Patent Publication Number: US-2020277677-A1

Title: Methylation markers for diagnosing cancer

Description:
CROSS-REFERENCE 
     This application claims the benefit of U.S. Provisional Application No. 62/569,459, filed Oct. 6, 2017, and U.S. Provisional Application No. 62/673,593, filed May 18, 2018, which each of the applications is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND OF THE DISCLOSURE 
     Cancer is a leading cause of deaths worldwide, with annual cases expected to increase from 14 million in 2012 to 22 million during the next two decades (WHO). Diagnostic procedures for liver cancer, in some cases, begin only after a patient is already present with symptoms, leading to costly, invasive, and sometimes time-consuming procedures. In addition, inaccessible areas sometimes prevent an accurate diagnosis. Further, high cancer morbidities and mortalities are associated with late diagnosis. 
     SUMMARY OF THE DISCLOSURE 
     In certain embodiments, disclosed herein is a method of selecting a subject suspected of having cancer for treatment, comprising: (a) contacting treated DNA with at least one probe from a probe panel to generate an amplified product, wherein the at least one probe hybridizes under high stringency condition to a target sequence of a cg marker selected from Table 1, Table 2, Table 7, Table 8, or Table 13, and wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the cg marker; (c) comparing the methylation profile to a reference model relating methylation profiles of cg markers from Tables 1, 2, 7, 8, and 13 to a set of cancers; (d) based on the comparison of step c), determining: (i) whether the subject has cancer; and (ii) which cancer type the subject has; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have cancer and the cancer type is determined. 
     In certain embodiments, disclosed herein is a method of detecting the methylation status of a set of cg markers, comprising: (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of a cg marker from Table 1, Table 2, Table 7, Table 8, Table 13, Table 14, or Table 20; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. 
     Disclosed herein, in certain embodiments, is a method of detecting a methylation pattern of a set of biomarkers in a subject suspected of having a cancer, the method comprising: (a) processing an extracted genomic DNA with a deaminating agent to generate a genomic DNA sample comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample from the subject suspected of having a cancer; and (b) detecting the methylation pattern of one or more biomarkers selected from Table 1, Table 2, Table 7, Table 8, Table 13, Table 14, or Table 20 from the extracted genomic DNA by contacting the extracted genomic DNA with a set of probes, wherein the set of probes hybridizes to the one or more biomarkers, and perform a DNA sequencing analysis to determine the methylation pattern of the one or more biomarkers. In some embodiments, said detecting comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some embodiments, the digital probe-based PCR is a digital droplet PCR. In some embodiments, the set of probes comprises a set of padlock probes. In some embodiments, step b) comprises detecting the methylation pattern of one or more biomarkers selected from Table 2, Table 13, Table 14, or Table 20. In some embodiments, step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg19516279, cg06100368, cg25945732, cg19155007, cg17952661, cg04072843, cg01250961, cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg01237565, cg16561543, cg13771313, cg13771313, cg08169020, cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg09095222, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, cg13169641, cg25352342, cg09921682, cg02504622, cg17373759, cg06547203, cg06826710, cg00902147, cg17609887, cg15721142, cg08116711, cg00736681, cg18834029, cg06969479, cg24630516, cg16901821, cg20349803, cg23610994, cg19313373, cg16508600, cg24096323, cg24746106, cg12288267, cg10430690, cg24408776, cg05630192, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, cg09921682, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, cg06405341, cg08557188, cg00690392, cg03421440, cg07077277, or cg20702527. In some embodiments, the subject is suspected of having a breast cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg19516279, cg06100368, cg20349803, cg23610994, cg19313373, cg16508600, and cg24096323. In some embodiments, the subject is determined to have a breast cancer if: at least one of the cg markers cg19516279 and cg06100368 is hypermethylated; at least one of the cg markers cg20349803, cg23610994, cg19313373, cg16508600, and cg24096323 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a liver cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg25945732, cg19155007, cg17952661, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, and cg26859666, or cg00456086. In some embodiments, the subject is determined to have a liver cancer if: at least one of the cg markers cg25945732, cg19155007, or cg17952661 is hypermethylated; at least one of the cg markers cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, or cg00456086 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a liver cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, 5-176829639, 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858. In some embodiments, the subject is determined to have a liver cancer if: at least one of the markers 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, or 5-176829639 is hypermethylated; at least one of the markers 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having an ovarian cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg04072843, cg01250961, cg24746106, cg12288267, and cg10430690. In some embodiments, the subject is determined to have an ovarian cancer if: at least one of the cg markers cg04072843 and cg01250961 is hypermethylated; at least one of the cg markers cg24746106, cg12288267, and cg10430690 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a colorectal cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, and cg09921682. In some embodiments, the subject is determined to have a colorectal cancer if: at least one of the cg markers cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, or cg00846300 is hypermethylated; at least one of the cg markers cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, or cg09921682 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a colorectal cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg10673833, cg10493436, cg10428836, cg27284288, cg16959747, cg17494199, cg23678254, cg24067911, or cg25459300. In some embodiments, the subject is suspected of having a colorectal cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195. In some embodiments, the subject is determined to have a colorectal cancer if: at least one of the cg markers cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195 is hypermethylated; at least one of the cg markers cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, or cg16391792 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a prostate cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg06547203, cg06826710, cg00902147, cg17609887, and cg15721142. In some embodiments, the subject is determined to have a prostate cancer if: at least one of the cg markers cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, or cg26149167 is hypermethylated; at least one of the cg markers cg06547203, cg06826710, cg00902147, cg17609887, or cg15721142 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having a pancreatic cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg01237565, cg16561543, and cg08116711. In some embodiments, the subject is determined to have a pancreatic cancer if: at least one of the cg markers cg01237565 or cg16561543 is hypermethylated; cg marker cg08116711 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having acute myeloid leukemia and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg13771313, cg13771313, and cg08169020. In some embodiments, the subject is suspected of having cervical cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, and cg13169641. In some embodiments, the subject is determined to have cervical cancer if: at least one of the cg markers cg08169020, cg21153697, cg07326648, cg14309384, or cg20923716 is hypermethylated; at least one of the cg markers cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, or cg13169641 is hypomethylated; or a combination thereof. In some embodiments, the subject is suspected of having sarcoma and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg09095222. In some embodiments, the subject is determined to have sarcoma if at least cg marker cg09095222 is hypermethylated. In some embodiments, the subject is suspected of having stomach cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg00736681 and cg18834029. In some embodiments, the subject is determined to have stomach cancer if at least one of the cg markers cg00736681 or cg18834029 is hypomethylated. In some embodiments, the subject is suspected of having thyroid cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg06969479, cg24630516, and cg16901821. In some embodiments, the subject is determined to have thyroid cancer if at least one of the cg markers cg06969479, cg24630516, or cg16901821 is hypomethylated. In some embodiments, the subject is suspected of having mesothelioma and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg05630192. In some embodiments, the subject is determined to have mesothelioma if cg marker cg05630192 is hypomethylated. In some embodiments, the subject is suspected of having glioblastoma and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg06405341. In some embodiments, the subject is suspected of having lung cancer and step b) comprises detecting the methylation pattern of one or more biomarkers selected from cg08557188, cg00690392, cg03421440, and cg07077277. In some embodiments, the subject is determined to have lung cancer if at least one of the cg markers cg08557188, cg00690392, cg03421440, or cg07077277 is hypomethylated. In some embodiments, the biological sample is a blood sample, a urine sample, a saliva sample, a sweat sample, or a tear sample. In some embodiments, the biological sample is a cell-free DNA sample. In some embodiments, the biological sample comprises circulating tumor cells. 
     In certain embodiments, disclosed herein is a kit comprising a set of nucleic acid probes that hybridizes to target sequences of cg markers illustrated in Table 1, Table 2, Table 7, Table 8, Table 13, Table 14, Table 20, or a combination thereof. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 1. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 2. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 7. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 8. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 13. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 14. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from Table 20. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg markers selected from cg19516279, cg06100368, cg25945732, cg19155007, cg17952661, cg04072843, cg01250961, cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg01237565, cg16561543, cg13771313, cg13771313, cg08169020, cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg09095222, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, cg13169641, cg25352342, cg09921682, cg02504622, cg17373759, cg06547203, cg06826710, cg00902147, cg17609887, cg15721142, cg08116711, cg00736681, cg18834029, cg06969479, cg24630516, cg16901821, cg20349803, cg23610994, cg19313373, cg16508600, cg24096323, cg24746106, cg12288267, cg10430690, cg24408776, cg05630192, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, cg09921682, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, cg06405341, cg08557188, cg00690392, cg03421440, cg07077277, cg00456086, and cg20702527. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg10673833, cg10493436, cg10428836, cg27284288, cg16959747, cg17494199, cg23678254, cg24067911, or cg25459300. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg10673833 or cg25462303. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, or cg16391792. In some embodiments, the set of nucleic acid probes hybridizes to target sequences of cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195. In some embodiments, the set of nucleic acid probes comprises a set of padlock probes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various aspects of the disclosure are set forth with particularity in the appended claims. The file of this patent contains at least one drawing/photograph executed in color. Copies of this patent with color drawing(s)/photograph(s) will be provided by the Office upon request and payment of the necessary fee. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which: 
         FIG. 1  illustrates the methylation status of biomarker 7-1577016. 
         FIG. 2  illustrates the methylation status of biomarker 11-67177103. 
         FIG. 3  illustrates the methylation status of biomarker 19-10445516 (cg17126555). 
         FIG. 4  illustrates the methylation status of biomarker 12-122277360. 
         FIG. 5  illustrates the methylation status of biomarker 6-72130742 (cg24772267). 
         FIG. 6  illustrates the methylation status of biomarker 3-15369681. 
         FIG. 7  illustrates the methylation status of biomarker 3-131081177. 
         FIG. 8  illustrates workflow chart of data generation and analysis. Whole genome methylation data on HCC and normal lymphocytes were used to identify 401 candidate markers. Diagnostic marker selection: Lasso and Random-forest analyses were applied to a training cohort of 715 HCC and 560 normal patients to identify a final selection of 10 markers. These ten markers were applied to a validation cohort of 383 HCC and 275 normal patients. Prognostic marker selection: univariant-cox and LASSO-Cox were applied to a training cohort of 680 HCC patients with survival data to identify a final selection of eight markers. These eight markers were applied to a validation cohort of 369 HCC with survival data. 
         FIG. 9A - FIG. 9H  illustrate cfDNA methylation analysis for diagnosis of HCC.  FIG. 9A  shows the heatmap of methylation of 28 pairs of matched HCC tumor DNA and plasma cfDNA, with a mean methylation value threshold of 0.1 as a cutoff.  FIG. 9B  shows the methylation values and standard deviations of ten diagnostic markers in normal plasma, HCC tumor DNA, and HCC patient cfDNA.  FIG. 9C  and  FIG. 9D  show the confusion tables of binary results of the diagnostic prediction model in the training ( FIG. 9C ) and validation datasets ( FIG. 9D ).  FIG. 9E  and  FIG. 9F  illustrate ROC of the diagnostic prediction model with methylation markers in the training ( FIG. 9E ) and validation datasets ( FIG. 9F ).  FIG. 9G  and  FIG. 9H  show the unsupervised hierarchical clustering of ten methylation markers selected for use in the diagnostic prediction model in the training ( FIG. 9G ) and validation datasets ( FIG. 9H ). 
         FIG. 10A - FIG. 10K  illustrate cfDNA methylation analysis and tumor burden, treatment response, and staging. The combined diagnosis score (cd-score) ( FIG. 10A ) and AFP ( FIG. 10B ) in healthy controls, individuals with liver diseases (HBV/HCV infection, cirrhosis, and fatty liver) and HCC patients.  FIG. 10C  shows the cd-score in normal controls and HCC patients with and without detectable tumor burden.  FIG. 10D  shows the cd-score in normal controls, HCC patients before treatment, with treatment response, and with progression.  FIG. 10E  shows the cd-score in normal controls and HCC patients before surgery, after surgery, and with recurrence.  FIG. 10F  shows the cd-score in normal controls and HCC patients from stage I-IV.  FIG. 10G  shows the ROC of cd-score and AFP for HCC diagnosis in whole HCC cohort. cd-score ( FIG. 10H ) and AFP ( FIG. 10I ) in HCC patients with initial diagnosis (before surgery or other treatment), with treatment response, with progression, and with recurrence. cd-score ( FIG. 10J ) and AFP ( FIG. 10K ) in HCC patients from stage I-IV. 
         FIG. 11A - FIG. 11G  illustrate cfDNA methylation analysis for prognostic prediction HCC survival.  FIG. 11A  and  FIG. 11B  show the overall survival curves of HCC patients with low or high risk of death at 6 months, according to the combined prognosis score (cp-score) in the training ( FIG. 11A ) and validation datasets ( FIG. 11B ). Survival curves of HCC patients with stage I/II and stage III/IV in the training ( FIG. 11C ) and validation datasets ( FIG. 11D ). The ROC for the cp-score, stage, and cp-score combined with stage in the training ( FIG. 11E ) and validation datasets ( FIG. 11F ).  FIG. 11G  shows the survival curves of HCC patients with combinations of cp-score risk and stage in the whole HCC cohort. 
         FIG. 12  illustrates an unsupervised hierarchical clustering of top 1000 methylation markers differentially methylated between HCC tumor DNA and normal blood. Each column represents an individual patient and each row represents a CpG marker. 
         FIG. 13A - FIG. 13B  illustrate an exemplary region encompassing two Blocks of Correlated Methylation (BCM) in cfDNA samples of from HCC and normal controls.  FIG. 13A  shows a genomic neighborhood of the BCM displayed within UCSC genome browser (Pearson correlation track showed correlation data by summing r values for a marker within a BCM. Cg marker names below the Pearson correlation graph (cg14999168, cg14088196, cg25574765) were methylation markers from TCGA. Gene name and common SNPs were also listed.  FIG. 13B  shows a not-to-scale representation of a set of analyzed cg markers belonging to two BCMs in this region. Boundaries between blocks are indicated by a black rectangle, whereas red squares indicate correlated methylation (r&gt;0.5) between two nearby markers. Correlation between any two markers is represented by a square at the intersection of (virtual) perpendicular lines originating from these two markers. White color indicates no significant correlation. 10 newly identified methylation markers in the left MCB anchored by marker cg14999168 or 11 newly identified methylation markers in the right MCB anchored by cg14088196/cg 25574765 were highly consistent and correlated among HCC ctDNA, normal cfDNA, and HCC tissue DNA. Using markers within the same MCB can significantly enhanced allele calling accuracy. Vertical lines at the bottom of panel b were genomic coordinates of boundaries of two MCBs. 
         FIG. 14  illustrates an unsupervised hierarchical clustering of exemplary methylation markers for Stage I-Stage IV HCC tumor. 
         FIG. 15  shows methylation values correlated with treatment outcomes in HCC patients with serial plasma samples.  FIG. 15A  shows a change in cd-score comparing patients after surgery, with clinical response, and with disease progression (***p&lt;0.001).  FIG. 15B  shows cd-score trends in individual patients after complete surgical resection with treatment response, and with disease progression. PRE: pre-treatment; POST: after-treatment. 
         FIG. 16  illustrates a dynamic monitoring of treatment outcomes in individual patients with cd-score and AFP. Dates of treatments are indicated by vertical blue arrows. PD, progressive disease; PR partial response; SD, stable disease; TACE, trans-catheter arterial chemoembolization. 
         FIG. 17A - FIG. 17C  illustrates data analysis of an exemplary marker cg10673833. 
         FIG. 18  illustrates a workflow for building the diagnostic and prognostic models. Whole genome methylation data on HCC, LUNC and normal blood were used to identify candidate markers for probe design. Left panel: diagnostic marker selection: LASSO analysis was applied to a training cohort of 444 HCC, 299 LUNC, and 1123 normal patients to identify a final selection of 77 markers. These 77 markers were applied to a validation cohort of 445 HCC, 300 LUNC, and 1124 normal patients. Right panel: prognostic marker selection: LASSO-Cox were applied to a training cohort of 433 HCC and 299 LUNC patients with survival data to identify a final selection of 20 markers. These 20 markers were applied to a validation cohort of 434 HCC and 300 LUNC with survival data. 
         FIG. 19A - FIG. 19D  illustrates cfDNA methylation analysis for diagnosis of LUNC and HCC.  FIG. 19A  shows receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the diagnostic prediction model (cd-score) using cfDNA methylation analysis in the validation cohort.  FIG. 19B  shows box plot of composite scores used to classify normal and cancer patients (left), and LUNC and HCC patients (right). Unsupervised hierarchical clustering of methylation markers differentially methylated between cancer (HCC and LUNC) and normal ( FIG. 19C ) and between HCC and LUNC ( FIG. 19D ). Each row represents an individual patient and each column represents a MCB marker. 
         FIG. 20A - FIG. 20D  illustrates methylation profiling in healthy control, high-risk patients and cancer patients.  FIG. 20A  shows methylation profiling differentiates HCC from high risk liver disease patients or normal controls. High risk liver diseases were defined as hepatitis, liver cirrhosis and fatty liver disease.  FIG. 20B  shows serum AFP differentiates HCC from high risk liver disease patients or normal controls.  FIG. 20C  shows methylation profiling differentiates LUNC from patients who smoke and normal controls.  FIG. 20D  shows serum CEA differentiates LUNC from high risk (smoking) patients. 
         FIG. 21A - FIG. 21R  illustrates cfDNA methylation analysis could predict tumor burden, staging, and treatment response using a composite diagnosis score in LUNC and HCC patients. cd-score in patients with and without detectable tumor burden in LUNC ( FIG. 21A ) when compared to CEA ( FIG. 21I ) and HCC ( FIG. 21E ) when compared to AFP ( FIG. 21M ); cd-score of patients with stage I/II and stage III/IV disease in LUNC ( FIG. 21B ) when compared to CEA ( FIG. 21J ) and HCC ( FIG. 21F ) patients when compared to AFP ( FIG. 21N ); cd-score in patients before intervention, after surgery, and with recurrence in LUNC ( FIG. 21C ) when compared to CEA ( FIG. 21K ) and HCC ( FIG. 21G ) when compared to AFP ( FIG. 21O ); cd-score in patients before intervention, with treatment response, and with worsening progression in LUNC ( FIG. 21D ) when compared to CEA ( FIG. 21L ) and HCC ( FIG. 21H ) when compared to AFP ( FIG. 21P );  FIG. 21Q : The ROC curve and the AUC of cd-score and AFP for LUNC diagnosis in the entire LUNC cohort.  FIG. 21R : The ROC curve and the AUC of cd-score and AFP for HCC diagnosis in the entire HCC cohort. 
         FIG. 22A - FIG. 22F  illustrates prognostic prediction in HCC and LUNC survival based on cfDNA methylation profiling.  FIG. 22A  shows the overall survival curves of HCC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation cohort.  FIG. 22B  shows the overall survival curves of LUNC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation dataset.  FIG. 22C  shows the survival curves of HCC patients with stage I/II and stage III/IV in the validation cohort.  FIG. 22D  shows the survival curves of patients with stage I/II and stage III/IV LUNC in the validation cohort. The ROC for 12 months survival predicted by cp-score, CEA, AFP, stage, and cp-score combined with stage of HCC ( FIG. 22E ) and LUNC ( FIG. 22F ) in the validation cohort. 
         FIG. 23A - FIG. 23B  illustrates early detection of LUNC using a cfDNA methylation panel.  208  smoker patients was enrolled with lung nodules between 10 mm and 30 mm in size in a prospective trial and measured a cfDNA LUNC methylation panel. Patients were divided into a training and a testing cohort ( FIG. 23A ); Receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the prediction of Stage I LUNC versus benign lung nodules in the validation cohort with 91.4% accuracy ( FIG. 23B ); table showing prediction results between Stage I LUNC versus benign lung nodules showing high sensitivity and specificity in the validation cohort. 
         FIG. 24A - FIG. 24D  illustrates methylation markers can differentiate between HCC and liver cirrhosis and Detect progression from liver cirrhosis to HCC. A prediction model was first built using 217 HCC and 241 cirrhosis patients and divided patients into a training and a testing cohort ( FIG. 24A ); Receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the prediction of Stage I HCC versus liver cirrhosis in the validation cohort with 89.9% accuracy ( FIG. 24B ); table showing prediction results between Stage I HCC and liver cirrhosis in a validation cohort ( FIG. 24C ); table showing prediction results on progression from liver cirrhosis to stage 1HCC with high sensitivity (89.5%) and specificity (98%) ( FIG. 24D ). 
         FIG. 25A  illustrates unsupervised hierarchical clustering of top 1000 methylation markers differentially methylated in DNA in HCC and LUNC primary tissues versus normal blood. 
         FIG. 25B  shows unsupervised hierarchical clustering of the top 1000 methylation markers differentially methylated between HCC and LUNC tissue DNA. Each column represents an individual patient and each row represents a CpG marker. 
         FIG. 25C  shows global view of supervised hierarchical clustering of all 888 MCBs in the entire cfDNA dataset. 
         FIG. 26  illustrates Boxplots showing the features of MCBs in cohorts. Top plot: Mean values and deviations of Lasso MCBs in each one versus rest comparison. 
         FIG. 27  illustrates methylation values correlated with treatment outcomes in HCC and LUNC patients with serial plasma samples. Summary graphs of change in methylation value comparing patients after surgery, with clinical response (Partial Remission (PR) or Stable Disease (SD), or with disease progression/recurrent (PD). 
         FIG. 28A  shows dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB in LUNC patients. 
         FIG. 28B  shows dynamic monitoring of treatment outcomes with the methylation value of an MCB in LUNC patients. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy. 
         FIG. 29  illustrates dynamic monitoring of treatment outcomes using the total methylation copy numbers of an MCB and CEA in HCC patients. 
         FIG. 30  shows dynamic monitoring of treatment outcomes with the methylation rate of an MCB in HCC patients. Dates of treatments are indicated in the figure. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy, TACE, trans-catheter arterial chemoembolization. 
         FIG. 31A - FIG. 31B  illustrate workflow chart described in Example 5.  FIG. 31A  illustrates an exemplary workflow for building the diagnostic model, prognostic model, and generating the subtype based ctDNA methylation.  FIG. 31B  shows the enrollment and outcomes of the prospective screening cohort study. 
         FIG. 32A - FIG. 32H  illustrate cfDNA methylation analysis for diagnosis of CRC.  FIG. 32A : exemplary workflow for building the diagnostic models.  FIG. 32B : Unsupervised hierarchical clustering of methylation markers differentially methylated between cancer (CRC) and normal in the training and the validation ( FIG. 32C ) testing cohort. Each row represents an individual patient and each column represents a CpG marker.  FIG. 32D : Receiver operating characteristic (ROC) curves and the associated Area Under Curves (AUCs) of the diagnostic prediction model (cd-score) using cfDNA methylation analysis in the training and the validation ( FIG. 32E ) testing cohort.  FIG. 32F : ROC curves and corresponding Area Under the Curve (AUCs) of cd-score and CEA for CRC diagnosis.  FIG. 32G : Confusion matrices built from diagnostic model prediction in the training and the validation (H) testing cohort. 
         FIG. 33A - FIG. 33E  illustrate prognostic prediction in CRC survival based on cfDNA methylation profiling.  FIG. 33A : an exemplary workflow for building the prognostic models.  FIG. 33B : Overall survival curves of CRC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the training testing cohort.  FIG. 33C : Overall survival curves of HCC patients with low or high risk of death, according to the combined prognosis score (cp-score) in the validation testing cohort. The ROC and corresponding AUCs for 12 months survival predicted by cp-score, Primary tumor location, TNM stage, CEA status and combined all in the training ( FIG. 33D ) and validation ( FIG. 33E ) testing cohort. 
         FIG. 34A  illustrates a nomogram for predicting one year overall survival of CRC patients using cp-score and other clinical factors. 
         FIG. 34B  illustrates a calibration plot of nomogram in external validation. 
         FIG. 35A - FIG. 35E  cfDNA methylation subtyping analysis in 801 patients with CRC.  FIG. 35A : A schematic diagram shown that the core algorithm utilized in the sample clustering.  FIG. 35B : Iteratively unsupervised clustering of cfDNA methylation markers identified two subtypes/clusters in training data. Clinical and molecular features are indicated by the annotation bars above the heatmap. Patients without such information were colored in white. Mutation status was defined by the mutation detected in one of the following genes: BRAF, KRAS, NRAS and PIK3CA.  FIG. 35C : Silhouette analysis of the clusters in the last iteration.  FIG. 35D : Predicted subtypes/clusters of validation using the 45 makers.  FIG. 35E : upper panel: overall survival for each of the cfDNA methylation in each subtypes. (log rank test p&lt;0.05). lower panel: proportion of III-IV stage CRC patients in two subtypes (Chi-squared test, **P&lt;0.01, *P&lt;0.05. left, training cohort; right, validation cohort). 
         FIG. 36A - FIG. 36B  presents a list of Methylation Correlated Blocks (MCBs) used for cd-score generation.  FIG. 36A : MCBs markers selected by muti-class LASSO.  FIG. 36B : Diagnostic marker selection: LASSO-based feature selection identified 13 markers and Random Forest-based feature selection identified 22 markers for discriminating cancer versus normal. There were 9 overlapping markers between these two methods. 
         FIG. 37A - FIG. 37F  show cfDNA methylation analysis could predict tumor burden, staging, and treatment response using a cd-score in CRC patients.  FIG. 37A : cfDNA methylation analysis cd-score in patients with and without detectable tumor burden;  FIG. 37B : cd-score of patients with stage I/II and stage III/IV disease;  FIG. 37C : cd-score in patients with primary tumor location on left or on right;  FIG. 37D : CEA in patients with stage VII and stage III/IV CRC;  FIG. 37E : cd-score in patients before treatment, after surgery, and with tumor recurrence;  FIG. 37F : CEA in CRC patients before treatment, after surgery, and with tumor recurrence; Recurrence was defined as tumor initially disappeared after treatment/surgery but recurred after a defined period. 
         FIG. 38A - FIG. 38C  illustrate comparison of subtype markers, diagnosis markers and prognosis markers.  FIG. 38A : Venn diagram shows the intersects of the three marker lists. Patients in cluster 2 had higher cpscores than those in cluster 1 from the both training cohort ( FIG. 38B ) and validation cohort ( FIG. 38C ). 
         FIG. 39  illustrates patient treatment monitoring with marker methylation level. Dynamic monitoring of treatment outcomes with the methylation value of CpG site cg10673833 (upper panel) and CEA (lower panel) in CRC patients #1-6. Dates of treatments are indicated in the figure. PD, progressive disease; PR partial response; SD, stable disease; chemo, chemotherapy. 
         FIG. 40A - FIG. 40B  illustrates methylation values correlated with treatment outcomes in CRC patients with serial plasma samples.  FIG. 40A : Summary graphs of change in methylation value comparing patients after surgery, with clinical response (Partial Remission (PR) or Stable Disease (SD), or with disease progression/recurrent (PD).  FIG. 40B : Methylation value trends in individual patients after complete surgical resection, with treatment response, and with disease progression. Delta methylation rate denotes the methylation value difference before treatment and after treatment. PRE: pre-treatment; POST: after-treatment. 
         FIG. 41  illustrates the methylation status of CpG marker cg00456086. 
         FIG. 42  illustrates the methylation status of biomarker 3-49757316, 8-27183116, 8-141607252, 17-29297711, and 3-49757306. 
         FIG. 43  illustrates the methylation status of biomarker 19-43979341, 8-141607236, 5-176829755, 18-13382140, and 15-65341965. 
         FIG. 44  illustrates the methylation status of biomarker 15-91129457, 2-1625431, 6-151373292, 6-151373294, and 20-25027093. 
         FIG. 45  illustrates the methylation status of biomarker 6-14284198, 10-4049295, 19-59023222, 1-184197132, and 2-131004117. 
         FIG. 46  illustrates the methylation status of biomarker 3-13152305, 17-29297770, 8-27183316, 5-176829740, and 19-41316693. 
         FIG. 47  illustrates the methylation status of biomarker 18-43830649, 15-65341957, 20-44539531, 7-30265625, and 2-131129567. 
         FIG. 48  illustrates the methylation status of biomarker 2-8995417, 12-10782319, 20-25027033, 6-151373256, and 8-86100970. 
         FIG. 49  illustrates the methylation status of biomarker 9-4839459, 17-41221574, 1-153926715, 20-25027044, and 20-20177325. 
         FIG. 50  illustrates the methylation status of biomarker 176829665, 3-13152273, 8-27183348, 3-49757302, and 19-41316697. 
         FIG. 51  illustrates the methylation status of biomarker 8-61821442, 20-44539525, 10-102883105, 11-65849129, and 5-176829639. 
         FIG. 52  illustrates the methylation status of biomarker 2-1625443, 20-25027085, 11-69420728, 1-229234865, and 6-13408877. 
         FIG. 53  illustrates the methylation status of biomarker 22-50643735, 6-151373308, 1-232119750, 8-134361508, and 6-13408858. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     Cancer is characterized by an abnormal growth of a cell caused by one or more mutations or modifications of a gene leading to dysregulated balance of cell proliferation and cell death. DNA methylation silences expression of tumor suppression genes, and presents itself as one of the first neoplastic changes. Methylation patterns found in neoplastic tissue and plasma demonstrate homogeneity, and in some instances are utilized as a sensitive diagnostic marker. For example, cMethDNA assay has been shown in one study to be about 91% sensitive and about 96% specific when used to diagnose metastatic breast cancer. In another study, circulating tumor DNA (ctDNA) was about 87.2% sensitive and about 99.2% specific when it was used to identify KRAS gene mutation in a large cohort of patients with metastatic colon cancer (Bettegowda et al., Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med, 6(224):ra24. 2014). The same study further demonstrated that ctDNA is detectable in &gt;75% of patients with advanced pancreatic, ovarian, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular, and head and neck cancers (Bettegowda et al). 
     Additional studies have demonstrated that CpG methylation pattern correlates with neoplastic progression. For example, in one study of breast cancer methylation patterns, P16 hypermethylation has been found to correlate with early stage breast cancer, while TIMP3 promoter hypermethylation has been correlated with late stage breast cancer. In addition, BMP6, CST6 and TIMP3 promoter hypermethylation have been shown to associate with metastasis into lymph nodes in breast cancer. 
     In some embodiments, DNA methylation profiling provides higher clinical sensitivity and dynamic range compared to somatic mutation analysis for cancer detection. In other instances, altered DNA methylation signature has been shown to correlate with the prognosis of treatment response for certain cancers. For example, one study illustrated that in a group of patients with advanced rectal cancer, ten differentially methylated regions were used to predict patients&#39; prognosis. Likewise, RASSF1A DNA methylation measurement in serum was used to predict a poor outcome in patients undergoing adjuvant therapy in breast cancer patients in a different study. In addition, SRBC gene hypermethylation was associated with poor outcome in patients with colorectal cancer treated with oxaliplatin in a different study. Another study has demonstrated that ESR1 gene methylation correlates with clinical response in breast cancer patients receiving tamoxifen. Additionally, ARM gene promoter hypermethylation was shown to be a predictor of long-term survival in breast cancer patients not treated with tamoxifen. 
     In some embodiments, disclosed herein include methods, probes, and kits for diagnosing the presence of cancer and/or a cancer type. In some instances, described herein is a method of profiling the methylation status of a set of CpG markers (or cg markers). In other instances, described herein is a method for selecting a patient based on the methylation status of a set of CpG markers (or cg markers) for treatment. 
     Methods of Use 
     DNA methylation is the attachment of a methyl group at the C5-position of the nucleotide base cytosine and the N6-position of adenine. Methylation of adenine primarily occurs in prokaryotes, while methylation of cytosine occurs in both prokaryotes and eukaryotes. In some instances, methylation of cytosine occurs in the CpG dinucleotides motif. In other instances, cytosine methylation occurs in, for example CHG and CHH motifs, where H is adenine, cytosine or thymine. In some instances, one or more CpG dinucleotide motif or CpG site forms a CpG island, a short DNA sequence rich in CpG dinucleotide. In some instances, a CpG island is present in the 5′ region of about one half of all human genes. CpG islands are typically, but not always, between about 0.2 to about 1 kb in length. Cytosine methylation further comprises 5-methylcytosine (5-mCyt) and 5-hydroxymethylcytosine. 
     The CpG (cytosine-phosphate-guanine) or CG motif refers to regions of a DNA molecule where a cytosine nucleotide occurs next to a guanine nucleotide in the linear strand. In some instances, a cytosine in a CpG dinucleotide is methylated to form 5-methylcytosine. In some instances, a cytosine in a CpG dinucleotide is methylated to form 5-hydroxymethylcytosine. 
     In some instances, one or more DNA regions are hypermethylated. In such cases, hypermethylation refers to an increase in methylation event of a region relative to a reference region. In some cases, hypermethylation is observed in one or more cancer types, and is useful, for example, as a diagnostic marker and/or a prognostic marker. 
     In some instances, one or more DNA regions are hypomethylated. In some cases, hypomethylation refers to a loss of the methyl group in the 5-methylcytosine nucleotide in a first region relative to a reference region. In some cases, hypomethylation is observed in one or cancer types, and is useful, for example, as a diagnostic marker and/or a prognostic marker. 
     In some embodiments, disclosed herein are CpG methylation markers for diagnosis of a cancer in a subject. In some instances, also disclosed herein is a method of selecting a subject suspected of having cancer for treatment. In some instances, the method comprises (a) contacting treated DNA with at least one probe from a probe panel to generate an amplified product, wherein the at least one probe hybridizes under high stringency condition to a target sequence of a cg marker selected from Table 1, Table 2, Table 7, Table 8, or Table 13, and wherein the treated DNA is processed from a biological sample obtained from the subject; (b) analyzing the amplified product to generate a methylation profile of the cg marker; (c) comparing the methylation profile to a reference model relating methylation profiles of cg markers from Tables 1, 2, 7, 8, and 13 to a set of cancers; (d) based on the comparison of step c), determining: (i) whether the subject has cancer; and (ii) which cancer type the subject has; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have cancer and the cancer type is determined. 
     In some instances, the method comprises (a) contacting treated DNA with the probe panel to generate amplified products, wherein each probe of the probe panel hybridizes under high stringency condition to a target sequence of a cg marker selected from Table 1, Table 2, Table 7, or Table 8; (b) analyzing the amplified products to generate a methylation profile of the cg markers targeted by the probe panel; (c) comparing the methylation profile to the reference model relating methylation profiles of cg markers from Tables 1, 2, 7, and 8 to a set of cancers; (d) evaluating an output from the model to determine: (i) whether the subject has cancer; and (ii) which cancer type the subject has; and (e) administering an effective amount of a therapeutic agent to the subject if the subject is determined to have cancer and the cancer type is determined. 
     In some cases, the biological sample is treated with a deaminating agent to generate the treated DNA. 
     In some cases, the at least one probe from the probe panel is a padlock probe. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 1. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 2. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 4. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 5. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 7. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 8. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 13. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg19516279, cg06100368, cg25945732, cg19155007, cg17952661, cg04072843, cg01250961, cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg01237565, cg16561543, cg13771313, cg13771313, cg08169020, cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg09095222, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, cg13169641, cg25352342, cg09921682, cg02504622, cg17373759, cg06547203, cg06826710, cg00902147, cg17609887, cg15721142, cg08116711, cg00736681, cg18834029, cg06969479, cg24630516, cg16901821, cg20349803, cg23610994, cg19313373, cg16508600, cg24096323, cg24746106, cg12288267, cg10430690, cg24408776, cg05630192, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, cg09921682, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, cg06405341, cg08557188, cg00690392, cg03421440, cg07077277, cg00456086, or cg20702527. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg19516279, cg06100368, cg20349803, cg23610994, cg19313373, cg16508600, or cg24096323. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg25945732, cg19155007, cg17952661, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, or cg00456086. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg04072843, cg01250961, cg24746106, cg12288267, or cg10430690. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, or cg09921682. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg06547203, cg06826710, cg00902147, cg17609887, or cg15721142. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg01237565, cg16561543, or cg08116711. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg13771313, cg13771313, or cg08169020. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, or cg13169641. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg09095222. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg00736681 or cg18834029. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg06969479, cg24630516, or cg16901821. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg24408776. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg05630192. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg06405341. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08557188, cg00690392, cg03421440, or cg07077277. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg10673833, cg10493436, cg10428836, cg27284288, cg16959747, cg17494199, cg23678254, cg24067911, or cg25459300. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of BMPR1A, PSD, ARHGAP25, KLF3, PLAC8, ATXN1, Chromosome 6:170, Chromosome 6:3, ATAD2, and Chromosome 8:20. 
     some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a gene selected from a gene panel consisting of MYO1G, ADAMTS4, BMPR1A, CD6, RBP5, Chr 13:10, LGAP5, ATXN1, and Chr 8:20. 
     In some instances, the reference model comprises methylation profiles of cg markers from Tables 1 and 2 generated from samples of known cancer types. In some cases, the reference model further comprises methylation profiles of cg markers from Tables 1 and 2 generated from normal samples. In some cases, the reference model comprises methylation profiles of cg markers from Tables 1 and 2 generated from tissue samples. 
     In some instances, the reference model comprises methylation profiles of cg markers from Tables 7 and 8 generated from samples of known cancer types. In some cases, the reference model further comprises methylation profiles of cg markers from Tables 7 and 8 generated from normal samples. In some cases, the reference model comprises methylation profiles of cg markers from Tables 7 and 8 generated from tissue samples. 
     In some instances, the reference model comprises methylation profiles of cg markers from Table 13 generated from samples of known cancer types. In some cases, the reference model further comprises methylation profiles of cg markers from Table 13 generated from normal samples. In some cases, the reference model comprises methylation profiles of cg markers from Table 13 generated from tissue samples. 
     In some cases, the reference model is developed using an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model. 
     In some embodiments, the analyzing described above comprises quantitatively detecting the methylation status of the amplified product. In some cases, the detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some cases, the detection comprises a real-time quantitative probe-based PCR. In other cases, the detection comprises a digital probe-based PCR, optionally, a digital droplet PCR. 
     In some embodiments, the treatment comprises a chemotherapeutic agent or an agent for a targeted therapy. Exemplary chemotherapeutic agents include, but are not limited to, cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof. In some cases, the chemotherapeutic agent comprises cisplatin, doxorubicin, fluoropyrimidine, gemcitabine, irinotecan, mitoxantrone, oxaliplatin, thalidomide, or a combination thereof. In some instances, the treatment comprises an agent for a targeted therapy. In additional instances, the treatment comprises surgery. 
     In some instances, the biological sample is a blood sample, an urine sample, a saliva sample, a sweat sample, or a tear sample. In some cases, the biological sample is a blood sample or an urine sample. In some cases, the biological sample is a tissue biopsy sample. In some cases, the biological sample is a cell-free DNA sample. In some cases, the biological sample comprises circulating tumor cells. 
     In some embodiments, also disclosed herein is a method of detecting the methylation status of a set of cg markers. In some embodiments, the method comprises (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of a cg marker from Table 1, Table 2, Table 7, Table 8, Table 13, Table 14, or Table 20; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. 
     In some embodiments, the method of detecting the methylation status of a set of cg markers comprises (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of a cg marker from Table 1 or Table 2; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. 
     In some cases, the detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some cases, the detection comprises a real-time quantitative probe-based PCR. In other cases, the detection comprises a digital probe-based PCR, optionally, a digital droplet PCR. 
     In some cases, the at least one probe from the probe panel is a padlock probe. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 1. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 2. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 4. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 5. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 7. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 8. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 13. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 14. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from Table 20. 
     In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg19516279, cg06100368, cg25945732, cg19155007, cg17952661, cg04072843, cg01250961, cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg01237565, cg16561543, cg13771313, cg13771313, cg08169020, cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg09095222, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, cg13169641, cg25352342, cg09921682, cg02504622, cg17373759, cg06547203, cg06826710, cg00902147, cg17609887, cg15721142, cg08116711, cg00736681, cg18834029, cg06969479, cg24630516, cg16901821, cg20349803, cg23610994, cg19313373, cg16508600, cg24096323, cg24746106, cg12288267, cg10430690, cg24408776, cg05630192, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, cg09921682, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, cg06405341, cg08557188, cg00690392, cg03421440, cg07077277, cg00456086, or cg20702527. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg19516279, cg06100368, cg20349803, cg23610994, cg19313373, cg16508600, or cg24096323. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg25945732, cg19155007, cg17952661, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, or cg00456086. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg04072843, cg01250961, cg24746106, cg12288267, or cg10430690. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, or cg09921682. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg06547203, cg06826710, cg00902147, cg17609887, or cg15721142. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg01237565, cg16561543, or cg08116711. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg13771313, cg13771313, or cg08169020. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, or cg13169641. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg09095222. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg00736681 or cg18834029. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg06969479, cg24630516, or cg16901821. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg24408776. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg05630192. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker cg06405341. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg08557188, cg00690392, cg03421440, or cg07077277. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg10673833, cg10493436, cg10428836, cg27284288, cg16959747, cg17494199, cg23678254, cg24067911, or cg25459300. In some instances, the at least one probe hybridizes under high stringency conditions to a target sequence of a cg marker selected from cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195. 
     In some embodiments, the methylation status or pattern of a set of cg markers is further used to determine whether the subject has a cancer. For example, the methylation status or pattern of at least one cg marker selected from cg19516279, cg06100368, cg20349803, cg23610994, cg19313373, cg16508600, and cg24096323 is used to determine whether the subject has a breast cancer. In some cases, the subject is determined to have a breast cancer if at least one of the cg markers cg19516279 and cg06100368 is hypermethylated. In other cases, the subject is determined to have a breast cancer if at least one of the cg markers cg20349803, cg23610994, cg19313373, cg16508600, and cg24096323 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg25945732, cg19155007, cg17952661, cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg00456086, and cg26859666 is used to determine whether the subject has a liver cancer. In some cases, the subject is determined to have a liver cancer if at least one of the cg markers cg25945732, cg19155007, or cg17952661 is hypermethylated. In other cases, the subject is determined to have a liver cancer if at least one of the cg markers cg25934700, cg14164596, cg24461337, cg23041410, cg07366553, cg26859666, or cg00456086 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one marker selected from 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, 5-176829639, 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858 is used to determine whether the subject has a liver cancer. In some instances, the subject is determined to have a liver cancer if at least one of the markers 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, or 5-176829639 is hypermethylated. In some cases, the subject is determined to have a liver cancer if at least one of the markers 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg04072843, cg01250961, cg24746106, cg12288267, and cg10430690 is used to determine whether the subject has an ovarian cancer. In some cases, the subject is determined to have an ovarian cancer if at least one of the cg markers cg04072843 and cg01250961 is hypermethylated. In other cases, the subject is determined to have an ovarian cancer if at least one of the cg markers cg24746106, cg12288267, and cg10430690 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, cg00846300, cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, and cg09921682 is used to determine whether the subject has a colorectal cancer. In some cases, the subject is determined to have a colorectal cancer if at least one of the cg markers cg08131100, cg03788131, cg17528648, cg07784526, cg18948743, cg23986470, or cg00846300 is hypermethylated. In other cases, the subject is determined to have a colorectal cancer if at least one of the cg markers cg25352342, cg09921682, cg02504622, cg17373759, cg12028674, cg24820270, cg12028674, cg26718707, cg10349880, or cg09921682 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg10673833, cg10493436, cg10428836, cg27284288, cg16959747, cg17494199, cg23678254, cg24067911, or cg25459300 is used to determine whether the subject has a colorectal cancer. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195 is used to determine whether the subject has a colorectal cancer. In some cases, the subject is determined to have a colorectal cancer if at least one of the cg markers cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195 is hypermethylated. In other cases, the subject is determined to have a colorectal cancer if at least one of the cg markers cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, or cg16391792 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, cg26149167, cg06547203, cg06826710, cg00902147, cg17609887, and cg15721142 is used to determine whether the subject has a prostate cancer. In some cases, the subject is determined to have a prostate cancer if at least one of the cg markers cg01029638, cg08350814, cg05098590, cg18085998, cg06532037, cg15313226, cg16232979, or cg26149167 is hypermethylated. In other cases, the subject is determined to have a prostate cancer if at least one of the cg markers cg06547203, cg06826710, cg00902147, cg17609887, or cg15721142 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg01237565, cg16561543, and cg08116711 is used to determine whether the subject has a pancreatic cancer. In some cases, the subject is determined to have a pancreatic cancer if at least one of the cg markers cg01237565 or cg16561543 is hypermethylated. In other cases, the subject is determined to have a pancreatic cancer if cg marker cg08116711 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg13771313, cg13771313, and cg08169020 is used to determine whether the subject has acute myeloid leukemia. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg08169020, cg21153697, cg07326648, cg14309384, cg20923716, cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, and cg13169641 is used to determine whether the subject has cervical cancer. In some instances, the subject is determined to have cervical cancer if at least one of the cg markers cg08169020, cg21153697, cg07326648, cg14309384, or cg20923716 is hypermethylated. In other instances, the subject is determined to have cervical cancer if at least one of the cg markers cg22220310, cg21950459, cg13332729, cg10802543, cg20707333, or cg13169641 is hypomethylated. 
