GENE METHYLATION IN THYROID CANCER DIAGNOSIS

Abstract

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

$\mathrm{Sensitivity}=\left[\frac{\left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{tumor}\mathrm{samples}\mathrm{above}\mathrm{the}\mathrm{threshold}\right)}{\left(\mathrm{the}\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{tumor}\mathrm{samples}\mathrm{tested}\right)}\right]\times 100$

The equation may also be stated as follows:

$\mathrm{Sensitivity}=\left[\frac{\left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{true}\mathrm{positive}\mathrm{samples}\right)}{\begin{array}{c}\left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{true}\mathrm{positive}\mathrm{samples}\right)+\\ \left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{false}\mathrm{negative}\mathrm{samples}\right)\end{array}}\right]\times 100$

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:

$\mathrm{Specificity}=\left[\frac{\left(\begin{array}{c}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{non}\text{-}\mathrm{tumor}\\ \mathrm{samples}\mathrm{below}\mathrm{the}\mathrm{threshold}\end{array}\right)}{\left(\mathrm{the}\mathrm{total}\mathrm{number}\mathrm{of}\mathrm{non}\text{-}\mathrm{tumor}\mathrm{samples}\mathrm{tested}\right)}\right]\times 100$

The equation may also be stated as follows:

$\mathrm{Specificity}=\left[\frac{\left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{true}\mathrm{negative}\mathrm{samples}\right)}{\begin{array}{c}\left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{true}\mathrm{negative}\mathrm{samples}\right)+\\ \left(\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{false}\mathrm{positive}\mathrm{samples}\right)\end{array}}\right]\times 100$

