portion from the Ct of the Treated Portion. Because McrBC-mediated cleavage between the two primers increases the Ct of the Treated Portion, increasing delta Ct values reflect increasing measurements of local DNA methylation densities. The average delta Ct between the two replicate Untreated/Treated qPCR reactions was calculated, as well as the standard deviation between the two delta Ct values.
    • Example 4 Validation of DNA Methylation Changes in Larger Number of Independent Ovarian Tumor, Normal Ovarian Samples, and Normal Blood Samples
  • The differential DNA methylation status of the loci was further validated by analyzing an independent panel of 26 ovarian carcinoma samples (17 Stage 1 and 9 Stage II), 27 normal ovarian tissue samples, and 23 normal blood samples. The normal ovarian tissues included in this panel were obtained from mastectomies unrelated to ovarian cancer. Each sample was split into two equal portions of 4 μg. One portion was digested with McrBC (Treated Portion) in a total volume of 200 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs). The second portion was mock treated under identical conditions, except that 3.2 μL sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in Example 3.
  • Table 3 indicates the percent sensitivity and specificity for each locus. Gain biomarkers are biomarkers that show more methylation in tumor samples than normal samples and loss biomarkers show conversely. For gain biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus. For loss biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as negative for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as positive for DNA methylation at each locus. As described above, an average delta Ct>1.0 (Treated Portion—Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population). Percent sensitivity of gain biomarkers was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates>1 cycle)×100. Percent specificity of gain biomarkers was calculated as (1−(the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus))×100. On the contrary percent sensitivity and specificity of loss biomarkers was calculated vice versa. As shown in Table 3, the loci have sensitivities>8% and specificities relative to normal ovarian samples>40%. Notably, at least 9 of the loci have 100% specificity relative to normal ovarian and relative to normal blood samples. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.
  • TABLE 3
    Sensitivity and Specificity of Differentially Methylated Loci in a
    Panel of 26 Ovarian Tumor Samples, 27 Normal Ovarian
    Samples, and 23 Normal Blood Samples
    Specificity
    Locus Specificity vs Normal
    Feature Name No. Sensitivity vs Normal Ovary Blood
    CHR01P001976799 1 96% 100% 0%
    CHR01P026794862 2 43% 60% 89%
    CHR01P043164342 3 42% 100% 100%
    CHR01P063154999 4 85% 100% 100%
    CHR01P204123050 5 76% 42% 5%
    CHR01P206905110 6 38% 100% 100%
    CHR01P225608458 7 85% 63% 4%
    CHR02P005061785 8 100% 89% 0%
    CHR02P042255672 9 81% 100% 0%
    CHR02P223364582 10 92% 52% 100%
    CHR03P027740753 11 77% 100% 100%
    CHR03P052525960 12 85% 67% 0%
    CHR03P069745999 13 8% 100% 100%
    CHR05P059799713 14 42% 100% 100%
    CHR05P059799813 15 35% 100% 100%
    CHR05P177842690 16 62% 96% 95%
    CHR06P010694062 17 88% 63% 0%
    CHR06P026333318 18 96% 89% 0%
    CHR08P102460854 19 84% 93% 0%
    CHR08P102461254 20 76% 96% 0%
    CHR08P102461554 21 80% 96% 0%
    CHR09P000107988 22 73% 96% 91%
    CHR09P021958839 23 88% 92% 90%
    CHR09P131048752 24 96% 96% 0%
    CHR10P118975684 25 35% 100% 0%
    CHR11P021861414 26 19% 100% 100%
    CHR12P004359362 27 38% 96% 87%
    CHR12P016001231 28 50% 96% 80%
    CHR14P018893344 29 85% 96% 0%
    CHR14P093230340 30 92% 89% 100%
    CHR16P000373719 31 38% 100% 0%
    CHR16P066389027 32 15% 96% 100%
    CHR16P083319654 33 38% 93% 87%
    CHR18P019705147 34 31% 100% 100%
    CHR19P018622408 35 92% 100% 0%
    CHR19P051892823 36 78% 89% 10%
    CHRXP013196410 37 96% 83% 14%
    CHRXP013196870 38 96% 80% 9%
    ha1p16_00179_l50 39 88% 96% 100%
    ha1p16_00182_l50 40 81% 96% 100%
    ha1p16_00257_l50 41 81% 86% 100%
    ha1p_12601_l50 42 69% 100% 0%
    ha1p_17147_l50 43 56% 100% 13%
    ha1p_42350_l50 44 43% 96% 0%
    ha1p_44897_l50 45 96% 40% 0%
    ha1p_61253_l50 46 71% 95% 0%
    CHR01P001005050 47 76% 100% 100%
    CHR16P001157479 48 29% 100% 100%
    • Example 5 Validation of DNA Methylation Changes in Independent Lung Tumor, Normal Lung Samples and in Normal Peripheral Blood Samples
  • The differential DNA methylation status of the 49 loci was validated by analyzing an independent panel of 4 lung non-small adenocarcinoma samples (1 Stage I, 1 Stage II, 1 Stage III, 1 Stage 1V) and 4 matched adjacent histologically normal as well as in 23 samples of peripheral blood of normal individuals. Each sample was split into two equal portions of 4 μg. One portion was digested with McrBC (Treated Portion) in a total volume of 200 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs). The second portion was mock treated under identical conditions, except that 3.2 μL sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in these Examples.
  • Table 4 indicates the percent sensitivity and specificity for each locus. Gain biomarkers are biomarkers that show more methylation in tumor samples than normal samples and loss biomarkers show conversely. For gain biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus. For loss biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as negative for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as positive for DNA methylation at each locus. As described above, an average delta Ct>1.0 (Treated Portion—Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population). Percent sensitivity of gain biomarkers was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates>1 cycle)×100. Percent specificity of gain biomarkers was calculated as (1−(the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus))×100. On the contrary percent sensitivity and specificity of loss biomarkers was calculated vice versa. As shown in Table 4, the 49 loci have sensitivities>32% up to 100% and specificities in range 8-100% in tissues and in range 0-100% specificity in peripheral blood. Notably, 33 of the 49 loci have 100% specificity in tissues, and 19 of the 49 loci have 100% specificity in blood. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.
  • TABLE 4
    Sensitivity and specificity of Differentially Methylated Loci in a Panel
    Of 13 Adjacent Normal Lung Samples, 13 Lung Tumor Samples
    and 23 Normal Blood Samples
    Specificity
    Feature Name Locus Number Sensitivity Adj. normal Blood
    ha1g_00681 49 58% 100% 0%
    ha1g_01966 50 75% 100% 95%
    ha1g_02153 51 67% 100% 100%
    ha1g_02319 52 50% 100% 100%
    ha1g_02335 53 64% 100% 0%
    ha1p_02799 58 38% 100% 95%
    ha1p_03567 59 77% 85% 89%
    ha1p_03671 60 33% 100% 100%
    ha1p_05803 61 77% 92% 23%
    ha1p_07131 62 38% 100% 100%
    ha1p_07989 63 62% 100% 100%
    ha1p_08588 64 54% 100% 100%
    ha1p_09700 65 38% 100% 94%
    ha1p_104458 66 100% 77% 0%
    ha1p_105287 67 69% 100% 100%
    ha1p_10702 68 62% 100% 80%
    ha1p_108469 69 62% 100% 100%
    ha1p_108849 70 92% 31% 0%
    ha1p_11016 71 100% 15% 0%
    ha1p_11023 72 92% 8% 26%
    ha1p_12974 73 46% 100% 100%
    ha1p_16027 74 54% 100% 100%
    ha1p_16066 75 85% 100% 95%
    ha1p_18911 76 100% 23% 95%
    ha1p_19254 77 77% 100% 44%
    ha1p_19853 78 69% 100% 100%
    ha1p_22257 79 64% 100% 91%
    ha1p_22519 80 77% 92% 91%
    ha1p_31800 81 100% 31% 50%
    ha1p_33290 82 83% 92% 50%
    ha1p_37635 83 91% 100% 45%
    ha1p_39189 84 50% 100% 100%
    ha1p_39511 85 100% 27% 31%
    ha1p_39752 86 75% 77% 85%
    ha1p_60945 87 85% 15% 21%
    ha1p_62183 88 50% 100% 95%
    ha1p_69418 89 75% 100% 100%
    ha1p_71224 90 92% 83% 67%
    ha1p_74221 91 64% 100% 42%
    ha1p_76289 92 64% 100% 90%
    ha1p_81050 93 54% 100% 100%
    ha1p_81674 94 83% 92% 100%
    ha1p_86355 95 69% 83% 94%
    ha1p_98491 96 54% 100% 100%
    ha1p_99426 97 62% 100% 100%
    ha1p16_00182 54 38% 100% 94%
    ha1p16_00185 55 38% 100% 100%
    ha1p16_00193 56 92% 77% 100%
    ha1p16_00259 57 82% 85% 95%
    • Example 6 Further Validation of Selected DNA Methylation Biomarkers in a Larger Panel of Lung Tumor Samples and Normal Lung Samples
  • A panel of 37 loci were selected for further validation in a panel of 25 additional lung carcinoma samples as well as 25 additional matched adjacent histologically normal samples, bringing the total number of tumor and normal samples analyzed to 38. The panel also included 22 lung samples from individuals who died from reasons other than cancer (i.e., benign samples). Samples were treated and analyzed as described in these Examples. As shown in Table 5, these loci display greater than 19% sensitivity, and all of them showed greater than 70% specificity relative to normal lung tissue, and 30 showed greater than 90% specificity relative to normal peripheral blood.
  • To address the applicability of the differential DNA methylation events as biomarkers for additional tumor types, a subset of claimed loci were analyzed in a panel of 10 cervical tumor samples and 8 benign cervical samples. All tumors were squamous cell cervical carcinomas with histology-confirmed neoplastic cellularity ranging from 75% to 95%. Benign cervical samples were obtained from hysterectomies of cervical cancer-free women. Some loci listed in Table 5 were analyzed using the same qPCR based assays as described in Example 3. Receiver-operator characteristic analysis (Lasko, et al. (2005) Journal of Biomedical Informatics 38(5):404-415.) was used to determine empirical threshold values for classification of tissue samples. The analysis was performed independently for each locus. Resulting sensitivity and specificity calculations are shown in Table 5 (columns labeled “cervical”). These results demonstrate that loci discovered to be differentially methylated in lung tumors relative to normal or benign tissue can also be relevant biomarkers of cancers other than lung cancer.
  • TABLE 5
    Sensitivity and specificity of differentially methylated loci in a panel of
    22 benign lung samples, 38 adjacent normal samples, 38 lung tumor
    samples and 23 normal blood samples using ROC analysis
    Adjacent &Benign Adjacent Normal Benign Samples Blood Cervical
    FeatureName Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity
    ha1g_01966 84.21% 85.00% 84.21% 84.21% 89.47% 90.91% 84.21% 82.61% 90.00% 87.50%
    ha1g_02153 78.95% 78.33% 78.95% 78.95% 78.95% 77.27% 86.84% 100.00%  100.00%  100.00% 
    ha1g_02319 84.21% 85.00% 78.95% 81.58% 86.84% 86.36% 94.74% 95.65% 90.00% 87.50%
    ha1p_02799 60.53% 60.00% 60.53% 60.53% 60.53% 59.09% 81.58% 82.61% — —
    ha1p_03567 84.21% 85.00% 81.58% 81.58% 86.84% 86.36% 78.95% 78.26% — —
    ha1p_03671 65.79% 68.33% 65.79% 68.42% 65.79% 68.18% 78.95% 78.26% 80.00% 75.00%
    ha1p_07131 89.47% 88.33% 86.84% 86.84% 92.11% 90.91% 94.74% 95.65% 70.00% 75.00%
    ha1p_07989 84.21% 85.00% 84.21% 84.21% 86.84% 86.36% 84.21% 82.61% — —
    ha1p_08588 84.21% 85.00% 84.21% 81.58% 86.84% 86.36% 86.84% 86.96% 60.00% 62.50%
    ha1p_09700 68.42% 68.33% 68.42% 68.42% 68.42% 68.18% 63.16% 65.22% — —
    ha1p_105287 78.95% 80.00% 78.95% 78.95% 84.21% 86.36% 94.74% 95.65% 70.00% 75.00%
    ha1p_10702 63.16% 66.67% 63.16% 73.68% 63.16% 63.64% 63.16% 78.26% — —
    ha1p_108469 78.95% 78.33% 81.58% 81.58% 78.95% 77.27% 86.84% 86.96% 70.00% 62.50%
    ha1p_12974 57.89% 58.33% 52.63% 52.63% 65.79% 68.18% 76.32% 100.00% 80.00% 75.00%
    ha1p_16027 78.95% 78.33% 76.32% 76.32% 86.84% 86.36% 100.00% 100.00% 70.00% 75.00%
    ha1p_16066 78.95% 80.00% 78.95% 78.95% 81.58% 81.82% 92.11% 91.30% — —
    ha1p_18911 73.68% 73.33% 73.68% 73.68% 73.68% 72.73% 76.32% 78.26% 60.00% 62.50%
    ha1p_19853 86.84% 86.67% 86.84% 86.84% 86.84% 86.36% 92.11% 91.30% 90.00% 87.50%
    ha1p_22257 76.32% 76.67% 78.95% 78.95% 68.42% 68.18% 73.68% 73.91% — —
    ha1p_22519 84.21% 83.33% 81.58% 81.58% 84.21% 86.36% 68.42% 69.57% — —
    ha1p_31800 84.21% 83.33% 89.47% 86.84% 81.58% 81.82% 60.53% 60.87% — —
    ha1p_33290 89.47% 90.00% 89.47% 89.47% 86.84% 86.36% 52.63% 52.17% — —
    ha1p_39189 84.21% 83.33% 84.21% 84.21% 84.21% 86.36% 81.58% 78.26% — —
    ha1p_39752 65.79% 65.00% 68.42% 68.42% 57.89% 59.09% 86.84% 86.96% — —
    ha1p_62183 84.21% 83.33% 81.58% 81.58% 86.84% 86.36% 94.74% 95.65% — —
    ha1p_69418 86.84% 86.67% 86.84% 86.84% 89.47% 90.91% 100.00% 100.00% 70.00% 75.00%
    ha1p_71224 76.32% 76.67% 76.32% 76.32% 76.32% 77.27% 78.95% 78.26% — —
    ha1p_76289 73.68% 73.33% 73.68% 73.68% 73.68% 72.73% 76.32% 78.26% — —
    ha1p_81050 89.47% 90.00% 84.21% 84.21% 94.74% 95.45% 94.74% 95.65% 70.00% 75.00%
    ha1p_81674 65.79% 68.33% 71.05% 68.42% 63.16% 63.64% 63.16% 65.22% 50.00% 50.00%
    ha1p_86355 57.89% 56.67% 55.26% 55.26% 60.53% 59.09% 84.21% 82.61% — —
    ha1p_98491 55.26% 55.00% 55.26% 55.26% 52.63% 54.55% 92.11% 91.30% 60.00% 62.50%
    ha1p_99426 86.84% 88.33% 86.84% 86.84% 86.84% 86.36% 94.74% 95.65% 100.00%  100.00% 
    ha1p16_00182 78.95% 78.33% 76.32% 76.32% 81.58% 81.82% 89.47% 91.30% 90.00% 87.50%
    ha1p16_00185 76.32% 76.67% 76.32% 76.32% 76.32% 77.27% 84.21% 86.96% 100.00%  100.00% 
    ha1p16_00193 78.95% 78.33% 73.68% 73.68% 84.21% 86.36% 86.84% 86.96% 80.00% 75.00%
    ha1p16_00259 78.95% 78.33% 78.95% 78.95% 78.95% 81.82% 78.95% 82.61% — —
    TH2B 76.32% 76.67% 73.68% 73.68% 78.95% 77.27% 86.84% 100.00% — —
    • Example 7 Determination of Sensitivity and Specificity by Receiver Operating Characteristics (ROC) Analysis
  • Receiver Operating Characteristic (ROC) analysis (see Lasko et al, Journal of Biomedical Informatics 38(5):404-415 (2005)) was used to determine empirical cut-off values for classification of tissue samples. The analysis was performed independently for each of the forty-two loci, as well as for each of the following comparisons: Tumor vs. Normal (non-diseased ovary) and Tumor vs. Blood. In Table 6, the calculated sensitivity and specificity are reported for both of the paradigms. In each case, sensitivity is reported as the true positive rate and 1-specificity is reported as the false positive rate. For each depicted locus, sensitivity refers to the percentage of tumor samples that report a value above (for a gain of DNA methylation event in tumor) or below (for a loss of DNA methylation event in tumor) a threshold value determined by ROC analysis. Specificity refers to the percentage of normal samples that report a value below (for a gain of DNA methylation event in tumor) or above (for a loss of DNA methylation event in tumor) a threshold value determined by ROC analysis.
  • TABLE 6
    Sensitivity and Specificity of Differentially Methylated Loci as Determined by
    ROC Analysis in a Panel of Tumor Ovary vs. Normal Ovary (Normal), Tumor
    Ovary vs. Normal Blood (Blood) and Tumor Cervix vs. Normal Cervix (Cervical)
    Feature Normal Blood Cervical
    Feature Name Seq Sensitivity Specificity Sensitivity Specificity Sensitivity Specificity
    CHR01P001976799 1 96.15% 96.30% 73.08% 86.96% — —
    CHR01P026794862 2 46.15% 44.44% 69.23% 69.57% — —
    CHR01P043164342 3 92.31% 100.00% 88.46% 86.96% 80.00% 75.00%
    CHR01P063154999 4 96.15% 96.30% 100.00% 100.00% 90.00% 87.50%
    CHR01P204123050 5 65.38% 66.67% 65.38% 65.22% — —
    CHR01P206905110 6 92.31% 96.30% 88.46% 86.96% 80.00% 75.00%
    CHR01P225608458 7 80.77% 81.48% 50.00% 47.83% — —
    CHR02P005061785 8 96.15% 96.30% 80.77% 86.96% — —
    CHR02P042255672 9 92.31% 92.59% 73.08% 73.91% — —
    CHR02P223364582 10 88.46% 88.89% 92.31% 91.30% 70.00% 75.00%
    CHR03P027740753 11 88.46% 88.89% 88.46% 86.96% 100.00%  100.00% 
    CHR03P052525960 12 76.92% 77.78% 84.62% 82.61% 60.00% 62.50%
    CHR03P069745999 13 80.77% 81.48% 69.23% 69.57% — —
    CHR05P059799713 14 69.23% 70.37% 92.31% 91.30% 60.00% 62.50%
    CHR05P059799813 15 73.08% 74.07% 96.15% 95.65% 80.00% 75.00%
    CHR05P177842690 16 84.62% 85.19% 80.77% 78.26% — —
    CHR06P010694062 17 88.46% 88.89% 69.23% 69.57% — —
    CHR06P026333318 18 96.15% 96.30% 73.08% 73.91% — —
    CHR08P102460854 19 88.46% 88.89% 76.92% 100.00% — —
    CHR08P102461254 20 92.31% 92.59% 88.46% 100.00% 80.00% 75.00%
    CHR08P102461554 21 88.46% 88.89% 92.31% 100.00% 80.00% 75.00%
    CHR09P000107988 22 84.62% 85.19% 84.62% 82.61% 90.00% 87.50%
    CHR09P021958839 23 88.46% 88.89% 92.31% 91.30% 90.00% 87.50%
    CHR09P131048752 24 96.15% 96.30% 73.08% 73.91% — —
    CHR10P118975684 25 76.92% 77.78% 88.46% 86.96% 100.00%  100.00% 
    CHR11P021861414 26 65.38% 66.67% 61.54% 60.87% — —
    CHR12P004359362 27 80.77% 81.48% 65.38% 65.22% — —
    CHR12P016001231 28 73.08% 74.07% 65.38% 65.22% — —
    CHR14P018893344 29 92.31% 92.59% 88.46% 86.96% 100.00%  100.00% 
    CHR14P093230340 30 92.31% 92.59% 100.00% 100.00% 90.00% 87.50%
    CHR16P066389027 32 73.08% 74.07% 80.77% 91.30% 60.00% 62.50%
    CHR16P083319654 33 76.92% 77.78% 69.23% 69.57% — —
    CHR18P019705147 34 96.15% 96.30% 80.77% 82.61% 90.00% 87.50%
    CHR19P018622408 35 92.31% 92.59% 84.62% 82.61% 100.00%  100.00% 
    CHRXP013196410 37 88.46% 88.89% 69.23% 69.57% — —
    CHRXP013196870 38 88.46% 88.89% 76.92% 78.26% — —
    ha1p16_00179_l50 39 88.46% 88.89% 92.31% 91.30% 90.00% 87.50%
    ha1p16_00182_l50 40 92.31% 92.59% 96.15% 95.65% 80.00% 75.00%
    ha1p_12601_l50 42 92.31% 92.59% 69.23% 69.57% — —
    ha1p_17147_l50 43 92.31% 92.59% 50.00% 47.83% — —
    ha1p_42350_l50 44 61.54% 62.96% 96.15% 95.65% 70.00% 75.00%
    ha1p_44897_l50 45 92.31% 88.89% 53.85% 52.17% — —
    CHR01P001005050 47 100.00% 100.00% 100.00% 100.00% 80.00% 100.00% 
    CHR16P001157479 48 92.86% 100.00% 100.00% 100.00% 71.43% 100.00% 
    • Example 8 Discriminatory Analysis to Determine which Locus or Combination of Loci Best Differentiate Between Cancerous and Non-Cancerous Tissue
  • To determine which locus or combination of loci best differentiate between cancerous (tumor) and non-cancerous (adjacent normal/benign disease) tissue, discriminant analysis (Fischer, R. A. “The Statistical Utilization of Multiple Measurements.” Annals of Eugenics, 8 (1938), 376-386.; Lachenbruch, P. A. Discriminant Analysis. New York: Hafner Press, 1975) was utilized. A training dataset consisted of delta Ct values for forty-two loci across a panel of ten tumor samples and ten normal samples. Discriminant analysis on the training set identified two combinations of four loci each that were able to correctly classify all twenty samples (i.e., error rate=0%) as shown in Tables 7 and 8. The models developed on the training set were validated on a test dataset of twenty-six tumor samples and twenty-seven normal samples. Error rates of 0% and 1.92% were achieved when classifying tumor vs. normal using each of the two models (see Table 9 and 10).
  • TABLE 7
    Discriminant analysis results from training data, Model 1:
    CHR01P001976799, CHR14P093230340, ha1p_42350_l50,
    ha1p_44897_l50.
    Overall error rate = 0%.
    Predicted Group
    Normal Tumor Total
    Known Group Normal 27  0 27
    100%  0%
    Tumor  0 26 26
     0% 100%
    Total 27 26
  • TABLE 8
    Discriminant analysis results from training data, Model 2:
    CHR14P093230340, ha1p_12601_l50,
    ha1p_42350_l50, ha1p_44897_l50.
    Overall error rate = 0%.
    Predicted Group
    Normal Tumor Total
    Known Group Normal 27  0 27
    100%  0%
    Tumor  0 26 26
     0% 100%
    Total 27 26
  • TABLE 9
    Discriminant analysis results from Model 1 (CHR01P001976799,
    CHR14P093230340, ha1p_42350_l50,
    ha1p_44897_l50) on test data.
    Overall error rate = 0%.
    Predicted Group
    Normal Tumor Total
    Known Group Normal 27  0 27
    100%  0%
    Tumor  0 26 26
     0% 100%
    Total 27 26
  • TABLE 10
    Discriminant analysis results from Model 2 (CHR14P093230340,
    ha1p_12601_l50, ha1p_42350_l50,
    ha1p_44897_l50) on test data.
    Overall error rate = 1.92%.
    Predicted Group
    Normal Tumor Total
    Known Group Normal 27  0 27
     100%    0%
    Tumor  1 25 26
    3.85% 47.17%
    Total 27 26
    • Example 9 Selection of Sequence Identified as Potential Region of Differential DNA Methylation
  • As described in the examples above, the loci identified as differentially methylated were originally discovered based on DNA methylation-dependent microarray analyses. The sequences of the microarray features reporting this differential methylation are indicated in Table 2 and in section “SEQUENCE LISTING.” The “wingspan” of genomic interrogation by each array feature is proportional to the size of the sheared target at the beginning of the experiment (e.g., 1 to 4 Kbp), therefore regions of the genome comprising the probe participated the interrogation for differential methylation. Because the DNA was randomly sheared the effective genomic region scanned is roughly twice the size of the average molecular weight. The smallest fragments in the molecular population were 1 Kb, this suggests the minimum region size. The largest fragments were 4 Kb in size, suggesting that each probe cannot monitor DNA methylation that is more than 4 Kbp proximal or distal to each probe. PCR primers that amplify an amplicon within a 1 kb region surrounding the sequence represented by each microarray feature were selected and used for independent verification and validation experiments. Primer sequences and amplicon sequences are indicated in Table 2 and in section “SEQUENCE LISTING.” To optimize successful PCR amplification, these amplicons were designed to be less than the entire 1 kb region represented by the wingspan of the microarray feature. However, it should be noted that differential methylation may be detectable anywhere within this 1-8 Kb sequence window adjacent to the probe.