     In some instances, the methylation status or pattern of one cg marker cg09095222 is used to determine whether the subject has sarcoma. In some cases, the subject is determined to have sarcoma if at least cg marker cg09095222 is hypermethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg00736681 and cg18834029 is used to determine whether the subject has stomach cancer. In some cases, the subject is determined to have stomach cancer if at least one of the cg markers cg00736681 or cg18834029 is hypomethylated. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg06969479, cg24630516, and cg16901821 is used to determine if the subject has thyroid cancer. In some cases, the subject is determined to have thyroid cancer if at least one of the cg markers cg06969479, cg24630516, or cg16901821 is hypomethylated. 
     In some instances, the methylation status or pattern of cg marker cg05630192 is used to determine whether the subject has mesothelioma. In some cases, the subject is determined to have mesothelioma if cg marker cg05630192 is hypomethylated. 
     In some instances, the methylation status or pattern of cg marker cg06405341 is used to determine whether the subject has glioblastoma. 
     In some instances, the methylation status or pattern of at least one cg marker selected from cg08557188, cg00690392, cg03421440, and cg07077277 is used to determine whether the subject has lung cancer. In some cases, the subject is determined to have lung cancer if at least one of the cg markers cg08557188, cg00690392, cg03421440, or cg07077277 is hypomethylated. 
     In some embodiments, the methylation status or pattern of one or more genes selected from MYO1G, ADAMTS4, BMPR1A, CD6, RBP5, Chr 13:10, LGAP5, ATX1N1, Chr 8:20, or a combination thereof is further used to determine whether the subject has a cancer. In some instances, the methylation status of one or more genes selected from MYO1G, ADAMTS4, BMPR1A, CD6, RBP5, Chr 13:10, LGAP5, ATXN1, Chr 8:20, or a combination thereof is further used to determine whether the subject has a colorectal cancer. 
     In some embodiments, also disclosed herein is a method of determining the prognosis of a cancer in a subject in need thereof based on the methylation status of a set of cg markers. In some embodiments, the method comprises (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of cg10673833 or cg25462303; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some instances, the cancer is a colorectal cancer. In some cases, the method of determining the prognosis of a colorectal cancer a subject in need thereof comprises (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of cg10673833 or cg25462303; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some cases, the methylation status of cg10673833, cg25462303, or a combination thereof is used for monitoring a treatment progression of a subject in need thereof. In additional cases, the methylation status of cg10673833, cg25462303, or a combination thereof is used as an early predictor of developing a cancer (e.g., CRC) of a subject in need thereof. 
     In some embodiments, additional disclosed herein is a method of determining the prognosis of a cancer in a subject in need thereof, comprising (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. In some instances, the cancer is a colorectal cancer. In some cases, the method of determining the prognosis of a colorectal cancer a subject in need thereof comprises (a) processing a biological sample obtained from a subject with a deaminating agent to generate treated DNA comprising deaminated nucleotides; (b) contacting the treated DNA with at least one probe that hybridizes under high stringency condition to a target sequence of cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195; and (c) quantitatively detecting the methylation status of the cg marker, wherein said detection comprises a real-time quantitative probe-based PCR or a digital probe-based PCR. 
     In some instances, if one or more cg markers cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, or cg25754195 are hypermethylated, the prognosis of the cancer is correlated with an advanced tumor stage and poor survival. 
     In some cases, the methylation status or pattern of cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, cg25754195, or a combination thereof is used for monitoring a treatment progression of a subject in need thereof. In additional cases, the methylation status of cg05205843, cg11841704, cg06699564, cg08924619, cg11959316, cg08924619, cg06699564, cg01824933, cg08924619, cg05205842, cg08924619, cg04049981, cg09026722, cg03616722, cg08924619, cg05928904, cg08704934, cg09776772, cg17494199, cg01824933, cg16296417, cg09776772, cg09776772, cg05338167, cg10493436, cg011251410, cg16391792, cg06393830, cg09366118, cg22513455, cg17583432, cg23881926, cg09638208, cg12441066, cg27284288, cg04441857, cg17583432, cg10673833, cg19757176, cg08670281, cg17583432, cg04460364, cg16959747, cg15011734, cg25754195, or a combination thereof is used as an early predictor of developing a cancer (e.g., CRC) of a subject in need thereof. 
     In some embodiments, the methylation status or pattern of one or more biomarkers selected from 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, 5-176829639, 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858 is used for determining the prognosis of a subject having a liver cancer. In additional cases, the methylation status or pattern of one or more biomarkers selected from 3-49757316, 8-27183116, 8-141607252, 17-29297711, 3-49757306, 19-43979341, 8-141607236, 5-176829755, 18-13382140, 15-65341965, 3-13152305, 17-29297770, 8-27183316, 5-176829740, 19-41316693, 18-43830649, 15-65341957, 20-44539531, 7-30265625, 2-131129567, 5-176829665, 3-13152273, 8-27183348, 3-49757302, 19-41316697, 8-61821442, 20-44539525, 10-102883105, 11-65849129, 5-176829639, 15-91129457, 2-1625431, 6-151373292, 6-151373294, 20-25027093, 6-14284198, 10-4049295, 19-59023222, 1-184197132, 2-131004117, 2-8995417, 12-10782319, 20-25027033, 6-151373256, 8-86100970, 9-4839459, 17-41221574, 1-153926715, 20-25027044, 20-20177325, 2-1625443, 20-25027085, 11-69420728, 1-229234865, 6-13408877, 22-50643735, 6-151373308, 1-232119750, 8-134361508, or 6-13408858 is used for monitoring a treatment progression of a subject having a liver cancer. 
     In some instances, the biological sample is a blood sample, a urine sample, a saliva sample, a sweat sample, or a tear sample. In some cases, the biological sample is a blood sample or a urine sample. In some cases, the biological sample is a tissue biopsy sample. In some cases, the biological sample is a cell-free DNA sample. In some cases, the biological sample comprises circulating tumor cells. 
     Detection Methods 
     In some embodiments, a number of methods are utilized to measure, detect, determine, identify, and characterize the methylation status/level of a gene or a biomarker (e.g., CpG island-containing region/fragment) in identifying a subject as having liver cancer, determining the liver cancer subtype, the prognosis of a subject having liver cancer, and the progression or regression of liver cancer in subject in the presence of a therapeutic agent. 
     In some instances, the methylation profile is generated from a biological sample isolated from an individual. In some embodiments, the biological sample is a biopsy. In some instances, the biological sample is a tissue sample. In some instances, the biological sample is a tissue biopsy sample. In some instances, the biological sample is a blood sample. In other instances, the biological sample is a cell-free biological sample. In other instances, the biological sample is a circulating tumor DNA sample. In one embodiment, the biological sample is a cell free biological sample containing circulating tumor DNA. 
     In some embodiments, a biomarker (or an epigenetic marker) is obtained from a liquid sample. In some embodiments, the liquid sample comprises blood and other liquid samples of biological origin (including, but not limited to, peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen, prostatic fluid, cowper&#39;s fluid or pre-ejaculatory fluid, female ejaculate, sweat, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, ascites, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions/flushing, synovial fluid, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, or umbilical cord blood. In some embodiments, the biological fluid is blood, a blood derivative or a blood fraction, e.g., serum or plasma. In a specific embodiment, a sample comprises a blood sample. In another embodiment, a serum sample is used. In another embodiment, a sample comprises urine. In some embodiments, the liquid sample also encompasses a sample that has been manipulated in any way after their procurement, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washed, or enriched for certain cell populations. 
     In some embodiments, a biomarker (or an epigenetic marker) is obtained from a tissue sample. In some instances, a tissue corresponds to any cell(s). Different types of tissue correspond to different types of cells (e.g., liver, lung, blood, connective tissue, and the like), but also healthy cells vs. tumor cells or to tumor cells at various stages of neoplasia, or to displaced malignant tumor cells. In some embodiments, a tissue sample further encompasses a clinical sample, and also includes cells in culture, cell supernatants, organs, and the like. Samples also comprise fresh-frozen and/or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, prepared for pathological analysis or study by immunohistochemistry. 
     In some embodiments, a biomarker (or an epigenetic marker) is methylated or unmethylated in a normal sample (e.g., normal or control tissue without disease, or normal or control body fluid, stool, blood, serum, amniotic fluid), most importantly in healthy stool, blood, serum, amniotic fluid or other body fluid. In other embodiments, a biomarker (or an epigenetic marker) is hypomethylated or hypermethylated in a sample from a patient having or at risk of a disease (e.g., one or more indications described herein); for example, at a decreased or increased (respectively) methylation frequency of at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, or about 100% in comparison to a normal sample. In one embodiment, a sample is also hypomethylated or hypermethylated in comparison to a previously obtained sample analysis of the same patient having or at risk of a disease (e.g., one or more indications described herein), particularly to compare progression of a disease. 
     In some embodiments, a methylome comprises a set of epigenetic markers or biomarkers, such as a biomarker described above. In some instances, a methylome that corresponds to the methylome of a tumor of an organism (e.g., a human) is classified as a tumor methylome. In some cases, a tumor methylome is determined using tumor tissue or cell-free (or protein-free) tumor DNA in a biological sample. Other examples of methylomes of interest include the methylomes of organs that contribute DNA into a bodily fluid (e.g. methylomes of tissue such as brain, breast, lung, the prostrate and the kidneys, plasma, etc.). 
     In some embodiments, a plasma methylome is the methylome determined from the plasma or serum of an animal (e.g., a human). In some instances, the plasma methylome is an example of a cell-free or protein-free methylome since plasma and serum include cell-free DNA. The plasma methylome is also an example of a mixed methylome since it is a mixture of tumor and other methylomes of interest. In some instances, the urine methylome is determined from the urine sample of a subject. In some cases, a cellular methylome corresponds to the methylome determined from cells (e.g., blood cells) of the patient. The methylome of the blood cells is called the blood cell methylome (or blood methylome). 
     In some embodiments, DNA (e.g., genomic DNA such as extracted genomic DNA or treated genomic DNA) is isolated by any means standard in the art, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated in by a cellular membrane the biological sample is disrupted and lysed by enzymatic, chemical or mechanical means. In some cases, the DNA solution is then cleared of proteins and other contaminants e.g. by digestion with proteinase K. The DNA is then recovered from the solution. In such cases, this is carried out by means of a variety of methods including salting out, organic extraction or binding of the DNA to a solid phase support. In some instances, the choice of method is affected by several factors including time, expense and required quantity of DNA. 
     Wherein the sample DNA is not enclosed in a membrane (e.g. circulating DNA from a cell free sample such as blood or urine) methods standard in the art for the isolation and/or purification of DNA are optionally employed (See, for example, Bettegowda et al. Detection of Circulating Tumor DNA in Early- and Late-Stage Human Malignancies. Sci. Transl. Med, 6(224): ra24. 2014). Such methods include the use of a protein degenerating reagent e.g. chaotropic salt e.g. guanidine hydrochloride or urea; or a detergent e.g. sodium dodecyl sulphate (SDS), cyanogen bromide. Alternative methods include but are not limited to ethanol precipitation or propanol precipitation, vacuum concentration amongst others by means of a centrifuge. In some cases, the person skilled in the art also make use of devices such as filter devices e.g. ultrafiltration, silica surfaces or membranes, magnetic particles, polystyrol particles, polystyrol surfaces, positively charged surfaces, and positively charged membranes, charged membranes, charged surfaces, charged switch membranes, and charged switched surfaces. 
     In some instances, once the nucleic acids have been extracted, methylation analysis is carried out by any means known in the art. A variety of methylation analysis procedures is known in the art and may be used to practice the methods disclosed herein. These assays allow for determination of the methylation state of one or a plurality of CpG sites within a tissue sample. In addition, these methods may be used for absolute or relative quantification of methylated nucleic acids. Such methylation assays involve, among other techniques, two major steps. The first step is a methylation specific reaction or separation, such as (i) bisulfite treatment, (ii) methylation specific binding, or (iii) methylation specific restriction enzymes. The second major step involves (i) amplification and detection, or (ii) direct detection, by a variety of methods such as (a) PCR (sequence-specific amplification) such as Taqman(R), (b) DNA sequencing of untreated and bisulfite-treated DNA, (c) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (d) pyrosequencing, (e) single-molecule sequencing, (f) mass spectroscopy, or (g) Southern blot analysis. 
     Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA may be used, e.g., the method described by Sadri and Hornsby (1996, Nucl. Acids Res. 24:5058-5059), or COBRA (Combined Bisulfite Restriction Analysis) (Xiong and Laird, 1997, Nucleic Acids Res. 25:2532-2534). COBRA analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific gene loci in small amounts of genomic DNA. Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Frommer et al, 1992, Proc. Nat. Acad. Sci. USA, 89, 1827-1831). PCR amplification of the bisulfite converted DNA is then performed using primers specific for the CpG sites of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from micro-dissected paraffin-embedded tissue samples. Typical reagents (e.g., as might be found in a typical COBRA-based kit) for COBRA analysis may include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); restriction enzyme and appropriate buffer; gene-hybridization oligo; control hybridization oligo; kinase labeling kit for oligo probe; and radioactive nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfo nation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components. 
     In an embodiment, the methylation profile of selected CpG sites is determined using methylation-Specific PCR (MSP). MSP allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al, 1996, Proc. Nat. Acad. Sci. USA, 93, 9821-9826; U.S. Pat. Nos. 5,786,146, 6,017,704, 6,200,756, 6,265,171 (Herman and Baylin); U.S. Pat. Pub. No. 2010/0144836 (Van Engeland et al); which are hereby incorporated by reference in their entirety). Briefly, DNA is modified by a deaminating agent such as sodium bisulfite to convert unmethylated, but not methylated cytosines to uracil, and subsequently amplified with primers specific for methylated versus unmethylated DNA. In some instances, typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis include, but are not limited to: methylated and unmethylated PCR primers for specific gene (or methylation-altered DNA sequence or CpG island), optimized PCR buffers and deoxynucleotides, and specific probes. One may use quantitative multiplexed methylation specific PCR (QM-PCR), as described by Fackler et al. Fackler et al, 2004, Cancer Res. 64(13) 4442-4452; or Fackler et al, 2006, Clin. Cancer Res. 12(11 Pt 1) 3306-3310. 
     In an embodiment, the methylation profile of selected CpG sites is determined using MethyLight and/or Heavy Methyl Methods. The MethyLight and Heavy Methyl assays are a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (Taq Man(R)) technology that requires no further manipulations after the PCR step (Eads, C. A. et al, 2000, Nucleic Acid Res. 28, e 32; Cottrell et al, 2007, J. Urology 177, 1753, U.S. Pat. No. 6,331,393 (Laird et al), the contents of which are hereby incorporated by reference in their entirety). Briefly, the MethyLight process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed either in an “unbiased” (with primers that do not overlap known CpG methylation sites) PCR reaction, or in a “biased” (with PCR primers that overlap known CpG dinucleotides) reaction. In some cases, sequence discrimination occurs either at the level of the amplification process or at the level of the fluorescence detection process, or both. In some cases, the MethyLight assay is used as a quantitative test for methylation patterns in the genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing of the biased PCR pool with either control oligonucleotides that do not “cover” known methylation sites (a fluorescence-based version of the “MSP” technique), or with oligonucleotides covering potential methylation sites. Typical reagents (e.g., as might be found in a typical MethyLight-based kit) for MethyLight analysis may include, but are not limited to: PCR primers for specific gene (or methylation-altered DNA sequence or CpG island); TaqMan(R) probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. 
     Quantitative MethyLight uses bisulfite to convert genomic DNA and the methylated sites are amplified using PCR with methylation independent primers. Detection probes specific for the methylated and unmethylated sites with two different fluorophores provides simultaneous quantitative measurement of the methylation. The Heavy Methyl technique begins with bisulfate conversion of DNA. Next specific blockers prevent the amplification of unmethylated DNA. Methylated genomic DNA does not bind the blockers and their sequences will be amplified. The amplified sequences are detected with a methylation specific probe. (Cottrell et al, 2004, Nuc. Acids Res. 32:e10, the contents of which is hereby incorporated by reference in its entirety). 
     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 and Jones, 1997, Nucleic Acids Res. 25, 2529-2531). 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. In some cases, small amounts of DNA are analyzed (e.g., micro-dissected pathology sections), and the method avoids utilization of restriction enzymes for determining the methylation status at CpG sites. Typical reagents (e.g., as is found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysis 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 specific gene; reaction buffer (for the Ms-SNuPE reaction); and radioactive 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 another embodiment, the methylation status of selected CpG sites is determined using differential Binding-based Methylation Detection Methods. For identification of differentially methylated regions, one approach is to capture methylated DNA. This approach uses a protein, in which the methyl binding domain of MBD2 is fused to the Fc fragment of an antibody (MBD-FC) (Gebhard et al, 2006, Cancer Res. 66:6118-6128; and PCT Pub. No. WO 2006/056480 A2 (Relhi), the contents of which are hereby incorporated by reference in their entirety). This fusion protein has several advantages over conventional methylation specific antibodies. The MBD FC has a higher affinity to methylated DNA and it binds double stranded DNA. Most importantly the two proteins differ in the way they bind DNA. Methylation specific antibodies bind DNA stochastically, which means that only a binary answer can be obtained. The methyl binding domain of MBD-FC, on the other hand, binds DNA molecules regardless of their methylation status. The strength of this protein—DNA interaction is defined by the level of DNA methylation. After binding genomic DNA, eluate solutions of increasing salt concentrations can be used to fractionate non-methylated and methylated DNA allowing for a more controlled separation (Gebhard et al, 2006, Nucleic Acids Res. 34: e82). Consequently this method, called Methyl-CpG immunoprecipitation (MCIP), not only enriches, but also fractionates genomic DNA according to methylation level, which is particularly helpful when the unmethylated DNA fraction should be investigated as well. 
     In an alternative embodiment, a 5-methyl cytidine antibody to bind and precipitate methylated DNA. Antibodies are available from Abcam (Cambridge, Mass.), Diagenode (Sparta, N.J.) or Eurogentec (c/o AnaSpec, Fremont, Calif.). Once the methylated fragments have been separated they may be sequenced using microarray based techniques such as methylated CpG-island recovery assay (MIRA) or methylated DNA immunoprecipitation (MeDIP) (Pelizzola et al, 2008, Genome Res. 18, 1652-1659; O&#39;Geen et al, 2006, BioTechniques 41(5), 577-580, Weber et al, 2005, Nat. Genet. 37, 853-862; Horak and Snyder, 2002, Methods Enzymol, 350, 469-83; Lieb, 2003, Methods Mol Biol, 224, 99-109). Another technique is methyl-CpG binding domain column/segregation of partly melted molecules (MBD/SPM, Shiraishi et al, 1999, Proc. Natl. Acad. Sci. USA 96(6):2913-2918). 
     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. Pat. Nos. 7,910,296; 7,901,880; and 7,459,274. In some embodiments, amplification can be performed using primers that are gene specific. 
     For example, there are methyl-sensitive enzymes that preferentially or substantially cleave or digest at their DNA recognition sequence if it is non-methylated. Thus, an unmethylated DNA sample is cut into smaller fragments than a methylated DNA sample. Similarly, a hypermethylated DNA sample is not cleaved. In contrast, there are methyl-sensitive enzymes that cleave at their DNA recognition sequence only if it is methylated. Methyl-sensitive enzymes that digest unmethylated DNA suitable for use in methods of the technology include, but are not limited to, Hpall, Hhal, Maell, BstUI and Acil. In some instances, an enzyme that is used is Hpall that cuts only the unmethylated sequence CCGG. In other instances, another enzyme that is used is Hhal that cuts only the unmethylated sequence GCGC. Both enzymes are available from New England BioLabs(R), Inc. Combinations of two or more methyl-sensitive enzymes that digest only unmethylated DNA are also used. Suitable enzymes that digest only methylated DNA include, but are not limited to, Dpnl, which only cuts at fully methylated 5′-GATC sequences, and McrBC, an endonuclease, which cuts DNA containing modified cytosines (5-methylcytosine or 5-hydroxymethylcytosine or N4-methylcytosine) and cuts at recognition site 5′ . . . PumC(N4o-3ooo) PumC . . . 3′ (New England BioLabs, Inc., Beverly, Mass.). Cleavage methods and procedures for selected restriction enzymes for cutting DNA at specific sites are well known to the skilled artisan. For example, many suppliers of restriction enzymes provide information on conditions and types of DNA sequences cut by specific restriction enzymes, including New England BioLabs, Promega Biochems, Boehringer-Mannheim, and the like. Sambrook et al. (See Sambrook et al. Molecular Biology: A Laboratory Approach, Cold Spring Harbor, N.Y. 1989) provide a general description of methods for using restriction enzymes and other enzymes. 
     In some instances, a methylation-dependent restriction enzyme is 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., Dpnl) and enzymes that cut at a sequence near but not at the recognition sequence (e.g., McrBC). For example, McrBC&#39;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, McrA, MrrA, Bisl, Glal and Dpnl. 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 with one or more methods described herein. 
     In some cases, a methylation-sensitive restriction enzyme is 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, 22(17) NUCLEIC ACIDS RES. 3640-59 (1994). 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, HinPl I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and Zra I. Suitable methylation-sensitive restriction enzymes that do not cleave DNA at or near their recognition sequence when an adenosine within the recognition sequence is methylated at position N6 include, e.g., Mbo I. One of skill in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use with one or more of the methods described herein. 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. 
     In alternative embodiments, adaptors are optionally added to the ends of the randomly fragmented DNA, the DNA is then digested with a methylation-dependent or methylation-sensitive restriction enzyme, and intact DNA is subsequently amplified using primers that hybridize to the adaptor sequences. In this case, a second step is 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 other 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. 
     In some instances, the quantity of methylation of a locus of DNA is 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. Pat. No. 7,910,296. 
     The methylated CpG island amplification (MCA) technique is a method that can be used to screen for altered methylation patterns in genomic DNA, and to isolate specific sequences associated with these changes (Toyota et al, 1999, Cancer Res. 59, 2307-2312, U.S. Pat. No. 7,700,324 (Issa et al), the contents of which are hereby incorporated by reference in their entirety). Briefly, restriction enzymes with different sensitivities to cytosine methylation in their recognition sites are used to digest genomic DNAs from primary tumors, cell lines, and normal tissues prior to arbitrarily primed PCR amplification. Fragments that show differential methylation are cloned and sequenced after resolving the PCR products on high-resolution polyacrylamide gels. The cloned fragments are then used as probes for Southern analysis to confirm differential methylation of these regions. Typical reagents (e.g., as might be found in a typical MCA-based kit) for MCA analysis may include, but are not limited to: PCR primers for arbitrary priming Genomic DNA; PCR buffers and nucleotides, restriction enzymes and appropriate buffers; gene-hybridization oligos or probes; control hybridization oligos or probes. 
     Additional methylation detection methods include those methods described in, e.g., U.S. Pat. Nos. 7,553,627; 6,331,393; U.S. patent Ser. No. 12/476,981; U.S. Patent Publication No. 2005/0069879; Rein, et al, 26(10) NUCLEIC ACIDS RES. 2255-64 (1998); and Olek et al, 17(3) NAT. GENET. 275-6 (1997). 
     In another embodiment, the methylation status of selected CpG sites is determined using Methylation-Sensitive High Resolution Melting (HRM). Recently, Wojdacz et al. reported methylation-sensitive high resolution melting as a technique to assess methylation. (Wojdacz and Dobrovic, 2007, Nuc. Acids Res. 35(6) e41; Wojdacz et al. 2008, Nat. Prot. 3(12) 1903-1908; Balic et al, 2009 J. Mol. Diagn. 11 102-108; and US Pat. Pub. No. 2009/0155791 (Wojdacz et al), the contents of which are hereby incorporated by reference in their entirety). A variety of commercially available real time PCR machines have HRM systems including the Roche LightCycler480, Corbett Research RotorGene6000, and the Applied Biosystems 7500. HRM may also be combined with other amplification techniques such as pyrosequencing as described by Candiloro et al. (Candiloro et al, 2011, Epigenetics 6(4) 500-507). 
     In another embodiment, the methylation status of selected CpG locus is determined using a primer extension assay, including an optimized PCR amplification reaction that produces amplified targets for analysis using mass spectrometry. The assay can also be done in multiplex. Mass spectrometry is a particularly effective method for the detection of polynucleotides associated with the differentially methylated regulatory elements. The presence of the polynucleotide sequence is verified by comparing the mass of the detected signal with the expected mass of the polynucleotide of interest. The relative signal strength, e.g., mass peak on a spectra, for a particular polynucleotide sequence indicates the relative population of a specific allele, thus enabling calculation of the allele ratio directly from the data. This method is described in detail in PCT Pub. No. WO 2005/012578A1 (Beaulieu et al), which is hereby incorporated by reference in its entirety. For methylation analysis, the assay can be adopted to detect bisulfite introduced methylation dependent C to T sequence changes. These methods are particularly useful for performing multiplexed amplification reactions and multiplexed primer extension reactions (e.g., multiplexed homogeneous primer mass extension (hME) assays) in a single well to further increase the throughput and reduce the cost per reaction for primer extension reactions. 
     Other methods for DNA methylation analysis include restriction landmark genomic scanning (RLGS, Costello et al, 2002, Meth. Mol Biol, 200, 53-70), methylation-sensitive-representational difference analysis (MS-RDA, Ushijima and Yamashita, 2009, Methods Mol Biol 507, 1 17-130). Comprehensive high-throughput arrays for relative methylation (CHARM) techniques are described in WO 2009/021141 (Feinberg and Irizarry). The Roche(R) NimbleGen(R) microarrays including the Chromatin Immunoprecipitation-on-chip (ChIP-chip) or methylated DNA immunoprecipitation-on-chip (MeDIP-chip). These tools have been used for a variety of cancer applications including melanoma, liver cancer and lung cancer (Koga et al, 2009, Genome Res., 19, 1462-1470; Acevedo et al, 2008, Cancer Res., 68, 2641-2651; Rauch et al, 2008, Proc. Nat. Acad. Sci. USA, 105, 252-257). Others have reported bisulfate conversion, padlock probe hybridization, circularization, amplification and next generation or multiplexed sequencing for high throughput detection of methylation (Deng et al, 2009, Nat. Biotechnol 27, 353-360; Ball et al, 2009, Nat. Biotechnol 27, 361-368; U.S. Pat. No. 7,611,869 (Fan)). As an alternative to bisulfate oxidation, Bayeyt et al. have reported selective oxidants that oxidize 5-methylcytosine, without reacting with thymidine, which are followed by PCR or pyro sequencing (WO 2009/049916 (Bayeyt et al). These references for these techniques are hereby incorporated by reference in their entirety. 
     In some instances, quantitative amplification methods (e.g., quantitative PCR or quantitative linear amplification) are 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., DeGraves, et al, 34(1) BIOTECHNIQUES 106-15 (2003); Deiman B, et al., 20(2) MOL. BIOTECHNOL. 163-79 (2002); and Gibson et al, 6 GENOME RESEARCH 995-1001 (1996). 
     Following reaction or separation of nucleic acid in a methylation specific manner, the nucleic acid in some cases are subjected to sequence-based analysis. For example, once it is determined that one particular genomic sequence from a sample is hypermethylated or hypomethylated compared to its counterpart, the amount of this genomic sequence can be determined. Subsequently, this amount can be compared to a standard control value and used to determine the present of liver cancer in the sample. In many instances, it is desirable to amplify a nucleic acid sequence using any of several nucleic acid amplification procedures which are well known in the art. Specifically, nucleic acid amplification is the chemical or enzymatic synthesis of nucleic acid copies which contain a sequence that is complementary to a nucleic acid sequence being amplified (template). The methods and kits may use any nucleic acid amplification or detection methods known to one skilled in the art, such as those described in U.S. Pat. No. 5,525,462 (Takarada et al); U.S. Pat. No. 6,114,117 (Hepp et al); U.S. Pat. No. 6,127,120 (Graham et al); U.S. Pat. No. 6,344,317 (Urnovitz); U.S. Pat. No. 6,448,001 (Oku); U.S. Pat. No. 6,528,632 (Catanzariti et al); and PCT Pub. No. WO 2005/111209 (Nakajima et al); all of which are incorporated herein by reference in their entirety. 
     In some embodiments, the nucleic acids are amplified by PCR amplification using methodologies known to one skilled in the art. One skilled in the art will recognize, however, that amplification can be accomplished by any known method, such as ligase chain reaction (LCR), Q-replicas amplification, rolling circle amplification, transcription amplification, self-sustained sequence replication, nucleic acid sequence-based amplification (NASBA), each of which provides sufficient amplification. Branched-DNA technology is also optionally used to qualitatively demonstrate the presence of a sequence of the technology, which represents a particular methylation pattern, or to quantitatively determine the amount of this particular genomic sequence in a sample. Nolte reviews branched-DNA signal amplification for direct quantitation of nucleic acid sequences in clinical samples (Nolte, 1998, Adv. Clin. Chem. 33:201-235). 
     The PCR process is well known in the art and include, for example, reverse transcription PCR, ligation mediated PCR, digital PCR (dPCR), or droplet digital PCR (ddPCR). For a review of PCR methods and protocols, see, e.g., Innis et al, eds., PCR Protocols, A Guide to Methods and Application, Academic Press, Inc., San Diego, Calif. 1990; U.S. Pat. No. 4,683,202 (Mullis). PCR reagents and protocols are also available from commercial vendors, such as Roche Molecular Systems. In some instances, PCR is carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available. 
     In some embodiments, amplified sequences are also measured using invasive cleavage reactions such as the Invader(R) technology (Zou et al, 2010, Association of Clinical Chemistry (AACC) poster presentation on Jul. 28, 2010, “Sensitive Quantification of Methylated Markers with a Novel Methylation Specific Technology; and U.S. Pat. No. 7,011,944 (Prudent et al)). 
     Suitable next generation sequencing technologies are widely available. Examples include the 454 Life Sciences platform (Roche, Branford, Conn.) (Margulies et al. 2005 Nature, 437, 376-380); lllumina&#39;s Genome Analyzer, GoldenGate Methylation Assay, or Infinium Methylation Assays, i.e., Infinium HumanMethylation 27K BeadArray or VeraCode GoldenGate methylation array (Illumina, San Diego, Calif.; Bibkova et al, 2006, Genome Res. 16, 383-393; U.S. Pat. Nos. 6,306,597 and 7,598,035 (Macevicz); U.S. Pat. No. 7,232,656 (Balasubramanian et al.)); QX200™ Droplet Digital™ PCR System from Bio-Rad; or DNA Sequencing by Ligation, SOLiD System (Applied Biosystems/Life Technologies; U.S. Pat. Nos. 6,797,470, 7,083,917, 7,166,434, 7,320,865, 7,332,285, 7,364,858, and 7,429,453 (Barany et al); the Helicos True Single Molecule DNA sequencing technology (Harris et al, 2008 Science, 320, 106-109; U.S. Pat. Nos. 7,037,687 and 7,645,596 (Williams et al); 7, 169,560 (Lapidus et al); U.S. Pat. No. 7,769,400 (Harris)), the single molecule, real-time (SMRT™) technology of Pacific Biosciences, and sequencing (Soni and Meller, 2007, Clin. Chem. 53, 1996-2001); semiconductor sequencing (Ion Torrent; Personal Genome Machine); DNA nanoball sequencing; sequencing using technology from Dover Systems (Polonator), and technologies that do not require amplification or otherwise transform native DNA prior to sequencing (e.g., Pacific Biosciences and Helicos), such as nanopore-based strategies (e.g., Oxford Nanopore, Genia Technologies, and Nabsys). These systems allow the sequencing of many nucleic acid molecules isolated from a specimen at high orders of multiplexing in a parallel fashion. Each of these platforms allows sequencing of clonally expanded or non-amplified single molecules of nucleic acid fragments. Certain platforms involve, for example, (i) sequencing by ligation of dye-modified probes (including cyclic ligation and cleavage), (ii) pyrosequencing, and (iii) single-molecule sequencing. 
     Pyrosequencing is a nucleic acid sequencing method based on sequencing by synthesis, which relies on detection of a pyrophosphate released on nucleotide incorporation. Generally, sequencing by synthesis involves synthesizing, one nucleotide at a time, a DNA strand complimentary to the strand whose sequence is being sought. Study nucleic acids may be immobilized to a solid support, hybridized with a sequencing primer, incubated with DNA polymerase, ATP sulfurylase, luciferase, apyrase, adenosine 5′ phosphsulfate and luciferin. Nucleotide solutions are sequentially added and removed. Correct incorporation of a nucleotide releases a pyrophosphate, which interacts with ATP sulfurylase and produces ATP in the presence of adenosine 5′ phosphsulfate, fueling the luciferin reaction, which produces a chemiluminescent signal allowing sequence determination. Machines for pyrosequencing and methylation specific reagents are available from Qiagen, Inc. (Valencia, Calif.). See also Tost and Gut, 2007, Nat. Prot. 2 2265-2275. An example of a system that can be used by a person of ordinary skill based on pyrosequencing generally involves the following steps: ligating an adaptor nucleic acid to a study nucleic acid and hybridizing the study nucleic acid to a bead; amplifying a nucleotide sequence in the study nucleic acid in an emulsion; sorting beads using a picoliter multiwell solid support; and sequencing amplified nucleotide sequences by pyrosequencing methodology (e.g., Nakano et al, 2003, J. Biotech. 102, 117-124). Such a system can be used to exponentially amplify amplification products generated by a process described herein, e.g., by ligating a heterologous nucleic acid to the first amplification product generated by a process described herein. 
     CpG Methylation Data Analysis Methods 
     In certain embodiments, the methylation values measured for biomarkers of a biomarker panel are mathematically combined and the combined value is correlated to the underlying diagnostic question. In some instances, methylated biomarker values are combined by any appropriate state of the art mathematical method. Well-known mathematical methods for correlating a biomarker combination to a disease status employ methods like discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), Discriminant Functional Analysis (DFA), Kernel Methods (e.g., SVM), Multidimensional Scaling (MDS), Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods), Generalized Linear Models (e.g., Logistic Regression), Principal Components based Methods (e.g., SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods. The skilled artisan will have no problem in selecting an appropriate method to evaluate an epigenetic marker or biomarker combination described herein. In one embodiment, the method used in a correlating methylation status of an epigenetic marker or biomarker combination, e.g. to diagnose liver cancer or a liver cancer subtype, is selected from DA (e.g., Linear-, Quadratic-, Regularized Discriminant Analysis), DFA, Kernel Methods (e.g., SVM), MDS, Nonparametric Methods (e.g., k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (e.g., Logic Regression, CART, Random Forest Methods, Boosting Methods), or Generalized Linear Models (e.g., Logistic Regression), and Principal Components Analysis. Details relating to these statistical methods are found in the following references: Ruczinski et al., 12 J. OF COMPUTATIONAL AND GRAPHICAL STATISTICS 475-511 (2003); Friedman, J. H., 84 J. OF THE AMERICAN STATISTICAL ASSOCIATION 165-75 (1989); Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome, The Elements of Statistical Learning, Springer Series in Statistics (2001); Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. Classification and regression trees, California: Wadsworth (1984); Breiman, L., 45 MACHINE LEARNING 5-32 (2001); Pepe, M. S., The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford Statistical Science Series, 28 (2003); and Duda, R. O., Hart, P. E., Stork, D. O., Pattern Classification, Wiley Interscience, 2nd Edition (2001). 
     In one embodiment, the correlated results for each methylation panel are rated by their correlation to the disease or tumor type positive state, such as for example, by p-value test or t-value test or F-test. Rated (best first, i.e. low p- or t-value) biomarkers are then subsequently selected and added to the methylation panel until a certain diagnostic value is reached. Such methods include identification of methylation panels, or more broadly, genes that were differentially methylated among several classes using, for example, a random-variance t-test (Wright G. W. and Simon R, Bioinformatics 19:2448-2455,2003). Other methods include the step of specifying a significance level to be used for determining the epigenetic markers that will be included in the biomarker panel. Epigenetic markers that are differentially methylated between the classes at a univariate parametric significance level less than the specified threshold are included in the panel. It doesn&#39;t matter whether the specified significance level is small enough to exclude enough false discoveries. In some problems better prediction is achieved by being more liberal about the biomarker panels used as features. In some cases, the panels are biologically interpretable and clinically applicable, however, if fewer markers are included. Similar to cross-validation, biomarker selection is repeated for each training set created in the cross-validation process. That is for the purpose of providing an unbiased estimate of prediction error. The methylation panel for use with new patient sample data is the one resulting from application of the methylation selection and classifier of the “known” methylation information, or control methylation panel. 
     Models for utilizing methylation profile to predict the class of future samples can also be used. These models may be based on the Compound Covariate Predictor (Radmacher et al. Journal of Computational Biology 9:505-511, 2002), Diagonal Linear Discriminant Analysis (Dudoit et al. Journal of the American Statistical Association 97:77-87, 2002), Nearest Neighbor Classification (also Dudoit et al.), and Support Vector Machines with linear kernel (Ramaswamy et al. PNAS USA 98:15149-54, 2001). The models incorporated markers that were differentially methylated at a given significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003). The prediction error of each model using cross validation, preferably leave-one-out cross-validation (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003 can be estimated. For each leave-one-out cross-validation training set, the entire model building process is repeated, including the epigenetic marker selection process. In some instances, it is also evaluated in whether the cross-validated error rate estimate for a model is significantly less than one would expect from random prediction. In some cases, the class labels are randomly permuted and the entire leave-one-out cross-validation process is then repeated. The significance level is the proportion of the random permutations that gives a cross-validated error rate no greater than the cross-validated error rate obtained with the real methylation data. 
     Another classification method is the greedy-pairs method described by Bo and Jonassen (Genome Biology 3(4):research0017.1-0017.11, 2002). The greedy-pairs approach starts with ranking all markers based on their individual t-scores on the training set. This method attempts to select pairs of markers that work well together to discriminate the classes. 
     Furthermore, a binary tree classifier for utilizing methylation profile is optionally used to predict the class of future samples. The first node of the tree incorporated a binary classifier that distinguished two subsets of the total set of classes. The individual binary classifiers are based on the “Support Vector Machines” incorporating markers that were differentially expressed among markers at the significance level (e.g. 0.01, 0.05 or 0.1) as assessed by the random variance t-test (Wright G. W. and Simon R. Bioinformatics 19:2448-2455, 2003). Classifiers for all possible binary partitions are evaluated and the partition selected is that for which the cross-validated prediction error is minimum. The process is then repeated successively for the two subsets of classes determined by the previous binary split. The prediction error of the binary tree classifier can be estimated by cross-validating the entire tree building process. This overall cross-validation includes re-selection of the optimal partitions at each node and re-selection of the markers used for each cross-validated training set as described by Simon et al. (Simon et al. Journal of the National Cancer Institute 95:14-18, 2003). Several-fold cross validation in which a fraction of the samples is withheld, a binary tree developed on the remaining samples, and then class membership is predicted for the samples withheld. This is repeated several times, each time withholding a different percentage of the samples. The samples are randomly partitioned into fractional test sets (Simon R and Lam A. BRB-ArrayTools User Guide, version 3.2. Biometric Research Branch, National Cancer Institute). 
     Thus, in one embodiment, the correlated results for each marker b) are rated by their correct correlation to the disease, preferably by p-value test. It is also possible to include a step in that the markers are selected d) in order of their rating. 
     In additional embodiments, factors such as the value, level, feature, characteristic, property, etc. of a transcription rate, mRNA level, translation rate, protein level, biological activity, cellular characteristic or property, genotype, phenotype, etc. can be utilized in addition prior to, during, or after administering a therapy to a patient to enable further analysis of the patient&#39;s cancer status. 
     In some embodiments, a diagnostic test to correctly predict status is measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic (“ROC”) curve. In some instances, sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. In some cases, an ROC curve provides the sensitivity of a test as a function of 1-specificity. The greater the area under the ROC curve, for example, the more accurate or powerful the predictive value of the test. Other useful measures of the utility of a test include positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative. 
     In some embodiments, one or more of the biomarkers disclosed herein show a statistical difference in different samples of at least p&lt;0.05, p&lt;10 −2 , p&lt;10 −3 , p&lt;10 −4  or p&lt;10 −5 . Diagnostic tests that use these biomarkers may show an ROC of at least 0.6, at least about 0.7, at least about 0.8, or at least about 0.9. In some instances, the biomarkers are differentially methylated in different subjects with or without liver cancer. In additional instances, the biomarkers for different subtypes of liver cancer are differentially methylated. In certain embodiments, the biomarkers are measured in a patient sample using the methods described herein and compared, for example, to predefined biomarker levels and are used to determine whether the patient has liver cancer, which liver cancer subtype does the patient have, and/or what is the prognosis of the patient having liver cancer. In other embodiments, the correlation of a combination of biomarkers in a patient sample is compared, for example, to a predefined set of biomarkers. In some embodiments, the measurement(s) is then compared with a relevant diagnostic amount(s), cut-off(s), or multivariate model scores that distinguish between the presence or absence of liver cancer, between liver cancer subtypes, and between a “good” or a “poor” prognosis. As is well understood in the art, by adjusting the particular diagnostic cut-off(s) used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. In some embodiments, the particular diagnostic cut-off is determined, for example, by measuring the amount of biomarker hypermethylation or hypomethylation in a statistically significant number of samples from patients with or without liver cancer and from patients with different liver cancer subtypes, and drawing the cut-off to suit the desired levels of specificity and sensitivity. 
     Kits/Article of Manufacture 
     In some embodiments, provided herein include kits for detecting and/or characterizing the methylation profile of a biomarker described herein. In some instances, the kit comprises a plurality of primers or probes to detect or measure the methylation status/levels of one or more samples. Such kits comprise, in some instances, at least one polynucleotide that hybridizes to at least one of the methylation marker sequences described herein and at least one reagent for detection of gene methylation. Reagents for detection of methylation include, e.g., sodium bisulfate, polynucleotides designed to hybridize to sequence that is the product of a marker sequence if the marker sequence is not methylated (e.g., containing at least one C-U conversion), and/or a methylation-sensitive or methylation-dependent restriction enzyme. In some cases, the kits provide solid supports in the form of an assay apparatus that is adapted to use in the assay. In some instances, the kits further comprise detectable labels, optionally linked to a polynucleotide, e.g., a probe, in the kit. 
     In some embodiments, the kits 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 of a biomarker described herein. Optionally, one or more detectably-labeled polypeptides capable of hybridizing to the amplified portion are also 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 of an epigenetic marker described herein. 
     In some embodiments, the kits comprise 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 of an epigenetic marker described herein. 
     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 of a marker described herein. 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.). 
     In some embodiments, the kit includes a packaging material. As used herein, the term “packaging material” can refer to a physical structure housing the components of the kit. In some instances, the packaging material maintains sterility of the kit components, and is made of material commonly used for such purposes (e.g., paper, corrugated fiber, glass, plastic, foil, ampules, etc.). Other materials useful in the performance of the assays are included in the kits, including test tubes, transfer pipettes, and the like. In some cases, the kits also include written instructions for the use of one or more of these reagents in any of the assays described herein. 
     In some embodiments, kits also include a buffering agent, a preservative, or a protein/nucleic acid stabilizing agent. In some cases, kits also include other components of a reaction mixture as described herein. For example, kits include one or more aliquots of thermostable DNA polymerase as described herein, and/or one or more aliquots of dNTPs. In some cases, kits also include control samples of known amounts of template DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a negative control sample, e.g., a sample that does not contain DNA molecules harboring the individual alleles of a locus. In some embodiments, the kit includes a positive control sample, e.g., a sample containing known amounts of one or more of the individual alleles of a locus. 
     Certain Terminology 
     Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the claimed subject matter belongs. It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of any subject matter claimed. In this application, the use of the singular includes the plural unless specifically stated otherwise. It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. In this application, the use of “or” means “and/or” unless stated otherwise. Furthermore, use of the term “including” as well as other forms, such as “include”, “includes,” and “included,” is not limiting. 
     As used herein, ranges and amounts can be expressed as “about” a particular value or range. About also includes the exact amount. Hence “about 5 μL” means “about 5 μL” and also “5 μL.” Generally, the term “about” includes an amount that would be expected to be within experimental error. 
     The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. 
     As used herein, the terms “individual(s)”, “subject(s)” and “patient(s)” mean any mammal. In some embodiments, the mammal is a human. In some embodiments, the mammal is a non-human. None of the terms require or are limited to situations characterized by the supervision (e.g. constant or intermittent) of a health care worker (e.g. a doctor, a registered nurse, a nurse practitioner, a physician&#39;s assistant, an orderly or a hospice worker). 
     A “site” corresponds to a single site, which in some cases is a single base position or a group of correlated base positions, e.g., a CpG site. A “locus” corresponds to a region that includes multiple sites. In some instances, a locus includes one site. 
     EXAMPLES 
     These examples are provided for illustrative purposes only and not to limit the scope of the claims provided herein. 
     Example 1 
     Table 1 illustrates top 100 cg markers per cancer type, subdivided based on tissue comparison categories. Table 1 is included at the end of the Examples section. 
     Table 2 illustrates exemplary 20 cg makers per cancer type. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
               