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′-Pu^mC (N_40-2000) Pu^mC-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		vs Normal	Specificity
Feature Name	No.	Sensitivity	Ovary	vs Normal 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
	Locus		Specificity
Feature Name	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
Feature Name	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
	% Methylation
		CG		Adj.
		position	Tumor	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
Feature Name	Number	Threshold	Sensitivity	Total	Specificity	Neg. of 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
ha1p16_00179_l50	39	1.44	88.89%	8 of 9	100.00%	9 of 9
ha1p16_00182_l50	40	1.45	77.78%	7 of 9	100.00%	9 of 9
ha1p16_00257_l50	41	1.42	66.67%	6 of 9	87.50%	7 of 8
ha1p_12601_l50	42	1.245	88.89%	8 of 9	100.00%	9 of 9
ha1p_17147_l50	43	1.12	75.00%	6 of 8	88.89%	8 of 9
ha1p_42350_l50	44	5.11	62.50%	5 of 8	50.00%	4 of 8
ha1p_44897_l50	45	1.645	100.00%	9 of 9	85.71%	6 of 7
ha1p_61253_l50	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	—	—	—	—	—
ha1g_00681	49	1.68	44%	4 of 9	100%	9 of 9
ha1g_01966	50	1.99	89%	8 of 9	100%	9 of 9
ha1g_02153	51	1.51	56%	5 of 9	100%	9 of 9
ha1g_02319	52	0.64	100%	9 of 9	89%	8 of 9
ha1g_02335	53	4.24	78%	7 of 9	33%	3 of 9
ha1p16_00182	54	0.73	100%	9 of 9	67%	6 of 9
ha1p16_00185	55	1.12	67%	6 of 9	100%	9 of 9
ha1p16_00193	56	1.94	56%	5 of 9	100%	9 of 9
ha1p16_00259	57	2.17	88%	7 of 8	78%	7 of 9
ha1p_02799	58	2.35	56%	5 of 9	100%	9 of 9
ha1p_03567	59	1.06	67%	6 of 9	75%	6 of 8
ha1p_03671	60	1.15	89%	8 of 9	89%	8 of 9
ha1p_05803	61	2.09	67%	6 of 9	100%	9 of 9
ha1p_07131	62	3.52	78%	7 of 9	89%	8 of 9
ha1p_07989	63	2.06	78%	7 of 9	88%	7 of 8
ha1p_08588	64	3.96	67%	6 of 9	100%	9 of 9
ha1p_09700	65	0.77	75%	6 of 8	100%	8 of 8
ha1p_104458	66	3.43	56%	5 of 9	100%	9 of 9
ha1p_105287	67	2.96	100%	9 of 9	89%	8 of 9
ha1p_10702	68	3.06	67%	6 of 9	100%	8 of 8
ha1p_108469	69	1.54	33%	3 of 9	100%	9 of 9
ha1p_108849	70	3.42	67%	6 of 9	100%	9 of 9
ha1p_11016	71	2.92	100%	9 of 9	100%	9 of 9
ha1p_11023	72	2.91	56%	5 of 9	100%	9 of 9
ha1p_12974	73	0.53	56%	5 of 9	78%	7 of 9
ha1p_16027	74	2.2	44%	4 of 9	89%	8 of 9
ha1p_16066	75	2.25	56%	5 of 9	78%	7 of 9
ha1p_18911	76	2.77	44%	4 of 9	100%	8 of 8
ha1p_19254	77	1.95	78%	7 of 9	100%	9 of 9
ha1p_19853	78	0.79	78%	7 of 9	89%	8 of 9
ha1p_22257	79	2.7	67%	6 of 9	100%	9 of 9
ha1p_22519	80	1.41	89%	8 of 9	78%	7 of 9
ha1p_31800	81	2.65	50%	3 of 6	89%	8 of 9
ha1p_33290	82	2.62	89%	8 of 9	100%	9 of 9
ha1p_37635	83	6	100%	9 of 9	0%	0 of 9
ha1p_39189	84	0.78	100%	9 of 9	78%	7 of 9
ha1p_39511	85	3.02	44%	4 of 9	100%	9 of 9
ha1p_39752	86	2.51	56%	5 of 9	100%	9 of 9
ha1p_60945	87	2	67%	6 of 9	100%	9 of 9
ha1p_62183	88	4.11	22%	2 of 9	100%	9 of 9
ha1p_69418	89	2.64	78%	7 of 9	100%	8 of 8
ha1p_71224	90	1.83	89%	8 of 9	100%	9 of 9
ha1p_74221	91	1.98	56%	5 of 9	89%	8 of 9
ha1p_76289	92	1.11	78%	7 of 9	100%	9 of 9
ha1p_81050	93	3.95	78%	7 of 9	89%	8 of 9
ha1p_81674	94	1.82	67%	6 of 9	100%	9 of 9
ha1p_86355	95	1.18	89%	8 of 9	75%	6 of 8
ha1p_98491	96	3.99	33%	3 of 9	100%	9 of 9
ha1p_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
ha1p16_00179_l50	39	1.4	30.00%	3 of 10	100.00%	10 of 10
ha1p16_00182_l50	40	0.99	30.00%	3 of 10	100.00%	10 of 10
ha1p16_00257_l50	41	2.5	30.00%	3 of 10	100.00%	10 of 10
ha1p_12601_l50	42	0.99	100.00%	10 of 10	80.00%	8 of 10
ha1p_17147_l50	43	0.99	100.00%	10 of 10	80.00%	8 of 10