  • In addition, the local CG density surrounding each region was calculated. Approximately 10 kb of sequence both upstream and downstream of each feature was extracted from the human genome. For each 20 kb portion of the genome, a sliding window of 500 bp moving in 100 bp steps was used to calculate the CG density. CG density was expressed as the ratio of CG dinucleotides per kb. In this example, it is obvious that a region anywhere within the ˜4 kb peak of CG density associated with the promoter region of the gene could be monitored for DNA methylation and could be important in development of a clinical diagnostic assay. As an obvious consequence, the more CG rich the DNA is adjacent to the probe, the more likely it is that the sequence would function redundantly to its neighboring sequences. Because of the technology platform's ability to monitor this adjacent DNA for methylation differences, the sequences indicated in Table 2 (DNA Region sequences) and in section “SEQUENCE LISTING” were selected using an 8 Kb criteria.
    • Example 10 Applicability of DNA Methylation-Based Biomarkers in Additional Tumor Types
  • To address the applicability of the differential DNA methylation events as biomarkers for additional tumor types, a subset of claimed loci were analyzed in a panel of 10 cervical tumor samples and 8 benign cervical samples. All tumors were squamous cell cervical carcinomas with histology-confirmed neoplastic cellularity ranging from 75% to 95%. Benign cervical samples were obtained from hysterectomies of cervical cancer-free women. Loci listed in Table 5 (columns labeled “cervical”) were analyzed using qPCR based assays. Receiver-operator characteristic analysis (Lasko, et al. (2005) Journal of Biomedical Informatics 38(5):404-415) was used to determine empirical threshold values for classification of tissue samples. The analysis was performed independently for each locus. Resulting sensitivity [the percentage of tumor samples above (gain biomarkers) or below (loss biomarkers) threshold] and specificity [the percentage of benign samples below (gain biomarkers) or above (loss biomarkers) threshold] calculations are shown in Table 5. These results demonstrate that loci discovered to be differentially methylated in tumors relative to normal or benign tissue are also relevant biomarkers of cancers.
    • Example 11 Bisulfite Sequencing Confirmation of Differential DNA Methylation of Additional Loci
  • Confirmation of differential DNA methylation was performed by bisulfite sequencing. Primers were designed to amplify a 400 bp amplicon within the 500 bp region of locus ha1p16—00182—150 analyzed by qPCR (as discussed in the examples above) from bisulfite converted genomic DNA. Primers sequences lack CpG dinucleotides, and therefore amplify bisulfite converted DNA independently of DNA methylation status. Products were amplified from one tumor sample (positive for DNA methylation) and from one normal sample. Amplicons were purified and cloned using TA cloning kits (Invitrogen). Ninety-six (96) independent clones were sequenced for the tumor sample. Ninety-six (96) independent clones were sequenced for the normal sample. Bisulfite treatment results in conversion of unmethylated cytosines to uracil, but does not convert methylated cytosines. The percent methylation of each CpG dinucleotide within the region was calculated as the number of sequence reads of C at each CpG divided by the total number of sequence reads. All 9 CpG dinucleotides are methylated in the tumor (occupancy ranging from 93.62 to 100%). However, methylation occupancy of CpG dinucleotides in normal sample was lower, ranging from 0 to 10%.
  • To provide further confirmation of DNA methylation differences and to justify the qPCR based strategy for high-throughput detection of DNA methylation, three additional loci CHR01P043164342, CHR01P063154999, CHR03P027740753, were analyzed by bisulfite genomic sequencing as described above. Ninety six independent clones were sequenced per amplicon per sample. The sequencing results were consistent with the results of qPCR. Note that CHR01P043164342 is a DNA methylation loss marker, and this sequence is less methylated in tumor sample relative to the normal sample. In addition, two other loci were analyzed by bisulfite genomic sequencing as described above. Between 10 and 24 independent clones were sequenced per amplicon per sample. The sequencing results were in line with the qPCR results (see Table 11). Note that the CG Position column in Table 11 refers to the CG position in the amplicons used for bisulfite sequencing.
  • TABLE 11
    Examples of Bisulfite Analysis of Differentially Methylated Loci.
    CG % of Methylation
    Feature Name Position Tumor Benign
    ha1p_03671 27   85%   18%
    58   90%   27%
    70   90%   18%
    79   85%    9%
    89   80%    9%
    96   85%    9%
    115   85%    9%
    125   83%    9%
    134   83%    0%
    # of clones 20 11
    qPCR result (delta Ct) 2.7 0.91
    CG % Methylation
    position Tumor Adj. Normal
    ha1p_08588 35   46%   90%
    67   38%   82%
    182   21%   50%
    200    7%   40%
    # of clones 24 11
    qPCR result (delta Ct) 0.95 6.23
    CG % Methylation
    position Tumor Normal
    CHR01P043164342 39  9.91% 90.52%
    51  7.21% 75.68%
    60  6.19% 70.99%
    87 10.00% 92.86%
    104  9.46% 83.81%
    139  3.77% 32.56%
    148 13.46% 86.42%
    174  9.80% 61.54%
    183  7.69% 48.68%
    255  6.38% 67.27%
    # of clones 96 96
    qPCR result (delta Ct) 0.025 7.46
    CHR01P063154999 32 76.40% 15.05%
    34 96.63%  7.53%
    55 96.63% 23.60%
    66 92.13%  4.44%
    73 97.70%  4.40%
    89 94.32%  2.20%
    91 92.13%  3.30%
    94 93.18%  0.00%
    100 92.13%  1.10%
    110 97.73%  1.14%
    118 96.59%  2.22%
    128 97.73%  2.25%
    # of clones 96 96
    qPCR result (delta Ct) 4.14 0
    CHR03P027740753 26 93.14% 11.76%
    28 96.04%  17.6%
    93 29.21%  6.67%
    136 52.24%  0.00%
    157 91.04%  0.00%
    159 92.42%  0.00%
    171 98.48% 13.33%
    180 81.54% 14.29%
    # of clones 96 96
    qPCR result (delta Ct) 4.675 0
    • Example 12 Analysis of DNA Methylation in Various Cancer Types
  • To address the applicability of the claimed DNA methylation biomarkers to cancer types other than one type of cancer, all claimed biomarkers were analyzed in panels of bladder, breast, cervical, colon, endometrial, esophageal, head and neck, liver, lung, melanoma, ovarian, prostate, renal, and thyroid tumors. Adjacent histology normal tissues were analyzed as controls. In addition, melanoma tumors were analyzed, although no adjacent normal tissues were available. The number of samples analyzed for each cancer type is provided in Table 12. DNA methylation was measured as described in these Examples. For each locus and each cancer type, the sensitivity and specificity for discriminating between tumor and adjacent normal tissues are reported in Tables 13-27. For melanoma tumors (Table 23), only sensitivity (the frequency of DNA methylation detection (ie. samples that report and average dCt≧1.0)) is reported due to the unavailability of adjacent normal tissues. For each locus, the optimal threshold for discriminating between tumor and adjacent normal tissue was calculated following ROC curve analysis. These data demonstrate that particular biomarker loci are applicable to more than just one cancer type.
  • TABLE 12
    Number of tumor and
    normal samples tested for the
    biomarker loci.
    Cancer Type Tumor Normal
    Bladder 9 9
    Breast 10 10
    Cervical 10 9
    Colon 10 10
    Endometrial 14 9
    Esophageal 9 10
    Head &Neck 9 5
    Liver 9 9
    Lung 20 20
    Melanoma 7 0
    Ovarian 34 35
    Prostate 9 9
    Renal 10 10
    Thyroid 10 10
  • TABLE 13
    Sensitivity and Specificity of differentially methylated loci in bladder
    tumors relative to adjacent histological normal bladder tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 77.78%   7 of 9 88.89%   8 of 9
    CHR01P026794862 2 0.52 62.50%   5 of 8 50.00%   3 of 6
    CHR01P043164342 3 4.69 66.67%   6 of 9 77.78%   7 of 9
    CHR01P063154999 4 0.955 100.00%    9 of 9 66.67%   6 of 9
    CHR01P204123050 5 1.265 50.00%   4 of 8 100.00%   7 of 7
    CHR01P206905110 6 2.685 88.89%   8 of 9 88.89%   8 of 9
    CHR01P225608458 7 1.655 100.00%    9 of 9 88.89%   8 of 9
    CHR02P005061785 8 4.945 66.67%   6 of 9 75.00%   6 of 8
    CHR02P042255672 9 2.5 66.67%   6 of 9 77.78%   7 of 9
    CHR02P223364582 10 1.65 66.67%   6 of 9 100.00%   9 of 9
    CHR03P027740753 11 1.225 100.00%    9 of 9 100.00%   8 of 8
    CHR03P052525960 12 3.225 88.89%   8 of 9 100.00%   9 of 9
    CHR03P069745999 13 3.255 55.56%   5 of 9 66.67%   6 of 9
    CHR05P059799713 14 1.13 77.78%   7 of 9 55.56%   5 of 9
    CHR05P059799813 15 0.715 100.00%    8 of 8 37.50%   3 of 8
    CHR05P177842690 16 2.79 77.78%   7 of 9 50.00%   4 of 8
    CHR06P010694062 17 4.32 66.67%   6 of 9 100.00%   9 of 9
    CHR06P026333318 18 4.31 77.78%   7 of 9 100.00%   9 of 9
    CHR08P102460854 19 0.805 77.78%   7 of 9 100.00%   9 of 9
    CHR08P102461254 20 1.09 88.89%   8 of 9 100.00%   9 of 9
    CHR08P102461554 21 0.97 77.78%   7 of 9 88.89%   8 of 9
    CHR09P000107988 22 1.65 88.89%   8 of 9 100.00%   9 of 9
    CHR09P021958839 23 1.73 77.78%   7 of 9 88.89%   8 of 9
    CHR09P131048752 24 4.21 88.89%   8 of 9 100.00%   9 of 9
    CHR10P118975684 25 1.295 77.78%   7 of 9 66.67%   6 of 9
    CHR11P021861414 26 2.96 88.89%   8 of 9 88.89%   8 of 9
    CHR12P004359362 27 2.25 66.67%   6 of 9 77.78%   7 of 9
    CHR12P016001231 28 0.965 25.00%   2 of 8 100.00%   8 of 8
    CHR14P018893344 29 3.335 55.56%   5 of 9 100.00%   8 of 8
    CHR14P093230340 30 1.91 77.78%   7 of 9 85.71%   6 of 7
    CHR16P000373719 31 1.305 100.00%    8 of 8 75.00%   3 of 4
    CHR16P066389027 32 1.47 55.56%   5 of 9 77.78%   7 of 9
    CHR16P083319654 33 1.575 66.67%   6 of 9 100.00%   9 of 9
    CHR18P019705147 34 3.85 100.00%    9 of 9 55.56%   5 of 9
    CHR19P018622408 35 2.795 100.00%    9 of 9 87.50%   7 of 8
    CHR19P051892823 36 2.095 80.00%   4 of 5 100.00%   4 of 4
    CHRXP013196410 37 2.63 66.67%   6 of 9 88.89%   8 of 9
    CHRXP013196870 38 2.255 55.56%   5 of 9 55.56%   5 of 9
    halp16_00179_150 39 1.44 88.89%   8 of 9 100.00%   9 of 9
    halp16_00182_150 40 1.45 77.78%   7 of 9 100.00%   9 of 9
    halp16_00257_150 41 1.42 66.67%   6 of 9 87.50%   7 of 8
    halp_12601_150 42 1.245 88.89%   8 of 9 100.00%   9 of 9
    halp_17147_150 43 1.12 75.00%   6 of 8 88.89%   8 of 9
    halp_42350_150 44 5.11 62.50%   5 of 8 50.00%   4 of 8
    halp_44897_150 45 1.645 100.00%    9 of 9 85.71%   6 of 7
    halp_61253_150 46 2.61 75.00%   6 of 8 100.00%   7 of 7
    CHR01P001005050 47 1.745 75.00%   6 of 8 100.00%   7 of 7
    CHR16P001157479 48 — — — — —
    halg_00681 49 1.68 44% 4 of 9 100% 9 of 9
    halg_01966 50 1.99 89% 8 of 9 100% 9 of 9
    halg_02153 51 1.51 56% 5 of 9 100% 9 of 9
    halg_02319 52 0.64 100%  9 of 9  89% 8 of 9
    halg_02335 53 4.24 78% 7 of 9  33% 3 of 9
    halp16_00182 54 0.73 100%  9 of 9  67% 6 of 9
    halp16_00185 55 1.12 67% 6 of 9 100% 9 of 9
    halp16_00193 56 1.94 56% 5 of 9 100% 9 of 9
    halp16_00259 57 2.17 88% 7 of 8  78% 7 of 9
    halp_02799 58 2.35 56% 5 of 9 100% 9 of 9
    halp_03567 59 1.06 67% 6 of 9  75% 6 of 8
    halp_03671 60 1.15 89% 8 of 9  89% 8 of 9
    halp_05803 61 2.09 67% 6 of 9 100% 9 of 9
    halp_07131 62 3.52 78% 7 of 9  89% 8 of 9
    halp_07989 63 2.06 78% 7 of 9  88% 7 of 8
    halp_08588 64 3.96 67% 6 of 9 100% 9 of 9
    halp_09700 65 0.77 75% 6 of 8 100% 8 of 8
    halp_104458 66 3.43 56% 5 of 9 100% 9 of 9
    halp_105287 67 2.96 100%  9 of 9  89% 8 of 9
    halp_10702 68 3.06 67% 6 of 9 100% 8 of 8
    halp_108469 69 1.54 33% 3 of 9 100% 9 of 9
    halp_108849 70 3.42 67% 6 of 9 100% 9 of 9
    halp_11016 71 2.92 100%  9 of 9 100% 9 of 9
    halp_11023 72 2.91 56% 5 of 9 100% 9 of 9
    halp_12974 73 0.53 56% 5 of 9  78% 7 of 9
    halp_16027 74 2.2 44% 4 of 9  89% 8 of 9
    halp_16066 75 2.25 56% 5 of 9  78% 7 of 9
    halp_18911 76 2.77 44% 4 of 9 100% 8 of 8
    halp_19254 77 1.95 78% 7 of 9 100% 9 of 9
    halp_19853 78 0.79 78% 7 of 9  89% 8 of 9
    halp_22257 79 2.7 67% 6 of 9 100% 9 of 9
    halp_22519 80 1.41 89% 8 of 9  78% 7 of 9
    halp_31800 81 2.65 50% 3 of 6  89% 8 of 9
    halp_33290 82 2.62 89% 8 of 9 100% 9 of 9
    halp_37635 83 6 100%  9 of 9  0% 0 of 9
    halp_39189 84 0.78 100%  9 of 9  78% 7 of 9
    halp_39511 85 3.02 44% 4 of 9 100% 9 of 9
    halp_39752 86 2.51 56% 5 of 9 100% 9 of 9
    halp_60945 87 2 67% 6 of 9 100% 9 of 9
    halp_62183 88 4.11 22% 2 of 9 100% 9 of 9
    halp_69418 89 2.64 78% 7 of 9 100% 8 of 8
    halp_71224 90 1.83 89% 8 of 9 100% 9 of 9
    halp_74221 91 1.98 56% 5 of 9  89% 8 of 9
    halp_76289 92 1.11 78% 7 of 9 100% 9 of 9
    halp_81050 93 3.95 78% 7 of 9  89% 8 of 9
    halp_81674 94 1.82 67% 6 of 9 100% 9 of 9
    halp_86355 95 1.18 89% 8 of 9  75% 6 of 8
    halp_98491 96 3.99 33% 3 of 9 100% 9 of 9
    halp_99426 97 1.34 67% 6 of 9 100% 9 of 9
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 14
    Sensitivity and Specificity of differentially methylated loci in breast
    tumors relative to adjacent histological normal breast tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 5.18 70.00%   7 of 10 100.00%    10 of 10
    CHR01P026794862 2 2.075 100.00%    8 of 8 33.33%   1 of 3
    CHR01P043164342 3 1.8 50.00%   5 of 10 80.00%   8 of 10
    CHR01P063154999 4 2.295 60.00%   6 of 10 90.00%   9 of 10
    CHR01P204123050 5 1.655 80.00%   8 of 10 60.00%   6 of 10
    CHR01P206905110 6 2.165 70.00%   7 of 10 90.00%   9 of 10
    CHR01P225608458 7 1.9 80.00%   8 of 10 90.00%   9 of 10
    CHR02P005061785 8 2.18 80.00%   8 of 10 90.00%   9 of 10
    CHR02P042255672 9 5.895 70.00%   7 of 10 70.00%   7 of 10
    CHR02P223364582 10 2.625 60.00%   6 of 10 70.00%   7 of 10
    CHR03P027740753 11 1.62 70.00%   7 of 10 100.00%    10 of 10
    CHR03P052525960 12 2.4 50.00%   5 of 10 100.00%    10 of 10
    CHR03P069745999 13 0.775 40.00%   4 of 10 100.00%    10 of 10
    CHR05P059799713 14 1.43 10.00%   1 of 10 100.00%    9 of 9
    CHR05P059799813 15 0.765 66.67%   6 of 9 55.56%   5 of 9
    CHR05P177842690 16 1.29 70.00%   7 of 10 80.00%   8 of 10
    CHR06P010694062 17 3.46 70.00%   7 of 10 90.00%   9 of 10
    CHR06P026333318 18 1.155 50.00%   5 of 10 100.00%    10 of 10
    CHR08P102460854 19 0.615 100.00%    10 of 10 40.00%   4 of 10
    CHR08P102461254 20 0.525 100.00%    10 of 10 50.00%   5 of 10
    CHR08P102461554 21 0.695 100.00%    10 of 10 40.00%   4 of 10
    CHR09P000107988 22 1.97 40.00%   4 of 10 80.00%   8 of 10
    CHR09P021958839 23 2.805 20.00%   2 of 10 100.00%    10 of 10
    CHR09P131048752 24 2.22 90.00%   9 of 10 90.00%   9 of 10
    CHR10P118975684 25 2.695 40.00%   4 of 10 100.00%    10 of 10
    CHR11P021861414 26 3.98 70.00%   7 of 10 100.00%    10 of 10
    CHR12P004359362 27 1.91 50.00%   5 of 10 80.00%   8 of 10
    CHR12P016001231 28 1.515 70.00%   7 of 10 62.50%   5 of 8
    CHR14P018893344 29 2.585 80.00%   8 of 10 100.00%    10 of 10
    CHR14P093230340 30 3.66 30.00%   3 of 10 100.00%    10 of 10
    CHR16P000373719 31 1.16 33.33%   3 of 9 88.89%   8 of 9
    CHR16P066389027 32 0.935 60.00%   6 of 10 90.00%   9 of 10
    CHR16P083319654 33 1.635 80.00%   8 of 10 90.00%   9 of 10
    CHR18P019705147 34 3.435 50.00%   5 of 10 90.00%   9 of 10
    CHR19P018622408 35 2.595 80.00%   8 of 10 100.00%    10 of 10
    CHR19P051892823 36 3.53 100.00%    4 of 4 100.00%    8 of 8
    CHRXP013196410 37 1.63 66.67%   6 of 9 88.89%   8 of 9
    CHRXP013196870 38 1.71 60.00%   6 of 10 100.00%    9 of 9
    halp16_00179_150 39 1.4 30.00%   3 of 10 100.00%    10 of 10
    halp16_00182_150 40 0.99 30.00%   3 of 10 100.00%    10 of 10
    halp16_00257_150 41 2.5 30.00%   3 of 10 100.00%    10 of 10
    halp_12601_150 42 0.99 100.00%    10 of 10 80.00%   8 of 10
    halp_17147_150 43 0.99 100.00%    10 of 10 80.00%   8 of 10
    halp_42350_150 44 5.27 50.00%   5 of 10 80.00%   8 of 10
    halp_44897_150 45 2.76 40.00%   4 of 10 90.00%   9 of 10
    halp_61253_150 46 1.37 80.00%   8 of 10 90.00%   9 of 10
    CHR01P001005050 47 0.605 70.00%   7 of 10 75.00%   6 of 8
    CHR16P001157479 48 — — — — —
    halg_00681 49 1.66 90% 9 of 10 100%  10 of 10
    halg_01966 50 3.37 60% 6 of 10 90% 9 of 10
    halg_02153 51 2.72 50% 5 of 10 90% 9 of 10
    halg_02319 52 2.03 50% 5 of 10 100%  10 of 10
    halg_02335 53 2.4 90% 9 of 10 90% 9 of 10
    halp16_00182 54 1.55 60% 6 of 10 50% 5 of 10
    halp16_00185 55 1.65 60% 6 of 10 90% 9 of 10
    halp16_00193 56 2.89 60% 6 of 10 80% 8 of 10
    halp16_00259 57 5.08 20% 2 of 10 100%  10 of 10
    halp_02799 58 4.22 80% 8 of 10 60% 6 of 10
    halp_03567 59 1.46 100%  10 of 10 50% 3 of 6
    halp_03671 60 0.59 40% 4 of 10 90% 9 of 10
    halp_05803 61 2.97 67% 4 of 6 86% 6 of 7
    halp_07131 62 5.55 100%  7 of 7 86% 6 of 7
    halp_07989 63 2.18 100%  9 of 9 88% 7 of 8
    halp_08588 64 5.9 100%  10 of 10 90% 9 of 10
    halp_9700 65 0.72 44% 4 of 9 89% 8 of 9
    halp_104458 66 6 20% 2 of 10 90% 9 of 10
    halp_105287 67 0.82 40% 4 of 10 80% 8 of 10
    halp_10702 68 1.57 60% 6 of 10 100%  9 of 9
    halp_108469 69 1.77 78% 7 of 9 90% 9 of 10
    halp_108849 70 1.78 80% 8 of 10 80% 8 of 10
    halp_11016 71 3.69 90% 9 of 10 90% 9 of 10
    halp_11023 72 3.02 80% 8 of 10 80% 8 of 10
    halp_12974 73 0.94 60% 6 of 10 63% 5 of 8
    halp_16027 74 1.81 80% 8 of 10 70% 7 of 10
    halp_16066 75 2.33 70% 7 of 10 90% 9 of 10
    halp_18911 76 3.31 100%  9 of 9 30% 3 of 10
    halp_19254 77 3.66 100%  10 of 10 90% 9 of 10
    halp_19853 78 1.18 80% 8 of 10 80% 8 of 10
    halp_22257 79 3.55 50% 5 of 10 100%  10 of 10
    halp_22519 80 1.64 100%  9 of 9 90% 9 of 10
    halp_31800 81 3.68 60% 6 of 10 100%  10 of 10
    halp_33290 82 2.26 90% 9 of 10 100%  10 of 10
    halp_37635 83 5.41 100%  10 of 10  0% 0 of 10
    halp_39189 84 1.12 88% 7 of 8 89% 8 of 9
    halp_39511 85 3.3 60% 6 of 10 78% 7 of 9
    halp_39752 86 3.99 30% 3 of 10 100%  10 of 10
    halp_60945 87 1.84 70% 7 of 10 50% 5 of 10
    halp_62183 88 4.05 90% 9 of 10 90% 9 of 10
    halp_69418 89 2.39 60% 6 of 10 90% 9 of 10
    halp_71224 90 2.01 50% 5 of 10 90% 9 of 10
    halp_74221 91 1.12 80% 8 of 10 50% 5 of 10
    halp_76289 92 1.12 89% 8 of 9 86% 6 of 7
    halp_81050 93 4.34 100%  10 of 10 90% 9 of 10
    halp_81674 94 1.9 90% 9 of 10 100%  8 of 8
    halp_86355 95 2.08 70% 7 of 10 100%  8 of 8
    halp_98491 96 4.17 70% 7 of 10 80% 8 of 10
    halp_99426 97 1.2 90% 9 of 10 90% 9 of 10
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 15
    Sensitivity and Specificity of differentially methylated loci in cervical tumors
    relative to adjacent histological normal cervical tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 2.985 100.00%  10 of 10  100.00%  9 of 9
    CHR01P026794862 2 0.985 50.00% 3 of 6  87.50% 7 of 8
    CHR01P043164342 3 2.535 60.00% 6 of 10 100.00%  8 of 8
    CHR01P063154999 4 1.165 90.00% 9 of 10 88.89% 8 of 9
    CHR01P204123050 5 1.87 60.00% 6 of 10 88.89% 8 of 9
    CHR01P206905110 6 2.83 80.00% 8 of 10 100.00%  9 of 9
    CHR01P225608458 7 2 80.00% 8 of 10 100.00%  9 of 9
    CHR02P005061785 8 1.99 100.00%  10 of 10  100.00%  9 of 9
    CHR02P042255672 9 2.02 80.00% 8 of 10 100.00%  9 of 9
    CHR02P223364582 10 2.11 70.00% 7 of 10 77.78% 7 of 9
    CHR03P027740753 11 0.96 90.00% 9 of 10 100.00%  9 of 9
    CHR03P052525960 12 1.74 70.00% 7 of 10 100.00%  9 of 9
    CHR03P069745999 13 2.955 60.00% 6 of 10 100.00%  9 of 9
    CHR05P059799713 14 0.89 50.00% 5 of 10 88.89% 8 of 9
    CHR05P059799813 15 0.735 30.00% 3 of 10 88.89% 8 of 9
    CHR05P177842690 16 1.885 40.00% 4 of 10 100.00%  9 of 9
    CHR06P010694062 17 2.38 90.00% 9 of 10 100.00%  9 of 9
    CHR06P026333318 18 1.97 90.00% 9 of 10 100.00%  7 of 7
    CHR08P102460854 19 0.925 90.00% 9 of 10 75.00% 6 of 8
    CHR08P102461254 20 1.305 80.00% 8 of 10 100.00%  9 of 9
    CHR08P102461554 21 1.1 80.00% 8 of 10 100.00%  9 of 9
    CHR09P000107988 22 1.6 70.00% 7 of 10 100.00%  9 of 9
    CHR09P021958839 23 1.27 70.00% 7 of 10 77.78% 7 of 9
    CHR09P131048752 24 3.68 70.00% 7 of 10 66.67% 6 of 9
    CHR10P118975684 25 1.28 80.00% 8 of 10 66.67% 6 of 9
    CHR11P021861414 26 5.505 80.00% 8 of 10 88.89% 8 of 9
    CHR12P004359362 27 3.645 30.00% 3 of 10 100.00%  9 of 9
    CHR12P016001231 28 1.56 100.00%  10 of 10  77.78% 7 of 9
    CHR14P018893344 29 1.255 100.00%  10 of 10  100.00%  9 of 9
    CHR14P093230340 30 1.695 88.89% 8 of 9  85.71% 6 of 7
    CHR16P000373719 31 2.135 87.50% 7 of 8  80.00% 4 of 5
    CHR16P066389027 32 1.22 60.00% 6 of 10 55.56% 5 of 9
    CHR16P083319654 33 2.885 100.00%  10 of 10  66.67% 6 of 9
    CHR18P019705147 34 2.815 80.00% 8 of 10 100.00%  9 of 9
    CHR19P018622408 35 1.525 100.00%  10 of 10  100.00%  9 of 9
    CHR19P051892823 36 1.445 66.67% 4 of 6  100.00%  4 of 4
    CHRXP013196410 37 1.545 80.00% 8 of 10 100.00%  9 of 9
    CHRXP013196870 38 1.48 80.00% 8 of 10 85.71% 6 of 7
    ha1p16_00179_l50 39 1.555 66.67% 6 of 9  87.50% 7 of 8
    ha1p16_00182_l50 40 1.205 70.00% 7 of 10 100.00%  9 of 9
    ha1p16_00257_l50 41 0.705 70.00% 7 of 10 77.78% 7 of 9
    ha1p_12601_l50 42 2.245 100.00%  10 of 10  100.00%  9 of 9
    ha1p_17147_l50 43 2.455 90.00% 9 of 10 100.00%  9 of 9
    ha1p_42350_l50 44 2.335 66.67% 6 of 9  77.78% 7 of 9
    ha1p_44897_l50 45 3.755 80.00% 8 of 10 85.71% 6 of 7
    ha1p_61253_l50 46 1.34 100.00%  10 of 10  100.00%  6 of 6
    CHR01P001005050 47 2.025 80.00% 8 of 10 100.00%  9 of 9
    CHR16P001157479 48 4.045 71.43% 5 of 7  100.00%  2 of 2
    ha1g_00681 49 0.91   80% 8 of 10   100% 9 of 9
    ha1g_01966 50 1.71   100% 10 of 10    67% 6 of 9
    ha1g_02153 51 1.41   100% 10 of 10    100% 9 of 9
    ha1g_02319 52 0.99   100% 10 of 10    89% 8 of 9
    ha1g_02335 53 4.96   60% 6 of 10   67% 6 of 9