             
            
               
                 BLCA 
                 Marker 
                 CpG 
                 BRCA 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 17-75447478 
                 cg14517217 
                 Hypermethylated 
                 6-33739558 
                 cg18005901 
               
               
                   
                 6-106958474 
                 cg09184502 
                   
                 9-129445599 
                 cg14100888 
               
               
                   
                 17-75447504 
                 cg18104645 
                   
                 6-33739406 
                 cg07979401 
               
               
                   
                 17-17686141 
                 cg19872299 
                   
                 1-6508954 
                 cg25097074 
               
               
                   
                 17-17685407 
                 cg25927164 
                   
                 1-6508592 
                 cg15274864 
               
               
                   
                 17-75447785 
                 cg11991516 
                   
                 2-241034966 
                 cg18663033 
               
               
                   
                 22-21319661 
                 cg07960806 
                   
                 9-129373499 
                 cg19337180 
               
               
                   
                 17-17685582 
                 cg15906409 
                   
                 2-100175805 
                 cg17165836 
               
               
                   
                 17-48619308 
                 cg00698906 
                   
                 11-65405492 
                 cg27068330 
               
               
                   
                 6-28911474 
                 cg05127899 
                   
                 2-100175704 
                 cg18261069 
               
               
                   
                 2-20866242 
                 cg19113641 
                   
                 9-129388668 
                 cg04693928 
               
               
                   
                 17-48619672 
                 cg27437806 
                   
                 21-46352817 
                 cg17648210 
               
               
                   
                 6-106958645 
                 cg22231056 
                   
                 1-155043322 
               
               
                   
                 17-8055756 
                 cg23363911 
                   
                 11-128392102 
                 cg18565473 
               
               
                   
                 2-20866231 
                 cg21039778 
                   
                 9-36037454 
               
               
                   
                 17-46671298 
                 cg02293936 
                   
                 6-160769754 
                 cg07237939 
               
               
                   
                 6-106958303 
                 cg04067276 
                   
                 11-65405760 
                 cg01685242 
               
               
                   
                 22-21319668 
                 cg11782550 
                   
                 2-100175768 
                 cg24797187 
               
               
                   
                 20-34189411 
                 cg13544006 
                   
                 19-11354132 
               
               
                   
                 6-106958640 
                 cg25944720 
                   
                 9-36037340 
               
               
                 Hypomethylated 
                 20-50419274 
                 cg12288473 
                 Hypomethylated 
                 8-128403369 
                 cg11118198 
               
               
                   
                 16-1576146 
                 cg27316811 
                   
                 6-32135186 
                 cg11108115 
               
               
                   
                 7-1932321 
                 cg23618217 
                   
                 19-45575023 
                 cg15732530 
               
               
                   
                 17-767334 
                 cg25236028 
                   
                 2-121036440 
                 cg20349803 
               
               
                   
                 4-141188341 
                 cg06775361 
                   
                 2-121036760 
                 cg25950520 
               
               
                   
                 8-123966105 
                 cg01886570 
                   
                 12-132286017 
                 cg13251533 
               
               
                   
                 13-114187990 
                 cg13241681 
                   
                 1-204531767 
                 cg00827973 
               
               
                   
                 2-25427108 
                 cg20422417 
                   
                 2-85622094 
                 cg27090078 
               
               
                   
                 17-767301 
                 cg13476078 
                   
                 9-108424905 
                 cg06119575 
               
               
                   
                 6-18368796 
                 cg23221431 
                   
                 6-33288804 
                 cg15070677 
               
               
                   
                 6-106582669 
                 cg25581448 
                   
                 2-121036705 
                 cg27505472 
               
               
                   
                 6-106582638 
                 cg17356964 
                   
                 1-245925961 
                 cg23610994 
               
               
                   
                 12-90313611 
                 cg20490773 
                   
                 8-126082720 
               
               
                   
                 1-8384659 
                 cg10508347 
                   
                 14-81865007 
                 cg10334928 
               
               
                   
                 5-180219707 
                 cg18114890 
                   
                 4-141564017 
                 cg03135535 
               
               
                   
                 19-8670086 
                 cg11745755 
                   
                 1-168250628 
                 cg17095936 
               
               
                   
                 3-12332780 
                 cg16827534 
                   
                 1-162694184 
                 cg19313373 
               
               
                   
                 2-219256018 
                 cg23212579 
                   
                 9-97856079 
                 cg13763287 
               
               
                   
                 17-25619946 
                 cg07564598 
                   
                 7-127984393 
                 cg08309135 
               
               
                   
                 2-219256101 
                 cg16926316 
                   
                 15-39472102 
                 cg13182816 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 CHOL 
                 Marker 
                 CpG 
                 COAD 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 6-32547019 
                 cg07984380 
                 Hypermethylated 
                 2-29338432 
                 cg08808128 
               
               
                   
                 1-198904195 
                 cg09517106 
                   
                 2-66803033 
                 cg02764245 
               
               
                   
                 5-174150712 
                 cg26092471 
                   
                 15-28351906 
                 cg04803843 
               
               
                   
                 12-108733275 
                 cg12142354 
                   
                 4-55991782 
                 cg07893544 
               
               
                   
                 1-113498106 
                 cg12551029 
                   
                 1-76080727 
               
               
                   
                 12-102872937 
                 cg11612617 
                   
                 5-38556435 
                 cg12587766 
               
               
                   
                 6-27782628 
                 cg05444312 
                   
                 20-61051039 
                 cg14980983 
               
               
                   
                 3-120627406 
                 cg05293820 
                   
                 7-149411652 
                 cg08131100 
               
               
                   
                 6-27840105 
                 cg05511483 
                   
                 20-34894648 
               
               
                   
                 2-158114424 
                   
                   
                 15-28352098 
                 cg03061682 
               
               
                   
                 2-136743460 
                 cg18110444 
                   
                 20-61051032 
                 cg20265733 
               
               
                   
                 6-32522683 
                 cg25140213 
                   
                 12-45444895 
               
               
                   
                 6-27782484 
                 cg16347724 
                   
                 8-97506675 
                 cg04261408 
               
               
                   
                 1-113497932 
                 cg16251079 
                   
                 1-115880865 
                 cg09008705 
               
               
                   
                 3-157824209 
                 cg14961949 
                   
                 6-127440000 
                 cg19365062 
               
               
                   
                 4-147443095 
                 cg20910303 
                   
                 9-707166 
                 cg14399863 
               
               
                   
                 1-113498174 
                 cg17020695 
                   
                 9-707387 
                 cg13877502 
               
               
                   
                 5-111115809 
                 cg11657018 
                   
                 4-55991860 
                 cg00989765 
               
               
                   
                 1-113497999 
                 cg07423363 
                   
                 4-157997554 
                 cg11163620 
               
               
                   
                 12-4381788 
                 cg15993083 
                   
                 1-57889035 
               
               
                 Hypomethylated 
                 13-111553101 
                 cg23052386 
                 Hypomethylated 
                 19-39306244 
                 cg12028674 
               
               
                   
                 1-23418405 
                 cg15897613 
                   
                 3-150238257 
                 cg02504622 
               
               
                   
                 6-36237037 
                 cg27319730 
                   
                 10-518370 
                 cg26718707 
               
               
                   
                 7-1946447 
                 cg25786640 
                   
                 20-57616191 
                 cg20726575 
               
               
                   
                 6-105854768 
                 cg18879177 
                   
                 4-187629336 
                 cg24820270 
               
               
                   
                 1-117489843 
                 cg23093116 
                   
                 4-187629387 
                 cg25352342 
               
               
                   
                 13-101241430 
                   
                   
                 4-187629328 
                 cg21399040 
               
               
                   
                 3-24213704 
                 cg25087352 
                   
                 17-48845009 
                 cg25518968 
               
               
                   
                 1-202559755 
                 cg11143857 
                   
                 7-150069026 
                 cg17677524 
               
               
                   
                 20-36033352 
                 cg14637027 
                   
                 4-6945745 
                 cg16574807 
               
               
                   
                 10-5542941 
                 cg26552733 
                   
                 5-179393917 
                 cg26722544 
               
               
                   
                 6-34231501 
                 cg15495717 
                   
                 2-106959257 
                 cg24419520 
               
               
                   
                 11-134118834 
                 cg22756211 
                   
                 17-80535428 
                 cg03454705 
               
               
                   
                 6-16147669 
                 cg15404791 
                   
                 1-1095607 
                 cg02971920 
               
               
                   
                 10-135097477 
                 cg27633010 
                   
                 2-8850020 
                 cg00240378 
               
               
                   
                 10-76801843 
                 cg23077498 
                   
                 7-73408058 
                 cg16233515 
               
               
                   
                 15-102261251 
                 cg14588686 
                   
                 11-2287734 
                 cg14907310 
               
               
                   
                 14-65210900 
                 cg11849213 
                   
                 7-73408101 
                 cg18780627 
               
               
                   
                 10-119101310 
                 cg17182036 
                   
                 2-106959384 
                 cg13656404 
               
               
                   
                 11-126072352 
                 cg26351663 
                   
                 2-112808038 
                 cg25890678 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 ESCA 
                 Marker 
                 CpG 
                 GBM 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 19-57078765 
                   
