# Patent

## Title
Gene methylation in cancer diagnosis

## Abstract

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

## Background
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 throid 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, Acd 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 I, Eag I, Fau I, Fse I, Hha I, HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapA1 I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N6 include, e.g., Mbo I. One of skill in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention. One of skill in the art will further appreciate that a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of a cytosine at or near its recognition sequence may be insensitive to the presence of methylation of an adenosine at or near its recognition sequence. Likewise, a methylation-sensitive restriction enzyme that fails to cut in the presence of methylation of an adenosine at or near its recognition sequence may be insensitive to the presence of methylation of a cytosine at or near its recognition sequence. For example, Sau3AI is sensitive (i.e., fails to cut) to the presence of a methylated cytosine at or near its recognition sequence, but is insensitive (i.e., cuts) to the presence of a methylated adenosine at or near its recognition sequence. One of skill in the art will also appreciate that some methylation-sensitive restriction enzymes are blocked by methylation of bases on one or both strands of DNA encompassing of their recognition sequence, while other methylation-sensitive restriction enzymes are blocked only by methylation on both strands, but can cut if a recognition site is hemi-methylated.
A “threshold value that distinguishes between individuals with and without” a particular disease refers to a value or range of values of a particular measurement that can be used to distinguish between samples from individuals with the disease and samples without the disease. Ideally, there is a threshold value or values that absolutely distinguishes between the two groups (i.e., values from the diseased group are always on one side (e.g., higher) of the threshold value and values from the healthy, non-diseased group are on the other side (e.g., lower) of the threshold value). However, in many instances, threshold values do not absolutely distinguish between diseased and non-diseased samples (for example, when there is some overlap of values generated from diseased and non-diseased samples).
The phrase “corresponding to a nucleotide in a biomarker” refers to a nucleotide in a DNA region that aligns with the same nucleotide (e.g. a cytosine) in a biomarker sequence. Generally, as described herein, biomarker sequences are subsequences of (i.e., have 100% identity with) the DNA regions. Sequence comparisons can be performed using any BLAST including BLAST 2.2 algorithm with default parameters, described in Altschul et al., Nuc. Acids Res. 25:3389 3402 (1977) and Altschul et al., J. Mol. Biol. 215:403 410 (1990), respectively.
“Sensitivity” of a given biomarker refers to the percentage of tumor samples that report a DNA methylation value above a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:




  Sensitivity
  =
  
    
      [
      
        
          
            
          
          ⁢
          
            
              
                
                  (
                  
                    the
                    ⁢
                    
                      
                    
                    ⁢
                    number
                    ⁢
                    
                      
                    
                    ⁢
                    of
                    ⁢
                    
                      
                    
                    ⁢
                    tumor
                  
                
              
            
            
              
                
                  
                    samples
                    ⁢
                    
                      
                    
                    ⁢
                    above
                    ⁢
                    
                      
                    
                    ⁢
                    the
                    ⁢
                    
                      
                    
                    ⁢
                    threshold
                  
                  )
                
              
            
          
        
        
          (
          
            the
            ⁢
            
              
            
            ⁢
            total
            ⁢
            
              
            
            ⁢
            number
            ⁢
            
              
            
            ⁢
            of
            ⁢
            
              
            
            ⁢
            tumor
            ⁢
            
              
            
            ⁢
            samples
            ⁢
            
              
                
              
              ⁢
              
                
              
            
            ⁢
            tested
          
          )
        
      
      ]
    
    ×
    100
  




The equation may also be stated as follows:





  Sensitivity
  =
  
    
      [
      
        
          (
          
            the
            ⁢
            
              
            
            ⁢
            number
            ⁢
            
              
            
            ⁢
            of
            ⁢
            
              
            
            ⁢
            true
            ⁢
            
              
            
            ⁢
            positive
            ⁢
            
              
            
            ⁢
            samples
          
          )
        
        
          
            (
            
              the
              ⁢
              
                
              
              ⁢
              number
              ⁢
              
                
              
              ⁢
              of
              ⁢
              
                
              
              ⁢
              true
              ⁢
              
                
              
              ⁢
              positive
              ⁢
              
                
              
              ⁢
              samples
            
            )
          
          +
          
            (
            
              the
              ⁢
              
                
              
              ⁢
              number
              ⁢
              
                
              
              ⁢
              of
              ⁢
              
                
              
              ⁢
              fal
              ⁢
              
                
              
              ⁢
              se
              ⁢
              
                
              
              ⁢
              negative
              ⁢
              
                
              
              ⁢
              samples
            
            )
          
        
      
      ]
    
    ×
    100
  




where true positive is defined as a histology-confirmed tumor sample that reports a DNA methylation value above the threshold value (i.e. the range associated with disease), and false negative is defined as a histology-confirmed tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given biomarker would detect the presence of a clinical condition when applied to a patient with that condition.

“Specificity” of a given biomarker refers to the percentage of non-tumor samples that report a DNA methylation value below a threshold value that distinguishes between tumor and non-tumor samples. The percentage is calculated as follows:




  Specificity
  =
  
    
      [
      
        
          
            
              
                
                  (
                  
                    the
                    ⁢
                    
                      
                    
                    ⁢
                    number
                    ⁢
                    
                      
                    
                    ⁢
                    of
                    ⁢
                    
                      
                    
                    ⁢
                    
                      non-tumor
                    
                  
                  ⁢
                  
                    
                  
                
              
            
            
              
                
                  
                    samples
                    ⁢
                    
                      
                    
                    ⁢
                    below
                    ⁢
                    
                      
                    
                    ⁢
                    the
                    ⁢
                    
                      
                    
                    ⁢
                    threshold
                  
                  )
                
              
            
          
          ⁢
          
            
          
        
        
          (
          
            the
            ⁢
            
              
            