    ha1p16_00182 54 1.13   80% 8 of 10   89% 8 of 9
    ha1p16_00185 55 0.96   80% 8 of 10   100% 9 of 9
    ha1p16_00193 56 1.74   80% 8 of 10   89% 8 of 9
    ha1p16_00259 57 1.84   90% 9 of 10   89% 8 of 9
    ha1p_02799 58 3.12   20% 2 of 10   100% 9 of 9
    ha1p_03567 59 1.89   100% 10 of 10    78% 7 of 9
    ha1p_03671 60 1.14   80% 8 of 10   100% 9 of 9
    ha1p_05803 61 1.3   100% 10 of 10    89% 8 of 9
    ha1p_07131 62 5.67   70% 7 of 10   100% 9 of 9
    ha1p_07989 63 4.72   89% 8 of 9    75% 6 of 8
    ha1p_08588 64 6   70% 7 of 10   89% 8 of 9
    ha1p_09700 65 0.59   70% 7 of 10   89% 8 of 9
    ha1p_104458 66 4.45   70% 7 of 10   78% 7 of 9
    ha1p_105287 67 2.56   70% 7 of 10   100% 9 of 9
    ha1p_10702 68 2.09   60% 6 of 10   100% 9 of 9
    ha1p_108469 69 1.27   100% 10 of 10    44% 4 of 9
    ha1p_108849 70 3.01   100% 10 of 10    78% 7 of 9
    ha1p_11016 71 3.18   100% 10 of 10    56% 5 of 9
    ha1p_11023 72 2.52   90% 9 of 10   100% 9 of 9
    ha1p_12974 73 0.6   50% 5 of 10   89% 8 of 9
    ha1p_16027 74 1.56   100% 10 of 10    56% 5 of 9
    ha1p_16066 75 2.15   60% 6 of 10   88% 7 of 8
    ha1p_18911 76 3.08   60% 6 of 10   78% 7 of 9
    ha1p_19254 77 4.53   90% 9 of 10   100% 8 of 8
    ha1p_19853 78 0.55   100% 9 of 9    67% 6 of 9
    ha1p_22257 79 2.35   60% 6 of 10   78% 7 of 9
    ha1p_22519 80 2.09   80% 8 of 10   100% 9 of 9
    ha1p_31800 81 2.88   90% 9 of 10   89% 8 of 9
    ha1p_33290 82 1.65   80% 8 of 10   100% 9 of 9
    ha1p_37635 83 6   10% 1 of 10   100% 9 of 9
    ha1p_39189 84 0.86   100% 10 of 10    78% 7 of 9
    ha1p_39511 85 3.18   63% 5 of 8    89% 8 of 9
    ha1p_39752 86 2.44   88% 7 of 8    100% 8 of 8
    ha1p_60945 87 1.44   80% 8 of 10   67% 6 of 9
    ha1p_62183 88 5.21   60% 6 of 10   56% 5 of 9
    ha1p_69418 89 3.61   100% 10 of 10    78% 7 of 9
    ha1p_71224 90 1.99   80% 8 of 10   100% 9 of 9
    ha1p_74221 91 1.61   89% 8 of 9    100% 8 of 8
    ha1p_76289 92 0.52   90% 9 of 10   100% 8 of 8
    ha1p_81050 93 6   50% 5 of 10   100% 9 of 9
    ha1p_81674 94 0.87   80% 8 of 10   78% 7 of 9
    ha1p_86355 95 1.56   60% 6 of 10   89% 8 of 9
    ha1p_98491 96 3.3   70% 7 of 10   67% 6 of 9
    ha1p_99426 97 0.76   90% 9 of 10   100% 9 of 9
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 16
    Sensitivity and Specificity of differentially methylated loci in colon tumors
    relative to adjacent histological normal colon tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 5.86 80.00% 8 of 10 90.00% 9 of 10
    CHR01P026794862 2 0.88 50.00% 4 of 8  87.50% 7 of 8 
    CHR01P043164342 3 1.78 80.00% 8 of 10 60.00% 6 of 10
    CHR01P063154999 4 1.14 80.00% 8 of 10 90.00% 9 of 10
    CHR01P204123050 5 1.655 40.00% 4 of 10 100.00%  10 of 10 
    CHR01P206905110 6 1.63 70.00% 7 of 10 80.00% 8 of 10
    CHR01P225608458 7 1.97 80.00% 8 of 10 90.00% 9 of 10
    CHR02P005061785 8 5.51 50.00% 5 of 10 80.00% 8 of 10
    CHR02P042255672 9 2.8 80.00% 8 of 10 100.00%  10 of 10 
    CHR02P223364582 10 2 80.00% 8 of 10 90.00% 9 of 10
    CHR03P027740753 11 1.26 90.00% 9 of 10 80.00% 8 of 10
    CHR03P052525960 12 2.825 100.00%  10 of 10  80.00% 8 of 10
    CHR03P069745999 13 3.6 70.00% 7 of 10 90.00% 9 of 10
    CHR05P059799713 14 1.025 50.00% 5 of 10 80.00% 8 of 10
    CHR05P059799813 15 0.89 40.00% 4 of 10 80.00% 8 of 10
    CHR05P177842690 16 3.58 90.00% 9 of 10 20.00% 2 of 10
    CHR06P010694062 17 3.055 90.00% 9 of 10 90.00% 9 of 10
    CHR06P026333318 18 3.175 90.00% 9 of 10 90.00% 9 of 10
    CHR08P102460854 19 0.825 90.00% 9 of 10 70.00% 7 of 10
    CHR08P102461254 20 0.69 80.00% 8 of 10 80.00% 8 of 10
    CHR08P102461554 21 1.015 90.00% 9 of 10 70.00% 7 of 10
    CHR09P000107988 22 0.985 90.00% 9 of 10 80.00% 8 of 10
    CHR09P021958839 23 1.46 90.00% 9 of 10 100.00%  10 of 10 
    CHR09P131048752 24 3.695 70.00% 7 of 10 100.00%  10 of 10 
    CHR10P118975684 25 1.41 80.00% 8 of 10 90.00% 9 of 10
    CHR11P021861414 26 3.79 90.00% 9 of 10 90.00% 9 of 10
    CHR12P004359362 27 3.51 50.00% 5 of 10 80.00% 8 of 10
    CHR12P016001231 28 1.295 40.00% 4 of 10 90.00% 9 of 10
    CHR14P018893344 29 2.805 80.00% 8 of 10 90.00% 9 of 10
    CHR14P093230340 30 1.66 90.00% 9 of 10 70.00% 7 of 10
    CHR16P000373719 31 1.525 40.00% 2 of 5  87.50% 7 of 8 
    CHR16P066389027 32 0.655 90.00% 9 of 10 80.00% 8 of 10
    CHR16P083319654 33 1.79 90.00% 9 of 10 60.00% 6 of 10
    CHR18P019705147 34 2.01 80.00% 8 of 10 60.00% 6 of 10
    CHR19P018622408 35 3.015 60.00% 6 of 10 100.00%  10 of 10 
    CHR19P051892823 36 0.6 57.14% 4 of 7  100.00%  7 of 7 
    CHRXP013196410 37 2.99 60.00% 6 of 10 100.00%  10 of 10 
    CHRXP013196870 38 2.92 60.00% 6 of 10 100.00%  9 of 9 
    ha1p16_00179_l50 39 1.425 80.00% 8 of 10 90.00% 9 of 10
    ha1p16_00182_l50 40 1.22 80.00% 8 of 10 90.00% 9 of 10
    ha1p16_00257_l50 41 1.085 90.00% 9 of 10 90.00% 9 of 10
    ha1p_12601_l50 42 0.68 80.00% 8 of 10 80.00% 8 of 10
    ha1p_17147_l50 43 1.025 90.00% 9 of 10 60.00% 6 of 10
    ha1p_42350_l50 44 3.865 80.00% 8 of 10 55.56% 5 of 9 
    ha1p_44897_l50 45 3.045 60.00% 6 of 10 100.00%  10 of 10 
    ha1p_61253_l50 46 1.88 100.00%  10 of 10  90.00% 9 of 10
    CHR01P001005050 47 1.03 70.00% 7 of 10 66.67% 6 of 9 
    CHR16P001157479 48 2.055 50.00% 4 of 8  100.00%  9 of 9 
    ha1g_00681 49 1.61   70% 7 of 10   90% 9 of 10
    ha1g_01966 50 1.76   100% 10 of 10    90% 9 of 10
    ha1g_02153 51 1.51   80% 8 of 10   90% 9 of 10
    ha1g_02319 52 0.53   80% 8 of 10   90% 9 of 10
    ha1g_02335 53 1.63   90% 9 of 10   80% 8 of 10
    ha1p16_00182 54 1.17   90% 9 of 10   90% 9 of 10
    ha1p16_00185 55 1.08   90% 9 of 10   80% 8 of 10
    ha1p16_00193 56 1.79   90% 9 of 10   80% 8 of 10
    ha1p16_00259 57 2.89   90% 9 of 10   100% 10 of 10 
    ha1p_02799 58 4.6   50% 5 of 10   100% 9 of 9 
    ha1p_03567 59 0.78   70% 7 of 10   30% 3 of 10
    ha1p_03671 60 2.03   50% 5 of 10   89% 8 of 9 
    ha1p_05803 61 2.22   80% 8 of 10   90% 9 of 10
    ha1p_07131 62 4.26   100% 10 of 10    80% 8 of 10
    ha1p_07989 63 2.48   67% 6 of 9    100% 10 of 10 
    ha1p_08588 64 4.17   90% 9 of 10   70% 7 of 10
    ha1p_09700 65 0.66   33% 3 of 9    100% 9 of 9 
    ha1p_104458 66 4.08   80% 8 of 10   90% 9 of 10
    ha1p_105287 67 1.61   70% 7 of 10   80% 8 of 10
    ha1p_10702 68 1.14   50% 5 of 10   80% 8 of 10
    ha1p_108469 69 1.72   90% 9 of 10   80% 8 of 10
    ha1p_108849 70 3.63   80% 8 of 10   100% 9 of 9 
    ha1p_11016 71 2.28   90% 9 of 10   90% 9 of 10
    ha1p_11023 72 2.04   90% 9 of 10   80% 8 of 10
    ha1p_12974 73 0.68   30% 3 of 10   100% 10 of 10 
    ha1p_16027 74 2.03   50% 5 of 10   90% 9 of 10
    ha1p_16066 75 2.09   80% 8 of 10   60% 6 of 10
    ha1p_18911 76 2.52   78% 7 of 9    90% 9 of 10
    ha1p_19254 77 3.07   100% 10 of 10    80% 8 of 10
    ha1p_19853 78 1.74   50% 5 of 10   100% 10 of 10 
    ha1p_22257 79 0.96   50% 5 of 10   80% 8 of 10
    ha1p_22519 80 2.62   70% 7 of 10   90% 9 of 10
    ha1p_31800 81 3.8   60% 6 of 10   100% 10 of 10 
    ha1p_33290 82 2.56   90% 9 of 10   90% 9 of 10
    ha1p_37635 83 6   100% 10 of 10     0% 0 of 10
    ha1p_39189 84 1.29   100% 9 of 9    80% 8 of 10
    ha1p_39511 85 1.99   70% 7 of 10   90% 9 of 10
    ha1p_39752 86 3.01   40% 4 of 10   90% 9 of 10
    ha1p_60945 87 1.11   100% 10 of 10    70% 7 of 10
    ha1p_62183 88 2.58   60% 6 of 10   80% 8 of 10
    ha1p_69418 89 1.68   70% 7 of 10   80% 8 of 10
    ha1p_71224 90 2.42   70% 7 of 10   90% 9 of 10
    ha1p_74221 91 0.98   88% 7 of 8    67% 6 of 9 
    ha1p_76289 92 1.84   80% 8 of 10   100% 9 of 9 
    ha1p_81050 93 5.74   60% 6 of 10   90% 9 of 10
    ha1p_81674 94 2.3   60% 6 of 10   100% 10 of 10 
    ha1p_86355 95 0.67   50% 5 of 10   80% 8 of 10
    ha1p_98491 96 1.63   50% 5 of 10   80% 8 of 10
    ha1p_99426 97 2.21   50% 5 of 10   100% 10 of 10 
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 17
    Sensitivity and Specificity of differentially methylated loci in endometrial
    tumors relative to adjacent histological normal endometrial tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 2.95 78.57% 11 of 14 100.00%  9 of 9
    CHR01P026794862 2 1.565 50.00% 2 of 4 100.00%  1 of 1
    CHR01P043164342 3 3.105 92.86% 13 of 14 100.00%  9 of 9
    CHR01P063154999 4 0.705 85.71% 12 of 14 77.78% 7 of 9
    CHR01P204123050 5 2.55 40.00%  4 of 10 100.00%  5 of 5
    CHR01P206905110 6 3.385 92.86% 13 of 14 100.00%  9 of 9
    CHR01P225608458 7 2.025 57.14%  8 of 14 88.89% 8 of 9
    CHR02P005061785 8 1.17 92.86% 13 of 14 88.89% 8 of 9
    CHR02P042255672 9 1.175 78.57% 11 of 14 100.00%  9 of 9
    CHR02P223364582 10 2.145 100.00%  14 of 14 66.67% 6 of 9
    CHR03P027740753 11 0.985 85.71% 12 of 14 100.00%  9 of 9
    CHR03P052525960 12 1.84 71.43% 10 of 14 88.89% 8 of 9
    CHR03P069745999 13 1.25 85.71% 12 of 14 44.44% 4 of 9
    CHR05P059799713 14 1.285 71.43% 10 of 14 88.89% 8 of 9
    CHR05P059799813 15 1.28 78.57% 11 of 14 77.78% 7 of 9
    CHR05P177842690 16 1.72 78.57% 11 of 14 88.89% 8 of 9
    CHR06P010694062 17 3.215 50.00%  7 of 14 100.00%  9 of 9
    CHR06P026333318 18 1.895 92.86% 13 of 14 100.00%  9 of 9
    CHR08P102460854 19 1.44 92.86% 13 of 14 88.89% 8 of 9
    CHR08P102461254 20 1.635 100.00%  14 of 14 100.00%  9 of 9
    CHR08P102461554 21 1.97 100.00%  14 of 14 88.89% 8 of 9
    CHR09P000107988 22 1.52 92.86% 13 of 14 88.89% 8 of 9
    CHR09P021958839 23 1.27 100.00%  14 of 14 66.67% 6 of 9
    CHR09P131048752 24 2.72 71.43% 10 of 14 77.78% 7 of 9
    CHR10P118975684 25 0.505 35.71%  5 of 14 88.89% 8 of 9
    CHR11P021861414 26 5.925 78.57% 11 of 14 100.00%  9 of 9
    CHR12P004359362 27 2.28 92.86% 13 of 14 100.00%  9 of 9
    CHR12P016001231 28 1.635 100.00%  14 of 14 100.00%  9 of 9
    CHR14P018893344 29 1.565 71.43% 10 of 14 100.00%  9 of 9
    CHR14P093230340 30 2.235 78.57% 11 of 14 77.78% 7 of 9
    CHR16P000373719 31 2.88 100.00%  10 of 10 80.00% 4 of 5
    CHR16P066389027 32 1.325 85.71% 12 of 14 50.00% 4 of 8
    CHR16P083319654 33 2.49 100.00%  14 of 14 88.89% 8 of 9
    CHR18P019705147 34 3.36 92.86% 13 of 14 100.00%  9 of 9
    CHR19P018622408 35 1.97 71.43% 10 of 14 100.00%  9 of 9
    CHR19P051892823 36 1.11 85.71% 6 of 7 100.00%  6 of 6
    CHRXP013196410 37 1.205 92.31% 12 of 13 77.78% 7 of 9
    CHRXP013196870 38 1.32 78.57% 11 of 14 71.43% 5 of 7
    ha1p16_00179_l50 39 1.325 100.00%  14 of 14 77.78% 7 of 9
    ha1p16_00182_l50 40 0.985 92.86% 13 of 14 66.67% 6 of 9
    ha1p16_00257_l50 41 1.06 85.71% 12 of 14 77.78% 7 of 9
    ha1p_12601_l50 42 3.35 100.00%  14 of 14 88.89% 8 of 9
    ha1p_17147_l50 43 2.68 100.00%  14 of 14 100.00%  9 of 9
    ha1p_42350_l50 44 2.175 38.46%  5 of 13 100.00%  7 of 7
    ha1p_44897_l50 45 4.065 78.57% 11 of 14 44.44% 4 of 9
    ha1p_61253_l50 46 1.115 100.00%  6 of 6 50.00% 1 of 2
    CHR01P001005050 47 1.75 80.00%  8 of 10 100.00%  7 of 7
    CHR16P001157479 48 — — — — —
    ha1g_00681 49 1.74   50%  7 of 14   100% 9 of 9
    ha1g_01966 50 2.26   71% 10 of 14   78% 7 of 9
    ha1g_02153 51 0.68   93% 13 of 14   78% 7 of 9
    ha1g_02319 52 0.62   36%  5 of 14   100% 9 of 9
    ha1g_02335 53 1.77   69%  9 of 13   44% 4 of 9
    ha1p16_00182 54 0.89   79% 11 of 14   78% 7 of 9
    ha1p16_00185 55 0.86   71% 10 of 14   89% 8 of 9
    ha1p16_00193 56 1.67   85% 11 of 13   78% 7 of 9
    ha1p16_00259 57 1.94   100% 14 of 14   78% 7 of 9
    ha1p_02799 58 4.32   93% 13 of 14   78% 7 of 9
    ha1p_03567 59 1.79   79% 11 of 14   89% 8 of 9
    ha1p_03671 60 0.83   64%  9 of 14   78% 7 of 9
    ha1p_05803 61 0.66   93% 13 of 14   89% 8 of 9
    ha1p_07131 62 6   86% 12 of 14   100% 9 of 9
    ha1p_07989 63 3.79   36%  5 of 14   100% 9 of 9
    ha1p_08588 64 6   93% 13 of 14   100% 9 of 9
    ha1p_09700 65 0.82   100% 13 of 13   25% 2 of 8
    ha1p_104458 66 3.93   29%  4 of 14   100% 9 of 9
    ha1p_105287 67 2.99   100% 14 of 14   100% 9 of 9
    ha1p_10702 68 0.75   50%  7 of 14   100% 8 of 8
    ha1p_108469 69 1.43   64%  9 of 14   67% 6 of 9
    ha1p_108849 70 2.92   79% 11 of 14   89% 8 of 9
    ha1p_11016 71 4.13   50%  7 of 14   100% 9 of 9
    ha1p_11023 72 1.61   64%  9 of 14   100% 9 of 9
    ha1p_12974 73 0.51    0%  0 of 14   89% 8 of 9
    ha1p_16027 74 1.93   64%  9 of 14   56% 5 of 9
    ha1p_16066 75 0.51   79% 11 of 14   78% 7 of 9
    ha1p_18911 76 2.53   43%  6 of 14   89% 8 of 9
    ha1p_19254 77 5.53   79% 11 of 14   100% 9 of 9
    ha1p_19853 78 0.85   79% 11 of 14   89% 8 of 9
    ha1p_22257 79 2.22   43%  6 of 14   100% 9 of 9
    ha1p_22519 80 2.02   64%  9 of 14   100% 9 of 9
    ha1p_31800 81 3.52   57%  8 of 14   89% 8 of 9
    ha1p_33290 82 1.24   71% 10 of 14   89% 8 of 9
    ha1p_37635 83 6    7%  1 of 14   100% 9 of 9
    ha1p_39189 84 0.63   86% 12 of 14   89% 8 of 9
    ha1p_39511 85 3.91   71% 10 of 14   56% 5 of 9
    ha1p_39752 86 1.66   86% 12 of 14   78% 7 of 9
    ha1p_60945 87 0.86   86% 12 of 14   33% 3 of 9
    ha1p_62183 88 4.01   71% 10 of 14   100% 9 of 9
    ha1p_69418 89 2.53   50%  7 of 14   100% 9 of 9
    ha1p_71224 90 1.46   71% 10 of 14   78% 7 of 9
    ha1p_74221 91 0.88   85% 11 of 13   63% 5 of 8
    ha1p_76289 92 0.74   71% 10 of 14   78% 7 of 9
    ha1p_81050 93 5.92   79% 11 of 14   100% 9 of 9
    ha1p_81674 94 0.82   86% 12 of 14   89% 8 of 9
    ha1p_86355 95 1.19   79% 11 of 14   75% 6 of 8
    ha1p_98491 96 2.11   62%  8 of 13   100% 9 of 9
    ha1p_99426 97 0.66   79% 11 of 14   89% 8 of 9
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 18
    Sensitivity and Specificity of differentially methylated loci in esophageal
    tumors relative to adjacent histological normal esophageal tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 100.00%  9 of 9  0.00% 0 of 10
    CHR01P026794862 2 1.075 12.50% 1 of 8 100.00%  6 of 6 
    CHR01P043164342 3 5.84 66.67% 6 of 9 100.00%  10 of 10 
    CHR01P063154999 4 1.02 100.00%  9 of 9 50.00% 5 of 10
    CHR01P204123050 5 1.515 62.50% 5 of 8 77.78% 7 of 9 
    CHR01P206905110 6 1.935 88.89% 8 of 9 66.67% 6 of 9 
    CHR01P225608458 7 1.52 100.00%  9 of 9 70.00% 7 of 10
    CHR02P005061785 8 3.345 55.56% 5 of 9 80.00% 8 of 10
    CHR02P042255672 9 3.095 88.89% 8 of 9 70.00% 7 of 10
    CHR02P223364582 10 1.765 88.89% 8 of 9 80.00% 8 of 10
    CHR03P027740753 11 0.97 100.00%  9 of 9 80.00% 8 of 10
    CHR03P052525960 12 1.93 55.56% 5 of 9 90.00% 9 of 10
    CHR03P069745999 13 3.435 77.78% 7 of 9 80.00% 8 of 10
    CHR05P059799713 14 1.38 77.78% 7 of 9 70.00% 7 of 10
    CHR05P059799813 15 1.57 55.56% 5 of 9 90.00% 9 of 10
    CHR05P177842690 16 2.435 62.50% 5 of 8 55.56% 5 of 9 
    CHR06P010694062 17 2.07 77.78% 7 of 9 77.78% 7 of 9 
    CHR06P026333318 18 1.34 88.89% 8 of 9 60.00% 6 of 10
    CHR08P102460854 19 0.555 66.67% 6 of 9 60.00% 6 of 10
    CHR08P102461254 20 0.86 62.50% 5 of 8 70.00% 7 of 10
    CHR08P102461554 21 0.905 88.89% 8 of 9 40.00% 4 of 10
    CHR09P000107988 22 1.025 100.00%  9 of 9 60.00% 6 of 10
    CHR09P021958839 23 0.965 100.00%  9 of 9 40.00% 4 of 10
    CHR09P131048752 24 3.61 77.78% 7 of 9 90.00% 9 of 10
    CHR10P118975684 25 1.455 66.67% 6 of 9 100.00%  10 of 10 
    CHR11P021861414 26 4.49 100.00%  9 of 9 80.00% 8 of 10
    CHR12P004359362 27 2.085 66.67% 6 of 9 90.00% 9 of 10
    CHR12P016001231 28 0.855 50.00% 4 of 8 90.00% 9 of 10
    CHR14P018893344 29 2.14 100.00%  9 of 9 90.00% 9 of 10
    CHR14P093230340 30 2.035 100.00%  9 of 9 90.00% 9 of 10
    CHR16P000373719 31 0.83 87.50% 7 of 8 66.67% 6 of 9 
    CHR16P066389027 32 0.75 100.00%  9 of 9 11.11% 1 of 9 
    CHR16P083319654 33 2.145 55.56% 5 of 9 90.00% 9 of 10
    CHR18P019705147 34 2.245 88.89% 8 of 9 50.00% 4 of 8 
    CHR19P018622408 35 1.78 100.00%  9 of 9 60.00% 6 of 10
    CHR19P051892823 36 2.295 25.00% 1 of 4 100.00%  5 of 5 
    CHRXP013196410 37 1.615 100.00%  9 of 9 40.00% 4 of 10
    CHRXP013196870 38 1.945 88.89% 8 of 9 50.00% 5 of 10
    ha1p16_00179_l50 39 1.405 44.44% 4 of 9 90.00% 9 of 10
    ha1p16_00182_l50 40 0.995 66.67% 6 of 9 77.78% 7 of 9 
    ha1p16_00257_l50 41 0.8 88.89% 8 of 9 40.00% 4 of 10
    ha1p_12601_l50 42 1.125 88.89% 8 of 9 60.00% 6 of 10
    ha1p_17147_l50 43 1.025 66.67% 6 of 9 80.00% 8 of 10
    ha1p_42350_l50 44 2.885 100.00%  8 of 8 88.89% 8 of 9 
    ha1p_44897_l50 45 1.715 100.00%  9 of 9 50.00% 5 of 10
    ha1p_61253_l50 46 1.57 77.78% 7 of 9 88.89% 8 of 9 
    CHR01P001005050 47 1.57 55.56% 5 of 9 66.67% 6 of 9 
    CHR16P001157479 48 4.79 100.00%  1 of 1 66.67% 2 of 3 
    ha1g_00681 49 0.69   89% 8 of 9   80% 8 of 10
    ha1g_01966 50 1.74   100% 9 of 9   70% 7 of 10
    ha1g_02153 51 1.42   78% 7 of 9   80% 8 of 10
    ha1g_02319 52 1.01   89% 8 of 9   80% 8 of 10
    ha1g_02335 53 3.54   89% 8 of 9   40% 4 of 10
    ha1p16_00182 54 0.78   89% 8 of 9   70% 7 of 10
    ha1p16_00185 55 0.85   89% 8 of 9   50% 5 of 10
    ha1p16_00193 56 1.45   89% 8 of 9   50% 5 of 10
    ha1p16_00259 57 2.28   50% 4 of 8   80% 8 of 10
    ha1p_02799 58 2.63   56% 5 of 9   100% 10 of 10 
    ha1p_03567 59 1.09   78% 7 of 9   80% 8 of 10
    ha1p_03671 60 0.91   100% 9 of 9   90% 9 of 10
    ha1p_05803 61 1.37   100% 9 of 9   90% 9 of 10
    ha1p_07131 62 5.25   100% 9 of 9   80% 8 of 10
    ha1p_07989 63 2.79   83% 5 of 6   80% 8 of 10
    ha1p_08588 64 5.89   89% 8 of 9   70% 7 of 10
    ha1p_09700 65 — — — — —
    ha1p_14458 66 3.23   78% 7 of 9   70% 7 of 10
    ha1p_105287 67 1.49   56% 5 of 9   90% 9 of 10
    ha1p_10702 68 0.68   56% 5 of 9   100% 10 of 10 
    ha1p_108469 69 1.7   33% 3 of 9   100% 9 of 9 
    ha1p_108849 70 2.84   89% 8 of 9   70% 7 of 10
    ha1p_11016 71 2.81   89% 8 of 9   70% 7 of 10
    ha1p_11023 72 2.33   56% 5 of 9   90% 9 of 10
    ha1p_12974 73 0.79   67% 6 of 9   80% 8 of 10
    ha1p_16027 74 1.15   67% 6 of 9   90% 9 of 10
    ha1p_16066 75 1.17   89% 8 of 9   100% 10 of 10 
    ha1p_18911 76 2.13   88% 7 of 8   80% 8 of 10
    ha1p_19254 77 3.38   89% 8 of 9   80% 8 of 10
    ha1p_19853 78 0.76   100% 9 of 9   70% 7 of 10
    ha1p_22257 79 1.64   89% 8 of 9   90% 9 of 10
    ha1p_22519 80 1.76   89% 8 of 9   90% 9 of 10
    ha1p_31800 81 3.47   80% 4 of 5   80% 8 of 10
    ha1p_33290 82 1.26   100% 9 of 9   60% 6 of 10
    ha1p_37635 83 6   100% 9 of 9    0% 0 of 10
    ha1p_39189 84 1.75   100% 9 of 9   100% 10 of 10 
    ha1p_39511 85 3.21   11% 1 of 9   100% 10 of 10 
    ha1p_39752 86 2.3   100% 9 of 9   70% 7 of 10
    ha1p_60945 87 1.81   89% 8 of 9   90% 9 of 10
    ha1p_62183 88 2.84   33% 3 of 9   100% 10 of 10 
    ha1p_69418 89 2.29   78% 7 of 9   80% 8 of 10
    ha1p_71224 90 1.69   78% 7 of 9   60% 6 of 10
    ha1p_74221 91 1.42   88% 7 of 8   70% 7 of 10
    ha1p_76289 92 1.45   67% 6 of 9   100% 8 of 8 
    ha1p_81050 93 6   89% 8 of 9   80% 8 of 10
    ha1p_81674 94 1.98   67% 6 of 9   90% 9 of 10
    ha1p_86355 95 2.09   33% 3 of 9   89% 8 of 9 
    ha1p_98491 96 2.89   44% 4 of 9   80% 8 of 10
    ha1p_99426 97 1.24   89% 8 of 9   90% 9 of 10
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 19
    Sensitivity and Specificity of differentially methylated loci in head and neck
    tumors relative to adjacent histological normal head and neck tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 87.50% 7 of 8 40.00% 2 of 5
    CHR01P026794862 2 0.615 57.14% 4 of 7 100.00%  1 of 1
    CHR01P043164342 3 4.85 77.78% 7 of 9 100.00%  5 of 5
    CHR01P063154999 4 0.875 100.00%  9 of 9 80.00% 4 of 5
    CHR01P204123050 5 1.605 44.44% 4 of 9 80.00% 4 of 5
    CHR01P206905110 6 1.92 55.56% 5 of 9 80.00% 4 of 5
    CHR01P225608458 7 1.8 77.78% 7 of 9 100.00%  5 of 5
    CHR02P005061785 8 3.455 66.67% 6 of 9 80.00% 4 of 5
    CHR02P042255672 9 4.075 77.78% 7 of 9 80.00% 4 of 5
    CHR02P223364582 10 1.7 88.89% 8 of 9 100.00%  5 of 5
    CHR03P027740753 11 1.05 88.89% 8 of 9 100.00%  5 of 5
    CHR03P052525960 12 2.66 55.56% 5 of 9 100.00%  5 of 5
    CHR03P069745999 13 4.04 77.78% 7 of 9 100.00%  5 of 5
    CHR05P059799713 14 1.595 66.67% 6 of 9 80.00% 4 of 5
    CHR05P059799813 15 1.745 57.14% 4 of 7 100.00%  5 of 5
    CHR05P177842690 16 1.12 100.00%  9 of 9 60.00% 3 of 5
    CHR06P010694062 17 2.695 66.67% 6 of 9 100.00%  5 of 5
    CHR06P026333318 18 2.335 77.78% 7 of 9 80.00% 4 of 5
    CHR08P102460854 19 0.625 55.56% 5 of 9 80.00% 4 of 5
    CHR08P102461254 20 0.64 88.89% 8 of 9 40.00% 2 of 5
    CHR08P102461554 21 0.77 33.33% 3 of 9 80.00% 4 of 5
    CHR09P000107988 22 0.97 100.00%  9 of 9 100.00%  5 of 5
    CHR09P021958839 23 1.09 88.89% 8 of 9 100.00%  5 of 5
    CHR09P131048752 24 4.29 77.78% 7 of 9 100.00%  5 of 5
    CHR10P118975684 25 1.405 87.50% 7 of 8 80.00% 4 of 5
    CHR11P021861414 26 4.21 88.89% 8 of 9 80.00% 4 of 5
    CHR12P004359362 27 1.065 77.78% 7 of 9 60.00% 3 of 5
    CHR12P016001231 28 1.25 66.67% 6 of 9 60.00% 3 of 5
    CHR14P018893344 29 2.43 88.89% 8 of 9 100.00%  5 of 5
    CHR14P093230340 30 1.26 100.00%  9 of 9 80.00% 4 of 5
    CHR16P000373719 31 1.215 100.00%  6 of 6 75.00% 3 of 4
    CHR16P066389027 32 1.645 77.78% 7 of 9 60.00% 3 of 5
    CHR16P083319654 33 3.81 100.00%  9 of 9 20.00% 1 of 5
    CHR18P019705147 34 1.73 55.56% 5 of 9 80.00% 4 of 5
    CHR19P018622408 35 2.245 88.89% 8 of 9 80.00% 4 of 5
    CHR19P051892823 36 3.875 80.00% 4 of 5 100.00%  3 of 3
    CHRXP013196410 37 1.72 100.00%  9 of 9 40.00% 2 of 5
    CHRXP013196870 38 1.38 88.89% 8 of 9 40.00% 2 of 5
    ha1p16_00179_l50 39 1.105 88.89% 8 of 9 100.00%  5 of 5
    ha1p16_00182_l50 40 0.735 100.00%  9 of 9 100.00%  5 of 5
    ha1p16_00257_l50 41 1.495 77.78% 7 of 9 100.00%  5 of 5
    ha1p_12601_l50 42 1.035 66.67% 6 of 9 60.00% 3 of 5
    ha1p_17147_l50 43 1.53 37.50% 3 of 8 100.00%  5 of 5
    ha1p_42350_l50 44 4.505 100.00%  7 of 7 80.00% 4 of 5
    ha1p_44897_l50 45 2.59 100.00%  8 of 8 80.00% 4 of 5
    ha1p_61253_l50 46 1.19 88.89% 8 of 9 75.00% 3 of 4
    CHR01P001005050 47 1.205 71.43% 5 of 7 100.00%  3 of 3
    CHR16P001157479 48 — — — — —
    ha1g_00681 49 0.73   100% 9 of 9   40% 2 of 5