                 Hypermethylated 
                 6-43237330 
                 cg23514619 
               
               
                   
                 19-56915732 
                   
                   
                 19-19336240 
                 cg25814383 
               
               
                   
                 19-56915650 
                   
                   
                 17-55938871 
                 cg22451358 
               
               
                   
                 19-56915658 
                   
                   
                 12-57504948 
                 cg01063813 
               
               
                   
                 19-56915655 
                   
                   
                 19-1468943 
                 cg10565187 
               
               
                   
                 19-56915653 
                   
                   
                 6-43237511 
                 cg20434586 
               
               
                   
                 7-37960873 
                   
                   
                 4-81123613 
                 cg22054918 
               
               
                   
                 19-57078780 
                   
                   
                 19-35531817 
                 cg24139898 
               
               
                   
                 19-12175935 
                   
                   
                 14-70038925 
                 cg02380334 
               
               
                   
                 19-57078783 
                   
                   
                 6-74233510 
                 cg09681335 
               
               
                   
                 19-53696642 
                   
                   
                 17-55938748 
                 cg00023001 
               
               
                   
                 19-12175631 
                   
                   
                 19-35531859 
                 cg21484586 
               
               
                   
                 15-48470516 
                   
                   
                 19-7937107 
                 cg00438215 
               
               
                   
                 19-40315143 
                 cg00498691 
                   
                 1-27894955 
                 cg12610832 
               
               
                   
                 7-37960317 
                   
                   
                 11-65314161 
               
               
                   
                 7-37960731 
                   
                   
                 19-2614086 
                 cg19250790 
               
               
                   
                 2-80531483 
                   
                   
                 1-26690537 
                 cg12813724 
               
               
                   
                 12-94543449 
                   
                   
                 16-744328 
                 cg05542681 
               
               
                   
                 14-60097247 
                   
                   
                 16-4233246 
                 cg05006755 
               
               
                   
                 19-53696649 
                 cg01620580 
                   
                 2-58273552 
                 cg18998670 
               
               
                 Hypomethylated 
                 1-1563500 
                 cg09159050 
                 Hypomethylated 
                 12-131452297 
                 cg23617848 
               
               
                   
                 5-131725325 
                 cg23475112 
                   
                 1-10240017 
                 cg27508545 
               
               
                   
                 X-2836027 
                   
                   
                 7-4747216 
                 cg01275887 
               
               
                   
                 7-157204364 
                 cg18362281 
                   
                 2-30041338 
                 cg00073794 
               
               
                   
                 7-112431576 
                 cg04876978 
                   
                 18-42325086 
                 cg07015525 
               
               
                   
                 1-19280186 
                 cg11697440 
                   
                 4-146804010 
                 cg01578875 
               
               
                   
                 19-57105832 
                 cg19641839 
                   
                 7-101849120 
                 cg06010390 
               
               
                   
                 7-63772811 
                   
                   
                 1-10240025 
                 cg23661000 
               
               
                   
                 9-139913436 
                   
                   
                 4-3372206 
               
               
                   
                 1-156232301 
                 cg10199857 
                   
                 1-6390793 
                 cg00081799 
               
               
                   
                 19-57082536 
                   
                   
                 6-24140899 
                 cg08908855 
               
               
                   
                 5-138032270 
                   
                   
                 7-4747261 
                 cg07076109 
               
               
                   
                 21-44195410 
                   
                   
                 16-50897271 
                 cg00900231 
               
               
                   
                 16-84446919 
                 cg00838040 
                   
                 5-140865433 
                 cg11830096 
               
               
                   
                 11-47213196 
                 cg05584439 
                   
                 10-405225 
                 cg20595750 
               
               
                   
                 8-19026658 
                   
                   
                 17-76220929 
                 cg07366188 
               
               
                   
                 6-163730283 
                 cg10584587 
                   
                 17-63535223 
                 cg06405341 
               
               
                   
                 1-228362509 
                 cg18277682 
                   
                 22-42611379 
                 cg26799416 
               
               
                   
                 9-139907691 
                 cg14173587 
                   
                 8-9537322 
                 cg10419849 
               
               
                   
                 1-43971496 
                 cg25211006 
                   
                 18-42324965 
                 cg03722909 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 KICH 
                 Marker 
                 CpG 
                 KIRC 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 1-184357556 
                 cg10061342 
                 Hypermethylated 
                 13-113623844 
                 cg09678836 
               
               
                   
                 16-4103492 
                 cg10177032 
                   
                 5-66299786 
                 cg02632185 
               
               
                   
                 15-70767570 
                 cg16973527 
                   
                 5-66300503 
                 cg27116842 
               
               
                   
                 4-74570525 
                 cg23099587 
                   
                 13-113623639 
                 cg00127894 
               
               
                   
                 5-43772975 
                   
                   
                 13-113623659 
                 cg15179805 
               
               
                   
                 X-78201182 
                   
                   
                 5-66300406 
                 cg17449851 
               
               
                   
                 7-28726216 
                 cg05083033 
                   
                 5-66300433 
                 cg11831286 
               
               
                   
                 X-107020894 
                   
                   
                 12-54427636 
                 cg16983211 
               
               
                   
                 1-9224198 
                 cg22373770 
                   
                 12-52214119 
                 cg19495013 
               
               
                   
                 15-96957617 
                   
                   
                 13-113623300 
                 cg16737670 
               
               
                   
                 16-474271 
                 cg09555736 
                   
                 12-54422350 
                 cg27441225 
               
               
                   
                 10-34320548 
                 cg10772185 
                   
                 8-107669856 
                 cg17176732 
               
               
                   
                 6-168134760 
                 cg01797450 
                   
                 5-66299875 
                 cg18118497 
               
               
                   
                 16-30347333 
                   
                   
                 8-107669783 
                 cg17136799 
               
               
                   
                 10-17273187 
                 cg26063719 
                   
                 13-113623233 
                 cg02223067 
               
               
                   
                 12-10022688 
                 cg12591315 
                   
                 8-107669787 
                 cg26622232 
               
               
                   
                 6-166970727 
                 cg11387340 
                   
                 17-18908235 
                 cg14807365 
               
               
                   
                 16-15766942 
                   
                   
                 2-73151879 
                 cg00486564 
               
               
                   
                 16-474430 
                 cg02102075 
                   
                 12-54427700 
                 cg15700739 
               
               
                   
                 11-7728814 
                 cg13563074 
                   
                 12-54422488 
                 cg22378817 
               
               
                 Hypomethylated 
                 4-185362534 
                 cg02354388 
                 Hypomethylated 
                 13-96086098 
                 cg20278383 
               
               
                   
                 16-87873837 
                 cg06665333 
                   
                 2-239169591 
               
               
                   
                 22-35694370 
                 cg27436324 
                   
                 8-30209391 
                 cg14895559 
               
               
                   
                 8-6616240 
                 cg24485696 
                   
                 16-81565917 
                 cg03212183 
               
               
                   
                 20-57471672 
                   
                   
                 13-111880998 
                 cg21980364 
               
               
                   
                 11-66494177 
                 cg22015277 
                   
                 4-87993109 
                 cg27000934 
               
               
                   
                 5-173307471 
                 cg13384849 
                   
                 16-15127193 
                 cg06978461 
               
               
                   
                 20-57471660 
                   
                   
                 1-217724849 
                 cg10325088 
               
               
                   
                 4-38006787 
                 cg23289854 
                   
                 20-6025272 
                 cg21209310 
               
               
                   
                 17-81045501 
                 cg14521546 
                   
                 7-101556684 
                 cg15600935 
               
               
                   
                 16-479994 
                 cg10357909 
                   
                 17-78700928 
                 cg06975080 
               
               
                   
                 10-126314680 
                 cg04426802 
                   
                 11-64323816 
                 cg21039563 
               
               
                   
                 7-98667581 
                 cg09221667 
                   
                 17-18855313 
                 cg00306284 
               
               
                   
                 7-76871793 
                   
                   
                 3-149051159 
                 cg11011913 
               
               
                   
                 3-196693809 
                 cg21429725 
                   
                 14-89628141 
                 cg02212698 
               
               
                   
                 19-45316509 
                 cg21978694 
                   
                 15-39958882 
               
               
                   
                 7-23723014 
                 cg20200811 
                   
                 17-40064155 
                 cg03950716 
               
               
                   
                 19-45316493 
                 cg12249345 
                   
                 1-101094117 
                 cg00902464 
               
               
                   
                 14-95585023 
                 cg23488578 
                   
                 7-101566857 
                 cg02307880 
               
               
                   
                 13-33768013 
                   
                   
                 5-111017556 
                 cg01885839 
               
               
                   
               
               
                 LIHC 
                 Marker 
                 CpG 
                 LUAD 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 2-264178 
                 cg20749741 
                 Hypermethylated 
                 6-152623304 
                 cg05917732 
               
               
                   
                 1-145395753 
                 cg17952661 
                   
                 10-8092165 
                 cg15803869 
               
               
                   
                 19-54369556 
                   
                   
                 6-152623479 
                 cg12023625 
               
               
                   
                 3-183543564 
                 cg19155007 
                   
                 1-23668691 
                 cg00907427 
               
               
                   
                 2-264199 
                 cg00108164 
                   
                 10-119304102 
               
               
                   
                 19-50096610 
                 cg15032314 
                   
                 1-23668792 
                 cg08718097 
               
               
                   
                 6-43044771 
                 cg21663580 
                   
                 21-40984780 
                 cg08448665 
               
               
                   
                 1-145395846 
                 cg02395363 
                   
                 10-119304104 
                 cg12894449 
               
               
                   
                 6-56819429 
                   
                   
                 7-2271722 
                 cg03694580 
               
               
                   
                 3-183543587 
                 cg09738156 
                   
                 21-40984777 
                 cg17900854 
               
               
                   
                 19-54369576 
                 cg23305567 
                   
                 11-31832246 
                 cg26848086 
               
               
                   
                 2-233792394 
                 cg08165971 
                   
                 17-70119120 
                 cg21126344 
               
               
                   
                 2-264204 
                 cg25945732 
                   
                 10-8096370 
                 cg15187550 
               
               
                   
                 19-6740952 
                 cg03705926 
                   
                 21-40984886 
                 cg03701992 
               
               
                   
                 5-176831297 
                 cg14126493 
                   
                 1-166134346 
               
               
                   
                 11-75917844 
                 cg17583449 
                   
                 11-31832353 
                 cg05840031 
               
               
                   
                 5-176831638 
                 cg05876864 
                   
                 10-8092264 
                 cg16710894 
               
               
                   
                 1-160370208 
                 cg13428480 
                   
                 6-50680997 
                 cg17489695 
               
               
                   
                 2-264164 
                 cg03468349 
                   
                 10-8091753 
                 cg22783180 
               
               
                   
                 1-21616619 
                 cg17216478 
                   
                 6-152623387 
                 cg02682457 
               
               
                 Hypomethylated 
                 22-50644755 
                 cg25934700 
                 Hypomethylated 
                 7-2473529 
                 cg17907628 
               
               
                   
                 5-16794882 
                 cg12852139 
                   
                 7-2473859 
                 cg18259487 
               
               
                   
                 15-99427016 
                 cg03310087 
                   
                 5-141722174 
                 cg02084814 
               
               
                   
                 10-1334207 
                 cg04992974 
                   
                 10-1120831 
               
               
                   
                 16-87970367 
                 cg05640992 
                   
                 6-25726912 
                 cg07077277 
               
               
                   
                 11-46741373 
                 cg26453360 
                   
                 16-3139060 
                 cg20364187 
               
               
                   
                 7-1197113 
                 cg03923535 
                   
                 2-232113244 
                 cg27273946 
               
               
                   
                 12-69199037 
                 cg10635494 
                   
                 14-74957673 
               
               
                   
                 8-142263802 
                 cg00971369 
                   
                 6-25727291 
               
               
                   
                 11-6462110 
                 cg14164596 
                   
                 14-91691190 
                 cg14304073 
               
               
                   
                 5-17275783 
                 cg01860297 
                   
                 13-113445870 
               
               
                   
                 16-87970376 
                 cg03550864 
                   
                 6-25727107 
               
               
                   
                 7-20269648 
                 cg19086110 
                   
                 13-98869844 
                 cg16689193 
               
               
                   
                 1-167882440 
                 cg24461337 
                   
                 14-91691129 
                 cg16996571 
               
               
                   
                 2-44065725 
                 cg20926720 
                   
                 4-37962455 
               
               
                   
                 7-4012372 
                 cg04609841 
                   
                 6-25726953 
               
               
                   
                 11-102638470 
                 cg03367136 
                   
                 10-52156632 
               
               
                   
                 17-26694939 
                 cg18536123 
                   
                 6-25726956 
               
               
                   
                 10-93390347 
                 cg24288527 
                   
                 1-207975699 
               
               
                   
                 22-21128411 
                 cg09634469 
                   
                 16-3641320 
               
               
                   
               
               
                 LUSC 
                 Marker 
                 CpG 
                 OV 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 19-10406145 
                 cg23936587 
                 Hypermethylated 
                 19-58281450 
                 cg14038484 
               
               
                 Hypomethylated 
                 2-132348705 
                 cg11467141 
                   
                 19-58281016 
                 cg18575209 
               
               
                   
                 15-73889528 
                 cg06332339 
                   
                 19-58280832 
                 cg03584535 
               
               
                   
                 2-239970152 
                 cg08781549 
                   
                 19-58281117 
                 cg22956410 
               
               
                   
                 2-239970075 
                 cg26126749 
                   
                 19-58280927 
                 cg13636880 
               
               
                   
                 1-183204836 
                 cg14420230 
                   
                 19-58280891 
                 cg07685728 
               
               
                   
                 12-21547892 
                 cg14191244 
                   
                 19-58280801 
                 cg27046034 
               
               
                   
                 12-63195796 
                 cg27185063 
                   
                 19-58281019 
                 cg20751795 
               
               
                   
                 1-183204789 
                 cg22684969 
                   
                 19-58280994 
                 cg24298255 
               
               
                   
                 5-14160853 
                   
                   
                 11-133582402 
                 cg11368617 
               
               
                   
                 15-73889561 
                 cg08435157 
                   
                 12-130529951 
                 cg16236851 
               
               
                   
                 3-185704206 
                 cg09562655 
                   
                 10-95327305 
                 cg05613447 
               
               
                   
                 12-11286360 
                 cg24741148 
                   
                 10-95327292 
                 cg10905401 
               
               
                   
                 12-132643897 
                 cg16860971 
                   
                 10-74870311 
                 cg01701415 
               
               
                   
                 12-93101181 
                 cg17344080 
                   
                 17-36104218 
               
               
                   
                 7-148481030 
                 cg25477176 
                   
                 8-54569668 
                 cg05483021 
               
               
                   
                 22-30572326 
                 cg24211826 
                   
                 10-74870240 
                 cg05898953 
               
               
                   
                 19-5789699 
                 cg22961623 
                   
                 1-205399945 
                 cg06849719 
               
               
                   
                 7-101603335 
                 cg05855116 
                   
                 13-110899996 
                 cg15416440 
               
               
                   
                 1-64992433 
                   
                   
                 1-205400197 
                 cg06968878 
               
               
                   
                 15-73524906 
                 cg08177743 
               
               
                   
               
               
                 PAAD 
                 Marker 
                 CpG 
                 PRAD 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 14-105715025 
                 cg01237565 
                 Hypermethylated 
                 19-51416098 
                 cg17355294 
               
               
                   
                 16-1202468 
                 cg16427096 
                   
                 11-58940866 
                 cg05415131 
               
               
                   
                 14-105714843 
                 cg16561543 
                   
                 19-16187631 
                 cg16232979 
               
               
                   
                 14-105714589 
                 cg23034818 
                   
                 6-146136749 
                 cg09094393 
               
               
                   
                 20-55204593 
                 cg12521353 
                   
                 7-32981826 
                 cg08946731 
               
               
                   
                 10-11059577 
                 cg23858040 
                   
                 10-74020976 
                 cg12799885 
               
               
                   
                 13-32605406 
                 cg22219254 
                   
                 2-191045309 
                 cg08350814 
               
               
                   
                 13-32605611 
                 cg16941656 
                   
                 7-120628874 
                 cg01029638 
               
               
                   
                 2-177053345 
                 cg19001226 
                   
                 5-43193681 
                 cg10641714 
               
               
                   
                 4-141293985 
                   
                   
                 19-16187889 
                 cg26149167 
               
               
                   
                 16-86231948 
                 cg07989221 
                   
                 9-27529339 
                 cg21244846 
               
               
                   
                 5-172756558 
                 cg16443866 
                   
                 15-35014270 
                 cg24922143 
               
               
                   
                 5-172756550 
                 cg08711858 
                   
                 17-46830260 
                 cg13132370 
               
               
                   
                 1-182584341 
                 cg00153856 
                   
                 12-89744701 
                 cg05769889 
               
               
                   
                 14-105714833 
                 cg15474367 
                   
                 19-51416517 
                 cg20115266 
               
               
                   
                 11-61276678 
                   
                   
                 6-146136563 
                 cg23095615 
               
               
                   
                 4-141294009 
                   
                   
                 10-111767379 
                 cg05098590 
               
               
                   
                 22-17849639 
                 cg26796679 
                   
                 1-116711077 
                 cg17933722 
               
               
                   
                 9-126773879 
                 cg10755973 
                   
                 2-191045668 
                 cg15313226 
               
               
                   
                 2-107503672 
                   
                   
                 1-33938224 
                 cg18085998 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 SKCM 
                 Marker 
                 CpG 
                 UCEC 
                 Marker 
                 CpG 
               
               
                   
               
               
                 Hypermethylated 
                 7-5468436 
                   
                 Hypermethylated 
                 17-42092403 
                 cg06511389 
               
               
                   
                 2-166650606 
                   
                   
                 17-42092315 
                 cg27116819 
               
               
                   
                 11-19798742 
                   
                   
                 17-42092187 
                 cg18801599 
               
               
                   
                 17-77815779 
                   
                   
                 19-50836561 
                 cg01017355 
               
               
                   
                 6-10419927 
                   
                   
                 17-42092370 
                 cg08372947 
               
               
                   
                 11-441986 
                   
                   
                 17-42092247 
                 cg24638647 
               
               
                   
                 12-112204829 
                   
                   
                 17-42092450 
                 cg13847066 
               
               
                   
                 14-61188431 
                   
                   
                 18-5238310 
                 cg20757519 
               
               
                   
                 12-48398374 
                 cg17054969 
                   
                 17-42091909 
                 cg04879755 
               
               
                   
                 6-1311342 
                   
                   
                 18-5238589 
                 cg10885961 
               
               
                   
                 10-25241427 
                   
                   
                 18-5238219 
                 cg15258847 
               
               
                   
                 6-10420079 
                   
                   
                 17-42092488 
                 cg25868286 
               
               
                   
                 1-111747228 
                   
                   
                 17-42092472 
                 cg01635193 
               
               
                   
                 6-26332204 
                   
                   
                 3-169482895 
                 cg15494117 
               
               
                   
                 21-38630728 
                   
                   
                 6-26045663 
                 cg25438963 
               
               
                   
                 10-25241462 
                   
                   
                 17-42092431 
                 cg12259256 
               
               
                   
                 7-5469535 
                   
                   
                 19-50836910 
                 cg03251287 
               
               
                   
                 11-19798914 
                   
                   
                 3-169482358 
                 cg27020690 
               
               
                   
                 1-6265894 
                   
                   
                 3-10206731 
                 cg11029301 
               
               
                   
                 4-186732926 
                 cg04392082 
                   
                 3-164914575 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 normal blood 
                 Marker 
                 CpG 
               
               
                   
                   
               
               
                   
                 Hypermethylated 
                 3-188012918 
                 cg02524983 
               
               
                   
                   
                 1-43814358 
                 cg18856478 
               
               
                   
                   
                 22-51136325 
                 cg18982286 
               
               
                   
                   
                 12-58129855 
                 cg16402452 
               
               
                   
                   
                 19-15568360 
                 cg08846870 
               
               
                   
                   
                 15-22798986 
               
               
                   
                   
                 19-39086923 
                 cg05258935 
               
               
                   
                   
                 4-159969703 
                 cg10393744 
               
               
                   
                   
                 16-67687754 
                 cg09316954 
               
               
                   
                   
                 11-57090226 
                 cg11180921 
               
               
                   
                   
                 6-31510729 
               
               
                   
                   
                 10-17391003 
                 cg05576619 
               
               
                   
                   
                 1-3135712 
               
               
                   
                   
                 2-30737741 
               
               
                   
                   
                 1-221613658 
                 cg24686918 
               
               
                   
                   
                 1-164581169 
                 cg20812370 
               
               
                   
                   
                 10-63809073 
                 cg14789659 
               
               
                   
                   
                 17-43318735 
               
               
                   
                   
                 6-12718370 
               
               
                   
                   
                 5-10551547 
                 cg24100671 
               
               
                   
                   
               
            
           
         
       
     
     Table 3 illustrates cancer name and its respective abbreviation. 
     
       
         
           
               
               
             
               
                   
               
               
                 Abbreviation 
                 Name 
               
               
                   
               
             
            
               
                 LAML 
                 Acute Myeloid Leukemia 
               
               
                 ACC 
                 Adrenocortical carcinoma 
               
               
                 BLCA 
                 Bladder Urothelial Carcinoma 
               
               
                 LGG 
                 Brain Lower Grade Glioma 
               
               
                 BRCA 
                 Breast invasive carcinoma 
               
               
                 CESC 
                 Cervical squamous cell carcinoma and endocervical 
               
               
                   
                 adenocarcinoma 
               
               
                 CHOL 
                 Cholangiocarcinoma 
               
               
                 LCML 
                 Chronic Myelogenous Leukemia 
               
               
                 COAD 
                 Colon adenocarcinoma 
               
               
                 CNTL 
                 Controls 
               
               
                 ESCA 
                 Esophageal carcinoma 
               
               
                 FPPP 
                 FFPE Pilot Phase II 
               
               
                 GBM 
                 Glioblastoma multiforme 
               
               
                 HNSC 
                 Head and Neck squamous cell carcinoma 
               
               
                 KICH 
                 Kidney Chromophobe 
               
               
                 KIRC 
                 Kidney renal clear cell carcinoma 
               
               
                 KIRP 
                 Kidney renal papillary cell carcinoma 
               
               
                 LIHC 
                 Liver hepatocellular carcinoma 
               
               
                 LUAD 
                 Lung adenocarcinoma 
               
               
                 LUSC 
                 Lung squamous cell carcinoma 
               
               
                 DLBC 
                 Lymphoid Neoplasm Diffuse Large B-cell Lymphoma 
               
               
                 MESO 
                 Mesothelioma 
               
               
                 MISC 
                 Miscellaneous 
               
               
                 OV 
                 Ovarian serous cystadenocarcinoma 
               
               
                 PAAD 
                 Pancreatic adenocarcinoma 
               
               
                 PCPG 
                 Pheochromocytoma and Paraganglioma 
               
               
                 PRAD 
                 Prostate adenocarcinoma 
               
               
                 READ 
                 Rectum adenocarcinoma 
               
               
                 SARC 
                 Sarcoma 
               
               
                 SKCM 
                 Skin Cutaneous Melanoma 
               
               
                 STAD 
                 Stomach adenocarcinoma 
               
               
                 TGCT 
                 Testicular Germ Cell Tumors 
               
               
                 THYM 
                 Thymoma 
               
               
                 THCA 
                 Thyroid carcinoma 
               
               
                 UCS 
                 Uterine Carcinosarcoma 
               
               
                 UCEC 
                 Uterine Corpus Endometrial Carcinoma 
               
               
                 UVM 
                 Uveal Melanoma 
               
               
                   
               
            
           
         
       
     
       FIG. 1  illustrates the methylation status of marker 7-1577016. In some instances, marker 7-1577016 is hypomethylated. In some cases, marker 7-1577016 is used as a pan cancer marker. 
       FIG. 2  illustrates the methylation status of marker 11-67177103. In some cases, marker 11-67177103 is used as a pan cancer marker. 
       FIG. 3  illustrates the methylation status of marker 19-10445516 (cg17126555). In some cases, marker 19-10445516 (cg17126555) is used as a pan cancer marker. 
       FIG. 4  illustrates the methylation status of marker 12-122277360. In some cases, marker 12-122277360 is used as a liver cancer diagnostic marker. 
       FIG. 5  illustrates the methylation status of marker 6-72130742 (cg24772267). In some cases, marker 6-72130742 (cg24772267) is used as a colon cancer diagnostic marker. 
       FIG. 6  illustrates the methylation status of marker 3-15369681. In some cases, marker 3-15369681 is used as a liver cancer diagnostic marker. 
       FIG. 7  illustrates the methylation status of marker 3-131081177. In some cases, marker 3-131081177 is used as a breast cancer diagnostic marker. 
     Example 2—Generating a Reference Cg Marker Panel 
     Data Sources 
     DNA methylation data from initial training set and first testing set were obtained from The Cancer Genome Atlas (TCGA). 
     Generating a Reference Cg Marker Set 
     Cancer type specific signature was identified by comparing the pair-wise methylation difference between a particular cancer type versus its corresponding normal tissue, the difference between two different cancer types, as well as difference between two different normal tissues, with a total of 12 tissue groups including 6 tumor groups and 6 normal tissue groups. Patient samples were randomly divided from the TCGA representing 9 cancer types from 6 different tissues with matched adjacent-normal tissue into training and validation cohorts. To do this, a total of 12*11/2=66 unique pair-wise comparisons were performed. Using an Illumina 450,000 CpG methylation microarray, 450k markers were compared from one group to another group using the [column t test] colttests( ) function in the R genefilter package. Markers with the lowest p values by t-statistic and the largest difference in a mean methylation fraction between each comparison were ranked and the top ten markers in each group were selected for further validation analysis. 
     Calculate Weights for Top Ten Markers in Each Comparison 
     The Principle Component analysis was applied to the top ten markers in each comparison group using the function in the stats environment: prcomp( ) and the weights in the first principle component of each group were extracted and matched with the ten corresponding markers in each group. There were 45 groupings of weights with markers. These markers were used to classify the samples with several algorithms including Neural Networks, Logistic Regression, Nearest Neighbor (NN) and Support Vector Machines (SVM), all of which generated consistent results. Analyses using SVM were found to be most robust and were therefore used in all subsequent analyses. 
     For each tumor type, samples were divided into two groups based on the resulting methylation signatures and their survival was plotted using Kaplan-Meier curves. Subgroups based on tumor stage and the presence of residual tumor following treatment was also analyzed. These methylation profiles were able to predict highly statistically significant differences in survival in all tumor types and most subgroups examined. 
     Generate Variables 
     45 variables for each of the samples in the data were generated. Using the weight/marker combination, each variable V was calculated using the following equation: 
     
       
         
           
             V 
             = 
             
               
                 ∑ 
                 
                   1 
                    
                   0 
                 
                 1 
               
                
               
                 ( 
                 
                   W 
                   * 
                   M 
                 
                 ) 
               
             
           
         
       
     
     where W is the weight and M is the methylation Beta-value between 0 and 1 of the corresponding marker. A matrix was generated where the dimensions are (1) the number of samples by (2) 190 variables. 
     Classifying Samples 
     The above mentioned matrix was used to classify the samples. There are several classification algorithms that were used here including Logistic Regression, Nearest Neighbor (NN) and Support Vector Machines (SVM). Analysis using SVM were used in all subsequent analyses. 
     The Kernel-Based Machine Learning Lab (kernlab) library for R was used to generate the Support Vector Machines. The best results were with the “RBF” kernel. The Crammer, Singer algorithm had slightly better results than the Weston, Watson algorithm. In the analysis, four potential types of classification errors were seen.
         1. Incorrect Tissue; e.g. colon tissue is identified as lung tissue.   2. False negative; e.g. lung cancer is identified as normal lung   3. False positive; e.g. normal colon is identified as colon cancer   4. Correct tissue, incorrect cancer type; e.g. kidney renal clear cell carcinoma is identified as kidney renal papillary cell carcinoma.       

     Three methods were used to validate the results:
         1. The samples were divided into five equal parts and 4 of the parts were used for training and the fifth part was used to test the results.   2. Leave one out scenario was used where all of the samples were used for training except one. The one left out was used for testing. This was repeated for each sample until they had all been tested.   3. Two stage replication study: The samples were divided into two sets at the beginning of the process. With the training set, 10 markers in each comparison with the highest t-test scores were identified. These markers were then used to generate principal components and then used these variables to create a SVM. The obtained markers were applied to the test set, and principal components and SVM results were generated.       