ha1p_42350_l50	44	5.27	50.00%	5 of 10	80.00%	8 of 10
ha1p_44897_l50	45	2.76	40.00%	4 of 10	90.00%	9 of 10
ha1p_61253_l50	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	—	—	—	—	—
ha1g_00681	49	1.66	90%	9 of 10	100%	10 of 10
ha1g_01966	50	3.37	60%	6 of 10	90%	9 of 10
ha1g_02153	51	2.72	50%	5 of 10	90%	9 of 10
ha1g_02319	52	2.03	50%	5 of 10	100%	10 of 10
ha1g_02335	53	2.4	90%	9 of 10	90%	9 of 10
ha1p16_00182	54	1.55	60%	6 of 10	50%	5 of 10
ha1p16_00185	55	1.65	60%	6 of 10	90%	9 of 10
ha1p16_00193	56	2.89	60%	6 of 10	80%	8 of 10
ha1p16_00259	57	5.08	20%	2 of 10	100%	10 of 10
ha1p_02799	58	4.22	80%	8 of 10	60%	6 of 10
ha1p_03567	59	1.46	100%	10 of 10	50%	3 of 6
ha1p_03671	60	0.59	40%	4 of 10	90%	9 of 10
ha1p_05803	61	2.97	67%	4 of 6	86%	6 of 7
ha1p_07131	62	5.55	100%	7 of 7	86%	6 of 7
ha1p_07989	63	2.18	100%	9 of 9	88%	7 of 8
ha1p_08588	64	5.9	100%	10 of 10	90%	9 of 10
ha1p_09700	65	0.72	44%	4 of 9	89%	8 of 9
ha1p_104458	66	6	20%	2 of 10	90%	9 of 10
ha1p_105287	67	0.82	40%	4 of 10	80%	8 of 10
ha1p_10702	68	1.57	60%	6 of 10	100%	9 of 9
ha1p_108469	69	1.77	78%	7 of 9	90%	9 of 10
ha1p_108849	70	1.78	80%	8 of 10	80%	8 of 10
ha1p_11016	71	3.69	90%	9 of 10	90%	9 of 10
ha1p_11023	72	3.02	80%	8 of 10	80%	8 of 10
ha1p_12974	73	0.94	60%	6 of 10	63%	5 of 8
ha1p_16027	74	1.81	80%	8 of 10	70%	7 of 10
ha1p_16066	75	2.33	70%	7 of 10	90%	9 of 10
ha1p_18911	76	3.31	100%	9 of 9	30%	3 of 10
ha1p_19254	77	3.66	100%	10 of 10	90%	9 of 10
ha1p_19853	78	1.18	80%	8 of 10	80%	8 of 10
ha1p_22257	79	3.55	50%	5 of 10	100%	10 of 10
ha1p_22519	80	1.64	100%	9 of 9	90%	9 of 10
ha1p_31800	81	3.68	60%	6 of 10	100%	10 of 10
ha1p_33290	82	2.26	90%	9 of 10	100%	10 of 10
ha1p_37635	83	5.41	100%	10 of 10	0%	0 of 10
ha1p_39189	84	1.12	88%	7 of 8	89%	8 of 9
ha1p_39511	85	3.3	60%	6 of 10	78%	7 of 9
ha1p_39752	86	3.99	30%	3 of 10	100%	10 of 10
ha1p_60945	87	1.84	70%	7 of 10	50%	5 of 10
ha1p_62183	88	4.05	90%	9 of 10	90%	9 of 10
ha1p_69418	89	2.39	60%	6 of 10	90%	9 of 10
ha1p_71224	90	2.01	50%	5 of 10	90%	9 of 10
ha1p_74221	91	1.12	80%	8 of 10	50%	5 of 10
ha1p_76289	92	1.12	89%	8 of 9	86%	6 of 7
ha1p_81050	93	4.34	100%	10 of 10	90%	9 of 10
ha1p_81674	94	1.9	90%	9 of 10	100%	8 of 8
ha1p_86355	95	2.08	70%	7 of 10	100%	8 of 8
ha1p_98491	96	4.17	70%	7 of 10	80%	8 of 10
ha1p_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.
Feature Name	Number	Threshold	Sensitivity	Total	Specificity	of 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			Pos. of		Neg. of
Feature	No.	Threshold	Sensitivity	Total	Specificity	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

1. A method for determining the presence or absence of thyroid 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 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.

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 thyroid 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 thyroid cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of thyroid 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 thyroid cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of thyroid cancer in the individual.

11. A computer program product for determining the presence or absence of thyroid 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 thyroid cancer, wherein the comparison of the methylation value to the threshold value is predictive of the presence or absence of thyroid cancer in the individual.