            ⁢
            total
            ⁢
            
              
            
            ⁢
            number
            ⁢
            
              
            
            ⁢
            of
            ⁢
            
              
            
            ⁢
            
              non-tumor
            
            ⁢
            
              
            
            ⁢
            samples
            ⁢
            
              
                
              
              ⁢
              
                
              
            
            ⁢
            tested
          
          )
        
      
      ]
    
    ×
    100
  




The equation may also be stated as follows:





  Specificity
  =
  
    
      [
      
        
          (
          
            the
            ⁢
            
              
            
            ⁢
            number
            ⁢
            
              
            
            ⁢
            of
            ⁢
            
              
            
            ⁢
            true
            ⁢
            
              
            
            ⁢
            negative
            ⁢
            
              
            
            ⁢
            samples
          
          )
        
        
          
            (
            
              the
              ⁢
              
                
              
              ⁢
              number
              ⁢
              
                
              
              ⁢
              of
              ⁢
              
                
              
              ⁢
              true
              ⁢
              
                
              
              ⁢
              negative
              ⁢
              
                
              
              ⁢
              samples
            
            )
          
          +
          
            (
            
              the
              ⁢
              
                
              
              ⁢
              number
              ⁢
              
                
              
              ⁢
              of
              ⁢
              
                
              
              ⁢
              fal
              ⁢
              
                
              
              ⁢
              se
              ⁢
              
                
              
              ⁢
              positve
              ⁢
              
                
              
              ⁢
              samples
            
            )
          
        
      
      ]
    
    ×
    100
  




where true negative is defined as a histology-confirmed non-tumor sample that reports a DNA methylation value below the threshold value (i.e. the range associated with no disease), and false positive is defined as a histology-confirmed non-tumor sample that reports DNA methylation value above the threshold value (i.e. the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given biomarker obtained from a known non-diseased sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given biomarker would detect the absence of a clinical condition when applied to a patient without that condition.

Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information. This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as defaults a wordlength (W) of 11, an expectation (E) of 10, M=5, N=−4 and a comparison of both strands. For amino acid sequences, the BLASTP program uses as defaults a wordlength of 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989)) alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparison of both strands.
As used herein, the terms “nucleic acid,” “polynucleotide” and “oligonucleotide” refer to nucleic acid regions, nucleic acid segments, primers, probes, amplicons and oligomer fragments. The terms are not limited by length and are generic to linear polymers of polydeoxyribonucleotides (containing 2-deoxy-D-ribose), polyribonucleotides (containing D-ribose), and any other N-glycoside of a purine or pyrimidine base, or modified purine or pyrimidine bases. These terms include double- and single-stranded DNA, as well as double- and single-stranded RNA.
A nucleic acid, polynucleotide or oligonucleotide can comprise, for example, phosphodiester linkages or modified linkages including, but not limited to phosphotriester, phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate, carbamate, thioether, bridged phosphoramidate, bridged methylene phosphonate, phosphorothioate, methylphosphonate, phosphorodithioate, bridged phosphorothioate or sulfone linkages, and combinations of such linkages.
A nucleic acid, polynucleotide or oligonucleotide can comprise the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil) and/or bases other than the five biologically occurring bases. For example, a polynucleotide of the invention can contain one or more modified, non-standard, or derivatized base moieties, including, but not limited to, N6-methyl-adenine, N6-tert-butyl-benzyl-adenine, imidazole, substituted imidazoles, 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine, 5-(carboxyhydroxymethyl)uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N6-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-methyladenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D mannosylqueosine, 5′-methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, uracil-5-oxyacetic acidmethylester, 3-(3-amino-3-N-2-carboxypropyl)uracil, (acp3)w, 2,6-diaminopurine, and 5-propynyl pyrimidine. Other examples of modified, non-standard, or derivatized base moieties may be found in U.S. Pat. Nos. 6,001,611; 5,955,589; 5,844,106; 5,789,562; 5,750,343; 5,728,525; and 5,679,785.
Furthermore, a nucleic acid, polynucleotide or oligonucleotide can comprise one or more modified sugar moieties including, but not limited to, arabinose, 2-fluoroarabinose, xylulose, and a hexose.