     Tumor DNA Extraction 
     Genomic DNA extraction from pieces of freshly frozen healthy or cancer tissues was performed with QIAamp DNA Mini Kit (Qiagen) according to manufacturer&#39;s recommendations. Roughly 0.5 mg of tissue was used to obtain on average 5 mg of genomic DNA. DNA was stored at −20° C. and analyzed within one week of preparation. 
     DNA Extraction from FFPE Samples 
     Genomic DNA from frozen FFPE samples was extracted using QIAamp DNA FFPE Tissue Kit with several modifications. DNA was stored at −20° C. for further analysis. 
     Bisulfite Conversion of Genomic DNA 
     1 μg of genomic DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer&#39;s protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was &gt;99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides. 
     Padlock Probe Designs 
     CpG markers whose methylation levels significantly differed in any of the comparison between a cancer tissue and normal tissue were used to design padlock probes for sequencing. 
     Padlock probes were designed using the ppDesigner software. The average length of the captured region was 70 bp, with the CpG marker located in the central portion of the captured region. To prevent bias introduced by unknown methylation status of CpG markers, capturing arms were positioned exclusively within sequences devoid of CG dinucleotides. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produce probes of equal length. The average length of probes was 91 bp. Probes incorporated a 6-bp unique molecular identifier (UMI) sequence to allow for the identification of individual molecular capture events and accurate scoring of DNA methylation levels. 
     Probes were synthesized as separate oligonucleotides using standard commercial synthesis methods. For capture experiments, probes were mixed, in-vitro phosphorylated with T4 PNK (NEB) according to manufacturer&#39;s recommendations and purified using P-30 Micro Bio-Spin columns (Bio-Rad). 
     Bis-DNA Capture 
     20 ng of bisulfite-converted DNA was mixed with a defined molar ratio of padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre). The optimal molar ratio of probes to DNA was determined experimentally to be 20,000:1. Reactions were covered with 50 μl of mineral oil to prevent evaporation. To anneal probes to DNA, 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second. Hybridization was left to complete for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2U of PfuTurboCx polymerase (pre-activated for 3 min at 95° C. (Agilent)), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5 hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C. and snap-cooled on ice. 5 μl of exonuclease mix (20 U of Exo I and 100 U of ExoIII, both from Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C. 
     Circular products of site specific capture were amplified by PCR with concomitant barcoding of separate samples. Amplification was carried out using primers specific to linker DNA within padlock probes, one of which contained specific 6 bp barcodes. Both primers contained Illumina next-generation sequencing adaptor sequences. PCR was done as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM final [c] of primers, using the following cycle: 10s @ 98° C., 8× of (1s @ 98° C., 5s @ 58° C., 10s @ 72° C.), 25× of (1s @ 98° C., 15s @ 72° C.), 60s @ 72° C. PCR reactions were mixed and the resulting library was size selected to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp) using Agencourt AMPure XP beads (Beckman Coulter). Purity of the libraries was verified by PCR using Illumina flowcell adaptor primers (P5 and P7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries were sequenced using MiSeq and HiSeq2500 systems (Illumina). 
     Optimization of Capture Coverage Uniformity 
     Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60-65% of captured regions with coverage &gt;0.2 of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at &gt;0.2 of average coverage. 
     Sequencing Data Analysis 
     Mapping of sequencing reads was done using the software tool bisReadMapper (Diep, D, Nat Methods. 2012 Feb. 5; 9(3):270-272) with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script generously provided by D. D. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2 (Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012, 9:357-359). Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single one. Methylation frequencies were extracted for all CpG markers for which padlock probes were designed. Markers with less than 20 reads in any sample were excluded from analysis. 
     Example 3—Generating a Cg Marker Panel for Diagnosis and Prognosis of Hepatocellular Carcinoma from cfDNA 
     Patient Data 
     Tissue DNA methylation data was obtained from The Cancer Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards. 
     A second independent Chinese cohort consisted of HCC patients at the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi&#39;an and the West China Hospital in Chengdu, China. Those who presented with HCC from stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Supplementary Table 1. The TNM staging classification for HCC is according to the 7th edition of the AJCC cancer staging manual. The TNM Staging System is one of the most commonly used tumor staging systems. This system was developed and is maintained by the American Joint Committee on Cancer (AJCC) and adopted by the Union for International Cancer Control (UICC). The TNM classification system was developed as a tool for oncologists to stage different types of cancer based on certain standard criteria. The TNM Staging System is based on the extent of the tumor (T), the extent of spread to the lymph nodes (N), and the presence of metastasis (M). This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Tumor and normal tissues were obtained as clinically indicated for patient care and were retained for this study. Human blood samples were collected by venipuncture and plasma samples were obtained by taking supernatant after centrifugation and stored at −80° C. before cfDNA extraction. The key raw data were verified and uploaded onto the Research Data Deposit public platform with an approval number RDDB2017000132. 
     Tumor DNA Extraction 
     Genomic DNA extraction from freshly frozen healthy or cancer tissues was performed with QIAamp DNA Mini Kit (Qiagen) according to manufacturer&#39;s recommendations. Roughly 0.5 mg of tissue was used to obtain on average 5 μg of genomic DNA. DNA was stored at −20° C. and analyzed within one week of preparation. 
     DNA Extraction from FFPE Samples 
     Genomic DNA from frozen FFPE samples was extracted using QIAamp DNA FFPE Tissue Kit with several modifications. DNA were stored at −20° C. for further analysis. 
     Cell-Free DNA Extraction from Plasma Samples 
     cfDNA extraction from 1.5 ml of plasma samples was performed with QIAamp cfDNA Kit (Qiagen) according to manufacturer&#39;s recommendations. 
     Bisulfite Conversion of Genomic DNA 
     About 10 ng of cf DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer&#39;s protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfate conversion was &gt;99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides. 
     Determination of DNA Methylation Levels by Deep Sequencing of Bis-DNA Captured with Molecular-Inversion (Padlock) Probes 
     CpG markers whose methylation levels significantly differed in any of the comparisons between any cancer tissue and any normal tissue were used to design padlock probes for capture and sequencing of cfDNA. Padlock-capture of bis-DNA was based on the technique on published methods with modifications. 
     Probe Design and Synthesis 
     Padlock probes were designed using the ppDesigner software. The average length of the captured region was 100 bp, with the CpG marker located in the central portion of the captured region. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produced probes of equal length. A 6-bp unique molecular identifier (UMI) sequence was incorporated in probe design to allow for the identification of unique individual molecular capture events and accurate scoring of DNA methylation levels. 
     Probes were synthesized as separate oligonucleotides using standard commercial synthesis methods (ITD). For capture experiments, probes were mixed, in-vitro phosphorylated with T4 PNK (NEB) according to manufacturer&#39;s recommendations and purified using P-30 Micro Bio-Spin columns (Bio-Rad). 
     Bis-DNA Capture 
     About 10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre). To anneal probes to DNA, 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second. Hybridization was left to complete for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5 hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C. 5 μl of exonuclease mix (20 U of Exo I and 100 U of ExoIII, both from Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C. 
     Circular products of site-specific capture were amplified by PCR with concomitant barcoding of separate samples. Amplification was carried out using primers specific to linker DNA within padlock probes, one of which contained specific 6 bp barcodes. Both primers contained Illumina next-generation sequencing adaptor sequences. PCR was done as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10s @ 98° C., 8× of (1s @ 98° C., 5s @ 58° C., 10s @ 72° C.), 25× of (1s @ 98° C., 15s @ 72° C.), 60s @ 72° C. PCR reactions were mixed and the resulting library was size selected to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp) using Agencourt AMPure XP beads (Beckman Coulter). Purity of the libraries was verified by PCR using Illumina flowcell adaptor primers (P5 and P7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries were sequenced using MiSeq and HiSeq2500 systems (Illumina). 
     Optimization of Capture Coverage Uniformity 
     Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60-65% of captured regions with coverage &gt;0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at &gt;0.2× of average coverage. 
     Sequencing Data Analysis 
     Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2[28]. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position. 
     Identification of Blocks of Correlated Methylation (BCM) 
     Pearson correlation coefficients between methylation frequencies of each pair of CpG markers separated by no more than 200 bp were calculated separately across 50 cfDNA samples from each of the two diagnostic categories, ie normal health blood and HCC. A value of Pearson&#39;s r&lt;0.5 was used to identify transition spots (boundaries) between any two adjacent markers indicating uncorrelated methylation. Markers not separated by a boundary were combined into Blocks of Correlated Methylation (BCM). This procedure identified a total of 1550 BCM in each diagnostic category within our padlock data, combining between 2 and 22 CpG positions in each block. Methylation frequencies for entire BCMs were calculated by summing up the numbers of Cs at all interrogated CpG positions within a BCM and dividing by the total number of C+Ts at those positions. 
     DNA Isolation and Digital Quantitative PCR 
     Tumor and corresponding plasma samples were obtained from patients undergoing surgical tumor resection; samples were frozen and preserved in at −80° C. until use. Isolation of DNA and RNA from samples was performed using AllPrep DNA/RNA Mini kit and a cfDNA extraction kit, respectively (Qiagen, Valencia, Calif.). 
     Data Sources 
     DNA methylation data of 485,000 sites generated using the Infinium 450K Methylation Array were obtained from the TCGA and dataset generated from our previous study (GSE40279) in which DNA methylation profiles for HCC and blood were analyzed. IDAT format files of the methylation data were generated containing the ratio values of each scanned bead. Using the minfi package from Bioconductor, these data files were converted into a score, referred to as a Beta value. Methylation values of the Chinese cohort were obtained by targeted bisulfate sequencing using a molecular inversion probe and analyzed as described below. 
     Statistical Analysis—DNA Methylation Marker Pre-Selection for Diagnostic and Prognostic Analysis 
     A differential methylation analysis on TCGA data using a “moderated t-statistics shrinking” approach was first performed and the P-value for each marker was then corrected by multiple testing by the Benjamini-Hochberg procedure to control FDR at a significance level of 0.05. The list was ranked by adjusted P-value and selected the top 1000 markers for designing padlock probes. cfDNA samples with low quality or fewer than 20,000 reads per sample were also eliminated. Methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts. Methylation markers with a range of methylation values less than 0.1 in matched tumor tissue and tumor blood samples were eliminated. 
     Building a Cg Marker Panel from cfDNA 
     The cfDNA dataset was randomly split into training and validation cohorts with a 2:1 ratio. Two variable selection methods suitable for high-dimensionality on the prescreened training dataset were applied: Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest based variable selection method using OOB error. As results can depend strongly on the arbitrary choice of a random sample split for sparse high-dimensional data, an analysis of the “multi-split” method were adopted, which improves variable selection consistency while controlling finite sample error. For LASSO selection operator, 75 percent of the dataset was subsampled without replacement 500 times and selected the markers with repeat occurrence frequency more than 450. The tuning parameters was determined according to the expected generalization error estimated from 10-fold cross-validation and information-based criteria AIC/BIC, and the largest value of lambda was adopted such that the error was within one standard error of the minimum, known as “1-se” lambda. For the random forest analysis, using the OOB error as a minimization criterion, variable elimination from the random forest was carried out by setting variable a dropping fraction of each iteration at 0.3. Ten overlapping methylation markers were chosen by the two methods for model building a binary prediction. A logistic regression model was fitted using these 10 markers as the covariates and obtained a combined diagnosis score (designated as cd-score) by multiplying the unbiased coefficient estimates and the marker methylation value matrix in both the training and validation datasets. The predictability of the model was evaluated by area under ROC (AUC, also known as C-index), which calculated the proportions of concordant pairs among all pairs of observations with 1.0 indicating a perfect prediction accuracy. Confusion tables were generated using an optimized cd-score cutoff with a maximum Youden&#39;s index. 
     The pre-treatment or initial methylation level was evaluated at baseline, and the post-methylation level was evaluated approximately 2 months after treatment, where the treatment referred to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) were defined according to the RECIST guideline. For patients treated with surgical removal and no recurrence at time of evaluation, it was assumed that they had complete response (CR). The difference of cd-score distribution between clinical categories was examined by Wilcoxon Rank Sum test as cd-score was tested to be non-normally distributed using a Shapiro-Wilk Test. 
     Patient and Sample Characteristics 
     Clinical characteristics and molecular profiling including methylation data for comparison between HCC and blood lymphocytes were assembled from sources including 377 HCC tumor samples from The Cancer Genome Atlas (TCGA) and 754 blood leukocyte samples of healthy control individuals from a dataset on aging (GSE40279). To study ctDNA in HCC, plasma samples were obtained from Chinese patients with HCC and randomly selected healthy controls undergoing routine health care maintenance, resulting in a training cohort of 715 HCC patients and 560 normal healthy controls and a validation cohort of 383 HCC patients and 275 healthy controls. All participants provided written informed consent. 
     Identification of Methylation Markers Differentiating HCC and Blood 
     It was hypothesized that CpG markers with a maximal difference in methylation between HCC and blood leukocytes in normal individuals would be most likely to demonstrate detectable methylation differences in the cfDNA of HCC patients when compared to that of normal controls. The “moderated t-statistics” method was used with Empirical Bayes for shrinking the variance, and the Benjamini-Hochberg procedure to control the FDR at a significance level of 0.05 to identify the top 1000 markers with the most significantly different rates of methylation (i.e. those with the lowest p-values) between HCC and blood. Unsupervised hierarchical clustering of these top 1000 markers was able to distinguish between HCC and blood leukocytes in normal individuals ( FIG. 12 ). Molecular inversion (padlock) probes was designed corresponding to these 1000 markers and tested them in 28 pairs of HCC tissue DNA and matched plasma ctDNA from the same patient. The methylation profiles in HCC tumor DNA and matched plasma ctDNA were consistent ( FIG. 9A ,  FIG. 9B ). 401 markers with a good experimental amplification profile and dynamic methylation range were selected for further analysis. 
     Methylation Block Structure for Improved Allele Calling Accuracy 
     The concept of genetic linkage disequilibrium (LD block) was employed to study the degree of co-methylation among different DNA strands, with the underlying assumption that DNA sites in close proximity are more likely to be co-methylated than distant sites. A paired-end Illumina sequencing reads was used to identify each individual methylation block (mBlock). A Pearson correlation method to quantify co-methylation or mBlock was used. All common mBlocks of a region were compiled by calculating different mBlock fractions. The genome was then partitioned into blocks of tightly co-methylated CpG sites termed methylation correlated blocks (MCBs), using an r 2  cutoff of 0.5. MCBs was surveyed in cfDNA of 500 normal samples and found that MCBs are highly consistent. It was next determined methylation levels within an MCB in the cfDNA from 500 HCC samples. It was found that a highly consistent methylation pattern in MCBs when comparing normal versus HCC cfDNA samples, which significantly enhanced allele-calling accuracy ( FIG. 13 ). This technique was employed in all subsequent sequencing analysis. 
     cfDNA Diagnostic Prediction for HCC 
     The methylation values of the 401 selected markers that showed good methylation ranges in cfDNA samples were analyzed by Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) methods to further reduce the number of markers by modeling them in 715 HCC ctDNA and 560 normal cfDNA samples ( FIG. 8 ). 24 markers were obtained using the Random Forest analysis. 30 markers were obtained using a LASSO analysis in which selected markers were required to appear over 450 times out of a total of 500 repetitions. There were 10 overlapping markers between these two methods (Table 4). Using a logistic regression method, a diagnostic prediction model was constructed with these 10 markers. Applying the model yielded a sensitivity of 94.3% and specificity of 85.7% for HCC in the training dataset of 715 HCC and 560 normal samples ( FIG. 9C ) and a sensitivity of 90.5% and specificity of 83.2% in the validation dataset of 383 HCC and 275 normal samples ( FIG. 9D ). It was also demonstrated this model could differentiate HCC from normal controls both in the training dataset (AUC=0.966) and the validation dataset (AUC=0.944) ( FIG. 9E ,  FIG. 9F ). Unsupervised hierarchical clustering of these 10 markers was able to distinguish HCC from normal controls with high specificity and sensitivity ( FIG. 9G ,  FIG. 10H ,  FIG. 14 ). 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Markers 
                 Ref Gene 
                 Coefficients 
                 SE 
                 z value 
                 p value 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                 15.595 
                 2.395 
                 6.513 
                 &lt;0.001 
               
               
                 cg10428836 
                 BMPRIA 
                 11.543 
                 0.885 
                 −13.040 
                 &lt;0.001 
               
               
                 cg26668608 
                 PSD 
                 4.557 
                 0.889 
                 5.129 
                 &lt;0.001 
               
               
                 cg25754195 
                 ARHGAP25 
                 2.519 
                 0.722 
                 3.487 
                 &lt;0.001 
               
               
                 cg05205842 
                 KLF3 
                 −3.612 
                 0.954 
                 −3.785 
                 &lt;0.001 
               
               
                 cg11606215 
                 PLAC8 
                 6.865 
                 1.095 
                 6.271 
                 &lt;0.001 
               
               
                 cg24067911 
                 ATXN1 
                 −5.439 
                 0.868 
                 −6.265 
                 &lt;0.001 
               
               
                 cg18196829 
                 Chr 6:170 
                 −9.078 
                 1.355 
                 −6.698 
                 &lt;0.001 
               
               
                 cg23211949 
                 Chr 6:3 
                 −5.209 
                 1.081 
                 −4.819 
                 &lt;0.001 
               
               
                 cg17213048 
                 ATAD2 
                 6.660 
                 1.422 
                 4.683 
                 &lt;0.001 
               
               
                 cg25459300 
                 Chr 8:20 
                 1.994 
                 1.029 
                 1.938 
                 0.053 
               
               
                   
               
               
                 SE: standard errors of coefficient; 
               
               
                 z value: Wald z-statistic value 
               
            
           
         
       
     
     It was next assessed that a combined diagnostic score (cd-score) of the model for differentiating between liver diseases (HBV/HCV infection, and fatty liver) and HCC, since these liver diseases are known major risk factors for HCC. It was found that the cd-score could differentiate HCC patients from those with liver diseases or healthy controls ( FIG. 10A ). These results were consistent and comparable with those predicted by AFP levels ( FIG. 10B ). 
     Methylation Markers Predicted Tumor Load, Treatment Response, and Staging 
     It was next studied that the utility of the cd-score in assessing treatment response, the presence of residual tumor following treatment, and staging of HCC. Clinical and demographic characteristics, such as age, gender, race, and AJCC stage were included in the analysis. The cd-scores of patients with detectable residual tumor following treatment (n=828) were significantly higher than those with no detectable tumor (n=270), and both were significantly greater than normal controls (n=835) (p&lt;0.0001,  FIG. 10C ). Similarly, cd-scores were significantly higher in patients before treatment (n=109) or with progression (n=381) compared to those with treatment response (n=248) (p&lt;0.0001,  FIG. 10D ). In addition, cd-scores were significantly lower in patients with complete tumor resection after surgery (n=170) compared with those before surgery (n=109), yet were higher in patients with recurrence (n=155) (p&lt;0.0001,  FIG. 10E ). Furthermore, there is good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p&lt;0.05,  FIG. 10F ). Collectively, these results suggest that the cd-score (i.e., the amount of ctDNA in plasma) correlates well with tumor burden and may have utility in predicting tumor response and surveillance for recurrence. 
     Utility of ctDNA Diagnostic Prediction and AFP 
     In some instances, the blood biomarker for risk assessment and surveillance of HCC is serum AFP levels. However, its low sensitivity makes it inadequate to detect all patients that will develop HCC and severely limits its clinical utility. In fact, many cirrhotic patients develop HCC without any increase in AFP levels. Strikingly, 40% patients of the HCC study cohort have a normal serum AFP (&lt;25 ng/ml). 
     In biopsy-proven HCC patients, the cd-score demonstrated superior sensitivity and specificity than AFP for HCC diagnosis (AUC 0.969 vs 0.816,  FIG. 10G ). In patients with treatment response, tumor recurrence, or progression, cd-score showed more significant changes compared to testing at initial diagnosis than AFP ( FIG. 10H ,  FIG. 10I ). In patients with serial samples, those with a positive treatment response had a concomitant significant decrease in cd-score compared to that prior to treatment, and there was an even further decrease in patients after surgery. By contrast, our patients with progressive or recurrent disease all had an increase in cd-score ( FIG. 15 ). By comparison, AFP was less sensitive for assessing treatment efficacy in individual patients ( FIG. 16 ). In addition, while cd-score correlated well with tumor stage ( FIG. 10J ), particularly among patients with stage I, II and III, there was no significant difference in AFP values in patients with different stages, except between patients with stage III and IV ( FIG. 10K ), indicating an advantage of cd-score over AFP in differentiation of early stage HCC. 
     ctDNA Prognostic Prediction for HCC 
     It was then investigated the potential of using methylation markers in ctDNA for prediction of prognosis in HCC in combination with clinical and demographic characteristics including age, gender, race, and AJCC stage. The 1049 HCC patients were randomly split with complete survival information into training and validation datasets with an allocation of 2:1. Unicox and LASSO-cox methods were implemented to reduce the dimensionality and constructed a cox-model to predict prognosis with an 8-marker panel (Table 5). Kaplan-Meier curves was generated in training and validation datasets using a combined prognosis score (cp-score) with these markers. The high-risk group (cp-score &gt;−0.24) had 341 observations with 53 events in training dataset and 197 observations with 26 events in validation dataset; and the low-risk group (cp-score ≤−0.24) has 339 observations with 7 events in training dataset and 172 observations with 9 events in validation dataset. Median survival was significantly different in both the training set (p&lt;0.0001) and the validation set (p=0.0014) by log-rank test ( FIG. 11A ,  FIG. 11B ). 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE 5 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 z 
                 p 
               
               
                 Markers 
                 Ref Gene 
                 Coefficients 
                 HR 
                 CI 
                 SE 
                 value 
                 value 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 cg23461741 
                 SH3PXD2A 
                 −1.264 
                 0.282 
                 0.024-3.340 
                 1.2604 
                 −1.003 
                 0.316 
               
               
                 cg06482904 
                 Cllorf9 
                 −0.247 
                 0.781 
                 0.067-9.100 
                 1.2530 
                 −0.197 
                 0.844 
               
               
                 cg25574765 
                 PPFIA1 
                 1.026 
                 2.790 
                  0.488-15.900 
                 0.8894 
                 1.153 
                 0.249 
               
               
                 cg07459019 
                 Chr 17:78 
                 −8.156 
                 0.000 
                 0.000-0.012 
                 1.9112 
                 −4.267 
                 &lt;0.001 
               
               
                 cg20490031 
                 SERPINB5 
                 6.082 
                 438.000 
                   13.200-14600.000 
                 1.7885 
                 3.400 
                 0.001 
               
               
                 cg01643250 
                 NOTCH3 
                 −5.368 
                 0.005 
                 0.000-0.140 
                 1.7357 
                 −3.093 
                 0.002 
               
               
                 cg11397370 
                 GRHL2 
                 1.497 
                 4.470 
                  1.030-19.400 
                 0.7506 
                 1.994 
                 0.046 
               
               
                 cg11825899 
                 TMEM8B 
                 2.094 
                 8.120 
                  0.957-68.900 
                 1.0909 
                 1.920 
                 0.055 
               
               
                   
               
               
                 HR: Hazard Ratio; 
               
               
                 CI: 95.0% confidence interval; 
               
               
                 SE: standard errors of coefficients; 
               
               
                 z value: Wald z-statistic value 
               
            
           
         
       