    ha1g_01966 50 1.59   100% 9 of 9   50% 2 of 4
    ha1g_02153 51 1.42   78% 7 of 9   100% 5 of 5
    ha1g_02319 52 0.86   89% 8 of 9   60% 3 of 5
    ha1g_02335 53 3.52   67% 6 of 9   100% 5 of 5
    ha1p16_00182 54 0.88   89% 8 of 9   100% 5 of 5
    ha1p16_00185 55 0.99   89% 8 of 9   80% 4 of 5
    ha1p16_00193 56 1.75   89% 8 of 9   80% 4 of 5
    ha1p16_00259 57 2.44   67% 6 of 9   80% 4 of 5
    ha1p_02799 58 2.27   100% 9 of 9   40% 2 of 5
    ha1p_03567 59 1.61   89% 8 of 9   40% 2 of 5
    ha1p_03671 60 0.54   100% 9 of 9   100% 4 of 4
    ha1p_05803 61 1.88   78% 7 of 9   80% 4 of 5
    ha1p_07131 62 3.46   78% 7 of 9   100% 5 of 5
    ha1p_07989 63 2.09   83% 5 of 6   80% 4 of 5
    ha1p_08588 64 5.5   67% 6 of 9   60% 3 of 5
    ha1p_09700 65 0.61   50% 4 of 8   80% 4 of 5
    ha1p_104458 66 4.3   78% 7 of 9   100% 5 of 5
    ha1p_105287 67 1.8   67% 6 of 9   80% 4 of 5
    ha1p_10702 68 1.79   33% 3 of 9   100% 5 of 5
    ha1p_108469 69 1.85   22% 2 of 9   100% 5 of 5
    ha1p_108849 70 2.7   67% 6 of 9   80% 4 of 5
    ha1p_11016 71 3.99   100% 9 of 9   80% 4 of 5
    ha1p_11023 72 2.14   100% 9 of 9   60% 3 of 5
    ha1p_12974 73 0.57   78% 7 of 9   60% 3 of 5
    ha1p_16027 74 0.57   100% 9 of 9   60% 3 of 5
    ha1p_16066 75 0.76   89% 8 of 9   80% 4 of 5
    ha1p_18911 76 2.73   89% 8 of 9   60% 3 of 5
    ha1p_19254 77 2.44   67% 6 of 9   100% 5 of 5
    ha1p_19853 78 0.7   89% 8 of 9   100% 5 of 5
    ha1p_22257 79 1.86   78% 7 of 9   100% 5 of 5
    ha1p_22519 80 2.18   56% 5 of 9   100% 5 of 5
    ha1p_31800 81 2.79   100% 7 of 7   40% 2 of 5
    ha1p_33290 82 1.86   78% 7 of 9   100% 5 of 5
    ha1p_37635 83 6   100% 8 of 8    0% 0 of 5
    ha1p_39189 84 0.97   86% 6 of 7   80% 4 of 5
    ha1p_39511 85 1.88   89% 8 of 9   40% 2 of 5
    ha1p_39752 86 3.02   56% 5 of 9   100% 5 of 5
    ha1p_60945 87 2.31   78% 7 of 9   80% 4 of 5
    ha1p_62183 88 4.34   67% 6 of 9   60% 3 of 5
    ha1p_69418 89 2.22   43% 3 of 7   100% 5 of 5
    ha1p_71224 90 1.89   78% 7 of 9   80% 4 of 5
    ha1p_74221 91 2.08   67% 6 of 9   80% 4 of 5
    ha1p_76289 92 1.5   78% 7 of 9   100% 5 of 5
    ha1p_81050 93 5.46   78% 7 of 9   80% 4 of 5
    ha1p_81674 94 1.88   67% 6 of 9   80% 4 of 5
    ha1p_86355 95 1.18   100% 9 of 9   50% 2 of 4
    ha1p_98491 96 2.27   22% 2 of 9   100% 5 of 5
    ha1p_99426 97 1.13   78% 7 of 9   100% 5 of 5
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 20
    Sensitivity and Specificity of differentially methylated loci in liver tumors
    relative to adjacent histological normal liver tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 100.00%  8 of 8  0.00% 0 of 9
    CHR01P026794862 2 1.295 42.86% 3 of 7 100.00%  9 of 9
    CHR01P043164342 3 5.27 87.50% 7 of 8 88.89% 8 of 9
    CHR01P063154999 4 1.4 77.78% 7 of 9 71.43% 5 of 7
    CHR01P204123050 5 2.08 33.33% 3 of 9 88.89% 8 of 9
    CHR01P206905110 6 5.88 33.33% 3 of 9 100.00%  9 of 9
    CHR01P225608458 7 2.62 55.56% 5 of 9 88.89% 8 of 9
    CHR02P005061785 8 4.8 55.56% 5 of 9 100.00%  9 of 9
    CHR02P042255672 9 3.835 77.78% 7 of 9 75.00% 6 of 8
    CHR02P223364582 10 1.73 55.56% 5 of 9 77.78% 7 of 9
    CHR03P027740753 11 1.47 55.56% 5 of 9 88.89% 8 of 9
    CHR03P052525960 12 4.685 44.44% 4 of 9 100.00%  9 of 9
    CHR03P069745999 13 4.745 55.56% 5 of 9 100.00%  8 of 8
    CHR05P059799713 14 3.63 50.00% 4 of 8 87.50% 7 of 8
    CHR05P059799813 15 2.405 44.44% 4 of 9 100.00%  8 of 8
    CHR05P177842690 16 2.12 66.67% 6 of 9 100.00%  9 of 9
    CHR06P010694062 17 4.24 66.67% 6 of 9 66.67% 6 of 9
    CHR06P026333318 18 5.665 55.56% 5 of 9 88.89% 8 of 9
    CHR08P102460854 19 1.305 87.50% 7 of 8 55.56% 5 of 9
    CHR08P102461254 20 1.985 66.67% 6 of 9 77.78% 7 of 9
    CHR08P102461554 21 1.545 88.89% 8 of 9 55.56% 5 of 9
    CHR09P000107988 22 1.705 33.33% 3 of 9 88.89% 8 of 9
    CHR09P021958839 23 2.335 66.67% 6 of 9 77.78% 7 of 9
    CHR09P131048752 24 5.305 33.33% 3 of 9 100.00%  9 of 9
    CHR10P118975684 25 1.59 50.00% 3 of 6 100.00%  7 of 7
    CHR11P021861414 26 4.58 33.33% 3 of 9 100.00%  9 of 9
    CHR12P004359362 27 1.855 77.78% 7 of 9 88.89% 8 of 9
    CHR12P016001231 28 1.515 66.67% 6 of 9 55.56% 5 of 9
    CHR14P018893344 29 3.45 66.67% 6 of 9 77.78% 7 of 9
    CHR14P093230340 30 1.58 66.67% 6 of 9 77.78% 7 of 9
    CHR16P000373719 31 4.565 60.00% 3 of 5 75.00% 3 of 4
    CHR16P066389027 32 1.955 88.89% 8 of 9 55.56% 5 of 9
    CHR16P083319654 33 1.24 77.78% 7 of 9 88.89% 8 of 9
    CHR18P019705147 34 3.76 100.00%  9 of 9 100.00%  6 of 6
    CHR19P018622408 35 3.325 66.67% 6 of 9 66.67% 6 of 9
    CHR19P051892823 36 4.08 100.00%  2 of 2 100.00%  1 of 1
    CHRXP013196410 37 1.215 50.00% 4 of 8 85.71% 6 of 7
    CHRXP013196870 38 1.465 75.00% 6 of 8 55.56% 5 of 9
    ha1p16_00179_l50 39 2.28 75.00% 6 of 8 62.50% 5 of 8
    ha1p16_00182_l50 40 1.775 77.78% 7 of 9 77.78% 7 of 9
    ha1p16_00257_l50 41 0.98 88.89% 8 of 9 66.67% 6 of 9
    ha1p_12601_l50 42 0.915 44.44% 4 of 9 100.00%  8 of 8
    ha1p_17147_l50 43 1.175 44.44% 4 of 9 88.89% 8 of 9
    ha1p_42350_l50 44 1.975 42.86% 3 of 7 100.00%  6 of 6
    ha1p_44897_l50 45 3.59 66.67% 6 of 9 75.00% 6 of 8
    ha1p_61253_l50 46 4.055 77.78% 7 of 9 88.89% 8 of 9
    CHR01P001005050 47 3.3 100.00%  9 of 9 77.78% 7 of 9
    CHR16P001157479 48 6 25.00% 2 of 8 100.00%  5 of 5
    ha1g_00681 49 2.66   89% 8 of 9   78% 7 of 9
    ha1g_01966 50 3.07   38% 3 of 8   100% 9 of 9
    ha1g_02153 51 0.72   56% 5 of 9   78% 7 of 9
    ha1g_02319 52 2.1   56% 5 of 9   89% 8 of 9
    ha1g_02335 53 2.62   50% 4 of 8   89% 8 of 9
    ha1p16_00182 54 1.73   89% 8 of 9   78% 7 of 9
    ha1p16_00185 55 1.65   67% 6 of 9   100% 9 of 9
    ha1p16_00193 56 2.73   56% 5 of 9   78% 7 of 9
    ha1p16_00259 57 3.77   78% 7 of 9   89% 8 of 9
    ha1p_02799 58 2.5   78% 7 of 9   86% 6 of 7
    ha1p_03567 59 0.68   33% 3 of 9   89% 8 of 9
    ha1p_03671 60 2.39   44% 4 of 9   78% 7 of 9
    ha1p_05803 61 2.52   56% 5 of 9   89% 8 of 9
    ha1p_07131 62 2.4   89% 8 of 9   89% 8 of 9
    ha1p_07989 63 1.5   88% 7 of 8   56% 5 of 9
    ha1p_08588 64 3.26   89% 8 of 9   89% 8 of 9
    ha1p_09700 65 0.81   50% 4 of 8   100% 9 of 9
    ha1p_104458 66 3.8   67% 6 of 9   56% 5 of 9
    ha1p_105287 67 3.67   44% 4 of 9   100% 8 of 8
    ha1p_10702 68 0.53   56% 5 of 9   67% 6 of 9
    ha1p_108469 69 2.31   44% 4 of 9   89% 8 of 9
    ha1p_108849 70 3.94   56% 5 of 9   78% 7 of 9
    ha1p_11016 71 4.36   44% 4 of 9   89% 8 of 9
    ha1p_11023 72 2.59   33% 3 of 9   100% 9 of 9
    ha1p_12974 73 0.65   89% 8 of 9   22% 2 of 9
    ha1p_16027 74 1.51   63% 5 of 8   89% 8 of 9
    ha1p_16066 75 1.43   67% 6 of 9   78% 7 of 9
    ha1p_18911 76 2.32   56% 5 of 9   67% 6 of 9
    ha1p_19254 77 1.79   100% 7 of 7   89% 8 of 9
    ha1p_19853 78 0.71   50% 4 of 8   78% 7 of 9
    ha1p_22257 79 2.56   67% 6 of 9   89% 8 of 9
    ha1p_22519 80 2.83   56% 5 of 9   78% 7 of 9
    ha1p_31800 81 2.81   67% 6 of 9   56% 5 of 9
    ha1p_33290 82 0.89   44% 4 of 9   100% 9 of 9
    ha1p_37635 83 6   44% 4 of 9   89% 8 of 9
    ha1p_39189 84 1.29   67% 6 of 9   89% 8 of 9
    ha1p_39511 85 2.01   100% 9 of 9   56% 5 of 9
    ha1p_39752 86 1.09   44% 4 of 9   100% 9 of 9
    ha1p_60945 87 1.74   44% 4 of 9   78% 7 of 9
    ha1p_62183 88 2.48   67% 6 of 9   100% 9 of 9
    ha1p_69418 89 6   56% 5 of 9   89% 8 of 9
    ha1p_71224 90 0.96   50% 4 of 8   89% 8 of 9
    ha1p_74221 91 2.22   22% 2 of 9   100% 9 of 9
    ha1p_76289 92 1.55   88% 7 of 8   78% 7 of 9
    ha1p_81050 93 5.95   56% 5 of 9   100% 9 of 9
    ha1p_81674 94 3.45   22% 2 of 9   100% 9 of 9
    ha1p_86355 95 1.61   56% 5 of 9   89% 8 of 9
    ha1p_98491 96 2.63   56% 5 of 9   100% 9 of 9
    ha1p_99426 97 1.06   100% 9 of 9   78% 7 of 9
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 21
    Sensitivity and Specificity of differentially methylated loci in lung tumors
    relative to adjacent histological normal lung tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 100.00%  20 of 20  0.00%  0 of 20
    CHR01P026794862 2 0.855 47.06%  8 of 17 88.24% 15 of 17
    CHR01P043164342 3 2.25 80.00% 16 of 20 68.42% 13 of 19
    CHR01P063154999 4 1.005 90.00% 18 of 20 90.00% 18 of 20
    CHR01P204123050 5 0.835 100.00%  20 of 20 15.00%  3 of 20
    CHR01P206905110 6 1.655 55.00% 11 of 20 90.00% 18 of 20
    CHR01P225608458 7 1.485 90.00% 18 of 20 90.00% 18 of 20
    CHR02P005061785 8 3.955 78.95% 15 of 19 57.89% 11 of 19
    CHR02P042255672 9 4.045 85.00% 17 of 20 90.00% 18 of 20
    CHR02P223364582 10 1.82 75.00% 15 of 20 90.00% 18 of 20
    CHR03P027740753 11 1.19 90.00% 18 of 20 100.00%  18 of 18
    CHR03P052525960 12 2.2 30.00%  6 of 20 95.00% 19 of 20
    CHR03P069745999 13 4.23 70.00% 14 of 20 50.00% 10 of 20
    CHR05P059799713 14 2.075 50.00% 10 of 20 80.00% 16 of 20
    CHR05P059799813 15 2.715 30.00%  6 of 20 100.00%  18 of 18
    CHR05P177842690 16 2.145 55.00% 11 of 20 70.00% 14 of 20
    CHR06P010694062 17 3.31 80.00% 16 of 20 85.00% 17 of 20
    CHR06P026333318 18 3.605 80.00% 16 of 20 95.00% 19 of 20
    CHR08P102460854 19 0.955  5.00%  1 of 20 100.00%  20 of 20
    CHR08P102461254 20 0.57 50.00% 10 of 20 75.00% 15 of 20
    CHR08P102461554 21 0.53 40.00%  8 of 20 80.00% 16 of 20
    CHR09P000107988 22 1.44 60.00% 12 of 20 85.00% 17 of 20
    CHR09P021958839 23 1.525 75.00% 15 of 20 90.00% 18 of 20
    CHR09P131048752 24 3.285 90.00% 18 of 20 70.00% 14 of 20
    CHR10P118975684 25 1.14 85.00% 17 of 20 100.00%  20 of 20
    CHR11P021861414 26 4.05 70.00% 14 of 20 90.00% 18 of 20
    CHR12P004359362 27 2.155 70.00% 14 of 20 95.00% 19 of 20
    CHR12P016001231 28 1.705 65.00% 13 of 20 68.42% 13 of 19
    CHR14P018893344 29 2.5 85.00% 17 of 20 78.95% 15 of 19
    CHR14P093230340 30 1.465 89.47% 17 of 19 95.00% 19 of 20
    CHR16P000373719 31 1.36 62.50% 10 of 16 88.24% 15 of 17
    CHR16P066389027 32 1.195 57.89% 11 of 19 66.67% 12 of 18
    CHR16P083319654 33 1.895 80.00% 16 of 20 80.00% 16 of 20
    CHR18P019705147 34 3.865 40.00%  8 of 20 100.00%  20 of 20
    CHR19P018622408 35 2.105 84.21% 16 of 19 90.00% 18 of 20
    CHR19P051892823 36 1.095 83.33% 10 of 12 76.92% 10 of 13
    CHRXP013196410 37 3.725 45.00%  9 of 20 95.00% 19 of 20
    CHRXP013196870 38 3.12 60.00% 12 of 20 80.00% 16 of 20
    ha1p16_00179_l50 39 1.575 65.00% 13 of 20 100.00%  20 of 20
    ha1p16_00182_l50 40 1.07 85.00% 17 of 20 80.00% 16 of 20
    ha1p16_00257_l50 41 1.045 78.95% 15 of 19 90.00% 18 of 20
    ha1p_12601_l50 42 0.75 50.00% 10 of 20 85.00% 17 of 20
    ha1p_17147_l50 43 0.7 60.00% 12 of 20 85.00% 17 of 20
    ha1p_42350_l50 44 2.205 94.12% 16 of 17 73.68% 14 of 19
    ha1p_44897_l50 45 2.22 70.00% 14 of 20 65.00% 13 of 20
    ha1p_61253_l50 46 1.895 77.78% 14 of 18 83.33% 15 of 18
    CHR01P001005050 47 0.52 63.16% 12 of 19 64.71% 11 of 17
    CHR16P001157479 48 — — — — —
    ha1g_00681 49 1.5   80%  8 of 10   90%  9 of 10
    ha1g_01966 50 1.37   87% 40 of 46   96% 46 of 48
    ha1g_02153 51 0.62   84% 38 of 45   90% 43 of 48
    ha1g_02319 52 0.73   74% 35 of 47   96% 45 of 47
    ha1g_02335 53 1.68   80%  8 of 10   90%  9 of 10
    ha1p16_00182 54 1.18   65% 30 of 46   90% 43 of 48
    ha1p16_00185 55 1.1   68% 30 of 44   91% 40 of 44
    ha1p16_00193 56 1.76   89% 40 of 45   68% 32 of 47
    ha1p16_00259 57 2.31   75% 33 of 44   93% 43 of 46
    ha1p_02799 58 2.06   47% 22 of 47   88% 42 of 48
    ha1p_03567 59 1.14   78% 36 of 46   83% 39 of 47
    ha1p_03671 60 1.42   76% 35 of 46   65% 31 of 48
    ha1p_05803 61 1.63   70%  7 of 10   90%  9 of 10
    ha1p_07131 62 3.88   83% 39 of 47   93% 43 of 46
    ha1p_07989 63 2.86   85% 40 of 47   90% 43 of 48
    ha1p_08588 64 3.9   80% 37 of 46   88% 42 of 48
    ha1p_09700 65 1.01   56% 25 of 45   93% 43 of 46
    ha1p_104458 66 3.94   80%  8 of 10   89% 8 of 9
    ha1p_105287 67 2.04   83% 40 of 48   73% 35 of 48
    ha1p_10702 68 0.5   66% 27 of 41   87% 39 of 45
    ha1p_108469 69 0.98   83% 38 of 46   89% 42 of 47
    ha1p_108849 70 3.7   70%  7 of 10   100% 10 of 10
    ha1p_11016 71 2.88   70%  7 of 10   100% 10 of 10
    ha1p_11023 72 3.41   100% 10 of 10   20%  2 of 10
    ha1p_12974 73 0.59   36% 17 of 47   90% 43 of 48
    ha1p_16027 74 1.02   73% 35 of 48   92% 44 of 48
    ha1p_16066 75 0.79   73% 35 of 48   85% 41 of 48
    ha1p_18911 76 2.87   70% 33 of 47   96% 45 of 47
    ha1p_19254 77 3.66   90%  9 of 10   90%  9 of 10
    ha1p_19853 78 0.75   79% 37 of 47   92% 44 of 48
    ha1p_22257 79 1.12   80% 37 of 46   85% 41 of 48
    ha1p_22519 80 1.84   78% 35 of 45   94% 45 of 48
    ha1p_31800 81 2.33   88% 42 of 48   92% 44 of 48
    ha1p_33290 82 1.71   87% 41 of 47   92% 44 of 48
    ha1p_37635 83 2.99   70%  7 of 10   80%  8 of 10
    ha1p_39189 84 0.89   83% 39 of 47   90% 38 of 42
    ha1p_39511 85 2.36   78% 7 of 9   78% 7 of 9
    ha1p_39752 86 1.79   65% 30 of 46   81% 38 of 47
    ha1p_60945 87 2.11   60%  6 of 10   100% 8 of 8
    ha1p_62183 88 4.23   82% 37 of 45   83% 39 of 47
    ha1p_69418 89 2.36   85% 39 of 46   93% 43 of 46
    ha1p_71224 90 1.98   77% 34 of 44   77% 30 of 39
    ha1p_74221 91 1.51   70%  7 of 10   100% 10 of 10
    ha1p_76289 92 1.13   81% 34 of 42   83% 33 of 40
    ha1p_81050 93 5.6   92% 33 of 36   87% 40 of 46
    ha1p_81674 94 1.81   74% 29 of 39   87% 27 of 31
    ha1p_86355 95 1.95   39% 17 of 44   88% 36 of 41
    ha1p_98491 96 2.13   38% 18 of 47   94% 44 of 47
    ha1p_99426 97 1.02   83% 39 of 47   92% 44 of 48
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 22
    Sensitivity and Specificity of differentially methylated loci in lung tumors
    relative to histologically normal lung tissue.
    Locus
    Feature No. Threshold Sensitivity Pos. of Total Specificity Neg. of Total
    ha1g_00681 49 — — — — —
    ha1g_01966 50 1.3  89% 24 of 27 95% 20 of 21
    ha1g_02153 51 0.66 81% 22 of 27 91% 20 of 22
    ha1g_02319 52 0.57 83% 24 of 29 95% 21 of 22
    ha1g_02335 53 — — — — —
    ha1p16_00182 54 1.05 85% 23 of 27 86% 19 of 22
    ha1p16_00185 55 1.13 76% 19 of 25 100%  22 of 22
    ha1p16_00193 56 1.83 81% 22 of 27 90% 19 of 21
    ha1p16_00259 57 2.31 77% 20 of 26 100%  21 of 21
    ha1p_02799 58 1.91 50% 14 of 28 100%  21 of 21
    ha1p_03567 59 1.34 81% 22 of 27 91% 20 of 22
    ha1p_03671 60 1.86 46% 13 of 28 90% 19 of 21
    ha1p_05803 61 — — — — —
    ha1p_07131 62 3.89 82% 23 of 28 100%  22 of 22
    ha1p_07989 63 3.1  86% 24 of 28 95% 21 of 22
    ha1p_08588 64 4.35 86% 24 of 28 95% 20 of 21
    ha1p_09700 65 1.01 64% 18 of 28 91% 20 of 22
    ha1p_104458 66 — — — — —
    ha1p_105287 67 1.96 79% 23 of 29 95% 20 of 21
    ha1p_10702 68 1.16 41%  9 of 22 100%  19 of 19
    ha1p_108469 69 0.99 78% 21 of 27 95% 20 of 21
    ha1p_108849 70 — — — — —
    ha1p_11016 71 — — — — —
    ha1p_11023 72 — — — — —
    ha1p_12974 73 0.61 21%  6 of 28 95% 21 of 22
    ha1p_16027 74 0.66 86% 25 of 29 86% 19 of 22
    ha1p_16066 75 0.79 72% 21 of 29 100%  20 of 20
    ha1p_18911 76 2.69 66% 19 of 29 100%  22 of 22
    ha1p_19254 77 — — — — —
    ha1p_19853 78 0.75 86% 24 of 28 91% 20 of 22
    ha1p_22257 79 1.56 54% 15 of 28 91% 20 of 22
    ha1p_22519 80 1.68 89% 24 of 27 95% 21 of 22
    ha1p_31800 81 2.59 76% 22 of 29 86% 19 of 22
    ha1p_33290 82 1.94 86% 25 of 29 95% 21 of 22
    ha1p_37635 83 — — — — —
    ha1p_39189 84 0.89 86% 24 of 28 86% 19 of 22
    ha1p_39511 85 — — — — —
    ha1p_39752 86 1.96 56% 15 of 27 82% 18 of 22
    ha1p_60945 87 — — — — —
    ha1p_62183 88 3.55 81% 21 of 26 100%  22 of 22
    ha1p_69418 89 2.67 85% 23 of 27 100%  21 of 21
    ha1p_71224 90 2.01 84% 21 of 25 79% 15 of 19
    ha1p_74221 91 — — — — —
    ha1p_76289 92 1.13 80% 20 of 25 83% 15 of 18
    ha1p_81050 93 6   88% 15 of 17 95% 21 of 22
    ha1p_81674 94 2.4  65% 13 of 20 100%  18 of 18
    ha1p_86355 95 1.7  44% 11 of 25 100%  22 of 22
    ha1p_98491 96 2.14 43% 12 of 28 95% 21 of 22
    ha1p_99426 97 1.02 86% 24 of 28 100%  22 of 22
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) benign normal samples.
    Neg. of Total: Number of negative benign normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 23
    Frequency of methylation of each locus in melanoma tumors.
    Locus
    Feature Name Number Threshold Sensitivity Pos. of Total
    CHR01P001976799 1 1.0 100.00%  7 of 7
    CHR01P026794862 2 1.0 33.33% 2 of 6
    CHR01P043164342 3 1.0 100.00%  7 of 7
    CHR01P063154999 4 1.0 100.00%  7 of 7
    CHR01P204123050 5 1.0 85.71% 6 of 7
    CHR01P206905110 6 1.0 100.00%  7 of 7
    CHR01P225608458 7 1.0 85.71% 6 of 7
    CHR02P005061785 8 1.0 100.00%  7 of 7
    CHR02P042255672 9 1.0 100.00%  7 of 7
    CHR02P223364582 10 1.0 42.86% 3 of 7
    CHR03P027740753 11 1.0 100.00%  7 of 7
    CHR03P052525960 12 1.0 100.00%  7 of 7
    CHR03P069745999 13 1.0 100.00%  7 of 7
    CHR05P059799713 14 1.0 100.00%  7 of 7
    CHR05P059799813 15 1.0 100.00%  7 of 7
    CHR05P177842690 16 1.0 100.00%  7 of 7
    CHR06P010694062 17 1.0 85.71% 6 of 7
    CHR06P026333318 18 1.0 85.71% 6 of 7
    CHR08P102460854 19 1.0 85.71% 6 of 7
    CHR08P102461254 20 1.0 100.00%  7 of 7
    CHR08P102461554 21 1.0 85.71% 6 of 7
    CHR09P000107988 22 1.0 85.71% 6 of 7
    CHR09P021958839 23 1.0 85.71% 6 of 7
    CHR09P131048752 24 1.0 57.14% 4 of 7
    CHR10P118975684 25 1.0 85.71% 6 of 7
    CHR11P021861414 26 1.0 100.00%  7 of 7
    CHR12P004359362 27 1.0 71.43% 5 of 7
    CHR12P016001231 28 1.0 85.71% 6 of 7
    CHR14P018893344 29 1.0 85.71% 6 of 7
    CHR14P093230340 30 1.0 83.33% 5 of 6
    CHR16P000373719 31 1.0 50.00% 2 of 4
    CHR16P066389027 32 1.0 71.43% 5 of 7
    CHR16P083319654 33 1.0 71.43% 5 of 7
    CHR18P019705147 34 1.0 100.00%  7 of 7
    CHR19P018622408 35 1.0 66.67% 4 of 6
    CHR19P051892823 36 1.0 25.00% 1 of 4
    CHRXP013196410 37 1.0 100.00%  5 of 5
    CHRXP013196870 38 1.0 85.71% 6 of 7
    ha1p16_00179_l50 39 1.0 85.71% 6 of 7
    ha1p16_00182_l50 40 1.0 42.86% 3 of 7
    ha1p16_00257_l50 41 1.0 57.14% 4 of 7
    ha1p_12601_l50 42 1.0 71.43% 5 of 7
    ha1p_17147_l50 43 1.0 100.00%  7 of 7
    ha1p_42350_l50 44 1.0 100.00%  4 of 4
    ha1p_44897_l50 45 1.0 100.00%  7 of 7
    ha1p_61253_l50 46 1.0 57.14% 4 of 7
    CHR01P001005050 47 1.0 100.00%  4 of 4
    CHR16P001157479 48 1.0 100.00%  1 of 1
    ha1g_00681 49 1.0   71% 5 of 7
    ha1g_01966 50 1.0   86% 6 of 7
    ha1g_02153 51 1.0   86% 6 of 7
    ha1g_02319 52 1.0   57% 4 of 7
    ha1g_02335 53 1.0   86% 6 of 7
    ha1p16_00182 54 1.0   43% 3 of 7
    ha1p16_00185 55 1.0   71% 5 of 7
    ha1p16_00193 56 1.0   100% 6 of 6
    ha1p16_00259 57 1.0   100% 7 of 7
    ha1p_02799 58 1.0   100% 7 of 7
    ha1p_03567 59 1.0   57% 4 of 7
    ha1p_03671 60 1.0    0% 0 of 6
    ha1p_05803 61 1.0   57% 4 of 7
    ha1p_07131 62 1.0   86% 6 of 7
    ha1p_07989 63 1.0   80% 4 of 5
    ha1p_08588 64 1.0   86% 6 of 7
    ha1p_09700 65 1.0   50% 2 of 4
    ha1p_104458 66 1.0   86% 6 of 7
    ha1p_105287 67 1.0   100% 7 of 7
    ha1p_10702 68 1.0   71% 5 of 7
    ha1p_108469 69 1.0   86% 6 of 7
    ha1p_108849 70 1.0   100% 7 of 7
    ha1p_11016 71 1.0   100% 7 of 7
    ha1p_11023 72 1.0   86% 6 of 7
    ha1p_12974 73 1.0   14% 1 of 7
    ha1p_16027 74 1.0   100% 7 of 7
    ha1p_16066 75 1.0   86% 6 of 7
    ha1p_18911 76 1.0   86% 6 of 7
    ha1p_19254 77 1.0   100% 7 of 7
    ha1p_19853 78 1.0   29% 2 of 7
    ha1p_22257 79 1.0   100% 7 of 7
    ha1p_22519 80 1.0   86% 6 of 7
    ha1p_31800 81 1.0   100% 7 of 7
    ha1p_33290 82 1.0   86% 6 of 7
    ha1p_37635 83 1.0   100% 7 of 7
    ha1p_39189 84 1.0   86% 6 of 7
    ha1p_39511 85 1.0   43% 3 of 7
    ha1p_39752 86 1.0   100% 7 of 7
    ha1p_60945 87 1.0   86% 6 of 7
    ha1p_62183 88 1.0   100% 7 of 7
    ha1p_69418 89 1.0   71% 5 of 7
    ha1p_71224 90 1.0   57% 4 of 7
    ha1p_74221 91 1.0   86% 6 of 7
    ha1p_76289 92 1.0   57% 4 of 7
    ha1p_81050 93 1.0   86% 6 of 7
    ha1p_81674 94 1.0   100% 7 of 7
    ha1p_86355 95 1.0   100% 7 of 7
    ha1p_98491 96 1.0   86% 6 of 7
    ha1p_99426 97 1.0   71% 5 of 7
    Sensitivity: % of positive (i.e., methylation score above 1.0) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Note that adjacent histology normal or normal skin samples were not available for analysis.
    Threshold for a positive methylation score was set at an average dCt of 1.0.
  • TABLE 24
    Sensitivity and Specificity of differentially methylated loci in ovarian
    tumors relative to histologically normal ovarian tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 1.405 91.18% 31 of 34 96.97% 32 of 33
    CHR01P026794862 2 1.32 25.00%  7 of 28 87.88% 29 of 33
    CHR01P043164342 3 5.35 91.18% 31 of 34 97.14% 34 of 35
    CHR01P063154999 4 0.8 85.29% 29 of 34 94.12% 32 of 34
    CHR01P204123050 5 1.165 72.73% 24 of 33 73.53% 25 of 34
    CHR01P206905110 6 5.6 88.24% 30 of 34 97.14% 34 of 35
    CHR01P225608458 7 1.51 73.53% 25 of 34 94.29% 33 of 35
    CHR02P005061785 8 1.565 93.94% 31 of 33 97.14% 34 of 35
    CHR02P042255672 9 0.835 85.29% 29 of 34 97.14% 34 of 35
    CHR02P223364582 10 1.485 85.29% 29 of 34 94.29% 33 of 35
    CHR03P027740753 11 0.69 85.29% 29 of 34 96.97% 32 of 33
    CHR03P052525960 12 1.985 55.88% 19 of 34 97.06% 33 of 34
    CHR03P069745999 13 4.645 73.53% 25 of 34 94.29% 33 of 35
    CHR05P059799713 14 1.735 64.71% 22 of 34 90.91% 30 of 33
    CHR05P059799813 15 1.78 64.71% 22 of 34 88.24% 30 of 34
    CHR05P177842690 16 1.545 85.29% 29 of 34 85.71% 30 of 35
    CHR06P010694062 17 1.235 85.29% 29 of 34 85.71% 30 of 35
    CHR06P026333318 18 1.705 94.12% 32 of 34 94.29% 33 of 35
    CHR08P102460854 19 1.045 81.82% 27 of 33 94.29% 33 of 35
    CHR08P102461254 20 1.835 87.88% 29 of 33 94.29% 33 of 35
    CHR08P102461554 21 1.57 84.85% 28 of 33 94.29% 33 of 35
    CHR09P000107988 22 0.75 88.24% 30 of 34 82.86% 29 of 35
    CHR09P021958839 23 1.575 79.41% 27 of 34 96.97% 32 of 33
    CHR09P131048752 24 1.055 91.18% 31 of 34 94.29% 33 of 35
    CHR10P118975684 25 2.51 61.76% 21 of 34 91.43% 32 of 35
    CHR11P021861414 26 4.195 47.06% 16 of 34 97.14% 34 of 35