DETAILED DESCRIPTION OF THE INVENTION
I. Introduction
The present invention is based, in part, on the discovery that sequences in certain DNA regions are methylated in cancer cells, but not normal cells. Specifically, the inventors have found that methylation of biomarkers within the DNA regions described herein are associated with various types of cancer.
In view of this discovery, the inventors have recognized that methods for detecting the biomarker sequences and DNA regions comprising the biomarker sequences as well as sequences adjacent to the biomarkers that contain a significant amount of CpG subsequences, methylation of the DNA regions, and/or expression of the genes regulated by the DNA regions can be used to detect cancer cells. Detecting cancer cells allows for diagnostic tests that detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor disease, and/or aid in the selection of treatment for a person with disease.
II. Methylation Biomarkers
In some embodiments, the presence or absence or quantity of methylation of the chromosomal DNA within a DNA region or portion thereof (e.g., at least one cytosine) selected from SEQ ID Nos: 389-485 is detected. Portions of the DNA regions described herein will comprise at least one potential methylation site (i.e., a cytosine) and can in some embodiments generally comprise 2, 3, 4, 5, 10, or more potential methylation sites. In some embodiments, the methylation status of all cytosines within at least 20, 50, 100, 200, 500 or more contiguous base pairs of the DNA region are determined.
Some of the DNA regions overlap with each other, indicating that methylation can be detected in a larger chromosomal region as defined by the boundaries of the overlapping sequences. For example, SEQ ID NO:402 overlaps with SEQ ID NO:403; SEQ ID NOs: 407, 408 and 409 overlap with each other; SEQ ID NO:425 overlaps with SEQ ID NO:426; SEQ ID NOs:411, 427, 428, 442, 443, 444 overlap with each other; and SEQ ID NO:429 overlaps with SEQ ID NO:445. Thus, for example, methylation can be detected for the purposes described herein to detect the methylation status of at least one cytosine in a sequence from:
either SEQ ID NO: 402 or 403;
any of SEQ ID NO:403; SEQ ID NOs: 407, 408 or 409;
either SEQ ID NO:425 or SEQ ID NO:426;
any of SEQ ID NOs:411, 427, 428, 442, 443, 444;
either SEQ ID NO:429 or SEQ ID NO:445.
In some embodiments, the methylation of more than one DNA region (or portion thereof) is detected. In some embodiments, the methylation status of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97 of the DNA regions is determined.
In some embodiments of the invention, the methylation of a DNA region or portion thereof is determined and then normalized (e.g., compared) to the methylation of a control locus. Typically the control locus will have a known, relatively constant, methylation status. For example, the control sequence can be previously determined to have no, some or a high amount of methylation, thereby providing a relative constant value to control for error in detection methods, etc., unrelated to the presence or absence of cancer. In some embodiments, the control locus is endogenous, i.e., is part of the genome of the individual sampled. For example, in mammalian cells, the testes-specific histone 2B gene (hTH2B in human) gene is known to be methylated in all somatic tissues except testes. Alternatively, the control locus can be an exogenous locus, i.e., a DNA sequence spiked into the sample in a known quantity and having a known methylation status.
A DNA region comprises a nucleic acid including one or more methylation sites of interest (e.g., a cytosine, a “microarray feature,” or an amplicon amplified from select primers) and flanking nucleic acid sequences (i.e., “wingspan”) of up to 4 kilobases (kb) in either or both of the 3′ or 5′ direction from the amplicon. This range corresponds to the lengths of DNA fragments obtained by randomly fragmenting the DNA before screening for differential methylation between DNA in two or more samples (e.g. carrying out methods used to initially identify differentially methylated sequences as described in the Examples, below). In some embodiments, the wingspan of the one or more DNA regions is about 0.5 kb, 0.75 kb, 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in both 3′ and 5′ directions relative to the sequence represented by the microarray feature.
The methylation sites in a DNA region can reside in non-coding transcriptional control sequences (e.g. promoters, enhancers, etc.) or in coding sequences, including introns and exons of the designated genes listed in Tables 1 and 2 and in section “SEQUENCE LISTING.” In some embodiments, the methods comprise detecting the methylation status in the promoter regions (e.g., comprising the nucleic acid sequence that is about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5′ from the transcriptional start site through to the transcriptional start site) of one or more of the genes identified in Tables 1 and 2 and in section “SEQUENCE LISTING.”
The DNA regions of the invention also include naturally occurring variants, including for example, variants occurring in different subject populations and variants arising from single nucleotide polymorphisms (SNPs). SNPs encompasses insertions and deletions of varying size and simple sequence repeats, such as dinucleotides and trinucleotide repeats. Variants include nucleic acid sequences from the same DNA region (e.g. as set forth in Tables 1 and 2 and in section “SEQUENCE LISTING”) sharing at least 90%, 95%, 98%, 99% sequence identity, i.e., having one or more deletions, additions, substitutions, inverted sequences, etc., relative to the DNA regions described herein.
III. Methods for Determining Methylation
Any method for detecting DNA methylation can be used in the methods of the present invention.
In some embodiments, methods for detecting methylation include randomly shearing or randomly fragmenting the genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme and subsequently selectively identifying and/or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., by size) and quantifying a sequence of interest that was cut or, alternatively, that was not cut. See, e.g., U.S. Pat. No. 7,186,512. Alternatively, the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby only amplifying DNA that was not cleaved by the restriction enzyme in the area amplified. See, e.g., U.S. patent application Ser. Nos. 10/971,986; 11/071,013; and 10/971,339. In some embodiments, amplification can be performed using primers that are gene specific. Alternatively, adaptors can be added to the ends of the randomly fragmented DNA, the DNA can be digested with a methylation-dependent or methylation-sensitive restriction enzyme, intact DNA can be amplified using primers that hybridize to the adaptor sequences. In this case, a second step can be performed to determine the presence, absence or quantity of a particular gene in an amplified pool of DNA. In some embodiments, the DNA is amplified using real-time, quantitative PCR.
In some embodiments, the methods comprise quantifying the average methylation density in a target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting genomic DNA with a methylation-dependent restriction enzyme or methylation-sensitive restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying intact copies of the locus; and comparing the quantity of amplified product to a control value representing the quantity of methylation of control DNA, thereby quantifying the average methylation density in the locus compared to the methylation density of the control DNA.
The quantity of methylation of a locus of DNA can be determined by providing a sample of genomic DNA comprising the locus, cleaving the DNA with a restriction enzyme that is either methylation-sensitive or methylation-dependent, and then quantifying the amount of intact DNA or quantifying the amount of cut DNA at the DNA locus of interest. The amount of intact or cut DNA will depend on the initial amount of genomic DNA containing the locus, the amount of methylation in the locus, and the number (i.e., the fraction) of nucleotides in the locus that are methylated in the genomic DNA. The amount of methylation in a DNA locus can be determined by comparing the quantity of intact DNA or cut DNA to a control value representing the quantity of intact DNA or cut DNA in a similarly-treated DNA sample. The control value can represent a known or predicted number of methylated nucleotides. Alternatively, the control value can represent the quantity of intact or cut DNA from the same locus in another (e.g., normal, non-diseased) cell or a second locus.
By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved and subsequently quantifying the remaining intact copies and comparing the quantity to a control, average methylation density of a locus can be determined. If the methylation-sensitive restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be directly proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Similarly, if a methylation-dependent restriction enzyme is contacted to copies of a DNA locus under conditions that allow for at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved, then the remaining intact DNA will be inversely proportional to the methylation density, and thus may be compared to a control to determine the relative methylation density of the locus in the sample. Such assays are disclosed in, e.g., U.S. patent application Ser. No. 10/971,986.
Kits for the above methods can include, e.g., one or more of methylation-dependent restriction enzymes, methylation-sensitive restriction enzymes, amplification (e.g., PCR) reagents, probes and/or primers.
Quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) can be used to quantify the amount of intact DNA within a locus flanked by amplification primers following restriction digestion. Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996); DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, et al., Mol. Biotechnol. 20(2):163-79 (2002). Amplifications may be monitored in “real time.”
Additional methods for detecting DNA methylation can involve genomic sequencing before and after treatment of the DNA with bisulfite. See, e.g., Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992). When sodium bisulfite is contacted to DNA, unmethylated cytosine is converted to uracil, while methylated cytosine is not modified.
In some embodiments, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA is used to detect DNA methylation. See, e.g., Sadri & Hornsby, Nucl. Acids Res. 24:5058-5059 (1996); Xiong & Laird, Nucleic Acids Res. 25:2532-2534 (1997).