     
     Multivariate variable analysis showed that the cp-score was significantly correlated with risk of death both in the training and validation dataset and that the cp-score was an independent risk factor of survival (hazard ratio [HR]: 2.512; 95% confidence interval [CI]: 1.966-3.210; p&lt;0.001 in training set; HR: 1.553, CI: 1.240-1.944; p&lt;0.001 in validation set. Interestingly, AFP was no longer significant as a risk factor when cp-score and other clinical characteristics were taken into account. 
     As expected, TNM stage predicted the prognosis of patients in the training and validation dataset ( FIG. 11C ,  FIG. 11D ). However, the combination of cp-score and TNM staging improved the ability to predict prognosis in both the training (AUC 0.7935,  FIG. 11E ) and validation datasets (AUC 0.7586,  FIG. 11F ). Kaplan-Meier curves also showed that patients separated by both cp-score and staging have different prognosis (p&lt;0.0001,  FIG. 11G ). These results demonstrate that ctDNA methylation analysis may contribute to risk stratification and prediction of prognosis in patients with HCC. 
     In this study, differentially methylated CpG sites were first determined between HCC tumor samples and blood leukocytes in normal individuals for an HCC-specific panel. A diagnostic prediction model then constructed was using a 10-methylation marker panel (cd-score) for use in cfDNA; the cd-score effectively discriminated patients with HCC from individuals with HBV/HCV infection, cirrhosis, and fatty liver as well as healthy controls. Given that patients with these liver diseases are the target screening population under current guidelines, it is important that a serum test reliably distinguish these disease states from HCC. In the study, the sensitivity of the cd-score for HCC is comparable to liver ultrasound, the current standard for HCC screening, markedly superior to AFP, and may represent a more cost-effective and less resource-intensive approach. Furthermore, the cd-score of the model showed high correlation with HCC tumor burden, treatment response, and stage, and is superior to the performance of AFP in the instant cohort. In some cases, the cd-score is useful for assessment of treatment response and surveillance for recurrence. 
     Example 4—Cell-Free DNA Methylation Markers for Diagnosis and Prognosis of Lung Cancer and Hepatocellular Carcinoma 
     Primary Tissue Patient Data 
     Both primary solid tissues from cancer patients and blood tissues from healthy donor were measured by Illumina 450k infimum bead chip. Primary tumor DNA methylation data of 485,000 sites was obtained from The Cancer Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards. Blood tissue DNA methylation data from healthy donor were obtained and generated based on study from Hannum et al., 2013, Mol Cell 49, 359-367 (GSE40279) in which DNA methylation profiles for HCC and blood were analyzed. 
     Serum Sample Patient Data 
     A second independent Chinese cohort consisted of LUNC and HCC patients at the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi&#39;an, and the West China Hospital in Chengdu, China. Patients who presented with LUNC and HCC from stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Table 9. The TNM staging classification for LUNC and HCC is according to the 7 th  edition of the AJCC cancer staging manual. This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Two prospective trials on early detection of LUNC and HCC patients using methylation markers for predicting cancer occurrence in high-risk populations were conducted. In the first study, patients were recruited from a group of smokers that were undergoing CT scan-based lung cancer screening from December 2015 to December 2016. Patients presenting with lung nodules (&lt;10 mm, n=232, Table 12) were selected to undergo methylation profiling at the time of screening and were subsequently followed through secondary testing to determine whether nodules were due to cancer or inflammatory or infectious conditions by tissue biopsy and pathology diagnosis verification. In the second trial, high risk patients with liver cirrhosis were enrolled (n=242). 
     Tumor and normal tissues were obtained as clinically indicated for patient care and were retained for this study. Human blood samples were collected by venipuncture, and plasma samples were obtained by taking the supernatant after centrifugation and stored at −80° C. before cfDNA extraction. 
     The pre-treatment serum samples were obtained at the initial diagnosis, and the post-treatment serum samples were evaluated approximately 2 months after treatment, where the treatment referred to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) was defined according to the RECIST guideline. For patients treated with surgical removal and no recurrence at time of evaluation, it was assumed that they had complete response (CR). 
     Extraction of cfDNA from Plasma 
     It was determined that the minimal volume of plasma required to get consistent amounts of cfDNA for targeted sequencing. As a rough guide, it was aimed at ˜20× coverage at 90% of markers covered by the padlock probe panel (see below). It was observed that 20,000 or more total unique reads per sample fulfilled this criterion. It was found that 1.5 ml or more plasma could reliably yield enough cfDNA to produce &gt;20,000 unique reads. The relationship between amount of cfDNA in 1.5 ml plasma and detected copy numbers was further investigated using digital droplet PCR. It was found that 1.5 ml of plasma yielded &gt;10 ng, what produced at least 140 copies of detected amplicons in each digital droplet PCR assay. It was therefore settled on using 15 ng/1.5 ml as a cutoff in all of our experiments to obtain consistent and reliable measurements of DNA methylation. 
     cfDNA from 1.5 ml of plasma was extracted using EliteHealth cfDNA extraction Kit (EliteHealth, Guangzhou Youze, China) according to manufacturer&#39;s recommendations. 
     Bisulfite Conversion of Genomic or cfDNA 
     10 ng of DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer&#39;s protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was &gt;99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides. 
     Marker Selection for Padlock Probe Panel Design 
     To identify markers to differentiate HCC, LUNC and normal blood methylation signatures the “moderated t-statistics shrinking” approach on 450k methylation data with Benjamini-Hochberg procedure was employed to control FDR at a significance level of 0.05 using pairwise comparisons of 377 HCC samples and 827 LUNC samples (TCGA) and 754 normal blood samples (GSE40279, our previous study (HANNUM REF)). The lists was ranked by adjusted p-value and selected the top 1000 markers for designing padlock probes for differentiating cancer (both LUNC and HCC) versus normal samples and a separate group of 1000 markers for differentiating LUNC versus HCC ( FIG. 25 ). 
     All 2000 markers were used to design padlock probes for capture and sequencing of cfDNA. Padlock-capture of bis-DNA was based on the technique on methods of Deng, et al., 2009, Nature Biotechnology 27, 353-360; Diep, et al., 2012, Nature Methods 9, 270-272; and Porreca, et al., 2007, Nature Methods 4, 931-936; and with further modifications. Because of a relatively modest total size of captured regions/cg markers, this approach offers much lower cost of sequencing than any current methods including whole methylome-wide seqnencing, therefore enabling us to evaluate a large number of samples. Furthermore, the direct targeted sequencing approach offers digital readout, and requires much less starting cfDNA material (10-15 ng) than more traditional recent methods based on hybridization on a chip (eg. Infinium, Illumina) or target-enrichment by hybridization (eg. SureSelect, Agilent). This approach is also less sensitive to unequal amplification as it utilizes molecular identifiers (UMIs). 
     Padlock Probe Design, Synthesis and Validation 
     All probes were designed using the ppDesigner software. The average length of the captured region was 70 bp, with the CpG marker located in the central 80% of the captured region. A 6 bp 6-bp unique molecular identifier (HMI) flanked capture arms to aid in eliminating amplification bias in determination of DNA methylation frequencies. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produce probes of equal length. Probes were synthesized as separate oligonucleotides (IDT). For capture experiments, probes were mixed in equimolar quantities and purified on Qiagen columns. 
     Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60-65% of captured regions with coverage &gt;0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at &gt;0.5× of average coverage. 
     Targeted Methylation Sequencing Using Bis-DNA Padlock Probe Capture 
     10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre). To anneal probes to DNA, 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second and incubation for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5-hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C. 5 μl of exonuclease mix (20 U of Exo I and 100 U ExoIII of, Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C. 
     Circular capture products were amplified by PCR using primers specific to linker DNA within padlock probes. Both primers contained 10 bp barcodes for unique dual-index multiplexing, and Illumina next-generation sequencing adaptor sequences. PCR was performed as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10s @ 98° C., 8× of (1s @ 98° C., 5s @ 58° C., 10s @ 72° C.), 25× of (1s @ 98° C., 15s @ 72° C.), 60s @ 72° C. PCR reactions were mixed and the resulting library was size selected on 2.5% agarose gels to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp). Purity of the libraries was verified by TapeStation (Agilent) and PCR using Illumina flowcell adaptor primers (p5 and p′7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries were sequenced on MiSeq and HiSeq2500 systems (Illumina) using PE100 reads. Median total reads for each sample was 500,000 and on-target mappability 25% (˜125,000 on-target non-unique reads). 
     Optimization of Capture Coverage Uniformity 
     Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60˜65% of captured regions with coverage &gt;0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at &gt;0.5× of average coverage. 
     Sequencing Data Analysis 
     Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position. 
     DNA Isolation and Digital Quantitative PCR 
     Tumor and corresponding plasma samples were obtained from patients undergoing surgical tumor resection; samples were frozen and preserved in at −80° C. until use. Isolation of DNA and RNA from samples was performed using DNA/RNA MiniPrep kit and a cfDNA extraction kit, respectively (EliteHealth, Guangzhou Youze, China). To estimate tumor cfDNA fractions, mixing experiments were performed with various fractions of normal cfDNA and HCC tumor genomic DNA (gDNA) and assayed methylation values and copy numbers by dPCR (see next section for details). Digital droplet PCR (ddPCR) was performed according to the manufacturer&#39;s specifications (Bio-Rad, Hercules, Calif.). The following ddPCR assay was used in this study: cg10590292-forward primer 5′-TGTTAGTTTTTATGGAAGTTT, reverse primer 5′-AAACIAACAAAATACTCAAA; fluorescent probe for methylated allele detection 5′/6-FAM/TGGGAGAGCGGGAGAT/BHQ1/-3; probe for unmethylated allele detection, 5′/HEX/TTTGGGAGAGTGGGAGATTT/BHQ1/-3′. ddPCR was performed according to the manufacturer&#39;s specifications (Bio-Rad, Hercules, Calif.). using the following cycling conditions: 1× of 10 mins @ 98° C., 40× of (30s @ 98° C., 60s @ 53° C.), 1× of 10 mins @ 98° C. 
     Calculation of Tumor cfDNA Fraction 
     It was assumed that a particular methylation value observed for an HCC cfDNA sample results from the combined contribution of normal and tumor cfDNA. The fraction of cfDNA originating from the tumor was estimated using the following formula: fraction contributed from tumor DNA in sample i=[methylation value in HCC cfDNA in sample i−mean methylation value of normal cfDNA]/[mean methylation value of tumor DNA−mean methylation value of normal cfDNA]. Using this approach, it was estimated that on average the tumor fraction is around 23% in HCC cfDNA samples. Samples were then grouped according to factors that evaluate tumor load, such as an advanced stage and pre-treatment status, since these factors are expected to affect the tumor fraction in ctDNA. Indeed, it was observed that conditions associated with a higher tumor staging and severity also tended to have a larger tumor fraction. To further vet this approach, a mixing experiment with different fractions of normal cfDNA (0-100%) and tumor genomic DNA (0-100%) was performed and assayed methylation values using digital PCR. It was shown that incremental addition of tumor genomic DNA can increase methylation fraction percentage up to the values observed in the HCC patient samples. Specifically, addition of 10%, 20%, 40%, 60% or 100% fraction of tumor genomic DNA can be predicted by the above formula, when using methylation values obtained from the experiment. 
     Statistical Analysis 
     DNA Methylation Marker Selection for Diagnostic and Prognostic Analysis 
     Out of 2000 initially designed padlock probes, only 1673 were informative, i.e able to give positive and specific PCR amplification signals, and thus were used as capture probes in the subsequent experiments in cfDNA samples. Sequencing depth was used as a sample inclusion criterion. Samples where less than 100 MCB (see below) showed 10× read coverage were excluded from further analysis. Since each MCB incorporated on average ˜3 CG markers, the 10× coverage ensured at least 30 methylation measurements per MCB. Using these criteria, 73% of all samples with a median of 34K mapped reads per sample were included. 
     After having obtained DNA methylation data for 1673 CG markers, the concept of MCBs to merge proximal CpG markers into a MCB was used, resulting in a total of 888 MCBs. For each MCB, the MCB-specific methylation value was quantified with two numbers: log 10 (total methylated read count+1) and log 10 (total unmethylated read count+1), using the log transform to reduce outlier effects. 
     About 1673 informative padlock probes were obtained that were able to give positive and specific PCR amplification signals and they were used as capture probes in the subsequent experiments in cfDNA samples. cfDNA samples with less than 100 MCB of &gt;30× coverage were also eliminated. Methylated reads for each marker were defined as total unique methylated reads and methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts. 
     cfDNA-Based Diagnostic Classifier Construction Using MCBs (Cd-Score) 
     cfDNA sample data obtained from patients diagnosed with liver cancer (HCC), lung cancer (LUNC) and normal controls were divided into training and validation cohorts. The full dataset was randomly split with a 1:1 ratio to form the training and validation cohorts. Marker selections: Within the training cohort, the “randomized lasso” scheme was adopted to reduce the sampling dependency and stabilize variable selection in order to select biomarkers with high confidence. The training set was first randomly divided with 1:1 ratio. The variable selection procedure on two thirds of the samples was conducted and withheld a third of the samples for evaluating performance of the feature selection process. The feature selection process consisted of two steps repeated 50 times. MCBs were included for training the final model if they were selected in 40 out of 50 feature selection iteration. A multi-class prediction system based on Friedman et al., 2010, J Stat Softw 33, 1-22 was constructed to predict the group membership of samples in the test data using the panel of MCBs selected. A confusion matrix and ROC curves were also provided to evaluate sensitivity and specificity, in addition to prediction accuracy based on the held out partition of the training set. 
     Classification process: a two-step classification process was employed: cancer vs normal, LUNC vs HCC by building two binary multinomial logistic regression models. The multinomial logistic regression has the advantage where it can yield an intuitive probability score and allow for easier interpretation. For example, if the cancer-vs-normal model yield a probability score of 70% for a given methylation profile, it suggests that the patient has a 70% chance of having cancer. In order to minimize the number of false cancer predictions, the cancer prediction confidence threshold was set to 80%. For patients with at least 80% chance of cancer, the cancer-vs-cancer regression model was applied for classifying between LUNC and HCC, the classification model would decide only if the classified sample has a confidence of over 55%. 
     Building a Predictive Model for Prognosis and Survival 
     The potential to use a combined prognosis score (cp-score) system based on both methylation reads and non-methylated reads was investigated for each MCB in cfDNA for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, and AJCC stage. For each type of cancer, a cp-score model was build and validate it by randomly selecting half of the observations from the full dataset as the training cohort, and treated the rest as the validation cohort. Variable selection on the training cohort was conducted and built the composite score on the validation cohort. Within the training cohort, the “randomized lasso” scheme was adopted to reduce the sampling dependency to stabilize the variable selection in order to select biomarkers with a high confidence. The entire cohort was randomly divided with a 1:1 ratio. The variable selection procedure was conducted on two-thirds of the training cohort. LASSO was implemented with an optimal tuning parameter determined by either the expected generalization error from the 10-fold cross validation or the information based criteria AIC/BIC, whichever yielded the highest (the proportion of explained randomness) with the selected biomarkers. The 10 most recurring features from HCC and in LUNC (Table 8) was then aggregated. To evaluate the predictability of each panel externally, a composite score was obtained for each patient in the validation cohort by multiplying the unbiased coefficient estimates from the Cox regression and the methylation reads. A Kaplan-Meier curve and log-rank test were generated using the dichotomized composite score, which formed a high-risk and low-risk group membership assignment according to its median. This segmentation was compatible with that formed by AJCC stage. Time-dependent ROC was used to summarize the discrimination potential of the composite score, AJCC stage and the combination of two, with ROC curves varying as a function of time and accommodating censored data. Finally, a multivariate Cox regression model was also fitted to assess the significance of potential risk factors. 
     All the analysis was conducted in R (version 3.2.3) and python (version 2.7.13) with the following packages used: ‘glmnet’, ‘limma’, ‘survival’, ‘sklearn’, ‘lifeline’, ‘survival ROC’, ‘survcomp’, 
     All hypothesis testing was done by two-sided with p-value &lt;0.05 considered to be statistically significant unless specifically stated otherwise. 
     Patient and Sample Characteristics 
     Clinical characteristics and molecular DNA methylation profiles were collected for 827 LUNC and 377 HCC tumor samples from The Cancer Genome Atlas (TCGA) and 754 normal samples from a dataset used in our previous methylation study on aging (GSE40279) (Hannum et al., 2013). Two cohorts of patients were studied. The first cohort was from solid tumor samples from TCGA and the second cohort was from plasma samples from China. To study cfDNA in LUNC and HCC, plasma samples were obtained from 2,396 Chinese patients with HCC or LUNC, and from randomly selected, population-matched healthy controls undergoing routine health care maintenance, resulting in a cohort of 892 LUNC and 1504 HCC patients and 2247 normal healthy controls. Informed written consent was obtained from each study participant. Clinical characteristics of all patients and controls are listed in Table 9. 
     Identification of Methylation Markers Differentiating LUNC and HCC and Blood 
     Previous reports indicate that plasma contains DNA released from tissues within the body. It was hypothesized that because cfDNA originating from tumor cells can be detected in a background of cfDNA predominantly released from leukocytes, CpG markers with a maximal difference in methylation values between LUNC or HCC versus normal leukocytes would be most likely to demonstrate detectable methylation differences in the cfDNA of HCC or LUNC patients when compared to that of normal controls. To identify putative markers, methylation data derived from cancer tissue DNA from the TCGA and normal blood including 827 LUNC, 377 HCC, and 754 blood samples from healthy controls were compared. In order to identify DNA sites with significantly different rates of methylation between LUNC or HCC and normal blood, a t-statistic with Empirical Bayes was used for shrinking the variance and selected the top 1000 significant markers, using the Benjamini-Hochberg procedure to control the FDR at a significance level of 0.05. Unsupervised hierarchical clustering of these top 1000 markers was able to distinguish between LUNC, HCC, and normal blood, and between LUNC and HCC ( FIG. 25 ). About 2,000 molecular inversion (padlock) probes corresponding to these 2000 markers for capture-sequencing cfDNA from plasma (1000 for cancer versus normal and 1000 for LUNC versus HCC) were then designed. 
     cfDNA Diagnostic Prediction Model for LUNC and HCC 
     The methylation data of the 888 selected Methylation Correlated Blocks (MCB) that showed good methylation ranges in cfDNA samples were further analyzed to identify MCBs that showed significantly different methylation between cancer samples (LUNC and HCC) versus normal control samples. Unsupervised hierarchical clustering of these selected MCBs using methylated reads across samples is shown in  FIG. 25C , and distributions of MCB methylated read values for normal, LUNC and HCC samples is shown in  FIG. 26 . The entire methylation dataset of 888 MCBs was therefore analyzed by Least Absolute Shrinkage and Selection Operator (LASSO) method and further reduced the number of MCBs. LASSO-based feature selection identified 28 MCBs for discriminating LUNC versus HCC and normal, 27 MCBs for discriminating of HCC versus LUNC and normal, 22 MCBs for discriminating of normal vs HCC and LUNC, resulting in 77 unique markers (5 MCBs overlap between models). This approach combined the information captured by the MCBs into a composite cfDNA-based score (composite diagnostic score: cd-score). The utility of this score was evaluated for predicting the presence of LUNC or HCC using a hold-out strategy where samples were randomly assigned to a training set and a validation set with a 1:1 ratio. The scoring system was trained using 229 LUNC, 444 HCC and 1123 normal control cfDNA samples and then validated on 300 LUNC, 445 HCC and 1124 normal samples. Applying the fitted model to the validation set samples yielded a sensitivity of 92.4% for HCC and 85.8% for LUNC, and a specificity of 99.0% for normal controls in a multi-classification scheme (Table 6A). It was found that this model could successfully differentiate LUNC and HCC samples from normal controls in the validation cohort (AUC cancer vs normal=0.979; AUC LUNC vs HCC=0.924;  FIG. 19A , Table 6B, Table 6C). Unsupervised hierarchical clustering of the 77 MCBs was able to distinguish HCC and LUNC from normal controls with high specificity and sensitivity ( FIG. 19C  and  FIG. 19D ). 
     Liver diseases, such acirrhosis, and fatty liver, are major risk factors for HCC. Thus, the cd-score of the model was assessed for differentiating between liver diseases and. It was found that the cd-score was able to differentiate HCC patients from those with liver diseases or healthy controls ( FIG. 20A ). These results were consistent and comparable with those predicted by AFP levels in HCC ( FIG. 20B ). The cd-score could also differentiate between LUNC patients and non-LUNC patients with a smoking history (&gt;1 pack/day for ten years) who were at an increased risk of LUNC ( FIG. 20C ). These results were consistent and comparable with those predicted by AFP levels in HCC ( FIG. 20D ). 
     Methylation Profiles Predicted Tumor Burden, Treatment Response and Staging 
     Next, the utility of the cd-score was studied in assessing treatment response, the presence of residual tumor following treatment, and staging of LUNC and HCC. In LUNC, the cd-scores of patients with detectable residual tumor following treatment (n=559) were significantly higher than those with no detectable tumor (n=160) (p&lt;0.001,  FIG. 21A ). Similarly, there was good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p=&lt;0.005,  FIG. 21B ). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=158) compared with those before surgery (n=67), yet became higher in patients with recurrence (n=56) (p&lt;0.01,  FIG. 21C ). Furthermore, the cd-scores were significantly higher in patients before treatment (n=67) or with progression (n=136) compared to those with a positive treatment response (n=328) (p&lt;0.001,  FIG. 21D ). In HCC, The cd-scores of patients with detectable residual tumor following treatment (n=889) were significantly higher than those with no detectable tumor (n=314) (p&lt;0.0001,  FIG. 21E ). Similarly, there was a highly positive correlation between cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p&lt;0.001,  FIG. 21F ). In addition, the cd-scores were significantly lower in patients with complete tumor resection after surgery (n=293) compared with those before intervention (n=109), yet became higher in patients with recurrence (n=155) (p&lt;0.01,  FIG. 21G ). Furthermore, the cd-scores were significantly higher in patients before treatment (n=109) or with progression (n=381) compared to those with treatment response (n=249) (p&lt;0.001,  FIG. 21H ). Serial longitudinal dynamic changes were obtained of methylation values of CpG site cg10673833 in several individuals with LUNC or HCC patient in order to monitor treatment response and found there was a high correlation between methylation values and treatment outcomes ( FIG. 28 ,  FIG. 29  and  FIG. 30 ). Collectively, the results showed the significant correlation between he cd-score (i.e., the amount of cfDNA in plasma) and tumor burden, demonstrating its utility for the prediction of tumor response and for surveillance to detect recurrence. 
     Diagnostic Utility of cfDNA as Compared with AFP and CEA 
     Despite enormous efforts, an effective noninvasive blood-based biomarker for surveillance and diagnosis of LUNC and HCC is still lacking. CEA (cancer embryonic antigen) and AFP have filled this role for lung cancer and HCC for decades, but its sensitivity and specificity are inadequate. Moreover, some patients with squamous cell carcinoma or small cell lung cancer will not have increased blood CEA levels. AFP has low sensitivity of 60%, making it inadequate for detection of all patients that will develop HCC and thus severely limiting its clinical utility. In fact, it is common for cirrhotic patients with HCC to show no increase in AFP levels. Strikingly, 30% patients of the HCC study cohort have a normal AFP value (&lt;25 ng/ml). In biopsy-proven LUNC patients of the entire cohort, the cd-score demonstrated superior sensitivity and specificity to CEA for LUNC diagnosis (AUC 0.977 (cd-score) vs 0.856 (CEA),  FIG. 21Q ). Both cd-score and CEA values were highly correlated with tumor stage ( FIG. 21J ,  FIG. 21B ). On other hand, the cd-score demonstrated superior sensitivity and specificity to AFP for HCC diagnosis (AUC 0.993 vs 0.835,  FIG. 4R ) in biopsy-proven HCC patients. Both cd-score and AFP values were highly correlated with tumor stage ( FIG. 21F  and  FIG. 21N ). In patients with treatment response, tumor recurrence, or progression, the cd-score showed more changes from initial diagnosis than that of AFP ( FIG. 21G  and  FIG. 21H ,  FIG. 21O  and  FIG. 21P ). In LUNC patients with treatment response, tumor recurrence, or progression, the cd-score showed more significant changes from initial diagnosis than that of CEA ( FIG. 21C  and  FIG. 21D ,  FIG. 21K  and  FIG. 21L ). In LUNC and HCC patients with serial samples, there was a concomitant and significant decrease in cd-score in patients with a positive treatment response than in patients prior to treatment. There was an even further reduction in cd-score in patients after surgery. In contrast, there was an increase in methylation rate in patients with progressive or recurrent disease ( FIG. 28 ). By comparison, CEA and AFP were less sensitive for assessing treatment efficacy in individual patients ( FIG. 29  and  FIG. 30 ). 
     cfDNA Prognostic Model for HCC and LUNC 
     The potential of using a combined prognosis score (cp-score) based on cfDNA methylation analysis for prediction of prognosis in LUNC and HCC in combination with clinical and demographic characteristics including age, gender, AJCC stage, and AFP value was investigated. Totally 599 LUNC patients and 867 HCC patients enrolled in prognosis analysis (patients without tumor burden are excluded from the analysis). The median follow up time was 9.5 months (rang 0.6-26 months) in LUNC cohort and 6.7 months (rang 1.2-21.0 months) in HCC cohort. In the HCC cohort, the training dataset contained 433 observations with 41 events and the validation dataset contained 434 observations with 58 events. By using statistical learning methods, a predictive model was constructed using 10 CpG MCBs (Table 8) that can separate the HCC cohort into high and low risk groups, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=24.323, df=1, p&lt;0.001) ( FIG. 22A ). In the LUNC cohort, the training dataset contained 299 observations with 61 events and the validation dataset contained 434 observations with 58 events. A panel of 10 CpG markers (Table 8) was able to divide the LUNC cohort into high and low risk groups, with median survival significantly greater in the low-risk group than in the high-risk group (log-rank test=6.697, df=1, p&lt;0.001) ( FIG. 22B ). 
     Multivariate Cox regression model showed that the cp-score was significantly correlated with incidence of mortality in both HCC and LUNC. The cp-score was an independent risk factor of survival both in HCC and borderline in LUNC validation cohorts (harzard ratio=2.4881, p=0.000721 in HCC; hazard ratio=1.74, p=0.068 in LUNC; p=0.0017 in LUNC; Table 10). Interestingly, when cp-score and other clinical characteristics were taken into account in HCC, AFP was no longer significant as a risk factor (Table 11). As expected, TNM stage (as defined by AJCC guidelines) predicted the prognosis of patients both in HCC ( FIG. 22C ) and LUNC ( FIG. 22D ). The combination of cp-score and TNM staging improved our ability to predict prognosis in both HCC (AUC 0.867,  FIG. 22E ) and LUNC cohort (AUC 0.825,  FIG. 22F ). 
     Methylation Markers in Early Diagnosis of LUNC and HCC 
     Since LUNC and HCC are very aggressive cancers with poor prognosis and survival, and surgical removal of cancer at stage 1 carries a much more favorable prognosis, early detection becomes a key strategy in reducing morbidity and mortality. The method of using methylation markers for predicting cancer occurrence in high-risk populations was investigated in two prospective studies. In the first study, consecutive patients were recruited from a group of patients with solid lung nodules &gt;10 mm identified on chest CT scans. These patients were enrolled in a study for early lung detection in smokers and underwent CT scan-based lung cancer screening. Patients presenting with a solid lung nodule (between 10 mm and 30 mm in size, n=208, Table 11) were selected to undergo methylation profiling at the time of screening. These patients were subsequently followed through secondary testing to determine whether nodules were due to LUNC or a benign condition due to inflammation or infection by tissue biopsy and pathology verification. The methylation profile was sufficient to differentiate patients with biopsy-proven stage 1 LUNC lesions compared to patients with benign nodules due to inflammatory or infectious conditions ( FIG. 23 , Table 12). Among the patients with at least 59% confidence for diagnosis, Positive predictive value (PPV) of stage I cancer was 95.9% and negative predictive value (NPV) was 97.4%. Similarly, high risk HCC patients with liver cirrhosis (n=236, Table 11) were prospectively enrolled. The methylation profile was able to predict progression to stage 1 HCC with 89.5% sensitivity and 98.2% specificity ( FIG. 24 , Table 12) among the patients with at least 58% confidence for diagnosis. PPV was 80.9% and NPV was 99.1%. 
     In this study, differentially methylated CpG sites were first determined in LUNC and HCC tumor samples versus normal blood. Then, these markers were interrogated in the cfDNA of a large cohort of LUNC and HCC patients as well normal controls. A diagnostic model (cd-score) was developed using methylation of cfDNA to predict the presence of cancer, while at the same time differentiating between LUNC and HCC. 
     The cd-score discriminated patients with HCC from individuals with HBV/HCV infection, cirrhosis, and fatty liver disease as well as healthy controls. In some instances, it is important that a serum test reliably distinguish these disease states from HCC. According to the results, the sensitivity of the cd-score for HCC is comparable to liver ultrasound, the current standard for HCC screening. In addition, in some instances it is superior to AFP, the only clinically used biomarker for HCC, making cd-score a more cost-effective and less resource-intensive approach. Furthermore, by showing its high correlation with HCC tumor burden, treatment response, and stage, the cd-score of the model demonstrated superior performance than AFP in the instant cohort (AFP values were within a normal range for 40% of our HCC patients during the entire course of their disease). In some cases, the cd-score may be particularly useful for assessment of treatment response and surveillance for recurrence in HCC. Since nearly all of the HCC patients had hepatitis (most likely hepatitis B) in the study, HCC arising from other etiologies may have different cfDNA methylation patterns, Similar to HCC, screening for lung cancer has a high cost, involving CT imaging of the chest, which has an associated radiation exposure and a high false-positive rate. In some cases, the cd-score reliably distinguished smokers and patients with lung cancer and may also have utility in improving screening and surveillance. 
     Prognostic prediction models were also constructed for HCC and LUNC from the cp-score. The cp-score effectively distinguished HCC and LUNC patients with different prognosis and was validated as an independent prognostic risk factor in a multi-variable analysis in our cohorts. Of note, for predicting prognosis in HCC, cfDNA analysis was again superior to AFP. In some cases, this type of analysis is helpful for identification of patients for whom more or less aggressive treatment and surveillance is needed. 
     
       
         
           
               
             
               
                 TABLE 6A 
               
             
            
               
                   
               
               
                 Contingency table of multi-classification 
               
               
                 diagnosis in validation cohort 
               
            
           
           
               
               
               
               
               
            
               
                   
                 Prediction 
                 HCC 
                 LUNC 
                 Normal 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 HCC 
                 329 
                 19 
                 6 
               
               
                   
                 LUNC 
                 23 
                 145 
                 6 
               
               
                   
                 Normal 
                 4 
                 5 
                 921 
               
               
                   
                 Undecided 
                 89 
                 131 
                 191 
               
               
                   
                 Totals 
                 354 
                 174 
                 930 
               
               
                   
                 Correct 
                 329 
                 145 
                 921 
               
               
                   
                 Sensitivity (%) 
                 92.4 
                 85.8 
               
               
                   
                 Specificity (%) 
                   
                   
                 99.0 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6B 
               
             
            
               
                   
               
               
                 Contingency table of binary classification 
               
               
                 diagnosis between HCC and normal 
               
            
           
           
               
               
               
               
            
               
                   
                 Prediction 
                 HCC 
                 Normal 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 cancer 
                 371 
                 16 
               
               
                   
                 normal 
                 4 
                 921 
               
               
                   
                 undecided 
                 70 
                 187 
               
               
                   
                 Totals 
                 375 
                 937 
               
               
                   
                 Correct 
                 371 
                 921 
               
               
                   
                 Sensitivity (%) 
                 98.9 
               
               
                   
                 Specificity (%) 
                   
                 98.3 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 6C 
               
             
            
               
                   
               
               
                 Contingency table of binary classification 
               
               
                 diagnosis between LUNC and normal 
               
            
           
           
               
               
               
               
            
               
                   
                 Prediction 
                 LUNC 
                 Normal 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                   
                 cancer 
                 188 
                 16 
               
               
                   
                 normal 
                 5 
                 921 
               
               
                   
                 undecided 
                 107 
                 187 
               
               
                   
                 Totals 
                 193 
                 937 
               
               
                   
                 Correct 
                 188 
                 921 
               
               
                   
                 Sensitivity (%) 
                 97.4 
               
               
                   
                 Specificity (%) 
                   
                 98.3 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 7 
               
               
                   
               
               
                 List of MCBs selected by multi-class LASSO and used 
               
               
                 for cd-score generation. A, normal; B, LUNC; C, HCC. 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Logistic regression 
               
               
                   
                 Read 
                   
                   
                 coefficients 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Diagnosis 
                 MCBs 
                 counts 
                 Target ID 
                 RefGene 
                 LUNC vs HCC 
                 cancer vs normal 
               
               
                   
               
            
           
           
               
            
               
                 A, Normal 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Normal 
                 1-1693966 
                 mc 
                 cg00100121 
                 C1orf114 
                 0.058289236 
                 −0.460201686 
               
               
                   
                   
                 non_mc 
                 cg00100121 
                 C1orf114 
                 −0.264294546 
                 −0.362527983 
               
               
                   
                 11-474165 
                 mc 
                 cg13912307 
                 SLC39A13 
                 −0.019821436 
                 −0.342748675 
               
               
                   
                   
                 non_mc 
                 cg13912307 
                 SLC39A13 
                 −0.119495297 
                 −0.097830945 
               
               
                   
                 11-791245 
                 mc 
                 cg08794954 
                 ODZ4 
                 −0.502459754 
                 −0.119001342 
               
               
                   
                   
                 non_mc 
                 cg08794954 
                 ODZ4 
                 −0.050537884 
                 −0.237868292 
               
               
                   
                 12-561356 
                 mc 
                 cg00344358 
                 GDF11 
                 0.013443025 
                 −0.152292508 
               
               
                   
                   
                 non_mc 
                 cg00344358 
                 GDF11 
                 0.210631385 
                 −0.24781796 
               
               
                   
                 16-7128 
                 mc 
                 cg05773599 
                 WDR90 
                 −0.220759943 
                 −0.438656833 
               
               
                   
                   
                 non_mc 
                 cg05773599 
                 WDR90 
                 0.123092944 
                 −0.14690629 
               
               
                   
                 16-856199 
                 mc 
                 cg10174683 
                 — 
                 0.05314766 
                 0.335012824 
               
               
                   
                   
                 non_mc 
                 cg10174683 
                 — 
                 −0.182838798 
                 −0.279797616 
               
               
                   
                 17-579157 
                 mc 
                 cg12054453 
                 TMEM49 
                 0.062499156 
                 −0.478242327 
               
               
                   
                   
                 non_mc 
                 cg12054453 
                 TMEM49 
                 0.549358501 
                 0.925660526 
               
               
                   
                 17-742484 
                 mc 
                 cg24166450 
                 — 
                 0.240682421 
                 0.067853997 
               
               
                   
                   
                 non_mc 
                 cg24166450 
                 — 
                 0.443449771 
                 0.566346654 
               
               
                   
                 2-1139315 
                 mc 
                 cg09366118 
                 PSD4 
                 0.098868936 
                 0.269196214 
               
               
                   
                   
                 non_mc 
                 cg09366118 
                 PSD4 
                 0.039975437 
                 −0.005683265 
               
               
                   
                 2-293390 
                 mc 
                 cg21972382 
                 CLIP4 
                 −0.099097051 
                 −0.61331161 
               
               
                   
                   
                 non_mc 
                 cg21972382 
                 CLIP4 
                 0.074745373 
                 0.2650865 
               
               
                   
                 22-321497 
                 mc 
                 cg03550506 
                 DEPDC5 
                 −0.068645581 
                 −0.755902786 
               
               
                   
                   
                 non_mc 
                 cg03550506 
                 DEPDC5 
                 −0.046222783 
                 −0.124629981 
               
               
                   
                 3-1138223 
                 mc 
                 cg06722069 
                 — 
                 −0.427422216 
                 −0.26910555 
               
               
                   
                   
                 non_mc 
                 cg06722069 
                 — 
                 0.089047399 
                 −0.071525276 
               
               
                   
                 4-13248 
                 mc 
                 cg07748255 
                 MAEA 
                 0.217866978 
                 −0.457591409 
               
               
                   
                   
                 non_mc 
                 cg07748255 
                 MAEA 
                 −0.011474644 
                 −0.340582424 
               
               
                   
                 5-1489298 
                 mc 
                 cg22928002 
                 CSNK1A1 
                 −0.19633718 
                 −0.006367323 
               
               
                   
                   
                 non_mc 
                 cg22928002 
                 CSNK1A1 
                 −0.131881054 
                 0.459975236 
               
               
                   
                 5-429520 
                 mc 
                 egl7757602 
                 — 
                 0.041309712 
                 0.029262464 
               
               
                   
                   
                 non_mc 
                 egl7757602 
                 — 
                 −0.133713183 
                 −0.355503949 
               
               
                   
                 5-429521 
                 mc 
                 egl7757602 
                 — 
                 0.127773182 
                 0.169659022 
               
               
                   
                   
                 non_mc 
                 egl7757602 
                 — 
                 −0.3026458 
                 −0.526377272 
               
               
                   
                 6-276491 
                 mc 
                 cg03161803 
                 — 
                 −0.134554024 
                 0.040255772 
               
               
                   
                   
                 non_mc 
                 cg03161803 
                 — 
                 −0.076945207 
                 −0.517353281 
               
               
                   
                 6-912971 
                 mc 
                 cg01087382 
                 MAP3K7 
                 0.132083258 
                 −0.263202543 
               
               
                   
                   
                 non_mc 
                 cg01087382 
                 MAP3K7 
                 0.023651284 
                 0.314783378 
               
               
                   
                 7-1017626 
                 mc 
                 cg06721601 
                 CUX1 
                 0.305704307 
                 0.347231675 
               
               
                   
                   
                 non_mc 
                 cg06721601 
                 CUX1 
                 −0.061678409 
                 0.340362292 
               
               
                   
                 7-1577443 
                 mc 
                 cg27104173 
                 PTPRN2 
                 0.029132702 
                 −0.207025939 
               
               
                   
                   
                 non_mc 
                 cg27104173 
                 PTPRN2 
                 0.004420766 
                 −0.098341178 
               
               
                   
                 9-885141 
                 mc 
                 cg13740515 
                 — 
                 0.028390415 
                 −0.269846773 
               
               
                   
                   
                 non_mc 
                 cg13740515 
                 — 
                 −0.208939054 
                 −0.193859618 
               
               
                   
                 X-27307 
                 mc 
                 cg13176022 
                 XG 
                 0.511857283 
                 0.042177783 
               
               
                   
                   
                 non_mc 
                 cg13176022 
                 XG 
                 0.048888339 
                 −0.232292001 
               
            
           
           
               
            
               
                 B: LUNC 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 LUNC 
                 1-1693966 
                 mc 
                 cg00100121 
                 C1orf114 
                 0.058289236 
                 −0.460201686 
               
               
                   
                   
                 non_mc 
                 cg00100121 
                 C1orf114 
                 −0.264294546 
                 −0.362527983 
               
               
                   
                 11-1169673 
                 mc 
                 cg16858415 
                 SIK3 
                 0.374145093 
                 0.446746019 
               
               
                   
                   
                 non_mc 
                 cg16858415 
                 SIK3 
                 0.297361321 
                 0.42281836 
               
               
                   
                 11-476248 
                 mc 
                 cg05585544 
                 — 
                 0.570677409 
                 0.329004001 
               
               
                   
                   
                 non_mc 
                 cg05585544 
                 — 
                 0.127193074 
                 −0.357434967 
               
               
                   
                 11-646423 
                 mc 
                 cg18518074 
                 EHD1 
                 0.305179647 
                 0.273679057 
               
               
                   
                   
                 non_mc 
                 cg18518074 
                 EHD1 
                 −0.170377122 
                 −0.416439448 
               
               
                   
                 11-779080 
                 mc 
                 cg03423942 
                 USP35 
                 0.049737225 
                 0.05832888 
               
               
                   
                   
                 non_mc 
                 cg03423942 
                 USP35 
                 −0.152714594 
                 0.278866832 
               
               
                   
                 11-791245 
                 mc 
                 cg08794954 
                 ODZ4 
                 −0.502459754 
                 −0.119001342 
               
               
                   
                   
                 non_mc 
                 cg08794954 
                 ODZ4 
                 −0.050537884 
                 −0.237868292 
               
               
                   
                 12-687588 
                 mc 
                 cg20323175 
                 — 
                 0.148739937 
                 0.171957693 
               
               
                   
                   
                 non_mc 
                 cg20323175 
                 — 
                 −0.249738102 
                 0.397951832 
               
               
                   
                 12-72767 
                 mc 
                 cg16959747 
                 RBP5 
                 0.09713964 
                 −0.079430598 
               
               
                   
                   
                 non_mc 
                 cg16959747 
                 RBP5 
                 0.055654584 
                 0.2763291 
               
               
                   
                 13-256210 
                 mc 
                 cg25366582 
                 — 
                 0.168798263 
                 −0.174946818 
               
               
                   
                   
                 non_mc 
                 cg25366582 
                 — 
                 0.031754087 
                 0.303827389 
               
               
                   
                 16-571473 
                 mc 
                 cg06880930 
                 CPNE2 
                 0.235333764 
                 0.168616245 
               
               
                   
                   
                 non_mc 
                 cg06880930 
                 CPNE2 
                 −0.072680275 
                 −0.290080025 
               
               
                   
                 17-48361 
                 mc 
                 cg25526759 
                 GP1BA 
                 −0.01244657 
                 0.090902282 
               
               
                   
                   
                 non_mc 
                 cg25526759 
                 GP1BA 
                 0.14744695 
                 0.344718909 
               
               
                   
                 17-579157 
                 mc 
                 cg12054453 
                 TMEM49 
                 0.062499156 
                 −0.478242327 
               
               
                   
                   
                 non_mc 
                 cg12054453 
                 TMEM49 
                 0.549358501 
                 0.925660526 
               
               
                   
                 17-742484 
                 mc 
                 cg24166450 
                 — 
                 0.240682421 
                 0.067853997 
               
               
                   
                   
                 non_mc 
                 cg24166450 
                 — 
                 0.443449771 
                 0.566346654 
               
               
                   
                 19-460568 
                 mc 
                 cg27391679 
                 OPA3 
                 0.044319948 
                 0.082373402 
               
               
                   
                   
                 non_mc 
                 cg27391679 
                 OPA3 
                 −0.243746298 
                 −0.096052603 
               
               
                   
                 2-293390 
                 mc 
                 cg21972382 
                 CLIP4 
                 −0.099097051 
                 −0.61331161 
               
               
                   
                   
                 non_mc 
                 cg21972382 
                 CLIP4 
                 0.074745373 
                 0.2650865 
               
               
                   
                 2-382010 
                 mc 
                 cg20626840 
                 FAM82A1 
                 0.237250003 
                 0.741677389 
               
               
                   
                   
                 non_mc 
                 cg20626840 
                 FAM82A1 
                 0.272198397 
                 0.04877136 
               
               
                   
                 2-95267 
                 mc 
                 cg15545942 
                 ASAP2 
                 −0.557516889 
                 −0.185093352 
               
               
                   
                   
                 non_mc 
                 cg15545942 
                 ASAP2 
                 −0.11738118 
                 −0.171014921 
               
               
                   
                 3-1138223 
                 mc 
                 cg06722069 
                 — 
                 −0.427422216 
                 −0.26910555 
               
               
                   
                   
                 non_mc 
                 cg06722069 
                 — 
                 0.089047399 
                 −0.071525276 
               
               
                   
                 5-1767847 
                 mc 
                 cg04466840 
                 RGS14 
                 −0.206022964 
                 −0.032074773 
               
               
                   
                   
                 non_mc 
                 cg04466840 
                 RGS14 
                 −0.029935092 
                 0.110871368 
               
               
                   
                 5-429520 
                 mc 
                 cg17757602 
                 — 
                 0.041309712 
                 0.029262464 
               
               
                   
                   
                 non_mc 
                 cg17757602 
                 — 
                 −0.133713183 
                 −0.355503949 
               
               
                   
                 6-1574304 
                 mc 
                 cg17475813 
                 ARID1B 
                 0.129475454 
                 0.045380298 
               
               
                   
                   
                 non_mc 
                 cg17475813 
                 ARID1B 
                 −0.398696708 
                 −0.14398793 
               
               
                   
                 6-283040 
                 mc 
                 cg08343881 
                 ZNF323 
                 0.002401379 
                 0.00651448 
               
               
                   
                   
                 non_mc 
                 cg08343881 
                 ZNF323 
                 0.255186684 
                 0.345026825 
               
               
                   
                 6-352655 
                 mc 
                 cg02919168 
                 DEF6 
                 −0.372390965 
                 −0.054727228 
               
               
                   
                   
                 non_mc 
                 cg02919168 
                 DEF6 
                 0.070785826 
                 −0.098822441 
               
               
                   
                 6-912971 
                 mc 
                 cg01087382 
                 MAP3K7 
                 0.132083258 
                 −0.263202543 
               
               
                   
                   
                 non_mc 
                 cg01087382 
                 MAP3K7 
                 0.023651284 
                 0.314783378 
               
               
                   
                 7-1017626 
                 mc 
                 cg06721601 
                 CUX1 
                 0.305704307 
                 0.347231675 
               
               
                   
                   
                 non_mc 
                 cg06721601 
                 CUX1 
                 −0.061678409 
                 0.340362292 
               
               
                   
                 7-1228399 
                 mc 
                 cg22024657 
                 SLC13A1 
                 0.013826947 
                 0.222664259 
               
               
                   
                   
                 non_mc 
                 cg22024657 
                 SLC13A1 
                 −0.056854833 
                 0.037841165 
               
               
                   
                 8-1025044 
                 mc 
                 cg18004756 
                 GRHL2 
                 −0.293288901 
                 0.034342458 
               
               
                   
                   
                 non_mc 
                 cg18004756 
                 GRHL2 
                 −0.043777572 
                 −0.203017346 
               
               
                   
                 8-1444164 
                 mc 
                 cg12188860 
                 TOP1MT 
                 0.094522888 
                 −0.083700307 
               
               
                   
                   
                 non_mc 
                 cg12188860 
                 TOP1MT 
                 −0.083425117 
                 −0.026557506 
               
               
                   
               
            
           
           
               
               
            
               
                 C: HCC 
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Logistic regression 
                   
               
               
                   
                   
                 Read 
                   
                   
                 coefficients 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Diagnosis 
                 MCBs 
                 Counts 
                 Target ID 
                 RefGene 
                 LUNC vs 
                 Cancer vs 
               
               
                   
               
               
                 HCC 
                 1-1695560 
                 mc 
                 cg16054275 
                 F5 
                 −0.214291458 
                 −0.606905543 
               
               
                   
                   
                 non_mc 
                 cg16054275 
                 F5 
                 0.524710881 
                 −0.648261504 
               
               
                   
                 1-2035950 
                 mc 
                 cg06637618 
                 ATP2B4 
                 −0.198233007 
                 −0.368854771 
               
               
                   
                   
                 non_mc 
                 cg06637618 
                 ATP2B4 
                 0.023774113 
                 0.157886336 
               
               
                   
                 1-2130901 
                 mc 
                 cg04607844 
                 — 
                 −0.293894301 
                 −0.012727778 
               
               
                   
                   
                 non_mc 
                 cg04607844 
                 — 
                 0.119395918 
                 0.246150856 
               
               
                   