    CHR12P004359362 27 2.52 67.65% 23 of 34 94.12% 32 of 34
    CHR12P016001231 28 1.375 71.88% 23 of 32 84.85% 28 of 33
    CHR14P018893344 29 1.185 82.35% 28 of 34 100.00%  34 of 34
    CHR14P093230340 30 1.695 91.18% 31 of 34 97.14% 34 of 35
    CHR16P000373719 31 2.57 84.21% 16 of 19 82.14% 23 of 28
    CHR16P066389027 32 0.595 64.71% 22 of 34 88.57% 31 of 35
    CHR16P083319654 33 1.355 67.65% 23 of 34 88.57% 31 of 35
    CHR18P019705147 34 6 88.24% 30 of 34 97.06% 33 of 34
    CHR19P018622408 35 0.87 91.18% 31 of 34 97.06% 33 of 34
    CHR19P051892823 36 1.03 64.29%  9 of 14 86.67% 13 of 15
    CHRXP013196410 37 0.905 96.97% 32 of 33 84.38% 27 of 32
    CHRXP013196870 38 0.795 100.00%  34 of 34 75.76% 25 of 33
    ha1p16_00179_l50 39 1.085 88.24% 30 of 34 94.29% 33 of 35
    ha1p16_00182_l50 40 1.315 79.41% 27 of 34 100.00%  35 of 35
    ha1p16_00257_l50 41 1.07 86.21% 25 of 29 90.00% 27 of 30
    ha1p_12601_l50 42 2.915 94.12% 32 of 34 94.12% 32 of 34
    ha1p_17147_l50 43 3.72 93.94% 31 of 33 94.12% 32 of 34
    ha1p_42350_l50 44 0.785 62.07% 18 of 29 96.67% 29 of 30
    ha1p_44897_l50 45 2.84 88.24% 30 of 34 96.97% 32 of 33
    ha1p_61253_l50 46 0.905 79.31% 23 of 29 96.30% 26 of 27
    CHR01P001005050 47 5.195 100.00%  28 of 28 87.50% 7 of 8
    CHR16P001157479 48 3 84.21% 16 of 19 91.67% 11 of 12
    ha1g_00681 49 0.75   78% 14 of 18   100% 18 of 18
    ha1g_01966 50 1.54   89% 16 of 18   100% 18 of 18
    ha1g_02153 51 3.62   39%  7 of 18   94% 17 of 18
    ha1g_02319 52 0.57   78% 14 of 18   94% 17 of 18
    ha1g_02335 53 4.77   61% 11 of 18   83% 15 of 18
    ha1p16_00182 54 0.87   83% 15 of 18   94% 17 of 18
    ha1p16_00185 55 1.39   88% 15 of 17   89% 16 of 18
    ha1p16_00193 56 1.96   89% 16 of 18   100% 18 of 18
    ha1p16_00259 57 1.91   83% 15 of 18   56% 10 of 18
    ha1p_02799 58 5.54   61% 11 of 18   78% 14 of 18
    ha1p_03567 59 1.21   56% 10 of 18   67% 12 of 18
    ha1p_03671 60 0.65   78% 14 of 18   56% 10 of 18
    ha1p_05803 61 1.96   78% 14 of 18   100% 18 of 18
    ha1p_07131 62 5.03   76% 13 of 17   89% 16 of 18
    ha1p_07989 63 2.75   39%  7 of 18   100% 16 of 16
    ha1p_08588 64 6   61% 11 of 18   100% 18 of 18
    ha1p_09700 65 0.55   25%  4 of 16   88% 14 of 16
    ha1p_104458 66 5.08   78% 14 of 18   100% 18 of 18
    ha1p_105287 67 5.01   94% 17 of 18   94% 17 of 18
    ha1p_10702 68 0.54   44%  8 of 18   100% 18 of 18
    ha1p_108469 69 1.42   78% 14 of 18   78% 14 of 18
    ha1p_108849 70 3.1   56% 10 of 18   100% 18 of 18
    ha1p_11016 71 4.39   94% 17 of 18   78% 14 of 18
    ha1p_11023 72 2.83   39%  7 of 18   100% 18 of 18
    ha1p_12974 73 0.55   44%  8 of 18   89% 16 of 18
    ha1p_16027 74 1.02   83% 15 of 18   100% 17 of 17
    ha1p_16066 75 2.47   78% 14 of 18   56% 10 of 18
    ha1p_18911 76 3.16   78% 14 of 18   100% 18 of 18
    ha1p_19254 77 3.77   67% 12 of 18   100% 18 of 18
    ha1p_19853 78 0.61   83% 15 of 18   89% 16 of 18
    ha1p_22257 79 1.7   72% 13 of 18   100% 18 of 18
    ha1p_22519 80 2.05   78% 14 of 18   100% 18 of 18
    ha1p_31800 81 2.82   82% 14 of 17   83% 15 of 18
    ha1p_33290 82 1.52   72% 13 of 18   94% 17 of 18
    ha1p_37635 83 6    6%  1 of 18   100% 17 of 17
    ha1p_39189 84 0.86   88% 14 of 16   76% 13 of 17
    ha1p_39511 85 3.87   44%  8 of 18   83% 15 of 18
    ha1p_39752 86 1.73   78% 14 of 18   100% 18 of 18
    ha1p_60945 87 0.93   61% 11 of 18   72% 13 of 18
    ha1p_62183 88 4.12   33%  6 of 18   100% 18 of 18
    ha1p_69418 89 2.67   61% 11 of 18   94% 17 of 18
    ha1p_71224 90 1.21   89% 16 of 18   89% 16 of 18
    ha1p_74221 91 1.85   59% 10 of 17   88% 15 of 17
    ha1p_76289 92 0.61   79% 11 of 14   88% 14 of 16
    ha1p_81050 93 5.34   78% 14 of 18   100% 18 of 18
    ha1p_81674 94 0.98   89% 16 of 18   89% 16 of 18
    ha1p_86355 95 1.6   78% 14 of 18   100% 18 of 18
    ha1p_98491 96 4.57   78% 14 of 18   100% 18 of 18
    ha1p_99426 97 0.53   83% 15 of 18   89% 16 of 18
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e. methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e. methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 25
    Sensitivity and Specificity of differentially methylated loci in prostate
    tumors relative to adjacent histological normal prostate tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 4.025 100.00%  9 of 9 33.33% 3 of 9
    CHR01P026794862 2 0.505  0.00% 0 of 3 80.00% 4 of 5
    CHR01P043164342 3 1.755 66.67% 6 of 9 77.78% 7 of 9
    CHR01P063154999 4 1.535 50.00% 4 of 8 100.00%  9 of 9
    CHR01P204123050 5 1.88 71.43% 5 of 7 87.50% 7 of 8
    CHR01P206905110 6 2.93 66.67% 6 of 9 100.00%  8 of 8
    CHR01P225608458 7 1.785 88.89% 8 of 9 77.78% 7 of 9
    CHR02P005061785 8 3.505 88.89% 8 of 9 77.78% 7 of 9
    CHR02P042255672 9 1.98 100.00%  9 of 9 77.78% 7 of 9
    CHR02P223364582 10 1.94 66.67% 6 of 9 100.00%  9 of 9
    CHR03P027740753 11 1.3 77.78% 7 of 9 100.00%  9 of 9
    CHR03P052525960 12 2.135 77.78% 7 of 9 77.78% 7 of 9
    CHR03P069745999 13 1.585 75.00% 6 of 8 66.67% 6 of 9
    CHR05P059799713 14 0.685 55.56% 5 of 9 77.78% 7 of 9
    CHR05P059799813 15 0.69 55.56% 5 of 9 66.67% 6 of 9
    CHR05P177842690 16 2.055 55.56% 5 of 9 77.78% 7 of 9
    CHR06P010694062 17 2.36 88.89% 8 of 9 88.89% 8 of 9
    CHR06P026333318 18 1.825 87.50% 7 of 8 100.00%  9 of 9
    CHR08P102460854 19 0.82 100.00%  9 of 9 66.67% 6 of 9
    CHR08P102461254 20 0.82 88.89% 8 of 9 66.67% 6 of 9
    CHR08P102461554 21 0.925 100.00%  9 of 9 62.50% 5 of 8
    CHR09P000107988 22 1.355 88.89% 8 of 9 77.78% 7 of 9
    CHR09P021958839 23 1.565 77.78% 7 of 9 100.00%  9 of 9
    CHR09P131048752 24 2.19 77.78% 7 of 9 100.00%  8 of 8
    CHR10P118975684 25 1.665 55.56% 5 of 9 87.50% 7 of 8
    CHR11P021861414 26 4.785 87.50% 7 of 8 88.89% 8 of 9
    CHR12P004359362 27 2.895 66.67% 6 of 9 100.00%  9 of 9
    CHR12P016001231 28 1.365 77.78% 7 of 9 66.67% 6 of 9
    CHR14P018893344 29 1.48 66.67% 6 of 9 100.00%  9 of 9
    CHR14P093230340 30 1.97 77.78% 7 of 9 88.89% 8 of 9
    CHR16P000373719 31 0.81 50.00% 3 of 6 100.00%  3 of 3
    CHR16P066389027 32 1.31 55.56% 5 of 9 77.78% 7 of 9
    CHR16P083319654 33 1.95 66.67% 6 of 9 88.89% 8 of 9
    CHR18P019705147 34 2.89 85.71% 6 of 7 88.89% 8 of 9
    CHR19P018622408 35 1.65 88.89% 8 of 9 77.78% 7 of 9
    CHR19P051892823 36 1.175 100.00%  3 of 3 66.67% 2 of 3
    CHRXP013196410 37 3.205 100.00%  9 of 9 77.78% 7 of 9
    CHRXP013196870 38 4.015 77.78% 7 of 9 87.50% 7 of 8
    ha1p16_00179_l50 39 1.55 66.67% 6 of 9 100.00%  8 of 8
    ha1p16_00182_l50 40 1.32 55.56% 5 of 9 100.00%  9 of 9
    ha1p16_00257_l50 41 1.52 77.78% 7 of 9 100.00%  9 of 9
    ha1p_12601_l50 42 1.01 100.00%  9 of 9 77.78% 7 of 9
    ha1p_17147_l50 43 1.425 88.89% 8 of 9 66.67% 6 of 9
    ha1p_42350_l50 44 3.88 66.67% 6 of 9 71.43% 5 of 7
    ha1p_44897_l50 45 3.845 55.56% 5 of 9 100.00%  9 of 9
    ha1p_61253_l50 46 1.4 62.50% 5 of 8 85.71% 6 of 7
    CHR01P001005050 47 0.69 75.00% 6 of 8 55.56% 5 of 9
    CHR16P001157479 48 — — — — —
    ha1g_00681 49 0.76   89% 8 of 9   89% 8 of 9
    ha1g_01966 50 2.54   89% 8 of 9   67% 6 of 9
    ha1g_02153 51 1.04   56% 5 of 9   78% 7 of 9
    ha1g_02319 52 0.86   100% 9 of 9   56% 5 of 9
    ha1g_02335 53 3.58   56% 5 of 9   67% 6 of 9
    ha1p16_00182 54 1.14   75% 6 of 8   78% 7 of 9
    ha1p16_00185 55 1.04   78% 7 of 9   78% 7 of 9
    ha1p16_00193 56 1.6   78% 7 of 9   78% 7 of 9
    ha1p16_00259 57 2.02   78% 7 of 9   78% 7 of 9
    ha1p_02799 58 4.92   22% 2 of 9   100% 9 of 9
    ha1p_03567 59 1.8   44% 4 of 9   78% 7 of 9
    ha1p_03671 60 1.26   89% 8 of 9   78% 7 of 9
    ha1p_05803 61 1.67   89% 8 of 9   89% 8 of 9
    ha1p_07131 62 6   67% 6 of 9   78% 7 of 9
    ha1p_07989 63 2.88   75% 6 of 8   89% 8 of 9
    ha1p_08588 64 5.59   44% 4 of 9   78% 7 of 9
    ha1p_09700 65 0.84   86% 6 of 7   100% 9 of 9
    ha1p_104458 66 3.59   100% 9 of 9   67% 6 of 9
    ha1p_105287 67 1.24   67% 6 of 9   67% 6 of 9
    ha1p_10702 68 2.81   78% 7 of 9   78% 7 of 9
    ha1p_108469 69 1.13   100% 9 of 9   78% 7 of 9
    ha1p_108849 70 2.24   56% 5 of 9   67% 6 of 9
    ha1p_11016 71 3.63   100% 9 of 9   78% 7 of 9
    ha1p_11023 72 1.8   78% 7 of 9   67% 6 of 9
    ha1p_12974 73 1.05   67% 6 of 9   100% 9 of 9
    ha1p_16027 74 1.24   89% 8 of 9   50% 4 of 8
    ha1p_16066 75 2.53   100% 9 of 9   44% 4 of 9
    ha1p_18911 76 2.49   100% 9 of 9   75% 6 of 8
    ha1p_19254 77 4.59   50% 4 of 8   78% 7 of 9
    ha1p_19853 78 0.97   88% 7 of 8   88% 7 of 8
    ha1p_22257 79 2.7   78% 7 of 9   67% 6 of 9
    ha1p_22519 80 1.51   100% 9 of 9   78% 7 of 9
    ha1p_31800 81 2.51   100% 9 of 9   56% 5 of 9
    ha1p_33290 82 1.77   78% 7 of 9   78% 7 of 9
    ha1p_37635 83 6   100% 9 of 9    0% 0 of 9
    ha1p_39189 84 1.52   89% 8 of 9   78% 7 of 9
    ha1p_39511 85 3.59   89% 8 of 9   44% 4 of 9
    ha1p_39752 86 1.82   56% 5 of 9   78% 7 of 9
    ha1p_60945 87 1.41   78% 7 of 9   56% 5 of 9
    ha1p_62183 88 4.21   67% 6 of 9   78% 7 of 9
    ha1p_69418 89 3.72   63% 5 of 8   78% 7 of 9
    ha1p_71224 90 1.92   56% 5 of 9   78% 7 of 9
    ha1p_74221 91 1.71   78% 7 of 9   44% 4 of 9
    ha1p_76289 92 0.6   100% 9 of 9   56% 5 of 9
    ha1p_81050 93 6   44% 4 of 9   78% 7 of 9
    ha1p_81674 94 1.2   100% 8 of 8   63% 5 of 8
    ha1p_86355 95 1.54   100% 8 of 8   56% 5 of 9
    ha1p_98491 96 2.91   78% 7 of 9   56% 5 of 9
    ha1p_99426 97 0.77   78% 7 of 9   78% 7 of 9
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 26
    Sensitivity and Specificity of differentially methylated loci in renal tumors
    relative to adjacent histological normal renal tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 4.745 66.67% 6 of 9  90.00% 9 of 10
    CHR01P026794862 2 0.525 75.00% 6 of 8  80.00% 4 of 5 
    CHR01P043164342 3 1.555 100.00%  10 of 10  80.00% 8 of 10
    CHR01P063154999 4 1.28 90.00% 9 of 10 80.00% 8 of 10
    CHR01P204123050 5 2.105 77.78% 7 of 9  55.56% 5 of 9 
    CHR01P206905110 6 4.285 60.00% 6 of 10 100.00%  10 of 10 
    CHR01P225608458 7 1.56 80.00% 8 of 10 90.00% 9 of 10
    CHR02P005061785 8 4.32 80.00% 8 of 10 40.00% 4 of 10
    CHR02P042255672 9 2.145 60.00% 6 of 10 90.00% 9 of 10
    CHR02P223364582 10 1.89 66.67% 6 of 9  90.00% 9 of 10
    CHR03P027740753 11 1.23 90.00% 9 of 10 90.00% 9 of 10
    CHR03P052525960 12 2.69 80.00% 8 of 10 90.00% 9 of 10
    CHR03P069745999 13 1.615 100.00%  10 of 10  90.00% 9 of 10
    CHR05P059799713 14 3.425 80.00% 8 of 10 100.00%  10 of 10 
    CHR05P059799813 15 3.395 80.00% 8 of 10 100.00%  10 of 10 
    CHR05P177842690 16 1.685 100.00%  10 of 10  40.00% 4 of 10
    CHR06P010694062 17 2.27 80.00% 8 of 10 70.00% 7 of 10
    CHR06P026333318 18 2.18 100.00%  10 of 10  100.00%  10 of 10 
    CHR08P102460854 19 1.06 90.00% 9 of 10 90.00% 9 of 10
    CHR08P102461254 20 1.255 90.00% 9 of 10 80.00% 8 of 10
    CHR08P102461554 21 1.375 90.00% 9 of 10 90.00% 9 of 10
    CHR09P000107988 22 1.33 100.00%  10 of 10  50.00% 5 of 10
    CHR09P021958839 23 1.405 90.00% 9 of 10 90.00% 9 of 10
    CHR09P131048752 24 2.51 90.00% 9 of 10 90.00% 9 of 10
    CHR10P118975684 25 1.265 60.00% 6 of 10 90.00% 9 of 10
    CHR11P021861414 26 4.58 100.00%  10 of 10  100.00%  10 of 10 
    CHR12P004359362 27 2.055 40.00% 4 of 10 100.00%  10 of 10 
    CHR12P016001231 28 0.72 100.00%  9 of 9  60.00% 6 of 10
    CHR14P018893344 29 1.52 90.00% 9 of 10 88.89% 8 of 9 
    CHR14P093230340 30 1.85 70.00% 7 of 10 88.89% 8 of 9 
    CHR16P000373719 31 1.585 88.89% 8 of 9  50.00% 4 of 8 
    CHR16P066389027 32 1.945 100.00%  10 of 10  77.78% 7 of 9 
    CHR16P083319654 33 2.525 90.00% 9 of 10 62.50% 5 of 8 
    CHR18P019705147 34 4.07 70.00% 7 of 10 100.00%  9 of 9 
    CHR19P018622408 35 1.7 90.00% 9 of 10 40.00% 4 of 10
    CHR19P051892823 36 2.7 57.14% 4 of 7  100.00%  6 of 6 
    CHRXP013196410 37 0.68 100.00%  10 of 10  66.67% 6 of 9 
    CHRXP013196870 38 0.905 80.00% 8 of 10 80.00% 8 of 10
    ha1p16_00179_l50 39 1.375 90.00% 9 of 10 80.00% 8 of 10
    ha1p16_00182_l50 40 0.92 88.89% 8 of 9  80.00% 8 of 10
    ha1p16_00257_l50 41 0.95 100.00%  10 of 10  88.89% 8 of 9 
    ha1p_12601_l50 42 0.745 100.00%  10 of 10  77.78% 7 of 9 
    ha1p_17147_l50 43 1.74 100.00%  9 of 9  90.00% 9 of 10
    ha1p_42350_l50 44 1.54 80.00% 8 of 10 88.89% 8 of 9 
    ha1p_44897_l50 45 4.92 40.00% 4 of 10 88.89% 8 of 9 
    ha1p_61253_l50 46 1.96 80.00% 8 of 10 70.00% 7 of 10
    CHR01P001005050 47 2.8 100.00%  8 of 8  80.00% 8 of 10
    CHR16P001157479 48 4.66 100.00%  2 of 2  100.00%  1 of 1 
    ha1g_00681 49 1.89   60% 6 of 10   78% 7 of 9 
    ha1g_01966 50 1.21   100% 10 of 10    88% 7 of 8 
    ha1g_02153 51 0.92   70% 7 of 10   90% 9 of 10
    ha1g_02319 52 1.04   90% 9 of 10   40% 4 of 10
    ha1g_02335 53 2.11   90% 9 of 10   90% 9 of 10
    ha1p16_00182 54 0.85   89% 8 of 9    90% 9 of 10
    ha1p16_00185 55 0.53   100% 9 of 9    70% 7 of 10
    ha1p16_00193 56 1.54   100% 7 of 7    80% 8 of 10
    ha1p16_00259 57 1.79   100% 10 of 10    90% 9 of 10
    ha1p_02799 58 3.52   60% 6 of 10   100% 10 of 10 
    ha1p_03567 59 1.82   50% 5 of 10   80% 8 of 10
    ha1p_03671 60 0.79   60% 6 of 10   80% 8 of 10
    ha1p_05803 61 1.82   70% 7 of 10   70% 7 of 10
    ha1p_07131 62 6   80% 8 of 10   100% 10 of 10 
    ha1p_07989 63 3.3   90% 9 of 10   100% 10 of 10 
    ha1p_08588 64 5.67   60% 6 of 10   100% 10 of 10 
    ha1p_09700 65 0.98   13% 1 of 8    100% 8 of 8 
    ha1p_104458 66 4.06   90% 9 of 10   90% 9 of 10
    ha1p_105287 67 4.48   100% 10 of 10    100% 10 of 10 
    ha1p_10702 68 0.95   80% 8 of 10   40% 4 of 10
    ha1p_108469 69 1.63   70% 7 of 10   40% 4 of 10
    ha1p_108849 70 2.75   100% 10 of 10    70% 7 of 10
    ha1p_11016 71 2.71   60% 6 of 10   90% 9 of 10
    ha1p_11023 72 1.67   100% 10 of 10    90% 9 of 10
    ha1p_12974 73 0.57   30% 3 of 10   80% 8 of 10
    ha1p_16027 74 1.99   50% 5 of 10   100% 10 of 10 
    ha1p_16066 75 1.52   90% 9 of 10   80% 8 of 10
    ha1p_18911 76 2.16   80% 8 of 10   100% 10 of 10 
    ha1p_19254 77 4.53   90% 9 of 10   90% 9 of 10
    ha1p_19853 78 0.5   100% 10 of 10    90% 9 of 10
    ha1p_22257 79 2.11   50% 5 of 10   90% 9 of 10
    ha1p_22519 80 1.47   80% 8 of 10   90% 9 of 10
    ha1p_31800 81 2.46   40% 4 of 10   90% 9 of 10
    ha1p_33290 82 1.09   89% 8 of 9    80% 8 of 10
    ha1p_37635 83 6   100% 10 of 10     0% 0 of 10
    ha1p_39189 84 0.78   90% 9 of 10   80% 8 of 10
    ha1p_39511 85 1.87   78% 7 of 9    78% 7 of 9 
    ha1p_39752 86 1.35   89% 8 of 9    89% 8 of 9 
    ha1p_60945 87 1.97   60% 6 of 10   100% 10 of 10 
    ha1p_62183 88 5.06   80% 8 of 10   100% 10 of 10 
    ha1p_69418 89 2.15   70% 7 of 10   90% 9 of 10
    ha1p_71224 90 1.69   70% 7 of 10   90% 9 of 10
    ha1p_74221 91 1.22   50% 5 of 10   90% 9 of 10
    ha1p_76289 92 0.97   90% 9 of 10   80% 8 of 10
    ha1p_81050 93 6   80% 8 of 10   100% 10 of 10 
    ha1p_81674 94 1.24   100% 10 of 10    30% 3 of 10
    ha1p_86355 95 1.17   78% 7 of 9    50% 5 of 10
    ha1p_98491 96 4.12   80% 8 of 10   80% 8 of 10
    ha1p_99426 97 0.65   90% 9 of 10   90% 9 of 10
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • TABLE 27
    Sensitivity and Specificity of differentially methylated loci in thyroid tumors
    relative to adjacent histological normal thyroid tissue.
    Locus Pos. of Neg. of
    Feature Name Number Threshold Sensitivity Total Specificity Total
    CHR01P001976799 1 6 100.00% 10 of 10  0.00% 0 of 10
    CHR01P026794862 2 1.095 66.67% 2 of 3  100.00% 1 of 1 
    CHR01P043164342 3 2.01 100.00% 10 of 10  10.00% 1 of 10
    CHR01P063154999 4 1.205 60.00% 6 of 10 100.00% 10 of 10 
    CHR01P204123050 5 2.795 85.71% 6 of 7  50.00% 3 of 6 
    CHR01P206905110 6 1.195 60.00% 6 of 10 100.00% 10 of 10 
    CHR01P225608458 7 0.62 90.00% 9 of 10 70.00% 7 of 10
    CHR02P005061785 8 4.49 70.00% 7 of 10 90.00% 9 of 10
    CHR02P042255672 9 3.895 100.00% 10 of 10  60.00% 6 of 10
    CHR02P223364582 10 2.015 90.00% 9 of 10 80.00% 8 of 10
    CHR03P027740753 11 0.77 100.00% 10 of 10  90.00% 9 of 10
    CHR03P052525960 12 3.265 90.00% 9 of 10 80.00% 8 of 10
    CHR03P069745999 13 1.06 80.00% 8 of 10 50.00% 5 of 10
    CHR05P059799713 14 0.55 80.00% 8 of 10 40.00% 4 of 10
    CHR05P059799813 15 0.615 70.00% 7 of 10 44.44% 4 of 9 
    CHR05P177842690 16 0.935 80.00% 8 of 10 40.00% 4 of 10
    CHR06P010694062 17 3.58 50.00% 5 of 10 80.00% 8 of 10
    CHR06P026333318 18 2 90.00% 9 of 10 90.00% 9 of 10
    CHR08P102460854 19 0.505 10.00% 1 of 10 90.00% 9 of 10
    CHR08P102461254 20 0.555 40.00% 4 of 10 80.00% 8 of 10
    CHR08P102461554 21 0.545 40.00% 4 of 10 70.00% 7 of 10
    CHR09P000107988 22 1.255 60.00% 6 of 10 80.00% 8 of 10
    CHR09P021958839 23 1.03 100.00% 10 of 10  80.00% 8 of 10
    CHR09P131048752 24 3.27 80.00% 8 of 10 88.89% 8 of 9 
    CHR10P118975684 25 0.975 90.00% 9 of 10 70.00% 7 of 10
    CHR11P021861414 26 6 90.00% 9 of 10 80.00% 8 of 10
    CHR12P004359362 27 2.985 50.00% 5 of 10 90.00% 9 of 10
    CHR12P016001231 28 1.21 100.00% 9 of 9  20.00% 2 of 10
    CHR14P018893344 29 1.21 70.00% 7 of 10 80.00% 8 of 10
    CHR14P093230340 30 1.84 60.00% 6 of 10 90.00% 9 of 10
    CHR16P000373719 31 0.965 66.67% 6 of 9  77.78% 7 of 9 
    CHR16P066389027 32 2.62 80.00% 8 of 10 80.00% 8 of 10
    CHR16P083319654 33 2.845 100.00% 10 of 10  80.00% 8 of 10
    CHR18P019705147 34 5.775 10.00% 1 of 10 100.00% 10 of 10 
    CHR19P018622408 35 2.425 80.00% 8 of 10 80.00% 8 of 10
    CHR19P051892823 36 1.585 75.00% 6 of 8  100.00% 6 of 6 
    CHRXP013196410 37 1.445 50.00% 5 of 10 80.00% 8 of 10
    CHRXP013196870 38 1.93 30.00% 3 of 10 90.00% 9 of 10
    ha1p16_00179_l50 39 1.055 100.00% 10 of 10  80.00% 8 of 10
    ha1p16_00182_l50 40 0.645 100.00% 10 of 10  80.00% 8 of 10
    ha1p16_00257_l50 41 0.515 88.89% 8 of 9  70.00% 7 of 10
    ha1p_12601_l50 42 0.66 80.00% 8 of 10 40.00% 4 of 10
    ha1p_17147_l50 43 0.61 80.00% 8 of 10 40.00% 4 of 10
    ha1p_42350_l50 44 3.685 70.00% 7 of 10 77.78% 7 of 9 
    ha1p_44897_l50 45 3.565 80.00% 8 of 10 90.00% 9 of 10
    ha1p_61253_l50 46 1.785 70.00% 7 of 10 55.56% 5 of 9 
    CHR01P001005050 47 1.4 50.00% 4 of 8  75.00% 6 of 8 
    CHR16P001157479 48 6 100.00% 2 of 2  0.00% 0 of 2 
    ha1g_00681 49 1.11 100.00% 10 of 10  80.00% 8 of 10
    ha1g_01966 50 2.22 70.00% 7 of 10 90.00% 9 of 10
    ha1g_02153 51 0.59 90.00% 9 of 10 80.00% 8 of 10
    ha1g_02319 52 0.53 70.00% 7 of 10 70.00% 7 of 10
    ha1g_02335 53 1.61 90.00% 9 of 10 80.00% 8 of 10
    ha1p16_00182 54 0.67 100.00% 10 of 10  80.00% 8 of 10
    ha1p16_00185 55 0.75 90.00% 9 of 10 80.00% 8 of 10
    ha1p16_00193 56 2.12 80.00% 8 of 10 100.00% 10 of 10 
    ha1p16_00259 57 1.23 100.00% 10 of 10  70.00% 7 of 10
    ha1p_02799 58 3.75 55.56% 5 of 9  70.00% 7 of 10
    ha1p_03567 59 2.52 66.67% 6 of 9  70.00% 7 of 10
    ha1p_03671 60 1.21 20.00% 2 of 10 100.00% 10 of 10 
    ha1p_05803 61 2.13 100.00% 10 of 10  50.00% 5 of 10
    ha1p_07131 62 6 80.00% 8 of 10 90.00% 9 of 10
    ha1p_07989 63 4.73 80.00% 8 of 10 80.00% 8 of 10
    ha1p_08588 64 5.31 30.00% 3 of 10 100.00% 10 of 10 
    ha1p_09700 65 0.77 11.11% 1 of 9  100.00% 9 of 9 
    ha1p_104458 66 4.53 50.00% 5 of 10 80.00% 8 of 10
    ha1p_105287 67 3.69 50.00% 5 of 10 80.00% 8 of 10
    ha1p_10702 68 0.57 40.00% 4 of 10 90.00% 9 of 10
    ha1p_108469 69 1.02 50.00% 5 of 10 77.78% 7 of 9 
    ha1p_108849 70 3.36 50.00% 5 of 10 100.00% 9 of 9 
    ha1p_11016 71 4.1 50.00% 5 of 10 100.00% 9 of 9 
    ha1p_11023 72 2.64 66.67% 6 of 9  55.56% 5 of 9 
    ha1p_12974 73 1.5 40.00% 4 of 10 100.00% 10 of 10 
    ha1p_16027 74 1.27 80.00% 8 of 10 100.00% 10 of 10 
    ha1p_16066 75 1.2 66.67% 6 of 9  100.00% 9 of 9 
    ha1p_18911 76 3.67 40.00% 4 of 10 80.00% 8 of 10
    ha1p_19254 77 3.55 90.00% 9 of 10 60.00% 6 of 10
    ha1p_19853 78 0.5 90.00% 9 of 10 50.00% 5 of 10
    ha1p_22257 79 1.29 50.00% 5 of 10 80.00% 8 of 10
    ha1p_22519 80 2.52 40.00% 4 of 10 100.00% 10 of 10 
    ha1p_31800 81 3.25 55.56% 5 of 9  90.00% 9 of 10
    ha1p_33290 82 1.68 44.44% 4 of 9  77.78% 7 of 9 
    ha1p_37635 83 4.19 80.00% 8 of 10 50.00% 5 of 10
    ha1p_39189 84 0.9 44.44% 4 of 9  80.00% 8 of 10
    ha1p_39511 85 0.76 80.00% 8 of 10 50.00% 5 of 10
    ha1p_39752 86 3.14 80.00% 8 of 10 40.00% 4 of 10
    ha1p_60945 87 1.9 70.00% 7 of 10 60.00% 6 of 10
    ha1p_62183 88 4.53 60.00% 6 of 10 100.00% 10 of 10 
    ha1p_69418 89 5.38 90.00% 9 of 10 100.00% 10 of 10 
    ha1p_71224 90 0.91 90.00% 9 of 10 60.00% 6 of 10
    ha1p_74221 91 1.58 100.00% 10 of 10  60.00% 6 of 10
    ha1p_76289 92 1.29 40.00% 4 of 10 90.00% 9 of 10
    ha1p_81050 93 5.86 70.00% 7 of 10 100.00% 10 of 10 
    ha1p_81674 94 1.36 70.00% 7 of 10 80.00% 8 of 10
    ha1p_86355 95 0.9 70.00% 7 of 10 80.00% 8 of 10
    ha1p_98491 96 5.45 50.00% 5 of 10 90.00% 9 of 10
    ha1p_99426 97 0.66 60.00% 6 of 10 80.00% 8 of 10
    Threshold: Average dCt value established by ROC curve analysis as optimal threshold for distinguishing tumor and adjacent normal tissues.
    Sensitivity: % of positive (i.e., methylation score above Threshold for gain of methylation markers or below Threshold for loss of methylation markers) tumors.
    Pos. of Total: Number of positive tumors relative to the total number of tumors analyzed.
    Specificity: % of negative (i.e., methylation score below Threshold for gain of methylation markers or above Threshold for loss of methylation markers) adjacent normal samples.
    Neg. of Total: Number of negative adjacent normal samples relative to the total number of adjacent normal samples analyzed.
  • Although the invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to one of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.
  • All publications, databases, Genbank sequences, patents, and patent applications cited in this specification are herein incorporated by reference as if each was specifically and individually indicated to be incorporated by reference.