In some embodiments, a MethyLight assay is used alone or in combination with other methods to detect DNA methylation (see, Eads et al., Cancer Res. 59:2302-2306 (1999)). Briefly, in the MethyLight process genomic DNA is converted in a sodium bisulfite reaction (the bisulfite process converts unmethylated cytosine residues to uracil). Amplification of a DNA sequence of interest is then performed using PCR primers that hybridize to CpG dinucleotides. By using primers that hybridize only to sequences resulting from bisulfite conversion of unmethylated DNA, (or alternatively to methylated sequences that are not converted) amplification can indicate methylation status of sequences where the primers hybridize. Similarly, the amplification product can be detected with a probe that specifically binds to a sequence resulting from bisulfite treatment of a unmethylated (or methylated) DNA. If desired, both primers and probes can be used to detect methylation status. Thus, kits for use with MethyLight can include sodium bisulfite as well as primers or detectably-labeled probes (including but not limited to Taqman or molecular beacon probes) that distinguish between methylated and unmethylated DNA that have been treated with bisulfite. Other kit components can include, e.g., reagents necessary for amplification of DNA including but not limited to, PCR buffers, deoxynucleotides; and a thermostable polymerase.
In some embodiments, a Ms-SNuPE (Methylation-sensitive Single Nucleotide Primer Extension) reaction is used alone or in combination with other methods to detect DNA methylation (see, Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531 (1997)). The Ms-SNuPE technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo & Jones, supra). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site(s) of interest.
Typical reagents (e.g., as might be found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysis can include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPE reaction); and detectably-labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery regents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.
In some embodiments, a methylation-specific PCR (“MSP”) reaction is used alone or in combination with other methods to detect DNA methylation. An MSP assay entails initial modification of DNA by sodium bisulfite, converting all unmethylated, but not methylated, cytosines to uracil, and subsequent amplification with primers specific for methylated versus unmethylated DNA. See, Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, (1996); U.S. Pat. No. 5,786,146.
Additional methylation detection methods include, but are not limited to, methylated CpG island amplification (see, Toyota et al., Cancer Res. 59:2307-12 (1999)) and those described in, e.g., U.S. Patent Publication 2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998); Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No. WO 00/70090.
It is well known that methylation of genomic DNA can affect expression (transcription and/or translation) of nearby gene sequences. Therefore, in some embodiments, the methods include the step of correlating the methylation status of at least one cytosine in a DNA region with the expression of nearby coding sequences, as described in Tables 1 and 2 and in section “SEQUENCE LISTING.” For example, expression of gene sequences within about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb in either the 3′ or 5′ direction from the cytosine of interest in the DNA region can be detected. Methods for measuring transcription and/or translation of a particular gene sequence are well known in the art. See, for example, Ausubel, Current Protocols in Molecular Biology, 1987-2006, John Wiley & Sons; and Sambrook and Russell, Molecular Cloning: A Laboratory Manual, 3rd Edition, 2000, Cold Spring Harbor Laboratory Press. In some embodiments, the gene or protein expression of a gene in Tables 1 and 2 and in section “SEQUENCE LISTING” is compared to a control, for example, the methylation status in the DNA region and/or the expression of a nearby gene sequence from a sample from an individual known to be negative for cancer or known to be positive for cancer, or to an expression level that distinguishes between cancer and noncancer states. Such methods, like the methods of detecting methylation described herein, are useful in providing diagnosis, prognosis, etc., of cancer.
In some embodiments, the methods further comprise the step of correlating the methylation status and expression of one or more of the gene regions identified in Tables 1 and 2 and in section “SEQUENCE LISTING.”
IV. Cancer Detection
The present biomarkers and methods can be used in the diagnosis, prognosis, classification, prediction of disease risk, detection of recurrence of disease, and selection of treatment of a number of types of cancers. A cancer at any stage of progression can be detected, such as primary, metastatic, and recurrent cancers. Information regarding numerous types of cancer can be found, e.g., from the American Cancer Society (available on the worldwide web at cancer.org), or from, e.g., Harrison's Principles of Internal Medicine, Kaspar, et al., eds., 16th Edition, 2005, McGraw-Hill, Inc. Exemplary cancers that can be detected include lung, breast, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
The present invention provides methods for determining whether or not a mammal (e.g., a human) has cancer, whether or not a biological sample taken from a mammal contains cancerous cells, estimating the risk or likelihood of a mammal developing cancer, classifying cancer types and stages, monitoring the efficacy of anti-cancer treatment, or selecting the appropriate anti-cancer treatment in a mammal with cancer. Such methods are based on the discovery that cancer cells have a different methylation status than normal cells in the DNA regions described in the invention. Accordingly, by determining whether or not a cell contains differentially methylated sequences in the DNA regions as described herein, it is possible to determine whether or not the cell is cancerous.
In numerous embodiments of the present invention, the presence of methylated nucleotides in the diagnostic biomarker sequences of the invention is detected in a biological sample, thereby detecting the presence or absence of cancerous cells in the biological sample.
In some embodiments, the biological sample comprises a tissue sample from a tissue suspected of containing cancerous cells. For example, in an individual suspected of having cancer, breast tissue, lymph tissue, lung tissue, brain tissue, or blood can be evaluated. Alternatively, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate, or skin tissue can be evaluated. The tissue or cells can be obtained by any method known in the art including, e.g., by surgery, biopsy, phlebotomy, swab, nipple discharge, stool, etc. In other embodiments, a tissue sample known to contain cancerous cells, e.g., from a tumor, will be analyzed for the presence or quantity of methylation at one or more of the diagnostic biomarkers of the invention to determine information about the cancer, e.g., the efficacy of certain treatments, the survival expectancy of the individual, etc. In some embodiments, the methods will be used in conjunction with additional diagnostic methods, e.g., detection of other cancer biomarkers, etc.
Genomic DNA samples can be obtained by any means known in the art. In cases where a particular phenotype or disease is to be detected, DNA samples should be prepared from a tissue of interest, or as appropriate, from blood. For example, DNA can be prepared from biopsy tissue to detect the methylation state of a particular locus associated with cancer. The nucleic acid-containing specimen used for detection of methylated loci (see, e.g. Ausubel et al., Current Protocols in Molecular Biology (1995 supplement)) may be from any source and may be extracted by a variety of techniques such as those described by Ausubel et al., Current Protocols in Molecular Biology (1995) or Sambrook et al., Molecular Cloning, A Laboratory Manual (3rd ed. 2001).
The methods of the invention can be used to evaluate individuals known or suspected to have cancer or as a routine clinical test, i.e., in an individual not necessarily suspected to have cancer. Further diagnostic assays can be performed to confirm the status of cancer in the individual.
Further, the present methods may be used to assess the efficacy of a course of treatment. For example, the efficacy of an anti-cancer treatment can be assessed by monitoring DNA methylation of the biomarker sequences described herein over time in a mammal having cancer. For example, a reduction or absence of methylation in any of the diagnostic biomarkers of the invention in a biological sample taken from a mammal following a treatment, compared to a level in a sample taken from the mammal before, or earlier in, the treatment, indicates efficacious treatment.
The methods detecting cancer can comprise the detection of one or more other cancer-associated polynucleotide or polypeptides sequences. Accordingly, detection of methylation of any one or more of the diagnostic biomarkers of the invention can be used either alone, or in combination with other biomarkers, for the diagnosis or prognosis of cancer.
The methods of the present invention can be used to determine the optimal course of treatment in a mammal with cancer. For example, the presence of methylated DNA within any of the diagnostic biomarkers of the invention or an increased quantity of methylation within any of the diagnostic biomarkers of the invention can indicate a reduced survival expectancy of a mammal with cancer, thereby indicating a more aggressive treatment for the mammal. In addition, a correlation can be readily established between the presence, absence or quantity of methylation at a diagnostic biomarker, as described herein, and the relative efficacy of one or another anti-cancer agent. Such analyses can be performed, e.g., retrospectively, i.e., by detecting methylation in one or more of the diagnostic genes in samples taken previously from mammals that have subsequently undergone one or more types of anti-cancer therapy, and correlating the known efficacy of the treatment with the presence, absence or levels of methylation of one or more of the diagnostic biomarkers.