                 10-80958 
                 mc 
                 cg18187680 
                 FLJ45983 
                 −0.028534778 
                 0.369256194 
               
               
                   
                   
                 non_mc 
                 cg18187680 
                 FLJ45983 
                 −0.130812947 
                 −0.247877838 
               
               
                   
                 12-1222773 
                 mc 
                 cg26386472 
                 HPD 
                 −0.472436655 
                 0.005424793 
               
               
                   
                   
                 non_mc 
                 cg26386472 
                 HPD 
                 0.131065837 
                 −0.084249928 
               
               
                   
                 15-555695 
                 mc 
                 cg02712036 
                 RAB27A 
                 −0.178526212 
                 0.136059681 
               
               
                   
                   
                 non_mc 
                 cg02712036 
                 RAB27A 
                 −0.136916211 
                 0.13904572 
               
               
                   
                 16-724595 
                 mc 
                 cg07864976 
                 — 
                 0.159138061 
                 0.220936294 
               
               
                   
                   
                 non_mc 
                 cg07864976 
                 — 
                 −0.174358026 
                 0.041182013 
               
               
                   
                 17-579157 
                 mc 
                 cg12054453 
                 TMEM49 
                 0.062499156 
                 −0.478242327 
               
               
                   
                   
                 non_mc 
                 cg12054453 
                 TMEM49 
                 0.549358501 
                 0.925660526 
               
               
                   
                 17-800195 
                 mc 
                 cg20651080 
                 DUS1L 
                 0.16564247 
                 0.179098878 
               
               
                   
                   
                 non_mc 
                 cg20651080 
                 DUS1L 
                 −0.072821844 
                 0.405200855 
               
               
                   
                 17-803588 
                 mc 
                 cg11252953 
                 C17orf101 
                 0.009872935 
                 0.197906181 
               
               
                   
                   
                 non_mc 
                 cg11252953 
                 C17orf101 
                 −0.088619833 
                 −0.047902639 
               
               
                   
                 19-459097 
                 mc 
                 cg06663668 
                 CD3EAP 
                 0.026258194 
                 0.077247979 
               
               
                   
                   
                 non_mc 
                 cg06663668 
                 CD3EAP 
                 −0.209537326 
                 0.55413999 
               
               
                   
                 19-546460 
                 mc 
                 cg11441617 
                 CNOT3 
                 −0.272150647 
                 0.31583956 
               
               
                   
                   
                 non_mc 
                 cg11441617 
                 CNOT3 
                 −0.485831577 
                 0.011388271 
               
               
                   
                 19-546461 
                 mc 
                 cg11441617 
                 CNOT3 
                 0.345456601 
                 0.601027916 
               
               
                   
                   
                 non_mc 
                 cg11441617 
                 CNOT3 
                 0.252775143 
                 0.151478291 
               
               
                   
                 2-1139315 
                 mc 
                 cg09366118 
                 PSD4 
                 0.098868936 
                 0.269196214 
               
               
                   
                   
                 non_mc 
                 cg09366118 
                 PSD4 
                 0.039975437 
                 −0.005683265 
               
               
                   
                 21-364214 
                 mc 
                 cg01519261 
                 RUNX1 
                 0.350276252 
                 0.086780025 
               
               
                   
                   
                 non_mc 
                 cg01519261 
                 RUNX1 
                 0.205448988 
                 −0.230292808 
               
               
                   
                 21-364215 
                 mc 
                 cg01519261 
                 RUNX1 
                 −0.068910744 
                 0.175787941 
               
               
                   
                   
                 non_mc 
                 cg01519261 
                 RUNX1 
                 −0.470630509 
                 −0.686309747 
               
               
                   
                 22-185277 
                 mc 
                 cg02415779 
                 — 
                 0.056697461 
                 0.313717112 
               
               
                   
                   
                 non_mc 
                 cg02415779 
                 — 
                 0.122239689 
                 −0.243806777 
               
               
                   
                 22-378130 
                 mc 
                 cg00107982 
                 ELFN2 
                 −0.03021978 
                 0.143360514 
               
               
                   
                   
                 non_mc 
                 cg00107982 
                 ELFN2 
                 −0.063348947 
                 0.490504707 
               
               
                   
                 4-13248 
                 mc 
                 cg07748255 
                 MAEA 
                 0.217866978 
                 −0.457591409 
               
               
                   
                   
                 non_mc 
                 cg07748255 
                 MAEA 
                 −0.011474644 
                 −0.340582424 
               
               
                   
                 4-840359 
                 mc 
                 cg19255783 
                 PLAC8 
                 0.024851345 
                 0.211399591 
               
               
                   
                   
                 non_mc 
                 cg19255783 
                 PLAC8 
                 −0.154998544 
                 −0.217029094 
               
               
                   
                 5-1715385 
                 mc 
                 cg25650256 
                 STK10 
                 −0.296078256 
                 0.069437129 
               
               
                   
                   
                 non_mc 
                 cg25650256 
                 STK10 
                 −0.134013643 
                 0.178025221 
               
               
                   
                 6-262504 
                 mc 
                 cg05414338 
                 HIST1H3F 
                 −0.291906307 
                 0.385669376 
               
               
                   
                   
                 non_mc 
                 cg05414338 
                 HIST1H3F 
                 −0.188285503 
                 0.279553121 
               
               
                   
                 6-315278 
                 mc 
                 cg06393830 
                 NFKBIL1 
                 0.120351939 
                 0.122094253 
               
               
                   
                   
                 non_mc 
                 cg06393830 
                 NFKBIL1 
                 0.206309569 
                 −0.082505936 
               
               
                   
                 6-329093 
                 mc 
                 cg00862588 
                 HLA-DMB 
                 −0.322857861 
                 0.005678509 
               
               
                   
                   
                 non_mc 
                 cg00862588 
                 HLA-DMB 
                 0.382920486 
                 −0.054366936 
               
               
                   
                 6-912971 
                 mc 
                 cg01087382 
                 MAP3K7 
                 0.132083258 
                 −0.263202543 
               
               
                   
                   
                 non_mc 
                 cg01087382 
                 MAP3K7 
                 0.023651284 
                 0.314783378 
               
               
                   
                 7-1000913 
                 mc 
                 cg03113878 
                 C7orf51 
                 −0.173651065 
                 −0.291203774 
               
               
                   
                   
                 non_mc 
                 cg03113878 
                 C7orf51 
                 −0.009576582 
                 0.090427416 
               
               
                   
                 7-759325 
                 mc 
                 cg21217886 
                 HSPB1 
                 −0.135144273 
                 −0.558395663 
               
               
                   
                   
                 non_mc 
                 cg21217886 
                 HSPB1 
                 −0.163997602 
                 −0.133445148 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 8 
               
             
            
               
                   
               
               
                 Characteristics of 10 MCBs in LUNC prognosis prediction 
               
               
                 and 10 MCBs in HCC prognosis prediction 
               
            
           
           
               
               
               
               
            
               
                   
                 Features 
                 Target ID 
                 RefGene 
               
               
                   
                   
               
            
           
           
               
               
               
               
            
               
                 LUNC 
                 mc_17-742484 
                 cg24166450 
                 — 
               
               
                   
                 non_mc_2-741532 
                 cg02478828 
                 DGUOK 
               
               
                   
                 mc_2-2355288 
                 cg08436738 
                 — 
               
               
                   
                 mc_1-295863 
                 cg04933208 
                 PTPRU 
               
               
                   
                 non_mc_22-358223 
                 cg20146967 
                 MCM5 
               
               
                   
                 non_mc_16-900927 
                 cg07860918 
                 GAS8 
               
               
                   
                 mc_20-374337 
                 cg16119522 
                 PPP1R16B 
               
               
                   
                 non_mc_3-1960650 
                 cg05556202 
                 TM4SF19 
               
               
                   
                 mc_12-687588 
                 cg20323175 
                 — 
               
               
                   
                 non_mc_10-299484 
                 cg13324103 
                 SVIL 
               
               
                 HCC 
                 mc_6-262503 
                 cg05414338 
                 HIST1H3F 
               
               
                   
                 mc_6-733300 
                 cg17126142 
                 KCNQ5 
               
               
                   
                 non_mc_7-450187 
                 cg06787669 
                 MYO1G 
               
               
                   
                 mc_19-185898 
                 cg06747543 
                 ELL 
               
               
                   
                 mc_12-1222773 
                 cg26386472 
                 HPD 
               
               
                   
                 non_mc_1-20665 
                 cg00866690 
                 PRKCZ 
               
               
                   
                 mc_12-939663 
                 cg11225410 
                 SOCS2 
               
               
                   
                 mc_7-450187 
                 cg06787669 
                 MYO1G 
               
               
                   
                 mc_6-283040 
                 cg08343881 
                 ZNF323 
               
               
                   
                 mc_6-733299 
                 cg17126142 
                 KCNQ5 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 9 
               
             
            
               
                   
               
               
                 Clinical characteristics of study cohort 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 TCGA 
                 TCGA 
                   
                   
                   
                   
               
               
                   
                 HCC 
                 LUNC 
                 GSE 
                 HCC 
                 LUNC 
                 Normal 
               
               
                 Characteristic 
                 tissue 
                 tissue 
                 Normal 
                 blood 
                 blood 
                 blood 
               
               
                   
               
               
                 Total (n) 
                 377  
                 827  
                 754 
                 1504   
                 892 
                 2247 
               
               
                 Gender 
               
               
                 Female-no. (%) 
                 122(32.4) 
                 340(41.1) 
                 401(53.2) 
                 146(9.7)  
                 263(29.5) 
                 507(22.6) 
               
               
                 Male-no. (%) 
                 255(67.6) 
                 487(58.9) 
                 353(46.8) 
                 991(65.9) 
                 487(54.6) 
                 480(21.4) 
               
               
                 NA 
                 0 
                 0 
                  0 
                 367(24.4) 
                 142(15.9) 
                 1260(56.1)  
               
               
                 Age (years) 
               
               
                 Mean 
                 61  
                 68  
                  63 
                 54  
                  58 
                  48 
               
               
                 Range 
                 16-90 
                 33-90 
                 19-101 
                 11-85 
                 19-85 
                 19-90 
               
               
                 Pathology 
               
               
                 Hepatocellular    
                 367(97.3) 
                 0 
                 NA 
                 1504(100)   
                  0 
                 NA 
               
               
                 Adenocarcinoma(%) 
                 0 
                 458(55.4) 
                 NA 
                 0 
                 402(45.1) 
                 NA 
               
               
                 Squamous cell    
                 0 
                 369(44.6) 
                 NA 
                 0 
                 138(15.5) 
                 NA 
               
               
                 Small Cell Lung    
                 0 
                 0 
                 NA 
                 0 
                 79(8.9) 
                 NA 
               
               
                 Others(%) 
                 10(2.7) 
                 0 
                 NA 
                 0 
                 273(30.6) 
                 NA 
               
               
                 Stage 
               
               
                 I (%) 
                 175(46.4) 
                 424(51.3) 
                 NA 
                 206(13.7) 
                 58(6.5) 
                 NA 
               
               
                 II (%) 
                  87(23.1) 
                 115(13.9) 
                 NA 
                 202(13.4) 
                 52(5.8) 
                 NA 
               
               
                 III (%) 
                  86(22.8) 
                 261(31.6) 
                 NA 
                 612(40.7) 
                 148(16.6) 
                 NA 
               
               
                 IV (%) 
                  6(1.6) 
                 25(3.0) 
                 NA 
                 134(8.9)  
                 463(51.9) 
                 NA 
               
               
                 NA (%) 
                 23(6.1) 
                  2(0.2) 
                 NA 
                 350(23.3) 
                 171(19.2) 
                 NA 
               
               
                 Tumor burden 
               
               
                 Tumor free (%) 
                 236(62.6) 
                 503(60.8) 
                 NA 
                 314(20.9) 
                 160(17.9) 
                 NA 
               
               
                 With tumor (%) 
                 114(30.2) 
                 159(19.2) 
                 NA 
                 889(59.1) 
                 599(67.2) 
                 NA 
               
               
                 NA (%) 
                 27(7.2) 
                 165(20.0) 
                 NA 
                 301(20.0) 
                 133(14.9) 
                 NA 
               
               
                 EGFR status 
               
               
                 Wide type (%) 
                 NA 
                 400(48.4) 
                 NA 
                 NA 
                 102(11.4) 
                 NA 
               
               
                 Mutation (%) 
                 NA 
                 100(12.1) 
                 NA 
                 NA 
                 69(7.7) 
                 NA 
               
               
                 NA (%) 
                 NA 
                 327(39.5) 
                 NA 
                 NA 
                 721(80.8) 
                 NA 
               
               
                 Hepatitis 
               
               
                 Positive (%) 
                 120(31.8) 
                 NA 
                 NA 
                 623(95.3) 
                 NA 
                 379(16.9) 
               
               
                 Negative (%) 
                 119(31.6) 
                 NA 
                 NA 
                 10(1.5) 
                 NA 
                 571(25.4) 
               
               
                 NA (%) 
                 138(36.6) 
                 NA 
                 NA 
                 21(3.2) 
                 NA 
                 1297(57.7)  
               
               
                 Smoking (%) 
               
               
                 Current smoker (%) 
                 NA 
                 725(87.7) 
                 NA 
                 NA 
                 NA 
                 192(8.5)  
               
               
                 Non-smoker (%) 
                 NA 
                 80(9.7) 
                 NA 
                 NA 
                 NA 
                 670(29.8) 
               
               
                 NA (%) 
                 NA 
                 22(2.6) 
                 NA 
                 NA 
                 NA 
                 1385(61.6)  
               
               
                   
               
               
                     indicates data missing or illegible when filed 
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 10 
               
             
            
               
                   
               
               
                 Multivariate survival analysis for HCC patients and LUNC 
               
               
                 patients with composite-score of methylation markers 
               
               
                 (cp-score) and relevant variables in validation cohorts 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                   
                 coef 
                 exp(coef) 
                 se(coef) 
                 z 
                 p 
                 lower 0.95 
                 upper 0.95 
               
               
                   
                   
               
            
           
           
               
            
               
                 HCC 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 cp-score 
                 0.9115 
                 2.4881 
                 0.2696 
                 3.3813 
                 0.0007 
                 0.3830 
                 1.4400 
               
               
                 stage 
                 0.3336 
                 1.3960 
                 0.3034 
                 1.0995 
                 0.2715 
                 −0.2612 
                 0.9284 
               
               
                 AFP 
                 −0.0861 
                 0.9175 
                 0.2115 
                 −0.4069 
                 0.6841 
                 −0.5008 
                 0.3286 
               
               
                 age 
                 −330.8028 
                 0.0000 
                 75.7050 
                 −4.3696 
                 0.0000 
                 −479.2145 
                 −182.3911 
               
               
                 Gender 
                 −0.0885 
                 0.9153 
                 0.2361 
                 −0.3748 
                 0.7078 
                 −0.5512 
                 0.3743 
               
            
           
           
               
            
               
                 LUNC 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 cp-score 
                 0.5577 
                 1.7467 
                 0.3056 
                 1.8249 
                 0.0680 
                 −0.0414 
                 1.1569 
               
               
                 stage 
                 0.4647 
                 1.5916 
                 0.8422 
                 0.5518 
                 0.5811 
                 −1.1863 
                 2.1157 
               
               
                 CEA 
                 0.2695 
                 1.3093 
                 0.6035 
                 0.4466 
                 0.6552 
                 −0.9135 
                 1.4525 
               
               
                 age 
                 −321.0835 
                 0.0000 
                 94.6796 
                 −3.3913 
                 0.0007 
                 −506.6928 
                 −135.4742 
               
               
                 Gender 
                 0.0897 
                 1.0938 
                 0.3858 
                 0.2325 
                 0.8162 
                 −0.6666 
                 0.8460 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 11 
               
             
            
               
                   
               
               
                 Clinical characteristics and sensitivity/specificity 
               
               
                 for detection of stage I LUNC and benign lung nodules 
               
               
                 Sensitivity and Specificity for the Detection of Lung Cancer 
               
            
           
           
               
               
               
            
               
                   
                 Stage I 
                   
               
               
                   
                 lung cancer 
                 Benign nodules 
               
               
                 Characteristic 
                 N = 116 
                 N = 116 
               
               
                   
               
            
           
           
               
               
               
            
               
                 Age-yr 
                   
                   
               
               
                 Median 
                 61 
                 52 
               
               
                 Range 
                 29-83 
                 26-86 
               
               
                 Gender 
               
               
                 Female 
                 80 
                 53 
               
               
                 male 
                 36 
                 63 
               
               
                 Pathology 
               
               
                 Adenocarcinoma 
                 100 
                 NA 
               
               
                 Squamous cell carcinoma 
                 6 
                 NA 
               
               
                 Small Cell Lung Cancer 
                 0 
                 NA 
               
               
                 Others 
                 0 
                 NA 
               
               
                 Nodule size-mm 
               
               
                 Mean 
                 14.6 ± 6.1 
                 7.2 ± 2.1 
               
               
                 Median 
                 15.0 
                   6.4 
               
               
                 Range 
                  3-58 
                  2.7-18.2 
               
               
                 Stage 
               
               
                 AIS 
                 7 
                 NA 
               
               
                 MIA 
                 8 
                 NA 
               
               
                 IA 
                 78 
                 NA 
               
               
                 IB 
                 13 
                 NA 
               
               
                 Sensitivity - % (95% CI) 
                 77.8 
               
               
                 Specificity - % (95% CI) 
                 85.3 
               
               
                 Positive predictive value - % (95% CI) 
                 81.0 
               
               
                 Negative predictive value - % (95% CI) 
                 84.1 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 12 
               
             
            
               
                   
               
               
                 Clinical characteristics and sensitivity/specificity for 
               
               
                 detection of progression to stage I HCC from liver cirrhosis 
               
               
                 Sensitivity and Specificity for the Detection of Liver Cancer 
               
            
           
           
               
               
               
            
               
                   
                 Stage I 
                   
               
               
                   
                 liver cancer 
                 Cirrhosis 
               
               
                 Characteristic 
                 N = 204 
                 N = 242 
               
               
                   
               
            
           
           
               
               
               
            
               
                 Age-yr 
                   
                   
               
               
                 Median 
                 62 
                 52 
               
               
                 Range 
                 21-81 
                 25-82 
               
               
                 Gender 
               
               
                 Female 
                 32 
                 47 
               
               
                 Male 
                 172 
                 195 
               
               
                 Hepatitis B 
               
               
                 Positive 
                 198 
                 193 
               
               
                 Negative 
                 4 
                 13 
               
               
                 NA 
                 2 
                 36 
               
               
                 AFP 
               
               
                 &lt;25 ng/ml 
                 113 
                 196 
               
               
                 &gt;25 
                 43 
                 29 
               
               
                 NA 
                 48 
                 17 
               
               
                 Sensitivity - % (95% CI) 
                 94.9 
               
               
                 Specificity - % (95% CI) 
                 92.6 
               
               
                 Positive predictive value - % (95% CI) 
                 94.9 
               
               
                 Negative predictive value - % (95% CI) 
                 92.8 
               
               
                   
               
            
           
         
       
     