Claims (11)

1. A method for determining the presence or absence of lung cancer in an individual, the method comprising:
a) determining the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NO: 436, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485;
b) comparing the methylation status of the at least one cytosine to a threshold value for the at least one cytosine, wherein the threshold value distinguishes between individuals with and without lung cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of lung cancer in the individual.
2. The method of claim 1, wherein the determining step comprises determining the methylation status of at least one cytosine in the DNA region corresponding to a nucleotide in a biomarker, wherein the biomarker is at least 90% identical to a sequence selected from the group consisting of SEQ ID NO: 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, and 388.
3. The method of claim 2, wherein the determining step comprises determining the methylation status of the DNA region corresponding to the biomarker.
4. The method of claim 1, wherein the sample is from blood serum, blood plasma, or a biopsy.
5. The method of claim 1, wherein the methylation status of at least one biomarker from the list is compared to the methylation value of a control locus.
6. The method of claim 5, wherein the control locus is an endogenous control.
7. The method of claim 5, wherein the control locus is an exogenous control.
8. The method of claim 1, wherein the determining step comprises determining the methylation status of at least one cytosine from at least two DNA regions.
9. A computer-implemented method for determining the presence or absence of lung cancer in an individual, the method comprising:
receiving, at a host computer, a methylation value representing the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NO: 436, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; and
comparing, in the host computer, the methylation value to a threshold value, wherein the threshold value distinguishes between individuals with and without lung cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of lung cancer in the individual.
10. The method of claim 9, wherein the receiving step comprises receiving at least two methylation values, the two methylation values representing the methylation status of at least one cytosine biomarkers from two different DNA regions; and
the comparing step comprises comparing the methylation values to one or more threshold value(s) wherein the threshold value distinguishes between individuals with and without lung cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of lung cancer in the individual.
11. A computer program product for determining the presence or absence of lung cancer in an individual, the computer readable product comprising:
a computer readable medium encoded with program code, the program code including:
program code for receiving a methylation value representing the methylation status of at least one cytosine within a DNA region in a sample from the individual where the DNA region is at least 90% identical to a sequence selected from the group consisting of SEQ ID NO: 436, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; and
program code for comparing the methylation value to a threshold value, wherein the threshold value distinguishes between individuals with and without lung cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of lung cancer in the individual.
US12/024,701 2007-02-02 2008-02-01 Gene methylation in lung cancer diagnosis Abandoned US20090098543A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US12/024,701 US20090098543A1 (en) 2007-02-02 2008-02-01 Gene methylation in lung cancer diagnosis