In making a diagnosis, prognosis, risk assessment, classification, detection of recurrence or selection of therapy based on the presence or absence of methylation in at least one of the diagnostic biomarkers, the quantity of methylation may be compared to a threshold value that distinguishes between one diagnosis, prognosis, risk assessment, classification, etc., and another. For example, a threshold value can represent the degree of methylation found at a particular DNA region that adequately distinguishes between cancer samples and normal samples with a desired level of sensitivity and specificity. It is understood that a threshold value will likely vary depending on the assays used to measure methylation, but it is also understood that it is a relatively simple matter to determine a threshold value or range by measuring methylation of a DNA sequence in cancer samples and normal samples using the particular desired assay and then determining a value that distinguishes at least a majority of the cancer samples from a majority of non-cancer samples. If methylation of two or more DNA regions is detected, two or more different threshold values (one for each DNA region) will often, but not always, be used. Comparisons between a quantity of methylation of a sequence in a sample and a threshold value can be performed in any way known in the art. For example, a manual comparison can be made or a computer can compare and analyze the values to detect disease, assess the risk of contracting disease, determining a predisposition to disease, stage disease, diagnose disease, monitor, or aid in the selection of treatment for a person with disease.
In some embodiments, threshold values provide at least a specified sensitivity and specificity for detection of a particular cancer type. In some embodiments, the threshold value allows for at least a 50%, 60%, 70%, or 80% sensitivity and specificity for detection of a specific cancer, e.g., breast, lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical, colon, endometrial, esophageal, prostate cancer or melanoma.
In embodiments involving prognosis of cancer (including, for example, the prediction of progression of non-malignant lesions to invasive carcinoma, prediction of metastasis, prediction of disease recurrance or prediction of a response to a particular treatment), in some embodiments, the threshold value is set such that there is at least 10, 20, 30, 40, 50, 60, 70, 80% or more sensitivity and at least 70% specificity with regard to detecting cancer.
In some embodiments, the methods comprise recording a diagnosis, prognosis, risk assessment or classification, based on the methylation status determined from an individual. Any type of recordation is contemplated, including electronic recordation, e.g., by a computer.
V. Kits
This invention also provides kits for the detection and/or quantification of the diagnostic biomarkers of the invention, or expression or methylation thereof using the methods described herein.
For kits for detection of methylation, the kits of the invention can comprise at least one polynucleotide that hybridizes to at least one of the diagnostic biomarker sequences of the invention and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfite, polynucleotides designed to hybridize to sequence that is the product of a biomarker sequence of the invention if the biomarker sequence is not methylated (e.g., containing at least one C→U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme. The kits can provide solid supports in the form of an assay apparatus that is adapted to use in the assay. The kits may further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit. Other materials useful in the performance of the assays can also be included in the kits, including test tubes, transfer pipettes, and the like. The kits can also include written instructions for the use of one or more of these reagents in any of the assays described herein.
In some embodiments, the kits of the invention comprise one or more (e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g., primers and/or probes) capable of specifically amplifying at least a portion of a DNA region where the DNA region is a sequence selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485. Optionally, one or more detectably-labeled polypeptide capable of hybridizing to the amplified portion can also be included in the kit. In some embodiments, the kits comprise sufficient primers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions or portions thereof, and optionally include detectably-labeled polynucleotides capable of hybridizing to each amplified DNA region or portion thereof. The kits further can comprise a methylation-dependent or methylation sensitive restriction enzyme and/or sodium bisulfite.
In some embodiments, the kits comprise sodium bisulfite, primers and adapters (e.g., oligonucleotides that can be ligated or otherwise linked to genomic fragments) for whole genome amplification, and polynucleotides (e.g., detectably-labeled 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 some embodiments, the kits comprise a methylation sensing restriction enzymes (e.g., a methylation-dependent restriction enzyme and/or a methylation-sensitive restriction enzyme), primers and adapters for whole genome amplification, and polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485.
In some embodiments, the kits comprise a methylation binding moiety and one or more polynucleotides to quantify the number of copies of at least a portion of a DNA region where the DNA region is selected from the group consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485. A methylation binding moiety refers to a molecule (e.g., a polypeptide) that specifically binds to methyl-cytosine. Examples include restriction enzymes or fragments thereof that lack DNA cutting activity but retain the ability to bind methylated DNA, antibodies that specifically bind to methylated DNA, etc.).
VI. Computer-Based Methods
The calculations for the methods described herein can involve computer-based calculations and tools. For example, a methylation value for a DNA region or portion thereof can be compared by a computer to a threshold value, as described herein. The tools are advantageously provided in the form of computer programs that are executable by a general purpose computer system (referred to herein as a “host computer”) of conventional design. The host computer may be configured with many different hardware components and can be made in many dimensions and styles (e.g., desktop PC, laptop, tablet PC, handheld computer, server, workstation, mainframe). Standard components, such as monitors, keyboards, disk drives, CD and/or DVD drives, and the like, may be included. Where the host computer is attached to a network, the connections may be provided via any suitable transport media (e.g., wired, optical, and/or wireless media) and any suitable communication protocol (e.g., TCP/IP); the host computer may include suitable networking hardware (e.g., modem, Ethernet card, WiFi card). The host computer may implement any of a variety of operating systems, including UNIX, Linux, Microsoft Windows, MacOS, or any other operating system.
Computer code for implementing aspects of the present invention may be written in a variety of languages, including PERL, C, C++, Java, JavaScript, VBScript, AWK, or any other scripting or programming language that can be executed on the host computer or that can be compiled to execute on the host computer. Code may also be written or distributed in low level languages such as assembler languages or machine languages.
The host computer system advantageously provides an interface via which the user controls operation of the tools. In the examples described herein, software tools are implemented as scripts (e.g., using PERL), execution of which can be initiated by a user from a standard command line interface of an operating system such as Linux or UNIX. Those skilled in the art will appreciate that commands can be adapted to the operating system as appropriate. In other embodiments, a graphical user interface may be provided, allowing the user to control operations using a pointing device. Thus, the present invention is not limited to any particular user interface.
Scripts or programs incorporating various features of the present invention may be encoded on various computer readable media for storage and/or transmission. Examples of suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and carrier signals adapted for transmission via wired, optical, and/or wireless networks conforming to a variety of protocols, including the Internet.
EXAMPLES
Example 1
Identification of Cancer DNA Methylation Biomarkers
Loci that are differentially methylated in tumors relative to matched adjacent histologically normal tissue were identified using a DNA microarray-based technology platform that utilizes the methylation-dependent restriction enzyme McrBC. See, e.g. U.S. Pat. No. 7,186,512. In the discovery phase, cancer tissue and normal tissue samples were analyzed. Purified genomic DNA from each sample (60 μg) was randomly sheared to a range of 1 to 4 kb. The sheared DNA of each sample was then split into four equal portions of 15 μg each. Two portions were digested with McrBC under the following conditions: 15 μg sheared genomic DNA, 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 120 units of McrBC enzyme (New England Biolabs) in a total volume of 600 μL at 37° C. for approximately 12 hours. These two portions represent a technical replicate of McrBC digestion (Treated 1 and Treated 2). The remaining two 15 μg portions were mock treated under identical conditions with the exception that 12 μL of sterile 50% glycerol were added instead of McrBC enzyme. These two portions represent a technical replicate of mock treatment (Untreated 1 and Untreated 2). All reactions were treated with 5 μL proteinase K (50 mg/mL) for 1 hour at 50° C., and precipitated with EtOH under standard conditions. Pellets were washed twice with 70% EtOH, dried and resuspended in 30 μL H2O, Samples were then resolved on a 1% low melting point SeaPlaque GTG Agarose gel (Cambridge Bio Sciences). Untreated 1 and Treated 1 portions were resolved side-by-side, as were Untreated 2 and Treated 2 portions. 1 kb DNA sizing ladder was resolved adjacent to each untreated/treated pair to guide accurate gel slice excision. Gels were visualized with long-wave UV, and gel slices including DNA within the modal size range of the untreated fraction (approximately 1-4 kb) were excised with a clean razor blade. DNA was extracted from gel slices using gel extraction kits (Qiagen).
McrBC recognizes a pair of methylated cytosine residues in the context 5′-PumC (N40-2000) PumC-3′ (where Pu=A or G, mC=5-methylcytosine, and N=any nucleotide), and cleaves within approximately 30 base-pairs from one of the methylated cytosine residues. Therefore, loci that include high local densities of Pu mC will be cleaved to a greater extent than loci that include low local densities of Pu mC. Since Untreated and Treated portions were resolved by agarose gel electrophoresis, and DNA within the modal size range of the Untreated portions were excised and gel extracted, the Untreated portions represent the entire fragmented genome of the sample while the Treated portions are depleted of DNA fragments including Pu mC. Fractions were analyzed using a duplicated dye swap microarray hybridization paradigm. For example, equal mass (200 ng) of Untreated 1 and Treated 1 fraction DNA were used as template for labeling with Cy3 and Cy5, repectively, 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: Q8IWI9]