     Example 5—Circulating Tumor DNA Methylation Profiles for Diagnosis and Prognosis of Colorectal Cancer 
     Colorectal cancer (CRC) is one of the most common cancers in the world. In some instances, detection of CRC at early and/or intermediate stage provides a better prognosis than detection at an advanced stage. Serum Carcinoembryonic antigen (CEA) quantification is a non-invasive tool for the detection of cancer. However, in some instances, the CEA test has a low sensitivity for the detection of CRC. 
     Circulating tumor DNA (ctDNA) is tumor-derived fragmented DNA in the circulatory system, comes, e.g., from dead tumor cells through necrosis and apoptosis. In this study, DNA methylation status of genes obtained from ctDNA samples are determined and are further utilized for detection of CRC. 
     Patient Data 
     Tissue DNA methylation data was obtained from The Cancer Genome Atlas (TCGA). Complete clinical, molecular, and histopathological datasets are available at the TCGA website. Whole blood DNA methylation profiles from healthy donors were generated in an aging study (GSE40279) in which DNA methylation profiles for CRC and blood were analyzed. Individual institutions that contributed samples coordinated the consent process and obtained informed written consent from each patient in accordance to their respective institutional review boards. 
     The cfDNA cohort consisted of 801 CRC patients and 1021 normal control from the Sun Yat-sen University Cancer Center in Guangzhou, Xijing Hospital in Xi&#39;an, and the West China Hospital in Chengdu, China. Patients who presented with CRC from stage I-IV were selected and enrolled in this study. Patient characteristics and tumor features are summarized in Table 18. The TNM staging classification for CRC is according to the 7 th  edition of the AJCC cancer staging manual. This project was approved by the institutional review board of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. Informed consent was obtained from all patients. Human blood samples were collected by venipuncture, and plasma samples were obtained by taking the supernatant after centrifugation and stored at −80° C. before cfDNA extraction. 
     Prospective CRC Screening Cohort Study 
     A CRC study was conducted using plasma samples on screening and early detection of a high-risk screening population in order to assess feasibility of using methylation markers for predicting CRC occurrence in high-risk populations. The cohort included individuals whom based on questionnaires were definition as high risk with CRC, and individuals whom are asymptomatic and aged 45 or above scheduled for screening colonoscopy from September 2015-December 2017. A total of 1450 subjects at high risk of CRC were scheduled for colonoscopy and cfDNA methylation test with the following situation: (i) age &gt;45 years, (ii) ever smoking, alcohol consumption, diabetes mellitus; (iii) family history present (two or more first degree relatives with CRC or one or more with CRC at age 50 years or less; or known Lynch syndrome or familial adenomatous polyposis); (iv) had positive results on fecal blood testing or change in bowel habit. excluded subjects were those who had a personal history of colorectal neoplasia, digestive cancer, or inflammatory bowel disease; had undergone colonoscopy within the previous 10 years or a barium enema, computed tomographic colonography, or sigmoidoscopy within the previous 5 years; had undergone colorectal resection for any reason other than sigmoid diverticula; had overt rectal bleeding within the previous 30 days. The present study prospectively recruited screening subjects who gave informed consent and received colonoscopy in this program. This project was approved by the IRBs of Sun Yat-sen University Cancer Center, Xijing Hospital, and West China Hospital. 
     Statistical Analysis 
     Solid Tumor and Whole Blood Methylation Profiles 
     DNA methylation data of 485,000 sites generated using the Infinium 450K Methylation Array were obtained from the TCGA and a dataset generated from an aging study (GSE40279) in which DNA methylation profiles for CRC and blood were analyzed. Both primary solid tissues from cancer patients and whole blood from healthy donor were measured by Illumina 450k infimum bead chip. IDAT format files of the methylation data were generated containing the ratio values of each scanned bead. Using the minfi package from Bioconductor, these data files were converted into a score, referred to as a Beta value. Methylation values of the Chinese cohort were obtained by targeted bisulfate sequencing using a molecular inversion probe and analyzed as described below. 
     DNA Methylation Marker Pre-Selection for Diagnostic and Prognostic Analysis 
     A differential methylation analysis on TCGA data using a “moderated t-statistics shrinking” approach was performed and the p-value for each marker was then corrected by multiple testing by the Benjamini-Hochberg procedure to control FDR at a significance level of 0.05. The list was ranked by adjusted p-value and the top 1000 markers were selected for designing padlock probes for differentiating CRC versus normal samples. 1,000 molecular inversion (padlock) probes were then designed corresponding to these 1000 markers for capture-sequencing of cfDNA in CRC plasma. All padlock probe design and capture of bis-DNA were based on published techniques with some modification. Only 544 padlock probes were able to give positive and specific PCR amplification signals and they were therefore used as capture probes in the subsequent experiments in cfDNA samples. cfDNA samples with low quality or fewer than 30,000 reads per sample were also eliminated. About 1822 cfDNA samples were included in the subsequent study (801 CRC blood samples and 1021 normal blood samples). Methylated reads for each marker were defined as total unique methylated reads and methylation values for each marker were defined as the proportion of read counts with methylation divided by total read counts. For particular methylation markers with less than 20 unique reads, an imputed mean methylation value of CRC or normal healthy controls were used. 
     Building a Diagnostic Model 
     cfDNA sample data obtained from patients diagnosed with CRC (n=801) and healthy control (n=1021) was randomly split into training and validation cohorts with a 2:1 ratio. Next, two variable selection methods were applied which were suitable for high-dimensionality on the prescreened training dataset: Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest based variable selection method using OOB error. As results can depend strongly on the arbitrary choice of a random sample split for sparse high-dimensional data, an analysis of the “multi-split” method was adopted, which improved variable selection consistency while controlling finite sample error. For LASSO selection operator, 75 percent of the dataset were subsampled replacement 500 times and the markers were selected with repeat occurrence frequency of more than 450. The tuning parameters was determined according to the expected generalization error estimated from 10-fold cross-validation and information-based criteria AIC/BIC, and the largest value of lambda was adopted such that the error was within one standard error of the minimum, known as “1-se” lambda. For the random forest analysis, using the OOB error as a minimization criterion, variable elimination was carried out from the random forest by setting variable a dropping fraction of each iteration at 0.3. The overlapping methylation markers were then selected by the two methods for model building a binary prediction. A logistic regression model was fitted using these 9 markers as the covariates and obtained a combined diagnosis score (designated as cd-score) by multiplying the unbiased coefficient estimates and the marker methylation value matrix in both the training and validation datasets. The predictability of the model was evaluated by area under ROC (AUC, also known as C-index), which calculated the proportions of concordant pairs among all pairs of observations with 1.0 indicating a perfect prediction accuracy. Confusion tables were generated using an optimized cd-score cutoff with a maximum Youden&#39;s index. 
     The pre-treatment or initial methylation level was obtained at the initial diagnosis, and the post-treatment level was evaluated approximately 2 months after treatment, where the treatment referred to either chemotherapy or surgical resection of tumor. The primary endpoint (including response to treatment: progressive disease (PD), partial response (PR) and stable disease (SD)) was defined according to the RECIST guideline. For patients treated with surgical removal and no recurrence at time of evaluation, it was assumed that they had complete response (CR). The difference of cd-score distribution between clinical categories was examined by two-sided t-test as the cd-score was shown to be non-normally distributed using a Shapiro-Wilk Test. 
     Building a Predictive Model for Prognosis and Survival 
     The potential to use a combined prognosis score (cp-score) system for prediction of prognosis in CRC combination with clinical and demographic characteristics including age, gender, and AJCC stage was investigated. Tumor samples were selected from diagnosis&#39;s training cohort as training data set, and the tumor samples from validation cohort as the validation data set. Variable selection was conducted on the training data set and built the composite score on the validation cohort. A univariate pre-screening procedure was first performed to remove excessive noise to facilitate the computational analysis, which is generally recommended prior to applying any variable selection method. A marker with p-value &lt;0.05 from the Wald statistic was retained in the dataset. Second, a similar subsampling strategy in a diagnosis marker selecting process based on LASSO-cox method was used to shrink the marker numbers to a reasonable range (less than events). Slightly different from the binary classifier, subsampling was carried out in the training dataset with replacement in case that the event proportion was too low for a model construction. The frequency cutoff was set as 50 to retain approximately 1/10th of total events. The above analysis generated 2 final markers to construct a prognostic signature (Table 14). The cp-score was then calculated from the linear predictor of multi variate Cox regression model with the 2 markers. A Kaplan-Meier curve and log-rank test were generated using the dichotomized cp-score, which formed a high-risk and low-risk group membership assignment according to its median. This segmentation was compatible with that formed by AJCC stage. Time-dependent ROC was used to summarize the discrimination potential of the cp-score, AJCC stage, CEA level, primary tumor location and the combination of all factors, with ROC curves varying as a function of time and accommodating censored data. Finally, a multivariate Cox regression model was fitted to assess the significance of potential risk factors. 
     All hypothesis testing in the prognostic analysis section were done by two-sided with p-value &lt;0.05 considered to be statistically significant. All the analysis was conducted in R version 3.4.3 with the following packages used: ‘glmnet’, ‘pROC’, ‘limma’, ‘survival’, ‘survival ROC’, ‘survcomp’. 
     Unsupervised Discovery of ctDNA Methylation-Based Subtypes 
     Training dataset (n=528) was adopted to discover CRC groups/subtypes. Aiming to narrow down markers and get meaningful clustering by methylation information provided itself, an algorithm was used with iterative refinement of key features that was modified for better represents each cluster. This algorithm was modified as follows. Briefly, the markers were first filtered out with low variability using a threshold of median absolute deviation &lt;0.5. The matrix was then adjusted by mean centering on markers and samples in training dataset. Secondly, an iterator procedure was used to classify and get a marker list for predicting subtypes: (i) Consensus Clustering was used to initially cluster the dataset and Cluster numbers was determined by relative change in area under CDF curve (AA, cutoff=0.05); (ii) Calculated centroid of each cluster and get correlation coefficient vector of each sample by multiply methylation matrix and centroid matrix. (iii) Re-cluster samples based on correlation coefficient vectors. The number of new clusters was determined by the maximum average silhouette width; (iv) Get differentially methylated markers among the new clusters by using a moderated F-test (from ‘limma’ package); Then feed the subset of matrix in terms of differential methylated markers into next iteration. The iteration stopped when differential methylated marker list did not change from the previous run and this marker set was finally used as subtype signature for predicting CRC subtypes. 
     Validation was performed on the predefined validation dataset (n=273). To get the subtype of validation samples, centroid methylation value of each cluster was first calculated with the methylation level of selected subtyping signature in the training dataset. Here, the centroid methylation value was defined as a representative methylation value of a cluster of samples by get mean value of signature across samples. Secondly, samples from validation cohort were assigned to clusters according to maximum Pearson correlation coefficient to each centroid value. 
     Cell-Free DNA Extraction from Plasma Samples 
     Plasma samples were frozen and preserved in at −80° C. until use. 20,000 or more total unique reads per sample were observed to fulfilled this criterion, and in order to reliably produce &gt;20,000 unique reads, a volume of 1.5 ml or more plasma was required. This relationship between the amount of cfDNA in plasma and detected copy number was further accessed using digital droplet PCR. In addition, it was found that 1.5 ml of plasma yielded &gt;10 ng of cfDNA, which produced at least 140 copies of detected amplicons in each digital droplet PCR assay. Based on these findings, 15 ng/1.5 ml was used as a cutoff to obtain reliable measurements of DNA methylation for all experiments in this study. For all cfDNA extractions, the EliteHealth cfDNA extraction Kit was used (EliteHealth, Guangzhou, China) according to the manufacturer&#39;s recommendations. 
     Bisulfite Conversion of Genomic or cfDNA 
     10 ng of DNA was converted to bis-DNA using EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer&#39;s protocol. Resulting bis-DNA had a size distribution of ˜200-3000 bp, with a peak around ˜500-1000 bp. The efficiency of bisulfite conversion was &gt;99.8% as verified by deep-sequencing of bis-DNA and analyzing the ratio of C to T conversion of CH (non-CG) dinucleotides. 
     Determination of DNA Methylation Levels by Deep Sequencing of Bis-DNA Captured with Molecular-Inversion (Padlock) Probes 
     In order to identify DNA sites with significantly different rates of methylation between CRC and normal blood, the variance was shrank using a t-statistic with Empirical Bayes36 and selected the top 1000 significant markers with the Benjamini-Hochberg procedure 37 controlling the FDR at a significance level of 0.05. CRC and normal blood were successfully distinguished by unsupervised hierarchical clustering of these top 1000 markers. 1,000 molecular inversion (padlock) probes corresponding to these 1000 markers were then designed for capture-sequencing of cfDNA. Capture and sequencing were performed in bisulfite-converted cfDNA samples. 
     Probe Design, Synthesis and Validation 
     All probes were designed using the ppDesigner software. The average length of the captured region was 100 bp, with the CpG marker located in the central portion of the captured region. Linker sequence between arms contained binding sequences for amplification primers separated by a variable stretch of Cs to produce probes of equal length. A 6-bp unique molecular identifier (UMI) sequence was incorporated in probe design to allow for the identification of unique individual molecular capture events and accurate scoring of DNA methylation levels. For capture experiments, probes were synthesized as separate oligonucleotides using standard commercial synthesis methods (IDT) and mixed in equimolar quantities, followed by purification using Qiagen columns. 
     There were significant differences between the number of reads captured by the most efficient probes and the non-efficient probes (60-65% of captured regions with coverage &gt;0.2× of average) as shown by deep sequencing of the original pilot capture experiments. To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios, which increased capture uniformity to 85% of regions at &gt;0.5× of average coverage. 
     Bis-DNA Capture 
     To anneal probes to DNA, 10 ng of bisulfite-converted DNA was mixed with padlock probes in 20 μl reactions containing 1× Ampligase buffer (Epicentre), followed by 30 second denaturation at 95° C. was followed by a slow cooling to 55° C. at a rate of 0.02° C. per second. Hybridization was left to complete for 15 hrs at 55° C. To fill gaps between annealed arms, 5 μl of the following mixture was added to each reaction: 2U of PfuTurboCx polymerase (Agilent), 0.5 U of Ampligase (Epicentre) and 250 pmol of each dNTP in 1× Ampligase buffer. After 5-hour incubation at 55° C., reactions were denatured for 2 minutes at 94° C. 5 μl of exonuclease mix (20 U of Exo I and 100 U of ExoIII, both from Epicentre) was added and single-stranded DNA degradation was carried out at 37° C. for 2 hours, followed by enzyme inactivation for 2 minutes at 94° C. 
     Circular products of site-specific capture were amplified by PCR with concomitant barcoding of separate samples. Amplification was carried out using primers specific to linker DNA within padlock probes, one of which contained specific 6 bp barcodes. Both primers contained Illumina next-generation sequencing adaptor sequences. PCR was performed as follows: 1× Phusion Flash Master Mix, 3 μl of captured DNA and 200 nM primers, using the following cycle: 10s @ 98° C., 8× of (1s @ 98° C., 5s @ 58° C., 10s @ 72° C.), 25× of (1s @ 98° C., 15s @ 72° C.), 60s @ 72° C. PCR reactions were mixed and the resulting library was size selected to include effective captures (˜230 bp) and exclude “empty” captures (˜150 bp) using Agencourt AMPure XP beads (Beckman Coulter). Purity of the libraries was verified by PCR using Illumina flowcell adaptor primers (P5 and P7) and the concentrations were determined using Qubit dsDNA HS assay (Thermo Fisher). Libraries were sequenced using MiSeq and HiSeq2500 systems (Illumina). 
     Optimization of Capture Coverage Uniformity 
     Deep sequencing of the original pilot capture experiments showed significant differences between number of reads captured by most efficient probes and non-efficient probes (60˜65% of captured regions with coverage &gt;0.2× of average). To ameliorate this, relative efficiencies were calculated from sequencing data and probes were mixed at adjusted molar ratios. This increased capture uniformity to 85% of regions at &gt;0.2× of average coverage. 
     Sequencing Data Analysis 
     Mapping of sequencing reads was done using the software tool bisReadMapper with some modifications. First, UMI were extracted from each sequencing read and appended to read headers within FASTQ files using a custom script. Reads were on-the-fly converted as if all C were non-methylated and mapped to in-silico converted DNA strands of the human genome, also as if all C were non-methylated, using Bowtie2. Original reads were merged and filtered for single UMI, i.e. reads carrying the same UMI were discarded leaving a single, unique read. Methylation frequencies were calculated for all CpG dinucleotides contained within the regions captured by padlock probes by dividing the numbers of unique reads carrying a C at the interrogated position by the total number of reads covering the interrogated position. 
     Identification of Methylation Correlated Blocks (MCB) 
     In order to maximize the ability to measure small differences in DNA methylation advantage was taken of the notion that closely positioned CpG tend to have similar methylation levels, what is believed to be a result of the processivity and lack of sequence-specificity of DNA methyltransferases and demethylases, as well as the concept of haplotype blocks in genetic linkage analysis. Sequencing data was used to a generate MCB map using CRC and normal cfDNA samples. Pearson correlation coefficients between methylation frequencies of each pair of CpG markers separated by no more than 200 bp were calculated separately across 50 cfDNA samples from each of the two diagnostic categories, e.g. normal blood and CRC. A value of Pearson&#39;s r&lt;0.5 was used to identify boundaries between adjacent markers with uncorrelated methylation. Markers not separated by a boundary were combined into Methylation Correlated Blocks (MCBs). This procedure identified a total of ˜1550 MCBs in each diagnostic category within the padlock data, combining between 2 and 22 CpG positions in each block. Methylation frequencies for entire MCBs were calculated by summing up the numbers of Cs at all interrogated CpG positions within a MCB and dividing by the total number of C+Ts at those positions. 
     Droplet Digital PCR 
     The methylation status of cg10673833 was determined for each of these samples using a droplet digital PCR paradigm featuring a Bio-Rad (Carlsbad, Calif.) QX-200 Droplet Reader and an Automated Droplet Generator (AutoDG). In brief, 10 ng of DNA from each subject was bisulfite converted using EZ DNA Methylation-Lightning™ Kit (Zymo Research). An aliquot of each sample was pre-amplified, diluted 1:3,000, and then PCR amplified using fluorescent, dual labeled primer probe sets specific for cg10673833 from Behavioral Diagnostics and Universal Digital PCR reagents and protocols from Bio-Rad. The number of droplets was determined using a QX-200 droplet counter and analyzed using QuantiSoft software. The results were expressed as a percent methylation. Reactions were excluded if fewer than 10,000 droplets were counted. 
     Patient and Sample Characteristics 
     The clinical characteristics and the methylation profiling of 459 CRC tumor samples were collected from The Cancer Genome Atlas (TCGA) and 754 normal samples from a dataset used in a previous methylation study on aging (GSE40279) to identify the CRC specific methylation markers. To study cfDNA in CRC, plasma samples were obtained from 801 Chinese patients with CRC, and from randomly selected, population-matched 1021 healthy controls undergoing routine health care maintenance ( FIG. 31A ). Informed written consent was obtained from each participant. Clinical characteristics of all patients and controls are listed in Table 18. 
     A total of 1450 participants with high-risks of CRC were enrolled in the prospective screening cohort study and undergoing colonoscopy and cfDNA methylation test ( FIG. 31B ). Among them 18 participants were found to have colorectal cancer on colonoscopy (prevalence, 1.2%), 78 participants had advanced precancerous lesions (prevalence, 5.3%). Clinical characteristics of all participants from this cohort are listed in Table 19. 
     Identification of Methylation Markers Differentiating CRC and Blood 
     To identify putative markers that differentiate between CRC and normal blood samples, methylation data derived from CRC tissue DNA from the TCGA and normal blood were compared. The methylation data was obtained from 459 CRC and 754 blood samples from healthy controls. A “moderated t-statistic” analysis with Empirical Bayes for shrinking the variance was used and the top 1000 significant markers were selected by controlling the false discovery rate (FDR) at significance level 0.05 using the Benjamini-Hochberg procedure. The molecular-inversion (padlock) probes corresponding to these 1000 markers were designed for capture-sequencing cfDNA from plasma and 544 markers were selected with good experimental amplification profile and dynamic methylation range for further analysis. The genetic linkage disequilibrium (LD block) concept was used to study the degree of co-methylation among different DNA strands, with the underlying assumption that DNA sites in close proximity are more likely to be co-methylated than distant sites. 
     cfDNA Diagnostic Prediction Model for CRC 
     The entire methylation dataset of 544 markers was analyzed by Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest to reduce the number of markers ( FIG. 36 ). 801 CRC samples and 1021 normal control samples were randomly assigned to a training set and a validation set with a 2:1 ratio ( FIG. 32A ). LASSO-based feature selection identified 13 markers and Random Forest-based feature selection identified 22 markers for discriminating CRC versus normal. There were 9 overlapping markers between these two methods (Table 13). A diagnostic prediction model was constructed with these 9 markers and a combined diagnostic score system (cd-score) was formulated according to the coefficients from the multinomial logistic regression. Applying this model, a high consistency was observed between predicted results and pathological diagnosis results in both the training dataset and validation dataset ( FIGS. 32B and 32C ). The area of AUC was 0.96 (95% CI 0.95-0.97) in training dataset ( FIG. 32D ) and 0.96 (95% CI 0.94-0.97) in validation dataset ( FIG. 32E ) respectively. By using a best cutoff value determined via Youden index method, the model yielded a sensitivity of 87.5% and a specificity of 89.9% for discriminating CRC from normal controls in the training dataset and a sensitivity of 87.9% and a specificity of 89.6% in the validation dataset ( FIGS. 32G and 32H ). While these results clearly demonstrated the potential of ctDNA methylation markers for predicting the presence of CRC. 
     The utility of the cd-score in assessing the staging of CRC was then examined, the presence of residual tumor after treatment, the response of treatment (such as surgery or chemotherapy). The cd-scores of patients with detectable residual tumor following treatment were significantly higher than those without detectable tumor (p=&lt;0.001,  FIG. 37A ). Similarly, there was good correlation between the cd-scores and tumor stage. Patients with early stage disease (I, II) had substantially lower cd-scores compared to those with advanced stage disease (III, IV) (p=&lt;0.001,  FIG. 37B ). Furthermore, the cd-scores were significantly higher in patients before treatment compared to those received surgery (p&lt;0.001,  FIG. 37E ). When the tumor recurrence, the cd-score increase again ( FIG. 37E ). 
     CEA has been used in diagnosis and surveillance of CRC for decades. But its sensitivity and specificity are not satisfied, which led to the necessity of invasive approaches, like colonoscopy, in most suspected CRC patients. In biopsy-proven CRC patients of the cohort, the cd-score demonstrated superior sensitivity and specificity to CEA for CRC diagnosis (AUC 0.96 vs 0.72,  FIG. 37F ). Both cd-score and CEA values were highly correlated with tumor stage ( FIG. 37B  and  FIG. 37D ). In patients with treatment response or tumor recurrence, the cd-score showed more significant changes from initial diagnosis than that of CEA ( FIG. 37E  and  FIG. 37F ). 
     cfDNA Prognostic Prediction Model for CRC 
     Next the potential to use a combined prognosis score (cp-score) based on ctDNA methylation analysis was investigated for prediction of prognosis of CRC in combination with clinical and demographic characteristics including age, gender, primary tumor site and AJCC stage. Follow-up information of 801 CRC patients for prognostic score-based analysis was completed. The median follow-up times were 11.9 months (rang 0.5-25.7 months). The same training dataset as diagnosis section containing 528 observations with 73 events and validation dataset containing 273 observations with 32 events was applied. A variable selection was conducted on the training set and built the composite score on the validation set. UniCox and LASSO-Cox methods were implemented to reduce the dimensionality and constructed a Cox-model to predict prognosis with a two-markers panel ( FIG. 33A  and Table 14). A Kaplan-Meier curve and log-rank test were generated using the dichotomized composite score, which resulted in a high-risk and low-risk group membership assignment according to its median. Median survival time in low-risk group was significantly better than that in high-risk group (p&lt;0.001) ( FIG. 33B  and  FIG. 33C ). 
     Time-depended ROC was used to summarize the discrimination potential of the composite score, AJCC stage, CEA level, primary tumor location, and the combination of all the existing biomarkers. Multivariate Cox regression indicated that the cp-score significantly correlated with risk of death and was an independent risk factor of survival both in training set and validation set (Table 15). TNM stage (as defined by AJCC guidelines), CEA level, primary tumor location also predicted the 12-month survival of patients with CRC (Table 15). Furthermore, the combination of cp-score and clinical characteristics improved the ability to predict prognosis (AUC 0.79, 95% CI 0.70-0.88 in training cohort and 0.85, 95% CI 0.75-0.96) in validation cohort) ( FIG. 33D  and  FIG. 33E ). 
     Nomograms create a simple graphical representation of a statistical predictive model that generates a numerical probability of a clinical event. It can reduce statistical predictive models into a single numerical estimate of the probability of death. Multivariate Cox regression analysis identified four variables as independent predictive factors (cp-score, CEA level, TNM stage and primary tumor location, Table 15) in both training and validation cohort. As such, a nomogram was developed with point scales of these 4 variables to predict overall survival for CRC patients ( FIG. 34A ). The sum of each variable point was plotted on the total point axis, and the estimated median 1- and 2-year overall survival rates were obtained by drawing a vertical line from the plotted total point axis straight down to the outcome axis. In validation cohort, the c-index of this model was 0.839, indicating a good discrimination.  FIG. 34B  showed the calibration graph for the nomogram, in which the probability of 1-year overall survival as predicted by the nomogram is plotted against the corresponding observed survival rates obtained by the Kaplan-Meier method. 
     cfDNA Methylation Based Subtyping of CRC 
     To generate a cfDNA methylation based subtypes of CRC, an unsupervised clustering method was utilized. The method applied an iteration strategy that iteratively got rid of markers had less contributions to the initial clusters ( FIG. 35A ). Using the same training dataset as in building prognosis model, two clusters of CRC samples were obtained with a combined total of 45 markers that were differential methylated between clusters ( FIG. 35B ). From in silhouette analysis of the last round of iterations, a high separation distance was observed ( FIG. 35C ). The validation set was also classified by computing their correlation coefficients to centroid profile of clusters from training set. 
     The validation set was divided into two groups that shows a distinctly different methylation profile of the 45 markers ( FIG. 35D ). To explore the clinical relevance of the two subtypes, associations between the subtype and all available clinical factors including TNM stage, tumor site, MMR status, MSS status, tumor burden, sex, mutation status of a limited gene panels and survival outcomes were systematically tested. As a result, the second cluster in both training and validation data set have a significant poor survival rate than that of the first cluster ( FIG. 35E , upper panel, both p&lt;0.01, Log-rank Test). It was also found that the proportion of high stage CRC in cluster 2 were significantly higher than cluster 1 ( FIG. 35E , lower panel, both p&lt;0.05, Chi-squared Test). Considering the potential multicollinearity of the subtype and TNM stage in discriminating cohort with poor survival, multivariate cox regression analysis was performed and both factors were mutually independent for predicting overall survivals (Table 16). 
     The 45 markers for subtyping was classified into two groups according to the hypo- or hyper-methylation in clusters (27 hypo and 18 hyper in cluster 2, Table 20). Among these markers, three was also identified in the list of diagnosis markers and one was identified in both the diagnosis and prognosis marker lists ( FIG. 38 ). Further analysis showed that cp-scores in cluster2 were significantly higher than cluster1 in two datasets ( FIG. 40B , p&lt;0.001, Wilcox Test). Given that 7-45018848 was identified in the three separate marker lists and was shown to be hyper-methylated in cluster 2, its presence may contribute to the vary survival outcomes between the two subtypes. 
     As CpG site cg10673833 (7-45018848) was the only overlapping marker in diagnosis, prognosis, and molecular subtype analysis, the utility of cg10673833 as a potential marker in monitoring treatment response was investigate. Serial longitudinal dynamic changes of methylation values of cg10673833 were obtained in a group of CRC patients. The results showed that there was a high correlation between methylation values of cg10673833 and treatment outcomes, which was greater than that observed for CEA ( FIG. 39 ). In patients with serial samples, those with a positive treatment response had a concomitant and significant decrease in methylation values of cg10673833 compared to that prior to treatment, and there was an even further reduction in methylation values of cg10673833 in patients after surgery. In contrast, patients with progressive or recurrent disease had an increase methylation rate ( FIG. 40 ). 
     Methylation Markers for Screening and Early Diagnosis of CRC in High-Risk Populations Using 7-45018848 
     The potential of cg10673833 as a methylation marker in detection of CRC and precancerous lesions in high-risk population based on plasma samples was investigated. From January 2015 through June 2017, 1450 participants who were recognized with high risks of CRC, were scheduled to undergo screening colonoscopy and methylation test of cg10673833 ( FIG. 31B ). 
     Table 17 showed the screening results of colonoscopy and cg10673833 methylation testing. cg10673833 methylation testing identified 9/10 participants with CRC and 7/8 participants with CRC in situ, for sensitivity of 88.9% (95% CI, 0.74-1.00) and specificity of 86.5% (95% CI, 0.85-0.88). Positive predictive value (PPV) and negative predictive value (NPV) were 0.077 (95% CI, 0.041 to 0.113) and 0.998 (95% CI, 0.996-1.00) (Table 21), respectively. For advanced precancerous lesions, the sensitivity was 33.3% (95% CI, 0.229-0.438), significant higher than the positively rate for subjects without any pathology (12.3%, 95% CI, 0.106-0.141). 
     
       
         
           
               
             
               
                 TABLE 13 
               
             
            
               
                   
               
               
                 Characteristics of night methylation markers 
               
               
                 and their coefficients in diagnosis. 
               
            
           
           
               
               
               
               
               
            
               
                   
                 MCBs 
                 Target ID 
                 Ref Gene 
                 Coefficients 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Intersect 
                   
                 −2.76 
               
               
                   
                 7-45018848 
                 cg10673833 
                 MYO1G 
                 15.98 
               
               
                   
                 1-161169007 
                 cg10493436 
                 ADAMTS4 
                 1.88 
               
               
                   
                 10-88684020 
                 cg10428836 
                 BMPR1A 
                 −6.05 
               
               
                   
                 11-60738995 
                 cg27284288 
                 CD6 
                 3.29 
               
               
                   
                 12-7276714 
                 cg16959747 
                 RBP5 
                 1.45 
               
               
                   
                 13-106834900 
                 cg17494199 
                 Chr 13:10 
                 −2.24 
               
               
                   
                 14-55647474 
                 cg23678254 
                 LGAP5 
                 2.71 
               
               
                   
                 6-16729606 
                 cg24067911 
                 ATXN1 
                 −3.41 
               
               
                   
                 8-20375578 
                 cg25459300 
                 Chr 8:20 
                 4.06 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 14 
               
             
            
               
                   
               
               
                 Characteristics of two methylation markers 
               
               
                 and their coefficients in prognosis. 
               
            
           
           
               
               
               
               
               
               
            
               
                 MCBs 
                 Target ID 
                 Ref Gene 
                 Coefficients 
                 HR 
                 CI (lower) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 7-45018848 
                 cg10673833 
                 MYO1G 
                 2.06 
                 7.81 
                 2.11 
               
               
                 3-111852156 
                 cg25462303 
                 GCET2 
                 3.05 
                 21.13 
                 5.98 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 15 
               
             
            
               
                   
               
               
                 Multivariable cox regression analysis with covariates 
               
               
                 including cp-score, gender, age, tumor location, 
               
               
                 TNM stage, CEA for overall survival. 
               
            
           
           
               
               
            
               
                   
                 Training dataset 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Exp 
                 Se 
                   
                   
                   
               
               
                 Factor 
                 coef 
                 (coef) 
                 (coef) 
                 z 
                 p 
                 coef 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 cp-score: H vs L 
                 0.75 
                 2.11 
                 0.13 
                 5.88 
                 &lt;0.001 
                 0.72 
               
               
                 Gender: M vs F 
                 −0.31 
                 0.73 
                 0.26 
                 −1.19 
                 0.232 
                 −0.15 
               
               
                 Age: &gt;=65 vs &lt;65 
                 0.02 
                 1.02 
                 0.01 
                 1.91 
                 0.056 
                 −0.01 
               
               
                 Stage: III/IV vs I/II 
                 0.92 
                 2.51 
                 0.27 
                 3.45 
                 0.001 
                 1.95 
               
               
                 Tumor site: R vs L 
                 0.62 
                 1.85 
                 0.26 
                 2.36 
                 0.018 
                 0.86 
               
               
                 CEA: &gt;= vs &lt;100 
                 0.7 
                 2.02 
                 0.31 
                 2.26 
                 0.024 
                 1.33 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 16 
               
             
            
               
                   
               
               
                 Association between cfDNA methylation based CRC subtypes 
               
               
                 and CRC prognosis in both the training and validation 
               
               
                 set (the same cohort as the prognosis model analysis). 
               
            
           
           
               
               
               
            
               
                   
                 Univariate 
                 Multivariate 
               
            
           
           
               
               
               
               
               
            
               
                   
                 HR (95% CI) 
                 P 
                 HR (95% CI) 
                 P 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                 Training Set 
                   
                   
                   
                   
               
               
                 Subtype: 
                  6.36 (3.98-10.17) 
                 &gt;0.001 
                 5.24 (3.26-8.4) 
                 &gt;0.001 
               
               
                 2 vs 1 
               
               
                 TNM stage: 
                 7.46 (2.35-23.7) 
                 &gt;0.001 
                  4.95 (1.54-15.9) 
                 0.007 
               
               
                 III-IV vs I-II 
               
               
                 Validation 
               
               
                 Set 
               
               
                 Subtype: 
                 2.61 (1.25-5.14) 
                 0.01 
                 2.28 (1.1-4.73) 
                 0.027 
               
               
                 2 vs 1 
               
               
                 TNM stage: 
                 10.65 (1.45-78.02) 
                 0.02 
                  9.54 (1.3-70.12) 
                 0.027 
               
               
                 III-IV vs I-II 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 17 
               
             
            
               
                   
               
               
                 Sensitivity and Specificity of the ctDNA methylation test for the findings on colonoscopy. 
               
            
           
           
               
               
            
               
                   
                 Colonoscopy Finding 
               
            
           
           
               
               
               
            
               
                   
                 Persons with Finding (n = 1450) 
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Positive 
                 Negative 
                 ctDNA methylation test (n = 1450) 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                 Results 
                 Results 
                 Sensitivity 
                 Specificity 
               
               
                 Colorectal cancer 
                 No. 
                 No. 
                 No. 
                 (95% CI) 
                 (95% CI) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Stage I-III 
                 10 
                 9 
                 1 
                 90.0 
                 (71.4-108) 
                   
                   
               
               
                 colorectal cancer 
               
               
                 High grade 
                 8 
                 7 
                 1 
                 87.5 
                 (64.6-110) 
               
               
                 dysplasia 
               
               
                 All Colorectal 
                   
                   
                   
                 88.9 
                 (74.4-103) 
               
               
                 Cancer 
               
               
                 Advanced 
                 78 
                 26 
                 52 
                 33.3 
                 (22.9-43.8) 
                 66.7 
                 (0.56-0.77) 
               
               
                 precancerous 
               
               
                 lesion* 
               
               
                 Non-advanced 
                 100 
                 24 
                 76 
                 24.0 
                 (15.6-32.2) 
                 76.0 
                 (0.68-0.84) 
               
               
                 adenoma 
               
               
                 Polyps 
                 250 
                 20 
                 230 
                 8.0 
                 (4.6-11.4) 
                 92.0 
                 (88.6-95.4) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Inflammatory 
                 10 
                 0 
                 10 
                   
                   
                 100 
               
               
                 bowel disease 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Negative on 
                 994 
                 123 
                 871 
                   
                   
                 87.6 
                 (85.6-89.7) 
               
               
                 colonoscopy 
               
               
                 All non-advanced 
                 1354 
                 167 
                 1187 
                   
                   
                 87.7 
                 (85.9-89.4) 
               
               
                 adenoma, non- 
               
               
                 neoplastic finding, 
               
               
                 and negative 
               
               
                 results on 
               
               
                 colonoscopy 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 18 
               
             
            
               
                   
               
               
                 Clinical characteristics of study cohort. 
               
            
           
           
               
               
               
               
               
            
               
                   
                 TCGA 
                 GSE 
                   
                   
               
               
                   
                 CRC 
                 Normal 
                 CRC 
                 Normal 
               
               
                 Characteristic 
                 tissue 
                 blood 
                 serum 
                 control 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 Total (n) 
                 459 
                 754 
                 801 
                 1021 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Gender 
                   
                   
                   
                   
                   
                   
                   
               
               
                 Female-no.(%) 
                 216 
                 (47.1) 
                 401 (53.2) 
                 305 
                 (38.1) 
                 486 
                 (47.6) 
               
               
                 Male-no.(%) 
                 243 
                 (52.9) 
                 353 (46.8) 
                 496 
                 (61.9) 
                 470 
                 (46.0) 
               
            
           
           
               
               
               
               
               
               
            
               
                 NA 
                 0 
                  0 
                 0 
                 65 
                 (6.4) 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Age (years) 
                   
                   
                   
                   
                   
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                 Mean 
                 68 
                  63 
                 58 
                  47 
               
            
           
           
               
               
               
               
               
            
               
                 Range 
                 33-90 
                 19-101 
                 24-85 
                 19-90 
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Stage 
                   
                   
                   
                   
                   
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 I 
                 76 
                 (16.6) 
                 NA 
                 38 
                 (4.7) 
                 NA 
               
               
                 II 
                 179 
                 (39.0) 
                 NA 
                 139 
                 (17.4) 
                 NA 
               
               
                 III 
                 131 
                 (28.5) 
                 NA 
                 209 
                 (26.1) 
                 NA 
               
               
                 IV 
                 65 
                 (14.2) 
                 NA 
                 406 
                 (50.7) 
                 NA 
               
               
                 NA 
                 8 
                 (1.7) 
                 NA 
                 9 
                 (1.1) 
                 NA 
               
               
                 Tumor Burden 
               
               
                 Tumor free 
                 199 
                 (43.4) 
                 NA 
                 290 
                 (36.2) 
                 NA 
               
               
                 With tumor 
                 215 
                 (46.8) 
                 NA 
                 511 
                 (63.8) 
                 NA 
               
            
           
           
               
               
               
               
               
               
            
               
                 NA 
                 45 
                 (9.8) 
                 NA 
                 0 
                 NA 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 RAS Status 
                   
                   
                   
                   
                   
                   
               
               
                 Wide type 
                 24 
                 (5.3) 
                 NA 
                 122 
                 (15.2) 
                 NA 
               
               
                 Mutation 
                 23 
                 (5.0) 
                 NA 
                 78 
                 (9.8) 
                 NA 
               
               
                 NA 
                 412 
                 (89.7) 
                 NA 
                 601 
                 (75.0) 
                 NA 
               
               
                 MMR Status 
               
               
                 Proficient 
                 81 
                 (17.7) 
                 NA 
                 476 
                 (59.4) 
                 NA 
               
               
                 Deficient 
                 12 
                 (2.6) 
                 NA 
                 35 
                 (4.4) 
                 NA 
               
               
                 NA 
                 366 
                 (79.7) 
                 NA 
                 290 
                 (36.2) 
                 NA 
               
               
                 Tumor Site 
               
               
                 Right colon 
                 257 
                 (55.9) 
                 NA 
                 197 
                 (24.6) 
                 NA 
               
               
                 Left colon 
                 182 
                 (39.7) 
                 NA 
                 593 
                 (74.0) 
                 NA 
               
               
                 NA 
                 20 
                 (4.4) 
                 NA 
                 11 
                 (1.4) 
                 NA 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 19 
               
             
            
               
                   
               
               
                 Clinical characteristic of screening study cohort. 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 CRC 
                 AA 
                 Others 
               
               
                   
                 Characteristic 
                 N = 18 
                 N = 78 
                 N = 1354 
               
               
                   
                   
               
               
                   
                 Age (mean [SD]) 
                   
                   
                   
               
               
                   
                 45-49 yr-no. (%) 
                 0 
                 4 (5.1) 
                 338 (25.0) 
               
               
                   
                 50-59 yr-no. (%) 
                  8 (44.4) 
                 18 (23.1) 
                 446 (32.9) 
               
               
                   
                 60-69 yr-no. (%) 
                 10 (55.6) 
                 42 (53.9) 
                 472 (34.9) 
               
               
                   
                 &gt;70 yr-no. (%) 
                 0 
                 14 (17.9) 
                 98 (7.2) 
               
               
                   
                 Sex 
               
               
                   
                 Female (%) 
                  8 (44.4) 
                 30 (38.5) 
                 698 (51.6) 
               
               
                   
                 Male (%) 
                 10 (55.6) 
                 48 (61.5) 
                 656 (48.4) 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 20 
               
             
            
               
                   
               
               
                 Characteristics of 45 methylation markers in 
               
               
                 ctDNA methylation based subtyping of CRC. 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 Methylation 
                   
               
               
                 MCBs 
                 Target ID 
                 status 
                 Ref Gene 
               
               
                   
               
               
                 4-38673144 
                 cg05205843 
                 Hypo in cluster 1 
                 KLF3 
               
               
                 17-75539913 
                 cg11841704 
                 Hypo in cluster 1 
                 NA 
               
               
                 8-95651048 
                 cg06699564 
                 Hypo in cluster 1 
                 NA 
               
               
                 13-111160399 
                 cg08924619 
                 Hypo in cluster 1 
                 COL4A2 
               
               
                 1-57001742 
                 cg11959316 
                 Hypo in cluster 1 
                 PPAP2B 
               
               
                 13-111160365 
                 cg08924619 
                 Hypo in cluster 1 
                 COL4A2 
               
               
                 8-95651086 
                 cg06699564 
                 Hypo in cluster 1 
                 NA 
               
               
                 17-75539901 
                 cg01824933 
                 Hypo in cluster 1 
                 NA 
               
               
                 13-111160418 
                 cg08924619 
                 Hypo in cluster 1 
                 COL4A2 
               
               
                 4-3867313 
                 cg05205842 
                 Hypo in cluster 1 
                 KLF3 
               
               
                 13-111160424 
                 cg08924619 
                 Hypo in cluster 1 
                 COL4A2 
               
               
                 17-48894963 
                 cg04049981 
                 Hypo in cluster 1 
                 NA 
               
               
                 1-15686708 
                 cg09026722 
                 Hypo in cluster 1 
                 PEAR1 
               
               
                 13-112602444 
                 cg03616722 
                 Hypo in cluster 1 
                 NA 
               
               
                 13-11116043 
                 cg08924619 
                 Hypo in cluster 1 
                 COL4A2 
               
               
                 17-16924597 
                 cg05928904 
                 Hypo in cluster 1 
                 NA 
               
               
                 3-194826585 
                 cg08704934 
                 Hypo in cluster 1 
                 C3orf21 
               
               
                 7-2150548 
                 cg09776772 
                 Hypo in cluster 1 
                 MAD1L1 
               
               
                 13-106834942 
                 cg17494199 
                 Hypo in cluster 1 
                 NA 
               
               
                 17-75539895 
                 cg01824933 
                 Hypo in cluster 1 
                 NA 
               
               
                 8-126285443 
                 cg16296417 
                 Hypo in cluster 1 
                 NSMCE2 
               
               
                 7-2150534 
                 cg09776772 
                 Hypo in cluster 1 
                 MAD1L1 
               
               
                 7-215055 
                 cg09776772 
                 Hypo in cluster 1 
                 MAD1L1 
               
               
                 15-68498251 
                 cg05338167 
                 Hypo in cluster 1 
                 CA1ML4 
               
               
                 1-161169007 
                 cg10493436 
                 Hypo in cluster 1 
                 ADAMTS4 
               
               
                 13-112612344 
                 cg011251410 
                 Hypo in cluster 1 
                 NA 
               
               
                 15-58723675 
                 cg16391792 
                 Hypo in cluster 1 
                 LIPC 
               
               
                 6-31527889 
                 cg06393830 
                 Hyper in cluster 2 
                 NA 
               
               
                 2-113931518 
                 cg09366118 
                 Hyper in cluster 2 
                 PSD4 
               
               
                 3-118955835 
                 cg22513455 
                 Hyper in cluster 2 
                 B4GALT4 
               
               
                 8-129005567 
                 cg17583432 
                 Hyper in cluster 2 
                 PVT1 
               
               
                 8-59058648 
                 cg23881926 
                 Hyper in cluster 2 
                 FAM110B 
               
               
                 16-29757318 
                 cg09638208 
                 Hyper in cluster 2 
                 C16orf54 
               
               
                 17-55456535 
                 cg12441066 
                 Hyper in cluster 2 
                 MSI2 
               
               
                 11-60738995 
                 cg27284288 
                 Hyper in cluster 2 
                 CD6 
               
               
                 10-14701815 
                 cg04441857 
                 Hyper in cluster 2 
                 FAM107B 
               
               
                 8-129005599 
                 cg17583432 
                 Hyper in cluster 2 
                 PVT1 
               
               
                 7-45018848 
                 cg10673833 
                 Hyper in cluster 2 
                 MYO1G 
               
               
                 1-154128002 
                 cg19757176 
                 Hyper in cluster 2 
                 TPM3 
               
               
                 19-1602335 
                 cg08670281 
                 Hyper in cluster 2 
                 CYP4F11 
               
               
                 8-129005583 
                 cg17583432 
                 Hyper in cluster 2 
                 PVT1 
               
               
                 17-8370004 
                 cg04460364 
                 Hyper in cluster 2 
                 NDEL1 
               
               
                 12-7276714 
                 cg16959747 
                 Hyper in cluster 2 
                 RBP5 
               
               
                 12-132269608 
                 cg15011734 
                 Hyper in cluster 2 
                 SFRS8 
               
               
                 2-69027039 
                 cg25754195 
                 Hyper in cluster 2 
                 ARHGAP25 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE 21 
               
             
            
               
                   
               
               
                 Positive and negative predictive values of cfDNA methylation test. 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 Positive 
                 Negative 
               
               
                   
                 N of 
                 Predictive Value 
                 Predictive Value 
               
               
                 Outcome 
                 participations 
                 (95% CI) 
                 (95% CI) 
               
               
                   
               
               
                 Colorectal 
                 18/1450 
                 0.077 (0.041-0.113) 
                 0.998 (0.996-1.00)  
               
               
                 cancer 
               
               
                 Advanced 
                 78/1450 
                 0.134 (0.087-0.183) 
                 0.042 (0.031-0.053) 
               
               
                 precancerous 
               
               
                 lesion 
               
               
                   
               
            
           
         
       
     
     While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby. 
       
     
       
         
           
               
             
               
                 Lengthy table referenced here 
               
             
            
               
                 US20200277677A1-20200903-T00001 
               
               
                 Please refer to the end of the specification for access instructions. 
               
            
           
         
       
     
       
     
       
         
           
               
             
               
                 LENGTHY TABLES 
               
             
            
               
                 The patent application contains a lengthy table section. A copy of the table is available in electronic form from the USPTO web site (http://seqdata.uspto.gov/?pageRequest=docDetail&amp;DocID=US20200277677A1). An electronic copy of the table will also be available from the USPTO upon request and payment of the fee set forth in 37 CFR 1.19(b)(3).