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US89921807P 2007-02-02 2007-02-02
US89913707P 2007-02-02 2007-02-02
US96869007P 2007-08-29 2007-08-29
US97032207P 2007-09-06 2007-09-06
US2184008P 2008-01-17 2008-01-17
US12/024,701 US20090098543A1 (en) 2007-02-02 2008-02-01 Gene methylation in lung cancer diagnosis

Publications (1)

Publication Number Publication Date
US20090098543A1 true US20090098543A1 (en) 2009-04-16

Family

ID=39682307

Family Applications (16)

Application Number Title Priority Date Filing Date
US12/024,461 Abandoned US20090170082A1 (en) 2007-02-02 2008-02-01 Gene methylation in renal cancer diagnosis
US12/024,769 Abandoned US20090075265A1 (en) 2007-02-02 2008-02-01 Gene methylation in thyroid cancer diagnosis
US12/024,672 Abandoned US20090075264A1 (en) 2007-02-02 2008-02-01 Gene methylation in liver cancer diagnosis
US12/024,458 Abandoned US20090075262A1 (en) 2007-02-02 2008-02-01 Gene Methylation In Endometrial Cancer Diagnosis
US12/024,589 Abandoned US20090170085A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Head and Neck Cancer Diagnosis
US12/024,583 Abandoned US20090176215A1 (en) 2007-02-02 2008-02-01 Gene methylation in prostate cancer diagnosis
US12/024,803 Active 2028-07-22 US7960112B2 (en) 2007-02-02 2008-02-01 Gene methylation in cancer diagnosis
US12/024,767 Abandoned US20090170087A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Cervical Cancer Diagnosis
US12/024,534 Abandoned US20090075263A1 (en) 2007-02-02 2008-02-01 Gene methylation in ovarian cancer diagnosis
US12/024,498 Abandoned US20090170083A1 (en) 2007-02-02 2008-02-01 Gene methylation in diagnosis of melanoma
US12/024,477 Abandoned US20090098542A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Colon Cancer Diagnosis
US12/024,417 Abandoned US20090170081A1 (en) 2007-02-02 2008-02-01 Gene methylation in bladder cancer diagnosis
US12/024,701 Abandoned US20090098543A1 (en) 2007-02-02 2008-02-01 Gene methylation in lung cancer diagnosis
US12/024,566 Abandoned US20090170084A1 (en) 2007-02-02 2008-02-01 Gene methylation in breast cancer
US12/024,621 Abandoned US20090170086A1 (en) 2007-02-02 2008-02-01 Gene Methylation In Esophageal Cancer Diagnosis
US13/101,992 Abandoned US20120122090A1 (en) 2007-02-02 2011-05-05 Gene methylation in cancer diagnosis