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 I and 9 Stage II), 27 normal ovarian tissue samples, and 23 normal blood samples. The normal ovarian tissues included in this panel were obtained from mastectomies unrelated to ovarian cancer. Each sample was split into two equal portions of 4 μg. One portion was digested with McrBC (Treated Portion) in a total volume of 200 μL including 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (New England Biolabs). The second portion was mock treated under identical conditions, except that 3.2 μL sterile 50% glycerol was added instead of McrBC enzyme (Untreated Portion). Samples were incubated at 37° C. for approximately 12 hours, followed by incubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions and data analysis were performed as described in Example 3.
Table 3 indicates the percent sensitivity and specificity for each locus. Gain biomarkers are biomarkers that show more methylation in tumor samples than normal samples and loss biomarkers show conversely. For gain biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as positive for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as negative for DNA methylation at each locus. For loss biomarkers, sensitivity reflects the frequency of scoring a known tumor sample as negative for DNA methylation at each locus while specificity reflects the frequency of scoring a known normal sample as positive for DNA methylation at each locus. As described above, an average delta Ct>1.0 (Treated Portion−Untreated Portion) was used as a threshold to score a sample as positive for DNA methylation at each locus (representing >50% depletion of amplifiable molecules in the DNA methylation-dependent restricted population relative to the untreated population). Percent sensitivity of gain biomarkers was calculated as the number of tumor samples with an average delta Ct>1.0 divided by the total number of tumor samples analyzed for that locus (i.e. excluding any measurements with a standard deviation between qPCR replicates >1 cycle)×100. Percent specificity of gain biomarkers was calculated as (1−(the number of normal samples with an average delta Ct>1.0 divided by the total number of normal samples analyzed for that locus))×100. On the contrary percent sensitivity and specificity of loss biomarkers was calculated vice versa. As shown in Table 3, the loci have sensitivities >8% and specificities relative to normal ovarian samples >40%. Notably, at least 9 of the loci have 100% specificity relative to normal ovarian and relative to normal blood samples. It is important to point out that the sensitivity and specificity of the differential DNA methylation status of any given locus may be increased by further optimization of the precise local genetic region interrogated by a DNA methylation-sensing assay.







TABLE 3







Sensitivity and Specificity of Differentially Methylated Loci in a Panel


of 26 Ovarian Tumor Samples, 27 Normal Ovarian Samples,


and 23 Normal Blood Samples















Specificity



Locus

Specificity vs
vs Normal


Feature Name
No.
Sensitivity
Normal Ovary
Blood














CHR01P001976799
1
96%
100%
0%


CHR01P026794862
2
43%
60%
89%


CHR01P043164342
3
42%
100%
100%


CHR01P063154999
4
85%
100%
100%


CHR01P204123050
5
76%
42%
5%


CHR01P206905110
6
38%
100%
100%


CHR01P225608458
7
85%
63%
4%


CHR02P005061785
8
100%
89%
0%


CHR02P042255672
9
81%
100%
0%


CHR02P223364582
10
92%
52%
100%


CHR03P027740753
11
77%
100%
100%


CHR03P052525960
12
85%
67%
0%


CHR03P069745999
13
8%
100%
100%


CHR05P059799713
14
42%
100%
100%


CHR05P059799813
15
35%
100%
100%


CHR05P177842690
16
62%
96%
95%


CHR06P010694062
17
88%
63%
0%


CHR06P026333318
18
96%
89%
0%


CHR08P102460854
19
84%
93%
0%


CHR08P102461254
20
76%
96%
0%


CHR08P102461554
21
80%
96%
0%


CHR09P000107988
22
73%
96%
91%


CHR09P021958839
23
88%
92%
90%


CHR09P131048752
24
96%
96%
0%


CHR10P118975684
25
35%
100%
0%


CHR11P021861414
26
19%
100%
100%


CHR12P004359362
27
38%
96%
87%


CHR12P016001231
28
50%
96%
80%


CHR14P018893344
29
85%
96%
0%


CHR14P093230340
30
92%
89%
100%


CHR16P000373719
31
38%
100%
0%


CHR16P066389027
32
15%
96%
100%


CHR16P083319654
33
38%
93%
87%


CHR18P019705147
34
31%
100%
100%


CHR19P018622408
35
92%
100%
0%


CHR19P051892823
36
78%
89%
10%


CHRXP013196410
37
96%
83%
14%


CHRXP013196870
38
96%
80%
9%


ha1p16_00179_l50
39
88%
96%
100%


ha1p16_00182_l50
40
81%
96%
100%


ha1p16_00257_l50
41
81%
86%
100%


ha1p_12601_l50
42
69%
100%
0%


ha1p_17147_l50
43
56%
100%
13%


ha1p_42350_l50
44
43%
96%
0%


ha1p_44897_l50
45
96%
40%
0%


ha1p_61253_l50
46
71%
95%
0%


CHR01P001005050
47
76%
100%
100%


CHR16P001157479
48
29%
100%
100%









Example 5
Validation of DNA Methylation Changes in Independent Lung Tumor, Normal Lung Samples and in Normal Peripheral Blood Samples
The differential DNA methylation status of the 49 loci was validated by analyzing an independent panel of 4 lung non-small adenocarcinoma samples (1 Stage I, 1 Stage II, 1 Stage III, 1 Stage IV) 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















CG
% Methylation














position
Tumor
Adj. Normal







ha1p_08588
35
  46%
  90%




67
  38%
  82%




182
  21%
  50%




200
   7%
  40%












# of clones

24
11



qPCR result (delta Ct)

0.95
6.23















CG
% Methylation














position
Tumor
Normal







CHR01P043164342
39
 9.91%
90.52%




51
 7.21%
75.68%




60
 6.19%
70.99%




87
10.00%
92.86%




104
 9.46%
83.81%




139
 3.77%
32.56%




148
13.46%
86.42%




174
 9.80%
61.54%




183
 7.69%
48.68%




255
 6.38%
67.27%












# of clones

96
96



qPCR result (delta Ct)

0.025
7.46















CG
% Methylation














position
Tumor
Normal







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















CG
% Methylation














position
Tumor
Normal







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




Feature Name
Number
Threshold
Sensitivity
Total
Specificity
Neg. of 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




Feature Name
Number
Threshold
Sensitivity
Total
Specificity
Neg. of 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




Feature Name
Number
Threshold
Sensitivity
Total
Specificity
Neg. 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




Feature Name
Number
Threshold
Sensitivity
Total
Specificity
Neg. of 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_104458
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




Neg. of


Feature Name
Number
Threshold
Sensitivity
Pos. of 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




Neg. of


Feature Name
Number
Threshold
Sensitivity
Pos. of 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




Neg. of


Feature Name
Number
Threshold
Sensitivity
Pos. of 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
Thresh-
Sen-
Pos. of
Speci-
Neg. of


Feature
No.
old
sitivity
Total
ficity
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




Neg. of


Feature Name
Number
Threshold
Sensitivity
Pos. of 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




Neg. of


Feature Name
Number
Threshold
Sensitivity
Pos. of 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 methylation status of an individual human, the method comprising:
obtaining a biological sample from the individual; and
determining the methylation status of at least one cytosine within a DNA region in the sample from the individual where the DNA region is on chromosome 3 and at least 90% identical to SEQ ID NO: 302 or is within 1.5 kb 5′ or 3′ on the human genome from SEQ ID NO:302.

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 SEQ ID NO: 302.

3. The method of claim 2, wherein the determining step comprises determining the methylation status of SEQ ID NO:302.

4. The method of claim 1, wherein the sample is from blood serum, blood plasma, urine, sputum, or tissue biopsy.

5. The method of claim 1, wherein the methylation status of at least one cytosine is compared to the methylation status 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 in at least two DNA regions.

9. The method of claim 1, wherein the determining step further comprises 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, 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.

10. The method of claim 1, wherein the determining step further comprises determining the methylation status of at least one cytosine in a 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, 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.

11. The method of claim 1, wherein the determining comprises detecting methylation density of the DNA region.

12. A method for determining the presence or absence of, or information about, 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 on chromosome 3 and at least 90% identical to SEQ ID NO: 399, wherein the determining step comprises at least one of: nucleic acid extraction, nucleic acid amplification, bisulfite conversion of DNA, or cleavage of DNA with a methylation-sensitive or methylation restriction enzyme;
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 cancer, or provides information about the cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cancer or provides information about the cancer in the individual.

13. The method of claim 12, 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 SEQ ID NO: 302.

14. The method of claim 13, wherein the determining step comprises determining the methylation status of the DNA region corresponding to the biomarker.

15. The method of claim 13, wherein the biomarker is SEQ ID NO:302.

16. The method of claim 12, wherein the determining step further comprises determining the methylation status of at least one cytosine in a 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, 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.

17. The method of claim 12, wherein the sample is from blood serum, blood plasma, or a biopsy.

18. The method of claim 12, wherein the methylation status of the DNA region is compared to the methylation value of a control locus.

19. The method of claim 18, wherein the control locus is an endogenous control.

20. The method of claim 18, wherein the control locus is an exogenous control.

21. The method of claim 12, wherein the determining step comprises determining the methylation status of at least one cytosine from at least two DNA regions.

22. The method of claim 12, wherein the DNA region is SEQ ID NO:399.

23. The method of claim 12, wherein the determining step further comprises 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, 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.

24. The method of claim 12, wherein step (b) comprises 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 cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cancer in the individual.

25. The method of claim 24, wherein the determining comprises detecting methylation density of the DNA region.

26. The method of claim 24, wherein the determining comprises detecting methylation density of a genomic region corresponding to SEQ ID NO:302.

27. The method of claim 12, wherein the determining comprises detecting methylation density of the DNA region.

28. The method of claim 12, wherein the determining comprises detecting methylation density of a genomic region corresponding to SEQ ID NO:302.

29. The method of claim 12, wherein the information comprises prognosis, cancer classification, prediction of disease risk, or selection of treatment.

30. A method for determining the presence or absence of, or information about, 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 on chromosome 3 and at least 90% identical to SEQ ID NO: 399;
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 cancer, or provides information about the cancer, wherein the comparison of the methylation status to the threshold value is predictive of the presence or absence of cancer or provides information about the cancer in the individual.