Family Applications Before (12)

Application Number Title Priority Date Filing Date
US12/024,461 Abandoned US20090170082A1 (en) 2007-02-02 2008-02-01 Gene methylation in renal cancer diagnosis
US12/024,769 Abandoned US20090075265A1 (en) 2007-02-02 2008-02-01 Gene methylation in thyroid cancer diagnosis
US12/024,672 Abandoned US20090075264A1 (en) 2007-02-02 2008-02-01 Gene methylation in liver cancer diagnosis
US12/024,458 Abandoned US20090075262A1 (en) 2007-02-02 2008-02-01 Gene Methylation In Endometrial Cancer Diagnosis
US12/024,589 Abandoned US20090170085A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Head and Neck Cancer Diagnosis
US12/024,583 Abandoned US20090176215A1 (en) 2007-02-02 2008-02-01 Gene methylation in prostate cancer diagnosis
US12/024,803 Active 2028-07-22 US7960112B2 (en) 2007-02-02 2008-02-01 Gene methylation in cancer diagnosis
US12/024,767 Abandoned US20090170087A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Cervical Cancer Diagnosis
US12/024,534 Abandoned US20090075263A1 (en) 2007-02-02 2008-02-01 Gene methylation in ovarian cancer diagnosis
US12/024,498 Abandoned US20090170083A1 (en) 2007-02-02 2008-02-01 Gene methylation in diagnosis of melanoma
US12/024,477 Abandoned US20090098542A1 (en) 2007-02-02 2008-02-01 Gene Methylation in Colon Cancer Diagnosis
US12/024,417 Abandoned US20090170081A1 (en) 2007-02-02 2008-02-01 Gene methylation in bladder cancer diagnosis

Family Applications After (3)

Application Number Title Priority Date Filing Date
US12/024,566 Abandoned US20090170084A1 (en) 2007-02-02 2008-02-01 Gene methylation in breast cancer
US12/024,621 Abandoned US20090170086A1 (en) 2007-02-02 2008-02-01 Gene Methylation In Esophageal Cancer Diagnosis
US13/101,992 Abandoned US20120122090A1 (en) 2007-02-02 2011-05-05 Gene methylation in cancer diagnosis

Country Status (5)

Country Link
US (16) US20090170082A1 (en)
EP (1) EP2115161A4 (en)
AU (1) AU2008214377A1 (en)
CA (1) CA2677085A1 (en)
WO (1) WO2008097543A2 (en)

Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011100604A2 (en) 2010-02-12 2011-08-18 Raindance Technologies, Inc. Digital analyte analysis
US8528589B2 (en) 2009-03-23 2013-09-10 Raindance Technologies, Inc. Manipulation of microfluidic droplets
WO2013165748A1 (en) 2012-04-30 2013-11-07 Raindance Technologies, Inc Digital analyte analysis
US8592221B2 (en) 2007-04-19 2013-11-26 Brandeis University Manipulation of fluids, fluid components and reactions in microfluidic systems
US8658430B2 (en) 2011-07-20 2014-02-25 Raindance Technologies, Inc. Manipulating droplet size
US8772046B2 (en) 2007-02-06 2014-07-08 Brandeis University Manipulation of fluids and reactions in microfluidic systems
US8841071B2 (en) 2011-06-02 2014-09-23 Raindance Technologies, Inc. Sample multiplexing
WO2014172288A2 (en) 2013-04-19 2014-10-23 Raindance Technologies, Inc. Digital analyte analysis
US8871444B2 (en) 2004-10-08 2014-10-28 Medical Research Council In vitro evolution in microfluidic systems
US9012390B2 (en) 2006-08-07 2015-04-21 Raindance Technologies, Inc. Fluorocarbon emulsion stabilizing surfactants
US9150852B2 (en) 2011-02-18 2015-10-06 Raindance Technologies, Inc. Compositions and methods for molecular labeling
US9273308B2 (en) 2006-05-11 2016-03-01 Raindance Technologies, Inc. Selection of compartmentalized screening method
US9328344B2 (en) 2006-01-11 2016-05-03 Raindance Technologies, Inc. Microfluidic devices and methods of use in the formation and control of nanoreactors
US9364803B2 (en) 2011-02-11 2016-06-14 Raindance Technologies, Inc. Methods for forming mixed droplets
US9366632B2 (en) 2010-02-12 2016-06-14 Raindance Technologies, Inc. Digital analyte analysis
US9399797B2 (en) 2010-02-12 2016-07-26 Raindance Technologies, Inc. Digital analyte analysis
US9448172B2 (en) 2003-03-31 2016-09-20 Medical Research Council Selection by compartmentalised screening
US9498759B2 (en) 2004-10-12 2016-11-22 President And Fellows Of Harvard College Compartmentalized screening by microfluidic control
US9562837B2 (en) 2006-05-11 2017-02-07 Raindance Technologies, Inc. Systems for handling microfludic droplets
US9562897B2 (en) 2010-09-30 2017-02-07 Raindance Technologies, Inc. Sandwich assays in droplets
US9839890B2 (en) 2004-03-31 2017-12-12 National Science Foundation Compartmentalised combinatorial chemistry by microfluidic control
US10052605B2 (en) 2003-03-31 2018-08-21 Medical Research Council Method of synthesis and testing of combinatorial libraries using microcapsules
EP3495817A1 (en) 2012-02-10 2019-06-12 Raindance Technologies, Inc. Molecular diagnostic screening assay
US10351905B2 (en) 2010-02-12 2019-07-16 Bio-Rad Laboratories, Inc. Digital analyte analysis
US10520500B2 (en) 2009-10-09 2019-12-31 Abdeslam El Harrak Labelled silica-based nanomaterial with enhanced properties and uses thereof
US10533998B2 (en) 2008-07-18 2020-01-14 Bio-Rad Laboratories, Inc. Enzyme quantification
US10647981B1 (en) 2015-09-08 2020-05-12 Bio-Rad Laboratories, Inc. Nucleic acid library generation methods and compositions
US10837883B2 (en) 2009-12-23 2020-11-17 Bio-Rad Laboratories, Inc. Microfluidic systems and methods for reducing the exchange of molecules between droplets
US10998178B2 (en) 2017-08-28 2021-05-04 Purdue Research Foundation Systems and methods for sample analysis using swabs
US11174509B2 (en) 2013-12-12 2021-11-16 Bio-Rad Laboratories, Inc. Distinguishing rare variations in a nucleic acid sequence from a sample
US11193176B2 (en) 2013-12-31 2021-12-07 Bio-Rad Laboratories, Inc. Method for detecting and quantifying latent retroviral RNA species
US11511242B2 (en) 2008-07-18 2022-11-29 Bio-Rad Laboratories, Inc. Droplet libraries
US11901041B2 (en) 2013-10-04 2024-02-13 Bio-Rad Laboratories, Inc. Digital analysis of nucleic acid modification

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8067168B2 (en) * 2006-05-31 2011-11-29 Orion Genomics Llc Gene methylation in cancer diagnosis
EP2558594A2 (en) * 2010-04-16 2013-02-20 Koninklijke Philips Electronics N.V. Methods for the analysis of breast cancer disorders
DK3594366T3 (en) * 2012-05-11 2021-09-13 Clinical Genomics Pty Ltd DIAGNOSTIC GENMARKER PANEL
WO2014071218A2 (en) * 2012-11-02 2014-05-08 University Of Utah Research Foundation Biomarkers for breast cancer and methods of using same
US20140274757A1 (en) 2013-03-14 2014-09-18 Marie K. Kirby Differential Methylation Level of CpG Loci That Are Determinative of a Biochemical Reoccurrence of Prostate Cancer
US20150218643A1 (en) * 2014-02-06 2015-08-06 Brittany N. Lasseigne Differential methylation level of cpg loci that are determinative of kidney cancer
KR101750146B1 (en) * 2014-12-30 2017-06-23 충북대학교 산학협력단 Use of RSPH9 for Prognostic Marker Diagnosis of Bladder Cancer
US9984201B2 (en) * 2015-01-18 2018-05-29 Youhealth Biotech, Limited Method and system for determining cancer status
WO2018005668A2 (en) * 2016-06-29 2018-01-04 Chopra Sameer Classification of subtypes of kidney tumors using dna methylation
GB201703406D0 (en) * 2017-03-02 2017-04-19 Ucl Business Plc Diagnostic and prognostic methods
CN111363817B (en) * 2018-12-26 2024-02-02 广州康立明生物科技股份有限公司 Lung cancer diagnostic agent and kit based on HOXD12 gene
US11427874B1 (en) 2019-08-26 2022-08-30 Epi One Inc. Methods and systems for detection of prostate cancer by DNA methylation analysis
CN116287222A (en) * 2021-12-09 2023-06-23 江苏鹍远生物科技股份有限公司 Methylation marker for diagnosis of benign and malignant thyroid cancer nodules and application thereof
EP4234720A1 (en) 2022-02-25 2023-08-30 Consejo Superior de Investigaciones Cientificas Epigenetic biomarkers for the diagnosis of thyroid cancer

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6031078A (en) * 1998-06-16 2000-02-29 Millennium Pharmaceuticals, Inc. MTbx protein and nucleic acid molecules and uses therefor
AU2001277521A1 (en) * 2000-06-30 2002-01-14 Epigenomics Ag Diagnosis of diseases associated with cell signalling
JP2004516821A (en) 2000-09-01 2004-06-10 エピゲノミクス アーゲー Method for quantifying the degree of methylation of specific cytosine in sequence context 5-CpG-3 on genomic DNA
JP4384603B2 (en) * 2002-06-26 2009-12-16 コールド スプリング ハーバー ラボラトリー Methods and compositions for measuring methylation profiles
US20070292866A1 (en) * 2005-12-08 2007-12-20 Ambergen, Inc. Diagnosing human diseases by detecting DNA methylation changes
US8399193B2 (en) * 2007-08-30 2013-03-19 City Of Hope DNA methylation biomarkers for lung cancer

Cited By (73)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11187702B2 (en) 2003-03-14 2021-11-30 Bio-Rad Laboratories, Inc. Enzyme quantification
US10052605B2 (en) 2003-03-31 2018-08-21 Medical Research Council Method of synthesis and testing of combinatorial libraries using microcapsules
US9448172B2 (en) 2003-03-31 2016-09-20 Medical Research Council Selection by compartmentalised screening
US9857303B2 (en) 2003-03-31 2018-01-02 Medical Research Council Selection by compartmentalised screening
US9925504B2 (en) 2004-03-31 2018-03-27 President And Fellows Of Harvard College Compartmentalised combinatorial chemistry by microfluidic control
US9839890B2 (en) 2004-03-31 2017-12-12 National Science Foundation Compartmentalised combinatorial chemistry by microfluidic control
US11821109B2 (en) 2004-03-31 2023-11-21 President And Fellows Of Harvard College Compartmentalised combinatorial chemistry by microfluidic control
US8871444B2 (en) 2004-10-08 2014-10-28 Medical Research Council In vitro evolution in microfluidic systems
US11786872B2 (en) 2004-10-08 2023-10-17 United Kingdom Research And Innovation Vitro evolution in microfluidic systems
US9029083B2 (en) 2004-10-08 2015-05-12 Medical Research Council Vitro evolution in microfluidic systems
US9186643B2 (en) 2004-10-08 2015-11-17 Medical Research Council In vitro evolution in microfluidic systems
US9498759B2 (en) 2004-10-12 2016-11-22 President And Fellows Of Harvard College Compartmentalized screening by microfluidic control
US9534216B2 (en) 2006-01-11 2017-01-03 Raindance Technologies, Inc. Microfluidic devices and methods of use in the formation and control of nanoreactors
US9328344B2 (en) 2006-01-11 2016-05-03 Raindance Technologies, Inc. Microfluidic devices and methods of use in the formation and control of nanoreactors
US9410151B2 (en) 2006-01-11 2016-08-09 Raindance Technologies, Inc. Microfluidic devices and methods of use in the formation and control of nanoreactors
US9273308B2 (en) 2006-05-11 2016-03-01 Raindance Technologies, Inc. Selection of compartmentalized screening method
US9562837B2 (en) 2006-05-11 2017-02-07 Raindance Technologies, Inc. Systems for handling microfludic droplets
US11351510B2 (en) 2006-05-11 2022-06-07 Bio-Rad Laboratories, Inc. Microfluidic devices
US9012390B2 (en) 2006-08-07 2015-04-21 Raindance Technologies, Inc. Fluorocarbon emulsion stabilizing surfactants
US9498761B2 (en) 2006-08-07 2016-11-22 Raindance Technologies, Inc. Fluorocarbon emulsion stabilizing surfactants
US9017623B2 (en) 2007-02-06 2015-04-28 Raindance Technologies, Inc. Manipulation of fluids and reactions in microfluidic systems
US8772046B2 (en) 2007-02-06 2014-07-08 Brandeis University Manipulation of fluids and reactions in microfluidic systems
US11819849B2 (en) 2007-02-06 2023-11-21 Brandeis University Manipulation of fluids and reactions in microfluidic systems
US10603662B2 (en) 2007-02-06 2020-03-31 Brandeis University Manipulation of fluids and reactions in microfluidic systems
US9440232B2 (en) 2007-02-06 2016-09-13 Raindance Technologies, Inc. Manipulation of fluids and reactions in microfluidic systems
US10960397B2 (en) 2007-04-19 2021-03-30 President And Fellows Of Harvard College Manipulation of fluids, fluid components and reactions in microfluidic systems
US11224876B2 (en) 2007-04-19 2022-01-18 Brandeis University Manipulation of fluids, fluid components and reactions in microfluidic systems
US10357772B2 (en) 2007-04-19 2019-07-23 President And Fellows Of Harvard College Manipulation of fluids, fluid components and reactions in microfluidic systems
US10675626B2 (en) 2007-04-19 2020-06-09 President And Fellows Of Harvard College Manipulation of fluids, fluid components and reactions in microfluidic systems
US8592221B2 (en) 2007-04-19 2013-11-26 Brandeis University Manipulation of fluids, fluid components and reactions in microfluidic systems
US11618024B2 (en) 2007-04-19 2023-04-04 President And Fellows Of Harvard College Manipulation of fluids, fluid components and reactions in microfluidic systems
US9068699B2 (en) 2007-04-19 2015-06-30 Brandeis University Manipulation of fluids, fluid components and reactions in microfluidic systems
US11596908B2 (en) 2008-07-18 2023-03-07 Bio-Rad Laboratories, Inc. Droplet libraries
US11534727B2 (en) 2008-07-18 2022-12-27 Bio-Rad Laboratories, Inc. Droplet libraries
US11511242B2 (en) 2008-07-18 2022-11-29 Bio-Rad Laboratories, Inc. Droplet libraries
US10533998B2 (en) 2008-07-18 2020-01-14 Bio-Rad Laboratories, Inc. Enzyme quantification
US8528589B2 (en) 2009-03-23 2013-09-10 Raindance Technologies, Inc. Manipulation of microfluidic droplets
US11268887B2 (en) 2009-03-23 2022-03-08 Bio-Rad Laboratories, Inc. Manipulation of microfluidic droplets
US10520500B2 (en) 2009-10-09 2019-12-31 Abdeslam El Harrak Labelled silica-based nanomaterial with enhanced properties and uses thereof
US10837883B2 (en) 2009-12-23 2020-11-17 Bio-Rad Laboratories, Inc. Microfluidic systems and methods for reducing the exchange of molecules between droplets
US10351905B2 (en) 2010-02-12 2019-07-16 Bio-Rad Laboratories, Inc. Digital analyte analysis
US9366632B2 (en) 2010-02-12 2016-06-14 Raindance Technologies, Inc. Digital analyte analysis
WO2011100604A2 (en) 2010-02-12 2011-08-18 Raindance Technologies, Inc. Digital analyte analysis
US8535889B2 (en) 2010-02-12 2013-09-17 Raindance Technologies, Inc. Digital analyte analysis
US10808279B2 (en) 2010-02-12 2020-10-20 Bio-Rad Laboratories, Inc. Digital analyte analysis
EP3392349A1 (en) 2010-02-12 2018-10-24 Raindance Technologies, Inc. Digital analyte analysis
US9074242B2 (en) 2010-02-12 2015-07-07 Raindance Technologies, Inc. Digital analyte analysis
US11390917B2 (en) 2010-02-12 2022-07-19 Bio-Rad Laboratories, Inc. Digital analyte analysis
US9228229B2 (en) 2010-02-12 2016-01-05 Raindance Technologies, Inc. Digital analyte analysis
US11254968B2 (en) 2010-02-12 2022-02-22 Bio-Rad Laboratories, Inc. Digital analyte analysis
US9399797B2 (en) 2010-02-12 2016-07-26 Raindance Technologies, Inc. Digital analyte analysis
US11635427B2 (en) 2010-09-30 2023-04-25 Bio-Rad Laboratories, Inc. Sandwich assays in droplets
US9562897B2 (en) 2010-09-30 2017-02-07 Raindance Technologies, Inc. Sandwich assays in droplets
US11077415B2 (en) 2011-02-11 2021-08-03 Bio-Rad Laboratories, Inc. Methods for forming mixed droplets
US9364803B2 (en) 2011-02-11 2016-06-14 Raindance Technologies, Inc. Methods for forming mixed droplets
US9150852B2 (en) 2011-02-18 2015-10-06 Raindance Technologies, Inc. Compositions and methods for molecular labeling
US11768198B2 (en) 2011-02-18 2023-09-26 Bio-Rad Laboratories, Inc. Compositions and methods for molecular labeling
US11168353B2 (en) 2011-02-18 2021-11-09 Bio-Rad Laboratories, Inc. Compositions and methods for molecular labeling
US11747327B2 (en) 2011-02-18 2023-09-05 Bio-Rad Laboratories, Inc. Compositions and methods for molecular labeling
US11754499B2 (en) 2011-06-02 2023-09-12 Bio-Rad Laboratories, Inc. Enzyme quantification
US8841071B2 (en) 2011-06-02 2014-09-23 Raindance Technologies, Inc. Sample multiplexing
US8658430B2 (en) 2011-07-20 2014-02-25 Raindance Technologies, Inc. Manipulating droplet size
US11898193B2 (en) 2011-07-20 2024-02-13 Bio-Rad Laboratories, Inc. Manipulating droplet size
EP3495817A1 (en) 2012-02-10 2019-06-12 Raindance Technologies, Inc. Molecular diagnostic screening assay
WO2013165748A1 (en) 2012-04-30 2013-11-07 Raindance Technologies, Inc Digital analyte analysis
EP3524693A1 (en) 2012-04-30 2019-08-14 Raindance Technologies, Inc. Digital analyte analysis
WO2014172288A2 (en) 2013-04-19 2014-10-23 Raindance Technologies, Inc. Digital analyte analysis
US11901041B2 (en) 2013-10-04 2024-02-13 Bio-Rad Laboratories, Inc. Digital analysis of nucleic acid modification
US11174509B2 (en) 2013-12-12 2021-11-16 Bio-Rad Laboratories, Inc. Distinguishing rare variations in a nucleic acid sequence from a sample
US11193176B2 (en) 2013-12-31 2021-12-07 Bio-Rad Laboratories, Inc. Method for detecting and quantifying latent retroviral RNA species
US10647981B1 (en) 2015-09-08 2020-05-12 Bio-Rad Laboratories, Inc. Nucleic acid library generation methods and compositions
US11710626B2 (en) 2017-08-28 2023-07-25 Purdue Research Foundation Systems and methods for sample analysis using swabs
US10998178B2 (en) 2017-08-28 2021-05-04 Purdue Research Foundation Systems and methods for sample analysis using swabs

Also Published As

Publication number Publication date
AU2008214377A1 (en) 2008-08-14
US20090170081A1 (en) 2009-07-02
US20090170088A1 (en) 2009-07-02
US20090170082A1 (en) 2009-07-02
CA2677085A1 (en) 2008-08-14
US20090170084A1 (en) 2009-07-02
WO2008097543A2 (en) 2008-08-14
US20090075264A1 (en) 2009-03-19
WO2008097543A8 (en) 2008-12-31
US20090075263A1 (en) 2009-03-19
US20090176215A1 (en) 2009-07-09
EP2115161A4 (en) 2010-12-15
US20090170085A1 (en) 2009-07-02
US20090075262A1 (en) 2009-03-19
EP2115161A2 (en) 2009-11-11
US20090170087A1 (en) 2009-07-02
US20120122090A1 (en) 2012-05-17
US7960112B2 (en) 2011-06-14
US20090075265A1 (en) 2009-03-19
US20090098542A1 (en) 2009-04-16
US20090170086A1 (en) 2009-07-02
US20090170083A1 (en) 2009-07-02

Similar Documents

Publication Publication Date Title
US7960112B2 (en) Gene methylation in cancer diagnosis
US8067168B2 (en) Gene methylation in cancer diagnosis
US8150627B2 (en) Methods and compositions for diagnosing lung cancer with specific DNA methylation patterns
US9957570B2 (en) DNA hypermethylation diagnostic biomarkers for colorectal cancer
CN114729399A (en) Detection of ovarian cancer
US20090186360A1 (en) Detection of GSTP1 hypermethylation in prostate cancer
TW202144586A (en) Methods and kits for screening colorectal neoplasm
US20110217706A1 (en) Gene methylation in cancer diagnosis
WO2022170984A1 (en) Screening, risk assessment, and prognosis method and kit for advanced colorectal adenomas
US20070224626A1 (en) Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets
US20090186359A1 (en) Detecting prostate cancer

Legal Events

Date Code Title Description
AS Assignment

Owner name: ORION GENOMICS LLC, MISSOURI

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BUDIMAN, MOHAMMAD A.;JEDDELOH, JEFFREY A.;MALONEY, REBECCA;AND OTHERS;REEL/FRAME:022236/0385;SIGNING DATES FROM 20081204 TO 20081220

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION