Method and system for determining cancer status

Abstract

The present application relates to methods and systems for determining cancer status. The present application discloses methods, systems, platforms, non-transitory computer-readable media, services and kits for determining the type of cancer in an individual. The present application also describes methods, systems, platforms, non-transitory computer-readable media and compositions for generating a CpG methylation profile database.

Description

[0001] This application is a divisional application of an application filed on January 15, 2016, with application number 201680004125.9 and invention name “Method and system for determining cancer status (the changed name is “System for determining cancer status”)”.

[0002] Cross Reference

[0003] This application claims the benefit of U.S. Provisional Application No. 62 / 104,785, filed January 18, 2015, and U.S. Application No. 14 / 986,520, filed December 31, 2015, which are incorporated herein by reference.

[0004] Sequence Listing

[0005] This application contains a sequence listing, which has been submitted in ASCII format via EFS and is incorporated herein by reference in its entirety. This ASCII copy, created on January 12, 2016, is named 49697-701.601_SL.txt and is 846 kilobytes in size. Background Art

[0006] Cancer is the leading cause of death worldwide, with annual cases projected to increase from 14 million in 2012 to 22 million over the next two decades (WHO). In some cases, diagnostic procedures begin only after patients have developed symptoms, making them costly, invasive, and time-consuming. Furthermore, inaccessible areas sometimes hinder accurate diagnosis. Furthermore, higher cancer morbidity and mortality rates are associated with late diagnosis. Summary of the Invention

[0007] Disclosed herein in certain embodiments are methods, systems, platforms, non-transitory computer-readable media, services, and kits for determining the type of cancer in an individual. In some embodiments, described herein also include methods, systems, platforms, non-transitory computer-readable media, services, and kits for early detection of cancer. In other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for non-invasive detection of cancer. In yet other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for distinguishing different stages of cancer. In other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for determining the prognosis of cancer in an individual in need, predicting treatment response, and monitoring treatment response. In other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for generating a CpG methylation profile database, as well as probes for generating CpG methylation data.

[0008] Disclosed herein, in certain embodiments, is a computing platform for generating a cancer CpG methylation profile database using CpG cancer methylation data, the computing platform comprising:

[0009] (a) A first computing device comprising a processor, a memory module, an operating system, and a computer program, the computer program comprising instructions executable by the processor to generate a data acquisition application to generate CpG methylation data for a set of biological samples, the data acquisition application comprising:

[0010] (1) a sequencing module configured to operate a sequencing device to generate CpG methylation data for a set of biological samples, wherein the set comprises a first cancer biological sample, a second cancer biological sample, a third cancer biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancer biological samples are different; and wherein the first, second, and third normal biological samples are different; and

[0011] (2) A data receiving module configured to receive:

[0012] (i) a first pair of CpG methylation datasets generated from a first cancerous biological sample and a first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0013] (ii) a second pair of CpG methylation datasets generated from a second normal biological sample and a third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

[0014] and

[0015] (iii) a third pair of CpG methylation datasets generated from a second cancerous biological sample and a third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

[0016] and

[0017] (b) a second computing device comprising a processor, a memory module, an operating system, and a computer program, wherein the computer program comprises instructions executable by the processor to generate a data analysis application to generate a cancer CpG methylation profile database, wherein the data analysis application comprises a data analysis module configured to:

[0018] (1) generating a pairwise methylation difference dataset using the first pair, the second pair, and the third pair of datasets; and

[0019] (2) Using machine learning methods and control datasets to analyze pairwise methylation difference datasets to generate a cancer CpG methylation profile database, where

[0020] (i) the machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest scores, and classifying the sample based on the plurality of markers and the plurality of weights; and

[0021] (ii) The cancer CpG methylation profile database comprises a set of CpG methylation profiles, and each CpG methylation profile represents a cancer type.

[0022] In some embodiments, generating a pairwise methylation difference dataset comprises: (a) calculating a difference between a first dataset and a second dataset in a first pair of datasets; (b) calculating a difference between a third dataset and a fourth dataset in a second pair of datasets; and (c) calculating a difference between a fifth dataset and a sixth dataset in a third pair of datasets. In some embodiments, generating a pairwise methylation difference dataset is further based on the calculated difference of the first pair of datasets, the calculated difference of the second pair of datasets, and the calculated difference of the third pair of datasets.

[0023] In some embodiments, the machine learning method comprises a semi-supervised learning method or an unsupervised learning method. In some embodiments, the machine learning method utilizes an algorithm selected from one or more of the following: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, and neural network model.

[0024] In some embodiments, CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent. In some embodiments, the data analysis module is further configured to analyze the extracted genomic DNA using a next-generation sequencing method to generate CpG methylation data. In some embodiments, the next-generation sequencing method is a digital PCR sequencing method.

[0025] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of Tables 15 to 18. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 15. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 16. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 17. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 18.

[0026] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg2668300 5. cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg247 06505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg 27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg2549014 5. cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg2741 0601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg 27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0027] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, or more biomarkers selected from Table 20. In some embodiments, the methylation profile comprises at least 10, 20, 30, 40, 50, 100, or more biomarkers selected from the group consisting of: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg27125093, cg26112797, cg24166457, cg19300307, ​​cg17122157, cg135 55415, cg11436362, cg10673833, cg09866569, cg08075204, cg056143 46. ​​cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19675731, cg19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14083015, cg140 58476, cg12192582, cg10590292, cg06787669, cg06439655, cg025221 96. cg02233149, cg00558804, cg26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622899, cg14999168, cg13925432, cg12967050, cg11105292, cg09419005, cg091 53080, cg07380416, cg06825448, cg05596756, cg03891050, cg016813 67. cg01456691, cg00015530, cg27410601, cg27366280, cg26683005,cg25666403, cg24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg203 36172, cg18610205, cg18456621, cg17518965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044.

[0028] In some embodiments, the methylation profile comprises at least 1, 2, 3, or 4 biomarkers selected from cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555.

[0029] In some embodiments, the cancer type is a solid cancer type or a hematological malignancy. In some embodiments, the cancer type is a metastatic cancer type or a relapsed or refractory cancer type. In some embodiments, the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brainstem glioma, low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic carcinoma, Burkitt lymphoma (Burkitt lymphoma), ovarian cancer (VEGF ... lymphoma), cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC), childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma sarcoma), extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasm, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma, bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinomacarcinoma), Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasms, nasal cavity cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papillary ductal carcinoma tumors, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer (such as prostate adenocarcinoma (PRAD)), rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sezary syndrome ( syndrome), cutaneous melanoma (SKCM), small cell lung cancer, small intestinal cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilm's tumor. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, stomach cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0030] In some embodiments, the control dataset comprises a set of methylation profiles, wherein each of the methylation profiles is generated using a biological sample obtained from a known cancer type.

[0031] In some embodiments, the biological sample comprises a cell-free biological sample. In some embodiments, the biological sample comprises a prevalent tumor DNA sample. In some embodiments, the biological sample comprises a biopsy sample. In some embodiments, the biological sample comprises a tissue sample.

[0032] In some embodiments, described herein is a computing system comprising a processor, a memory module, an operating system configured to execute machine-readable instructions, and a computer program comprising instructions executable by the processor to generate an analysis application to generate a cancer CpG methylation profile database, the analysis application comprising:

[0033] (a) a data receiving module configured to receive:

[0034] (1) a first pair of CpG methylation datasets generated from a first cancerous biological sample and a first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset in the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset in the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0035] (2) a second pair of CpG methylation datasets generated from a second normal biological sample and a third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different; and

[0036] (3) a third pair of CpG methylation datasets generated from a second cancerous biological sample and a third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different; and

[0037] (b) a data analysis module configured to:

[0038] (1) generating a pairwise methylation difference dataset using the first pair, the second pair, and the third pair of datasets; and

[0039] (2) Using machine learning methods and control datasets to analyze pairwise methylation difference datasets to generate a cancer CpG methylation profile database, where

[0040] (i) the machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest scores, and classifying the sample based on the plurality of markers and the plurality of weights; and

[0041] (ii) The cancer CpG methylation profile database comprises a set of CpG methylation profiles, and each CpG methylation profile represents a cancer type.

[0042] Disclosed herein, in certain embodiments, is a computer-implemented method for generating a cancer CpG methylation profile database, comprising:

[0043] a. generating CpG methylation data for a set of biological samples by a sequencing method, wherein the set comprises a first cancerous biological sample, a second cancerous biological sample, a third cancerous biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancerous biological samples are different; and wherein the first, second, and third normal biological samples are different;

[0044] b. obtaining, using a first processor, a first pair of CpG methylation datasets generated from a first cancerous biological sample and a first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0045] c. obtaining, using the first computing device, a second pair of CpG methylation datasets generated from a second normal biological sample and a third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

[0046] d. obtaining, using the first computing device, a third pair of CpG methylation datasets generated from a second cancerous biological sample and a third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

[0047] e. generating a pairwise methylation difference dataset from the first, second, and third pairs of datasets using a second processor; and

[0048] f. Using a machine learning method, analyzing a pairwise methylation difference dataset using a control dataset to generate a cancer CpG methylation profile database, wherein

[0049] (1) The machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest score, and classifying the sample based on the plurality of markers and the plurality of weights; and

[0050] (2) The cancer CpG methylation profile database contains a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

[0051] In some embodiments, step e) further comprises (a) calculating a difference between the first dataset and the second dataset in the first pair of datasets; (b) calculating a difference between the third dataset and the fourth dataset in the second pair of datasets; and (c) calculating a difference between the fifth dataset and the sixth dataset in the third pair of datasets. In some embodiments, step e) further comprises generating, using a second processor, a pairwise methylation difference dataset from the calculated differences in the first pair of datasets, the calculated differences in the second pair of datasets, and the calculated differences in the third pair of datasets.

[0052] In some embodiments, the machine learning method comprises a semi-supervised learning method or an unsupervised learning method. In some embodiments, the machine learning method utilizes an algorithm selected from one or more of the following: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, and neural network model.

[0053] In some embodiments, CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent.

[0054] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of Tables 15 to 18. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 15. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 16. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 17. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 18.

[0055] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg2668300 5. cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg247 06505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg 27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg2549014 5. cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg2741 0601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg 27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0056] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, or more biomarkers selected from Table 20. In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, or more biomarkers selected from the group consisting of: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg11 8384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg27125093, cg26112797, cg24166457, cg193003 07, cg17122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204, cg05614346, cg02053964, cg27377213, cg24480810, cg 24301930, cg22513455, cg19693177, cg19675731, cg19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556 909, cg14083015, cg14058476, cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804, cg26680502, c g23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622899, cg14999168, cg13925432, cg12967050, cg1110 5292, cg09419005, cg09153080, cg07380416, cg06825448, cg05596756, cg03891050, cg01681367, cg01456691, cg00015530, cg27410601,cg27366280, cg26683005, cg25666403, cg24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621, cg17518965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044.

[0057] In some embodiments, the methylation profile comprises at least 1, 2, 3, or 4 biomarkers selected from cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555.

[0058] In some embodiments, the cancer type is a solid cancer type or a hematological malignancy type. In some embodiments, the cancer type is a relapsed or refractory cancer type. In some embodiments, the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain stem glioma, brain low grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic Myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma , pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasm, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer Lung cancer, oral cancer, oral cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumors, ovarian low malignant potential tumors, pancreatic cancer, papilloma, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer (such as prostate adenocarcinoma (PRAD)), rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma,Rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézare syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small bowel cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, stomach cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0059] In some embodiments, the control dataset comprises a set of methylation profiles, wherein each of the methylation profiles is generated using a biological sample obtained from a known cancer type.

[0060] In some embodiments, the biological sample comprises a cell-free biological sample. In some embodiments, the biological sample comprises a prevalent tumor DNA sample. In some embodiments, the biological sample comprises a biopsy sample. In some embodiments, the biological sample comprises a tissue sample.

[0061] In some embodiments, described herein is a computer-implemented method of diagnosing cancer in an individual in need thereof, comprising:

[0062] a. obtaining, using the first processor, a fourth pair of CpG methylation datasets generated from a fourth cancerous biological sample and a fourth normal biological sample, wherein the CpG methylation data generated from the fourth cancerous biological sample forms a seventh dataset within the fourth pair of datasets, the CpG methylation data generated from the first normal biological sample forms an eighth dataset within the fourth pair of datasets, and the fourth cancerous biological sample and the fourth normal biological sample are from the same biological sample source;

[0063] b. obtaining, using the first processor, a fifth pair of CpG methylation datasets generated from a fifth normal biological sample and a sixth normal biological sample, wherein the CpG methylation data generated from the fifth normal biological sample forms a ninth dataset within the fifth pair of datasets, the CpG methylation data generated from the sixth normal biological sample forms a tenth dataset within the fifth pair of datasets, and the fourth, fifth, and sixth normal biological samples are different;

[0064] c. obtaining, using the first processor, a sixth pair of CpG methylation datasets generated from a fifth cancerous biological sample and a sixth cancerous biological sample, wherein the CpG methylation data generated from the fifth cancerous biological sample forms an eleventh dataset within the sixth pair of datasets, the CpG methylation data generated from the sixth cancerous biological sample forms a twelfth dataset within the sixth pair of datasets, and the fourth, fifth, and sixth cancerous biological samples are different;

[0065] d. generating a second pairwise methylation difference dataset from the fourth, fifth, and sixth pairs of datasets using a second processor; and

[0066] e. Analyzing a second pairwise methylation difference dataset using the cancer CpG methylation profile database, wherein a correlation between the second pairwise methylation difference dataset and the CpG methylation profile in the cancer CpG methylation profile database determines the cancer type of the individual.

[0067] In some embodiments, the first processor is located on a first computing device and the second processor is located on a second computing device.In some embodiments, the method further comprises implementing a treatment regimen based on the diagnosed cancer type.

[0068] In some embodiments, described herein is a computer-implemented method for distinguishing between a primary tumor and metastatic cancer in an individual in need thereof, comprising:

[0069] a. obtaining, using the first processor, a fourth pair of CpG methylation datasets generated from a fourth cancerous biological sample and a fourth normal biological sample, wherein the CpG methylation data generated from the fourth cancerous biological sample forms a seventh dataset within the fourth pair of datasets, the CpG methylation data generated from the first normal biological sample forms an eighth dataset within the fourth pair of datasets, and the fourth cancerous biological sample and the fourth normal biological sample are from the same biological sample source;

[0070] b. obtaining, using the first processor, a fifth pair of CpG methylation datasets generated from a fifth normal biological sample and a sixth normal biological sample, wherein the CpG methylation data generated from the fifth normal biological sample forms a ninth dataset within the fifth pair of datasets, the CpG methylation data generated from the sixth normal biological sample forms a tenth dataset within the fifth pair of datasets, and the fourth, fifth, and sixth normal biological samples are different;

[0071] c. obtaining, using the first processor, a sixth pair of CpG methylation datasets generated from a fifth cancerous biological sample and a sixth cancerous biological sample, wherein the CpG methylation data generated from the fifth cancerous biological sample forms an eleventh dataset within the sixth pair of datasets, the CpG methylation data generated from the sixth cancerous biological sample forms a twelfth dataset within the sixth pair of datasets, and the fourth, fifth, and sixth cancerous biological samples are different;

[0072] d. generating a second pairwise methylation difference dataset from the fourth, fifth, and sixth pairs of datasets using a second processor; and

[0073] e. Analyze the second pairwise methylation difference dataset using the cancer CpG methylation profile database, wherein the correlation between the second pairwise methylation difference dataset and the CpG methylation profiles in the cancer CpG methylation profile database distinguishes between primary tumors and metastatic cancers in an individual.

[0074] In some embodiments, described herein is a computer-implemented method of monitoring cancer progression in an individual in need thereof, comprising:

[0075] a. obtaining, using the first processor, a fourth pair of CpG methylation datasets generated from a fourth cancerous biological sample and a fourth normal biological sample, wherein the CpG methylation data generated from the fourth cancerous biological sample forms a seventh dataset within the fourth pair of datasets, the CpG methylation data generated from the first normal biological sample forms an eighth dataset within the fourth pair of datasets, and the fourth cancerous biological sample and the fourth normal biological sample are from the same biological sample source;

[0076] b. obtaining, using the first processor, a fifth pair of CpG methylation datasets generated from a fifth normal biological sample and a sixth normal biological sample, wherein the CpG methylation data generated from the fifth normal biological sample forms a ninth dataset within the fifth pair of datasets, the CpG methylation data generated from the sixth normal biological sample forms a tenth dataset within the fifth pair of datasets, and the fourth, fifth, and sixth normal biological samples are different;

[0077] c. obtaining, using the first processor, a sixth pair of CpG methylation datasets generated from a fifth cancerous biological sample and a sixth cancerous biological sample, wherein the CpG methylation data generated from the fifth cancerous biological sample forms an eleventh dataset within the sixth pair of datasets, the CpG methylation data generated from the sixth cancerous biological sample forms a twelfth dataset within the sixth pair of datasets, and the fourth, fifth, and sixth cancerous biological samples are different;

[0078] d. generating a second pairwise methylation difference dataset from the fourth, fifth, and sixth pairs of datasets using a second processor; and

[0079] e. Analyzing a second pairwise methylation difference dataset using the cancer CpG methylation profile database, wherein a correlation between the second pairwise methylation difference dataset and the CpG methylation profile in the cancer CpG methylation profile database indicates whether the individual has cancer progression.

[0080] In some embodiments, the individual has received treatment prior to obtaining the first cancerous biological sample and the first normal biological sample.

[0081] In some embodiments, described herein are computer-implemented methods of determining cancer progression in an individual in need thereof, comprising:

[0082] a. obtaining, using the first processor, a fourth pair of CpG methylation datasets generated from a fourth cancerous biological sample and a fourth normal biological sample, wherein the CpG methylation data generated from the fourth cancerous biological sample forms a seventh dataset within the fourth pair of datasets, the CpG methylation data generated from the first normal biological sample forms an eighth dataset within the fourth pair of datasets, and the fourth cancerous biological sample and the fourth normal biological sample are from the same biological sample source;

[0083] b. obtaining, using the first processor, a fifth pair of CpG methylation datasets generated from a fifth normal biological sample and a sixth normal biological sample, wherein the CpG methylation data generated from the fifth normal biological sample forms a ninth dataset within the fifth pair of datasets, the CpG methylation data generated from the sixth normal biological sample forms a tenth dataset within the fifth pair of datasets, and the fourth, fifth, and sixth normal biological samples are different;

[0084] c. obtaining, using the first processor, a sixth pair of CpG methylation datasets generated from a fifth cancerous biological sample and a sixth cancerous biological sample, wherein the CpG methylation data generated from the fifth cancerous biological sample forms an eleventh dataset within the sixth pair of datasets, the CpG methylation data generated from the sixth cancerous biological sample forms a twelfth dataset within the sixth pair of datasets, and the fourth, fifth, and sixth cancerous biological samples are different;

[0085] d. generating a second pairwise methylation difference dataset from the fourth, fifth, and sixth pairs of datasets using a second processor; and

[0086] e. Analyzing a second pairwise methylation difference dataset using the cancer CpG methylation profile database, wherein the correlation between the second pairwise methylation difference dataset and the CpG methylation profile in the cancer CpG methylation profile database determines the cancer prognosis of the individual.

[0087] In some embodiments, the cancer prognosis is related to the cancer stage. In some embodiments, the cancer prognosis is not related to the cancer stage. In some embodiments, the cancer prognosis indicates the potential for an individual to respond to treatment.

[0088] Disclosed herein, in certain embodiments, are probe sets comprising a plurality of probes, each of which is a probe of Formula I:

[0089]

[0090] in:

[0091] A is the first target binding region;

[0092] B is the second target binding region; and

[0093] L is the linker region;

[0094] wherein A has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 30 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775; B has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 12 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775; L is linked to A; and B is linked to A or L.

[0095] In some embodiments, L is connected to A and B is connected to L. In some embodiments, the plurality of probes comprises at least 10, 20, 30, 50, 100 or more probes. In some embodiments, the plurality of probes is used in a solution-based next-generation sequencing reaction to generate CpG methylation data. In some embodiments, the solution-based next-generation sequencing reaction is a droplet digital PCR sequencing method. In some embodiments, each probe is associated with a CpG site. In some embodiments, the length of L is between 10 and 60, 15 and 55, 20 and 50, 25 and 45, and 30 and 40 nucleotides. In some embodiments, L further comprises an adapter region. In some embodiments, the adapter region comprises a sequence for identifying each probe.

[0096] In certain embodiments, disclosed herein is a non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, perform steps comprising:

[0097] a. generating CpG methylation data for a set of biological samples by a sequencing method, wherein the set comprises a first cancerous biological sample, a second cancerous biological sample, a third cancerous biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancerous biological samples are different; and wherein the first, second, and third normal biological samples are different;

[0098] b. obtaining, using a first processor, a first pair of CpG methylation datasets generated from a first cancerous biological sample and a first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0099] c. obtaining, using the first computing device, a second pair of CpG methylation datasets generated from a second normal biological sample and a third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

[0100] d. obtaining, using the first computing device, a third pair of CpG methylation datasets generated from a second cancerous biological sample and a third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

[0101] e. generating a pairwise methylation difference dataset from the first, second, and third pairs of datasets using a second processor; and

[0102] f. Using a machine learning method, analyzing a pairwise methylation difference dataset using a control dataset to generate a cancer CpG methylation profile database, wherein

[0103] (1) The machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest score, and classifying the sample based on the plurality of markers and the plurality of weights; and

[0104] (2) The cancer CpG methylation profile database contains a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

[0105] In some embodiments, step e) further comprises (a) calculating a difference between the first dataset and the second dataset in the first pair of datasets; (b) calculating a difference between the third dataset and the fourth dataset in the second pair of datasets; and (c) calculating a difference between the fifth dataset and the sixth dataset in the third pair of datasets. In some embodiments, step e) further comprises generating, using a second processor, a pairwise methylation difference dataset from the calculated differences in the first pair of datasets, the calculated differences in the second pair of datasets, and the calculated differences in the third pair of datasets.

[0106] In some embodiments, the machine learning method comprises a semi-supervised learning method or an unsupervised learning method. In some embodiments, the machine learning method utilizes an algorithm selected from one or more of the following: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, and neural network model.

[0107] In some embodiments, CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent.

[0108] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of Tables 15 to 18. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 15. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 16. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 17. In some embodiments, the methylation profile comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 biomarkers selected from the group consisting of Table 18.

[0109] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg2668300 5. cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg247 06505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg 27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg2549014 5. cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg2741 0601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg 27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0110] In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, or more biomarkers selected from Table 20. In some embodiments, the methylation profile comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100, or more biomarkers selected from the group consisting of: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg11 8384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg27125093, cg26112797, cg24166457, cg193003 07, cg17122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204, cg05614346, cg02053964, cg27377213, cg24480810, cg 24301930, cg22513455, cg19693177, cg19675731, cg19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556 909, cg14083015, cg14058476, cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804, cg26680502, c g23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622899, cg14999168, cg13925432, cg12967050, cg1110 5292, cg09419005, cg09153080, cg07380416, cg06825448, cg05596756, cg03891050, cg01681367, cg01456691, cg00015530, cg27410601,cg27366280, cg26683005, cg25666403, cg24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621, cg17518965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044.

[0111] In some embodiments, the methylation profile comprises at least 1, 2, 3, or 4 biomarkers selected from cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555.

[0112] In some embodiments, the cancer type is a solid cancer type or a hematological malignancy type. In some embodiments, the cancer type is a relapsed or refractory cancer type. In some embodiments, the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain stem glioma, brain low grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic Myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma , pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasm, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer Lung cancer, oral cancer, oral cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumors, ovarian low malignant potential tumors, pancreatic cancer, papilloma, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer (such as prostate adenocarcinoma (PRAD)), rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma,Rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézare syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small bowel cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, stomach cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0113] In some embodiments, the control dataset comprises a set of methylation profiles, wherein each of the methylation profiles is generated using a biological sample obtained from a known cancer type.

[0114] In some embodiments, the biological sample comprises a cell-free biological sample. In some embodiments, the biological sample comprises a prevalent tumor DNA sample. In some embodiments, the biological sample comprises a biopsy sample. In some embodiments, the biological sample comprises a tissue sample.

[0115] Disclosed herein, in certain embodiments, is a method of determining the presence of cancer in an individual in need thereof, comprising: (a) treating extracted genomic DNA with a deaminating agent to produce treated genomic DNA comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample of the individual; (b) generating a methylation profile comprising one or more biomarkers selected from Table 20 from the treated genomic DNA; and (c) comparing the methylation profile to a control profile, wherein a correlation between the methylation profile and the control profile determines the presence of cancer in the individual.

[0116] In some embodiments, the methylation profile further comprises one or more biomarkers selected from Tables 15 to 18.

[0117] In some embodiments, the methylation profile further comprises one or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09 844573, cg03087897, cg24706505, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419 005, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg2 7410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721 , cg27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg2611 2797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg 23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg0530497 9. cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg230 93496, cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, c g03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg224608 96. cg07116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10 590292, cg00037681, cg05596756, cg03569637, cg02522196, cg11655490,cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985、cg18856478、cg23229016、cg21004490、cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0118] In some embodiments, the comparison further comprises generating a pairwise methylation difference dataset comprising (i) a first difference between the methylation profile of the treated genomic DNA and the methylation profile of the first normal sample; (ii) a second difference between the methylation profile of the second normal sample and the methylation profile of the third normal sample; and (iii) a third difference between the methylation profile of the first primary cancer sample and the methylation profile of the second primary cancer sample. In some embodiments, the comparison further comprises analyzing the pairwise methylation difference dataset using a control to generate a methylation profile by a machine learning method. In some embodiments, the comparison further comprises determining the cancer type of the individual. In some embodiments, the machine learning method utilizes an algorithm selected from one or more of the following: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, and neural network model.

[0119] In some embodiments, the generating further comprises hybridizing each of the one or more biomarkers with a probe and performing a DNA sequencing reaction to quantify the methylation of each of the one or more biomarkers. In some embodiments, the DNA sequencing reaction is a digital PCR reaction.

[0120] In some embodiments, the control profile comprises a methylation profile of a normal sample. In some embodiments, the control profile comprises a methylation profile of a cancer sample.

[0121] In some embodiments, the cancer type is a solid cancer type or a hematological malignancy type. In some embodiments, the cancer type is a metastatic cancer type or a relapsed or refractory cancer type. In some embodiments, the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain stem glioma, brain low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia , chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular Melanoma, pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasms, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin Skin cancer, non-small cell lung cancer, mouth cancer, oral cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papilloma, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer (such as prostate adenocarcinoma (PRAD)), rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer,Retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézare syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small bowel cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, stomach cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0122] In some embodiments, the biological sample comprises a prevalent tumor DNA sample or a tissue sample. In some embodiments, the biological sample is a biopsy sample. In some embodiments, the biological sample is a cell-free biological sample.

[0123] A method of selecting a therapy for a subject having cancer comprises: (a) identifying the subject's cancer type according to the methods described herein; and (b) selecting a therapy appropriate for the cancer type based on step (a).

[0124] A method for distinguishing between a primary tumor and metastatic cancer in an individual in need thereof, comprising: (a) treating extracted genomic DNA with a deaminating agent to produce treated genomic DNA comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample of the individual; (b) generating a methylation profile comprising one or more biomarkers selected from Table 20 from the treated genomic DNA; and (c) comparing the methylation profile to a control profile, wherein a correlation between the methylation profile and the control profile distinguishes between a primary tumor and metastatic cancer in the individual.

[0125] A method of monitoring cancer progression in an individual in need thereof, comprising (a) treating extracted genomic DNA with a deaminating agent to produce treated genomic DNA comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample of the individual; (b) generating a methylation profile comprising one or more biomarkers selected from Table 20 from the treated genomic DNA; and (c) comparing the methylation profile to a second methylation profile, wherein the second methylation profile is generated from treated genomic DNA obtained from a second biological sample obtained from the individual prior to obtaining the first biological sample, and wherein a difference between the first methylation profile and the second methylation profile indicates whether cancer has progressed in the individual.

[0126] In certain embodiments, disclosed herein also include kits comprising the above-described probe sets.

[0127] In certain embodiments, disclosed herein further include services comprising the above-described methods.

[0128] Specifically, this application provides the following:

[0129] 1. A method for determining the presence of cancer in an individual in need thereof, the method comprising:

[0130] a. treating the extracted genomic DNA with a deaminating agent to produce treated genomic DNA comprising deamine nucleotides, wherein the extracted genomic DNA is obtained from a biological sample of the individual;

[0131] b. generating a methylation profile comprising one or more biomarkers selected from Table 20 in the treated genomic DNA; and

[0132] c. comparing the methylation profile to a control, wherein a correlation between the methylation profile and the control determines the presence of cancer in the individual.

[0133] 2. The method of claim 1 , wherein the methylation profile further comprises one or more biomarkers selected from Tables 15 to 18.

[0134] 3. The method of item 1 or 2, wherein the methylation profile further comprises one or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg1712215 7. cg09844573, cg03087897, cg24706505, cg13911392, cg18901104, cg259 82880, cg15797834, cg27125093, cg17518965, cg20695297, cg04858553, cg 09419005, cg11252953, cg18456621, cg07058988, cg17864646, cg0615392 5. cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg1575 9721, cg27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg 26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906 , cg23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg0530 4979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg2 3093496, cg07641284, cg01681367, cg26769927, cg08480068, cg02914427 , cg03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460 896, cg07116712, cg10186131, cg06380123, cg18610205, cg12353452, cg1 0590292, cg00037681, cg05596756, cg03569637, cg02522196, cg11655490,cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985、cg18856478、cg23229016、cg21004490、cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0135] 4. The method of item 1, wherein the comparing further comprises generating a pairwise methylation difference dataset, the pairwise methylation difference dataset comprising:

[0136] (i) a first difference between the methylation profile of the treated genomic DNA and the methylation profile of a first normal sample;

[0137] (ii) a second difference between the methylation profile of the second normal sample and the methylation profile of the third normal sample; and

[0138] (iii) a third difference between the methylation profile of the first primary cancer sample and the methylation profile of the second primary cancer sample.

[0139] 5. The method of item 1 or 4, wherein the comparison further comprises analyzing the pairwise methylation difference dataset using a control dataset by a machine learning method to generate the methylation profile.

[0140] 6. The method of item 1, 4 or 5, wherein the comparison further comprises determining the cancer type of the individual.

[0141] 7. A method as described in any of items 1-6, wherein the machine learning method utilizes one or more algorithms selected from the following: principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine and neural network model.

[0142] 8. The method of any one of items 1-7, wherein the generating further comprises hybridizing each of the one or more biomarkers with a probe, and performing a DNA sequencing reaction to quantify the methylation of each of the one or more biomarkers.

[0143] 9. The method of any one of items 1-8, wherein the cancer type is a solid cancer type or a hematological malignancy type.

[0144] 10. The method of any one of items 1-9, wherein the cancer type is a metastatic cancer type or a recurrent or refractory cancer type.

[0145] 11. The method of any one of items 1-10, wherein the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brainstem glioma, low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic carcinoma, Burkitt lymphoma (Burkitt lymphoma), lymphoma), cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC), childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma sarcoma), extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasm, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma, bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinomacarcinoma), Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasms, nasal cavity cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papillomatosis, paranasal sinus cancer carcinoma, parathyroid carcinoma, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal gland tumor, pinealoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma; prostate cancer, such as prostate adenocarcinoma (PRAD); rectal cancer, such as rectal adenocarcinoma (READ); kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sezary syndrome (Sezary syndrome) syndrome), cutaneous melanoma (SKCM), small cell lung cancer, small intestinal cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilm's tumor.

[0146] 12. The method of any one of items 1-11, wherein the biological samples comprise circulating tumor DNA samples or tissue samples.

[0147] 13. A method for selecting a therapy for an individual suffering from cancer, the method comprising: (a) identifying the cancer type of the individual according to the method of items 1 to 12; and (b) selecting a therapy appropriate for the cancer type based on step (a).

[0148] 14. A computing platform for generating a cancer CpG methylation profile database using CpG cancer methylation data, the computing platform comprising:

[0149] (a) A first computing device comprising a processor, a memory module, an operating system, and a computer program, the computer program comprising instructions executable by the processor to generate a data acquisition application to generate CpG methylation data for a set of biological samples, the data acquisition application comprising:

[0150] (1) a sequencing module configured to operate a sequencing device to generate CpG methylation data for a set of biological samples, wherein the set comprises a first cancer biological sample, a second cancer biological sample, a third cancer biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancer biological samples are different; and wherein the first, second, and third normal biological samples are different; and

[0151] (2) A data receiving module configured to receive:

[0152] (i) a first pair of CpG methylation datasets generated from the first cancerous biological sample and the first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0153] (ii) a second pair of CpG methylation datasets generated from the second normal biological sample and the third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different; and

[0154] (iii) a third pair of CpG methylation datasets generated from the second cancerous biological sample and the third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different; and

[0155] (b) a second computing device comprising a processor, a memory module, an operating system, and a computer program, wherein the computer program comprises instructions executable by the processor to generate a data analysis application to generate a cancer CpG methylation profile database, wherein the data analysis application comprises a data analysis module configured to:

[0156] (1) generating a pairwise methylation difference dataset using the first pair, the second pair, and the third pair of datasets; and

[0157] (2) Analyzing the pairwise methylation difference dataset using a control dataset by a machine learning method to generate the cancer CpG methylation profile database, wherein

[0158] (i) the machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest scores, and classifying the samples based on the plurality of markers and the plurality of weights; and

[0159] (ii) The cancer CpG methylation profile database comprises a set of CpG methylation profiles, and each CpG methylation profile represents a cancer type.

[0160] 15. The platform of item 14, wherein generating the pairwise methylation difference dataset comprises:

[0161] (a) calculating the difference between the first data set and the second data set in the first pair of data sets;

[0162] (b) calculating the difference between the third data set and the fourth data set in the second pair of data sets; and

[0163] (c) calculating the difference between the fifth data set and the sixth data set in the third pair of data sets.

[0164] 16. The platform of item 14 or 15, wherein the machine learning method utilizes one or more algorithms selected from the group consisting of principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machines, and neural network models.

[0165] 17. The platform of any one of items 14-16, wherein the CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent.

[0166] 18. The platform according to any one of items 14 to 17, wherein the sequencer is further configured to analyze the extracted genomic DNA by a next generation sequencing method to generate the CpG methylation data.

[0167] 19. The platform of claim 18, wherein the next generation sequencing method is a digital PCR sequencing method.

[0168] 20. The platform of any one of items 14-19, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200 or more biomarkers selected from the group consisting of Tables 15 to 18.

[0169] 21. The platform of any one of items 14-20, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200, or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, and cg26683005 , cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg2470 6505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg2 7125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145 , cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg27410 601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg2 7023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0170] 22. The platform of any one of items 14-21, wherein the methylation profile comprises at least 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100 or more biomarkers selected from Table 20.

[0171] 23. The platform of any one of items 14-22, wherein the cancer type is a solid cancer type or a hematological malignancy cancer type.

[0172] 24. The platform of any one of items 14-23, wherein the cancer type is a metastatic cancer type or a relapsed or refractory cancer type.

[0173] 25. The method of any one of items 14-24, wherein the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brainstem glioma, low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchial tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer, bile duct cancer (CHC) OL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD) ), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplasia syndrome, myeloproliferative neoplasm, nasal cancer, Pharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papillomatosis, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma; prostate cancer, such as prostate adenocarcinoma (PRAD);Rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézaré syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor.

[0174] 26. The platform of any one of items 14-25, wherein the control dataset comprises a set of methylation profiles, wherein each of the methylation profiles is generated from a biological sample obtained from a known cancer type.

[0175] 27. The platform of any one of items 14-26, wherein the biological samples comprise circulating tumor DNA samples or tissue samples.

[0176] 28. A computer-implemented method for generating a cancer CpG methylation profile database, the method comprising:

[0177] a. generating CpG methylation data for a set of biological samples by a sequencing method, wherein the set comprises a first cancerous biological sample, a second cancerous biological sample, a third cancerous biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancerous biological samples are different; and wherein the first, second, and third normal biological samples are different;

[0178] b. obtaining, using a first processor, a first pair of CpG methylation datasets generated from the first cancerous biological sample and the first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0179] c. obtaining, using a first computing device, a second pair of CpG methylation datasets generated from the second normal biological sample and the third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

[0180] d. obtaining, using the first computing device, a third pair of CpG methylation datasets generated from the second cancerous biological sample and the third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

[0181] e. generating a pairwise methylation difference dataset from the first, second, and third pairs of datasets using a second processor; and

[0182] f. Analyzing the pairwise methylation difference dataset using a control dataset by a machine learning method to generate the cancer CpG methylation profile database, wherein

[0183] (1) The machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest scores, and classifying the samples based on the plurality of markers and the plurality of weights; and

[0184] (2) The cancer CpG methylation profile database includes a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

[0185] 29. The computer-implemented method of item 28, wherein step e) further comprises

[0186] a. Calculating the difference between the first data set and the second data set within the first pair of data sets;

[0187] b. calculating the difference between the third data set and the fourth data set in the second pair of data sets; and

[0188] c. Calculating the difference between the fifth data set and the sixth data set in the third pair of data sets.

[0189] 30. The computer-implemented method of claim 28 or 29, wherein the machine learning method utilizes one or more algorithms selected from the group consisting of principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machines, and neural network models.

[0190] 31. The computer-implemented method of any one of items 28-30, wherein the CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent.

[0191] 32. The computer-implemented method of any one of items 28-31, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200 or more biomarkers selected from the group consisting of Tables 15 to 18.

[0192] 33. The computer-implemented method of any one of items 28-32, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200 or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26 683005, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797 834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg2 5490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925 , cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg1575 9721, cg27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg 26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg0414790 6. cg23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg053 04979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, c g23093496, cg07641284, cg01681367, cg26769927, cg08480068, cg029144 27. cg03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg22 460896, cg07116712, cg10186131, cg06380123, cg18610205, cg12353452,cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0193] 34. The computer-implemented method of any one of items 28-33, wherein the methylation profile comprises at least 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100 or more biomarkers selected from Table 20.

[0194] 35. The computer-implemented method of any one of items 28-34, wherein the cancer type is a solid cancer type or a hematological malignancy cancer type.

[0195] 36. The computer-implemented method of any one of items 28-35, wherein the cancer type is a relapsed or refractory cancer type.

[0196] 37. A computer-implemented method as described in any of items 28-36, wherein the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain stem glioma, brain low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, central nervous system atypical teratoid / rhabdoid tumor, central nervous system embryonal tumor, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer, bile duct Cancer (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNC), NSD), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplasia syndrome, myeloproliferative neoplasm, nasal cancer, Nasopharyngeal carcinoma, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papillomatosis, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma; prostate cancer, such as prostate adenocarcinoma (PRAD);Rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézaré syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor.

[0197] 38. The computer-implemented method of any one of items 28-37, wherein the biological samples comprise circulating tumor DNA samples or tissue samples.

[0198] 39. A probe panel comprising a plurality of probes, each probe being a probe of Formula I:

[0199]

[0200] in:

[0201] A is the first target binding region;

[0202] B is the second target binding region; and

[0203] L is the linker region;

[0204] wherein A has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 30 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775; B has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 12 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775; L is linked to A; and B is linked to A or L.

[0205] 40. The probe set of item 39, wherein L is linked to A and B is linked to L.

[0206] 41. The probe panel of item 39 or 40, wherein the plurality of probes comprises at least 10, 20, 30, 50, 100 or more probes.

[0207] 42. The probe set of any one of items 39-41, wherein the plurality of probes are used in a solution-based next-generation sequencing reaction to generate CpG methylation data.

[0208] 43. The probe set of item 42, wherein the solution-based next-generation sequencing reaction is a droplet digital PCR sequencing method.

[0209] 44. The probe set of any one of items 39-43, wherein the length of L is between 10 and 60 nucleotides, between 15 and 55 nucleotides, between 20 and 50 nucleotides, between 25 and 45 nucleotides, and between 30 and 40 nucleotides.

[0210] 45. The probe set of any one of items 39-44, wherein L further comprises an adapter region for identifying each probe.

[0211] 46. ​​A non-transitory computer readable medium having stored thereon instructions which, when executed by a processor, perform the steps comprising:

[0212] a. generating CpG methylation data for a set of biological samples by a sequencing method, wherein the set comprises a first cancerous biological sample, a second cancerous biological sample, a third cancerous biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancerous biological samples are different; and wherein the first, second, and third normal biological samples are different;

[0213] b. obtaining, using a first processor, a first pair of CpG methylation datasets generated from the first cancerous biological sample and the first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source;

[0214] c. obtaining, using a first computing device, a second pair of CpG methylation datasets generated from the second normal biological sample and the third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

[0215] d. obtaining, using the first computing device, a third pair of CpG methylation datasets generated from the second cancerous biological sample and the third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

[0216] e. generating a pairwise methylation difference dataset from the first, second, and third pairs of datasets using a second processor; and

[0217] f. Analyzing the pairwise methylation difference dataset using a control dataset by a machine learning method to generate the cancer CpG methylation profile database, wherein

[0218] (1) The machine learning method comprises: identifying a plurality of markers and a plurality of weights based on the highest scores, and classifying the samples based on the plurality of markers and the plurality of weights; and

[0219] (2) The cancer CpG methylation profile database includes a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

[0220] 47. The non-transitory computer-readable medium of item 46, wherein step e) further comprises

[0221] a. Calculating the difference between the first data set and the second data set within the first pair of data sets;

[0222] b. calculating the difference between the third data set and the fourth data set within the second pair of data sets; and

[0223] c. Calculating the difference between the fifth data set and the sixth data set in the third pair of data sets.

[0224] 48. The non-transitory computer-readable medium of item 46 or 47, wherein step e) further comprises generating the pairwise methylation difference dataset using the calculated difference of the first pair of datasets, the calculated difference of the second pair of datasets, and the calculated difference of the third pair of datasets using the second processor.

[0225] 49. The non-transitory computer-readable medium of any one of items 46-48, wherein the CpG methylation data is generated from extracted genomic DNA treated with a deaminating agent.

[0226] 50. The non-transitory computer-readable medium of any one of items 46-49, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200 or more biomarkers selected from the group consisting of Tables 15 to 18.

[0227] 51. The non-transitory computer-readable medium of any one of items 46-50, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200 or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg2 g26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg030878 97. cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15 797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg061539 25. cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15 759721, cg27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147 906, cg23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg05 304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496, cg07641284, cg01681367, cg26769927, cg08480068, cg02914 427, cg03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg2 2460896, cg07116712, cg10186131, cg06380123, cg18610205, cg12353452,cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0228] 52. The non-transitory computer-readable medium of any one of items 46-51, wherein the methylation profile comprises at least 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100 or more biomarkers selected from Table 20.

[0229] 53. The non-transitory computer-readable medium of any one of items 46-52, wherein the cancer type is a solid cancer type or a hematological malignancy cancer type.

[0230] 54. The non-transitory computer-readable medium of any one of items 46-53, wherein the cancer type is a relapsed or refractory cancer type.

[0231] 55. The non-transitory computer-readable medium of any one of items 46-54, wherein the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brainstem glioma, brain low-grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchogenic tumor, Burkitt lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancer , bile duct cancer (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasms, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplasia syndrome, myeloproliferative neoplasm, nasal cancer , nasopharyngeal carcinoma, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papilloma, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma; prostate cancer, such as prostate adenocarcinoma (PRAD);Rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézaré syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, gastric cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor.

[0232] 56. The non-transitory computer-readable medium of any one of items 46-55, wherein the biological samples comprise circulating tumor DNA samples or tissue samples.

[0233] Join by reference

[0234] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0235] The novel features of the present invention are set forth in detail in the appended claims. The features and advantages of the present invention will be better understood by reference to the following detailed description, taken in conjunction with the accompanying drawings, which describes illustrative embodiments utilizing the principles of the present invention:

[0236] Figure 1A and Figure 1B An overview of the methods, platforms, and systems disclosed herein is provided.

[0237] Figure 2 A diagram illustrating a computer system disclosed herein.

[0238] Figure 3 The yield of cell-free DNA from urine is shown. Urine concentrations range from 1-30 ng / ml, which is approximately one-fifth the concentration observed in plasma. This range varies between samples from different individuals and also depends on other factors, such as gender and certain disease states.

[0239] Figure 4This study demonstrates the effect of urine stability buffer (USB) on cell-free DNA yield in urine. Urine samples were mixed with either USB buffer or another commercial striping buffer and then maintained at room temperature for 14 days. After 14 days, genomic DNA was released from the buffer-free samples, resulting in significantly higher DNA yields. However, either USB or striping buffers hindered the release of cellular DNA.

[0240] Figure 5 Figure 2. DNA yields obtained using different operating concentrations of USB. Urine DNA yields obtained using different ratios of urine stability buffer compared to commercial striping buffer or no buffer demonstrate that USB buffer works at dilutions of 1:10 to 1:50 relative to urine.

[0241] Figure 6 This figure illustrates the fold change in the detection signal of fetal DNA in plasma compared to urine. Starting with a 4ml initial plasma and urine sample, q-RT-PCR detected the presence of male fetal DNA in cell-free DNA, based on the male-specific SRY gene. Given the same volume, the signal in plasma was approximately 2-8 times stronger than that in urine.

[0242] Figure 7 The yield of cell-free DNA in urine and lung fluid of a lung cancer patient at different time points is shown. The average cell-free DNA in lung fluid is about 130 ng / mL and the average cell-free DNA in urine is about 20 ng / mL.

[0243] Figure 8 Unsupervised hierarchical clustering and heatmaps illustrating the methylation profiles associated with different cancer types.

[0244] Figures 9A to 9C The present invention describes the use of unsupervised hierarchical clustering and heatmaps relative to reference methylation profiles of different cancer types to distinguish methylation profiles of different cancer types within the same tissue type. Figure 9A The illustrated heatmaps were obtained from 511 LGG, 138 GBM and 150 normal brain tissue samples (based on 1409 markers). Figure 9B The heatmaps shown were obtained from 311 LUAD, 359 LUSC, and 74 normal lung tissue samples (based on 926 markers). Figure 9C The illustrated heatmaps were obtained from 321 KIRC, 226 KIRP, and 205 normal kidney tissue samples (based on 716 markers).

[0245] FIG. 10A to FIG. 10BGraph illustrating exemplary methylation markers for predicting overall survival in patients with different types of cancer, including LGG, KIRP, KIRC, LUSC, and LUAD, and stratified by tumor status and tumor stage.

[0246] 11A to 11D This indicates that methylation-based survival classification is related to the driver gene mutation status. Figure 11A Unsupervised hierarchical clustering and heatmaps illustrating the association between methylation profiles and driver gene mutations in LGG. Figure 11B The 5-year survival curve of LGG patients is shown according to the combination of PCA value and IDH mutation. Figure 11C Unsupervised hierarchical clustering and heatmaps illustrating the association between methylation profiles and frequently mutated genes in LIHC. Figure 11D Unsupervised hierarchical clustering and heatmaps illustrating associations between KIRC methylation profiles and frequently mutated genes.

[0247] Figure 12 Illustrated is a heat map comparing the differential expression of hypermethylated genes in breast or liver cancer compared to matched normal tissues.

[0248] 13A to 13C Description: RNA-seq data from TCGA was used as a discovery cohort to calculate the differential expression of hypermethylated genes in breast cancer or liver cancer compared with matched normal tissues.

[0249] Figure 14 The figures shown illustrate that methylation patterns are associated with gene expression profiles and cancer behavior. mRNA expression of differentially methylated genes in breast and liver cancers was determined using qPCR. mRNA expression in tumor samples was normalized to expression in adjacent normal tissue from the same patient. Results are shown as the mean percent change in expression for multiple samples (n=3-7), with 3 technical replicates per sample. All samples were pooled and statistically analyzed using the Wilcoxon sign-rank test to determine whether gene expression was reversed by methylation as predicted; the p-value for the pooled samples was determined to be 1.21x10 -21 .

[0250] Figures 15A to 15J The engineered gene was transduced into the breast cancer cell line to inhibit its growth. Figure 15A and Figure 15F The corresponding CpG methylation sites are indicated. Figure 15B and Figure 15G Shown are tumors that were excised and measured after transduction of engineered genes into nude mice or implantation of control cells into nude mice. Figure 15D and Figure 15I Shown are the quantitative growth of these tumors over time. Figure 15C 、 Figure 15E 、 Figure 15H and Figure 15J In vitro colony formation of cells transduced with engineered genes relative to control cells is shown.

[0251] Figure 16 Illustration of DNA methylation markers associated with colon cancer. Unsupervised hierarchical clustering and heatmaps were performed on the methylation profiles of 435 TCGA samples (colon cancer: 390; normal colon: 45) using a set of 311 CpG markers. Each row represents an individual patient, and each column represents an individual CpG marker.

[0252] Figure 17 Illustrate DNA methylation signatures associated with colon, lung, and liver cancer. Unsupervised hierarchical clustering and heatmaps are associated with methylation profiles of 1108 TCGA samples (colon cancer: 390; normal colon: 45; liver cancer: 238; normal liver: 50; lung cancer: 311; normal lung: 74), based on 2793 CpG markers. Each row represents an individual patient, and each column represents an individual CpG marker.

[0253] Figure 18 Illustrates DNA methylation signatures associated with primary and metastatic colon, liver, and lung cancer in Chinese populations. Unsupervised hierarchical clustering and heatmap analysis were performed on the methylation profiles of 567 primary tumor samples based on 104 markers.

[0254] Figures 19A to 19E The methylation markers used to predict overall survival of patients with colon adenocarcinoma (COAD) using Kaplan-Meier curves are described. Figure 19A The 5-year survival rates stratified by methylation profiles are shown. The survival rate of the group with Pca values ​​> 0 (n = 127) was improved (81.2%) compared to the group with Pca values ​​< 0 (n = 145) (42%) (P = 0.007). Figure 19B The 5-year survival rate of stage I-II patients stratified by methylation profile analysis showed that the survival probability of the group with Pca value > 0 (n = 73) (100%) was improved compared with the survival probability of the group with Pca value < 0 (n = 77) (51.3%) (P = 0.007). Figure 19CThe 5-year survival rate of stage III-IV patients stratified by methylation profile analysis showed that the survival probability of the group with Pca value > 0 (n = 49) (81.1%) was improved compared with the survival probability of the group with Pca value < 0 (n = 66) (42%) (P = 0.01). Figure 19D The 5-year survival rate of stage II patients stratified by methylation profile analysis showed that the survival probability of the group with Pca value > 0 (n = 51) (100%) was improved compared with the survival probability of the group with Pca value < 0 (n = 58) (53.4%) (P = 0.029). Figure 19E The 5-year survival rate of stage III patients stratified by methylation profile analysis showed that the survival probability of the group with Pca value > 0 (n = 34) (94.1%) was improved compared with the survival probability of the group with Pca value < 0 (n = 46) (57.2%) (P = 0.021).

[0255] 20A to 20F This indicates that methylation-based survival classification is related to the driver gene mutation status. Figure 20A The 5-year survival curve of COAD patients was illustrated based on PCA values. Figure 20B Five-year survival curves of COAD patients according to gene mutations. Figure 20C The 5-year survival curve of COAD patients was illustrated based on the combination of PCA values ​​and gene mutations. Figure 20D Unsupervised hierarchical clustering and heatmaps showing associations between COAD methylation profiles and frequently mutated genes. Figure 20E P values ​​are shown for genes significantly associated with overall survival. Figure 20F The relationship between mutation status and patient survival is illustrated (P value estimated by log-rank test).

[0256] Figure 21 Describe the characteristics of the patient population.

[0257] Figure 22 Figure 3 illustrates mRNA expression of differentially methylated genes in colon cancer as determined by qPCR. mRNA expression in tumor samples was normalized to expression in adjacent normal tissue from the same patient. Results are presented as the mean percent change in expression across multiple samples (n=3-7), with three technical replicates per sample. All samples were combined and statistically analyzed using the Wilcoxon signed-rank test to determine whether gene expression was inversely altered by methylation as predicted; the p-value for the combined samples was determined to be 1.21x10 -21 .

[0258] Figures 23A to 23E To demonstrate the effect of PCDH17 on the growth inhibition of colon cancer cell lines, PCDH17 was transduced into HCT116 cells. Figure 23A Illustrate CpG methylation profiles. Figure 23B Shown are tumors that were excised and measured after implantation of cells transduced with engineered genes or control cells into nude mice. Figure 23C Shown are the quantitative growth of these tumors over time. Figure 23D and Figure 23E In vitro colony formation of cells transduced with engineered genes relative to control cells is shown.

[0259] Figure 24 Illustrates unsupervised hierarchical clustering and heatmap associated with the methylation profile of AML relative to normal blood.

[0260] Figure 25 Unsupervised hierarchical clustering and heatmap illustrating the association of the replication cohort with the methylation profile of AML relative to normal blood samples.

[0261] Figure 26 Illustrated are unsupervised hierarchical clustering and heatmaps associated with the methylation profiles of ALL (according to the color scale shown) relative to normal blood samples.

[0262] Figure 27 This suggests that methylation profiles can differentiate leukemia subtypes. Hierarchical clustering and heat maps are associated with ALL and AML cancer types.

[0263] FIG. 28A to FIG. 28B Illustrate the methylation marker profile. Figure 28A A methylation signature was shown to predict five-year overall survival in AML patients. Figure 28B A methylation signature was shown to predict five-year overall survival in ALL patients.

[0264] Figure 29 Illustrated are the methylation ratios of four exemplary CpG sites (cg06747543, cg15536663, cg22129276, and cg07418387) in colon cancer tissue and normal colon tissue samples (distant sites).

[0265] Figure 30 Methylation ratios of five exemplary CpG sites in metastatic colon cancer tissue samples, primary colon cancer reference samples, and normal lymphocyte genomic DNA reference samples are illustrated.

[0266] Figures 31A to 31C Shown are methylation signatures in cell-free DNA (cfDNA) samples derived from colon cancer. Figure 31A Shows methylated regions of genomic cfDNA and Figure 31B Illustrates the non-methylated regions of genomic cfDNA. Figure 31CMethylation ratios of CpG site cg10673833 from three patients (2043089, 2042981, and 2004651), a normal cfDNA reference sample, a primary colon tissue reference sample, and a normal blood reference sample are shown. Patients 2043089 and 2042981 had primary colon cancer, and patient 2004651 had metastatic colon cancer.

[0267] Figures 32A to 32C Displays methylation profiles of primary liver, breast, and lung cancers. Figure 32A The methylation ratios of CpG site cg00401797 in liver cancer cfDNA samples, normal cfDNA samples, primary liver cancer tissue reference samples (genomic DNA), and normal lymphocyte reference samples (genomic DNA) are shown. Figure 32B The methylation ratios of CpG site cg07519236 in breast cancer cfDNA samples, normal cfDNA samples, primary breast cancer tissue reference samples (genomic DNA), and normal lymphocyte reference samples (genomic DNA) are shown. Figure 32C The methylation ratios of the CpG site cg02877575 in lung cancer cfDNA samples, normal cfDNA samples, primary lung cancer tissue reference samples (genomic DNA), and normal lymphocyte reference samples (genomic DNA) are shown.

[0268] Figures 33A to 33B Two different probes are shown to differentiate primary colon cancer from normal samples. Figure 33A Shown are the methylation profiles of probe Cob-2 targeting the CpG site cg10673833 and cfDNA samples from three colon cancer patients, normal cfDNA samples, a primary colon cancer tissue reference sample (genomic DNA), and a normal lymphocyte reference sample (genomic DNA). Two of the three patients (2043089 and 2042981) had primary colon cancer. The remaining patient (2004651) had metastatic colon cancer. Figure 33B Shown are the methylation profiles of the probe Brb-2 targeting the CpG site cg07974511 and cfDNA samples from two primary colon cancer patients (2043089 and 2042981), normal cfDNA samples, a primary colon cancer tissue reference sample (genomic DNA), and a normal lymphocyte reference sample (genomic DNA).

[0269] Figures 34A to 34D Analysis of cfDNA from breast cancer patients is shown. Four probes were used: Brb-3 ( Figure 34A )、Brb-4( Figure 34B )、Brb-8( Figure 34C ) and Brb-13( Figure 34DThe methylation ratios of primary breast cancer cfDNA were compared with those of normal cfDNA samples, primary breast cancer tissue reference samples (genomic DNA), and normal lymphocyte reference samples (genomic DNA).

[0270] Figures 35A to 35B The detection of metastatic colon cancer in tissue samples from 49 patients was demonstrated using two probes, cob_3 and brb_13.

[0271] Figure 36 The analysis method described in this paper using PCA and ICA filtering is described.

[0272] Figure 37 The clinicopathological characteristics of the training cohort are shown.

[0273] Figure 38 Shown are the clinicopathological characteristics of validation cohort 1.

[0274] Figure 39 Shown are the clinicopathological characteristics of validation cohort 2.

[0275] Figure 40 Shows clinicopathological characteristics of brain cancer, kidney cancer, and lung cancer subtypes.

[0276] Figure 41 Shown are DNA methylation markers associated with primary breast cancer, breast cancer liver metastasis, normal breast, liver cancer, and normal liver tissue in a Chinese population. Unsupervised hierarchical clustering and heatmaps are associated with methylation profiles based on 128 markers for 126 tumor and 75 normal samples. Color bars indicate relative methylation.

[0277] Figures 42A to 42B This indicates that methylation-based survival classification is related to the driver gene mutation status. Figure 42A The 5-year survival curve of LIHC patients was illustrated based on the combination of PCA value and gene mutation status. Figure 42B The 5-year survival curve of KIRC patients was illustrated based on the combination of PCA value and gene mutation status.

[0278] Figures 43A to 43B Describe the relationship between various selected differentially methylated CpG markers and BRCA ( Figure 43A ) and LIHC( Figure 43B) in the expression of genes in the human body. Each point on the scatter plot represents the degree of methylation of a specified CpG marker drawn for the expression of the corresponding gene. The first column in the figure shows the hypermethylated CpG sites associated with reduced gene expression in cancer; the second column in the figure shows the hypermethylated CpG sites that do not seem to affect gene expression in cancer; and the third column in the figure shows the relevant hypermethylated CpG sites in cancer. Each point on the scatter plot represents the degree of methylation of a specified CpG marker drawn for the expression of the corresponding gene. The first column in the figure shows the hypermethylated CpG sites associated with reduced gene expression in cancer; the second column in the figure shows the hypermethylated CpG sites that do not affect gene expression in cancer; and the third column in the figure shows the relevant hypermethylated CpG sites in cancer.

[0279] Figures 44A to 44B Shows the clinicopathological characteristics of TCGA and Chinese patient cohorts.

[0280] Figure 45 Displays the relationship between differentially methylated markers and gene expression. Each point on the scatter plot shows the methylation level of a given CG marker plotted against the expression of the corresponding gene. The first row of plots shows hypermethylation associated with decreased gene expression; the second row of plots shows no correlation between methylation level and gene expression; and the third row of plots shows no relationship at all between methylation level and gene expression.

[0281] Figure 46 Illustrated are changes in the methylation profiles of two illustrative CpG sites (CpG Site 1 and CpG Site 2) measured using two different colon cancer probes (cob-2 and cob-9).

[0282] Figure 47 Illustrated are changes in methylation profiles of four patient samples (patients 2045021, 2044629, 2044645, and 2045021) following surgery based on colon cancer probes cob-2 and cob-9.

[0283] Figure 48 The methylation patterns of colon cancer probes cob-2 and cob-9 in samples of different cancer stages are shown.

[0284] Figures 49A to 49J The methylation profiles of ten illustrative CpG sites are shown in 19 different cancer types and normal blood samples. The cancer types include bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0285] Figures 50A to 50NThe methylation profiles of approximately 280 CpG sites (biomarkers) in breast cancer, colon cancer, liver cancer, lung cancer, and normal blood samples are described. DETAILED DESCRIPTION

[0286] Cancer is characterized by abnormal cell growth caused by one or more gene mutations or modifications, which leads to an imbalance in the balance between cell proliferation and cell death. DNA methylation silences the expression of tumor suppressor genes and presents itself as one of the first neoplastic changes. The methylation patterns found in neoplastic tissues and plasma show homogeneity and are used as sensitive diagnostic markers in some cases. For example, cMethDNA analysis has been shown in one study to have a sensitivity of about 91% and a specificity of about 96% when used to diagnose metastatic breast cancer. In another study, when it was used to identify KRAS gene mutations in a large group of metastatic colon cancer patients, circulating tumor DNA (ctDNA) had a sensitivity of about 87.2% and a specificity of about 99.2% (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 was detectable in >75% of patients with advanced pancreatic, ovarian, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular carcinoma, and head and neck cancer (Bettegowda et al.).

[0287] Other studies have demonstrated that CpG methylation patterns are associated with neoplastic progression. For example, in a study of breast cancer methylation patterns, hypermethylation of p16 was found to be associated with early-stage breast cancer, while hypermethylation of the TIMP3 promoter has been associated with advanced breast cancer. Furthermore, hypermethylation of the BMP6, CST6, and TIMP3 promoters has been shown to be associated with breast cancer metastasis to lymph nodes.

[0288] In some embodiments, for cancer detection, the clinical sensitivity and dynamic range provided by DNA methylation profiling are higher than somatic mutation analysis. In other cases, the DNA methylation markers that change have been shown to be associated with the prognosis of the treatment response of specific cancers. For example, a study illustrates that in a group of patients with advanced colorectal cancer, ten differentially methylated regions were used to predict patient prognosis. Similarly, the RASSF1A DNA methylation measurement results in serum are used to predict the adverse outcomes in patients undergoing adjuvant therapy for breast cancer patients in different studies. In addition, SRBC gene hypermethylation is associated with adverse outcomes in colorectal cancer patients treated with oxaliplatin (oxaliplatin) in different studies. Another study has demonstrated that ESR1 gene methylation is associated with the clinical response in breast cancer patients receiving tamoxifen (tamoxifen). In addition, ARHI gene promoter hypermethylation is shown as a predictor of long-term survival in breast cancer patients not treated with tamoxifen.

[0289] In some cases, DNA methylation profiling is tailored to specific cancer types. In some cases, DNA methylation profiling cannot distinguish between different cancer types in a pan-cancer context. In other cases, DNA methylation profiling lacks reproducibility and is less sensitive at lower sample concentrations (e.g., at ng concentrations) than at higher sample concentrations.

[0290] Disclosed herein are methods, systems, platforms, non-transitory computer-readable media, services, and kits for determining the type of cancer in an individual. In some embodiments, described herein also include methods, systems, platforms, non-transitory computer-readable media, services, and kits for early detection of cancer. In other embodiments, described herein include methods, systems, non-transitory computer-readable media, services, and kits for non-invasive detection of cancer. In yet other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for distinguishing different stages of cancer. In other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for determining the prognosis of cancer in an individual in need, predicting treatment response, and monitoring treatment response. In other embodiments, described herein include methods, systems, platforms, non-transitory computer-readable media, services, and kits for generating a CpG methylation profile database, as well as probes for generating CpG methylation data.

[0291] Determine the patient's cancer status

[0292] 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 mainly occurs in prokaryotes, while methylation of cytosine occurs in prokaryotes and eukaryotes. In some cases, cytosine methylation occurs in CpG dinucleotide motifs. In other cases, cytosine methylation occurs in, for example, CHG and CHH motifs, where H is adenine, cytosine, or thymine. In some cases, one or more CpG dinucleotide motifs or CpG sites form CpG islands (short DNA sequences rich in CpG dinucleotides). In some cases, CpG islands are present in approximately half of the 5' regions of all human genes. The length of CpG islands is typically, but not necessarily always, about 0.2 to about 1 kb. Cytosine methylation further includes 5-methylcytosine (5-mCyt) and 5-hydroxymethylcytosine.

[0293] The CpG (cytosine-phosphate-guanine) or CG motif refers to a region in a DNA molecule where cytosine nucleotides are present in a linear chain next to guanine nucleotides. In some cases, the cytosine in a CpG dinucleotide is methylated to form 5-methylcytosine. In some cases, the cytosine in a CpG dinucleotide is methylated to form 5-hydroxymethylcytosine.

[0294] CpG methylation profile database

[0295] In some embodiments, a plurality of CpG methylation data are generated and integrated into a CpG methylation profile database. In some cases, the CpG methylation profile database is used as a reference database for the methods, systems, non-transitory computer-readable media, services, or kits described herein. In some cases, the CpG methylation profile database contains a library of CpG methylation profiles, wherein each CpG methylation profile is associated with a cancer type (e.g., breast cancer, colorectal cancer, brain cancer, and the like). In some cases, each of the CpG methylation profiles is further associated with a cancer subtype (e.g., triple-negative breast cancer, colorectal adenocarcinoma, astrocytoma, and the like).

[0296] In some embodiments, a CpG methylation profile database is generated, such as Figure 1AIn some cases, genomic DNA (e.g., nuclear DNA or circulating DNA) is isolated from a biological sample and then treated with a deaminating agent to produce extracted genomic DNA (101). In some cases, the extracted genomic DNA (e.g., extracted nuclear DNA or extracted circulating DNA) is optionally treated with one or more restriction enzymes to produce a set of DNA fragments, which are then submitted for sequencing analysis to generate CpG methylation data (102). The CpG methylation data is then input into a machine learning / classification program (103) to generate a CpG methylation profile database (105).

[0297] In some cases, a set of biological samples is generated and subsequently input into a machine learning / classification program (103). In some cases, the set of biological samples comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, or more than 30 biological samples. In some cases, the set of biological samples comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, or more than 30 normal biological samples. In some cases, the set of biological samples comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, or more than 30 cancer biological samples. In some cases, the set of biological samples comprises a first cancer biological sample, a second cancer biological sample, a third cancer biological sample, a first normal biological sample, a second normal biological sample, and a third normal biological sample; wherein the first, second, and third cancer biological samples are different; and wherein the first, second, and third normal biological samples are different. In some cases, three pairs of datasets are generated, wherein the three pairs of datasets include a first pair of CpG methylation datasets generated from a first cancerous biological sample and a first normal biological sample, wherein the CpG methylation data generated from the first cancerous biological sample forms a first dataset within the first pair of datasets, the CpG methylation data generated from the first normal biological sample forms a second dataset within the first pair of datasets, and the first cancerous biological sample and the first normal biological sample are from the same biological sample source; a second pair of CpG methylation datasets generated from a second normal biological sample and a third normal biological sample, wherein the CpG methylation data generated from the second normal biological sample forms a third dataset within the second pair of datasets, the CpG methylation data generated from the third normal biological sample forms a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different; and a third pair of CpG methylation datasets generated from the second cancerous biological sample and the third cancerous biological sample, wherein the CpG methylation data generated from the second cancerous biological sample forms a fifth dataset within the third pair of datasets, the CpG methylation data generated from the third cancerous biological sample forms a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different. In some cases, the differences within each pair of data sets are calculated and then input into a machine learning / classification program (103). In some cases, pairwise methylation difference data sets are generated from the first, second, and third pairs of data sets, and then analyzed by a machine learning / classification method (103) in the presence of a control data set or a training data set (104) to generate a cancer CpG methylation profile database (105). In some cases, the machine learning method includes identifying a plurality of markers and a plurality of weights based on the highest score (e.g., t-test value, beta test value), and classifying the sample based on the plurality of markers and a plurality of weights. In some cases, the cancer CpG methylation profile database (105) includes a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

[0298] In some embodiments, a first biomarker panel is used to detect the presence of cancer. In some cases, at least one additional biomarker panel (e.g., at least 2, 3, 4, 5, 6, or more biomarker panels) is used to determine the type of cancer. In some cases, at least one additional biomarker panel (e.g., at least 2, 3, 4, 5, 6, or more biomarker panels) is used to determine the stage of cancer, distinguish between primary and metastatic cancer subtypes, monitor cancer progression, determine cancer prognosis, or monitor treatment regimens, as appropriate.

[0299] In some embodiments, the CpG methylation profile database is used as a reference database for diagnosing cancer types. In some cases, the use of the CpG methylation profile database as a reference database for diagnosing cancer is as follows: Figure 1B As described in . In some cases, genomic DNA (e.g., nuclear DNA or circulating DNA) is isolated from a biological sample and then treated with a deaminating agent to produce extracted genomic DNA (111). In some cases, the extracted genomic DNA (e.g., extracted nuclear DNA or extracted circulating DNA) is optionally treated with one or more restriction enzymes to produce a set of DNA fragments, which are then submitted for sequencing analysis to generate CpG methylation data. The CpG methylation data are further analyzed and compiled into a CpG methylation profile of interest (112). The CpG methylation profile of interest is optionally input into a machine learning / classification program (114) and then compared with a CpG methylation profile in a CpG methylation profile database (115). A match between the CpG methylation profile in the CpG methylation profile database and the CpG methylation profile of interest indicates a cancer type.

[0300] In some cases, the CpG methylation profile database is further used as a reference database for determining primary and metastatic cancer subtypes or for monitoring cancer progression.

[0301] In some embodiments, a CpG methylation atlas database is generated from CpG methylation data of a biopsy sample. In some cases, a CpG methylation atlas database is generated from CpG methylation data of a tissue sample. In some cases, a CpG methylation atlas database is generated using CpG methylation data from a cell-free biological sample. In some cases, a CpG methylation atlas database is generated using CpG methylation data from a prevalent tumor DNA (ctDNA) sample.

[0302] Biomarkers

[0303] In some embodiments, the methylation of the biomarkers (or markers) described herein in cancer is different from that in normal tissue. In some embodiments, a biomarker indicates or represents a methylation marker, such as a CpG methylation site, a methylation state, a methylation index, or a methylation profile. In some cases, a set of biomarker descriptions produces a methylation marker set such as a methylation profile (methylation of one or more genes) and its analogs. In some cases, biomarkers are used individually or collectively as diagnostic tools, or are used in combination or converted to a biomarker panel for use. In some embodiments, biomarkers in one or more genes are assessed, and in some cases are further compared with the methylation profile of one or more genes (such as a reference methylation profile) to characterize the cancer state.

[0304] In some embodiments, methods, systems, platforms, and non-transitory computer-readable media for determining cancer type based on the methylation profile or methylation signature of one or more biomarkers are described herein. In some embodiments, one or more biomarkers are used for early detection of cancer. In other embodiments, one or more biomarkers are used for non-invasive detection of cancer. In yet other embodiments, one or more biomarkers are used to distinguish between different cancer stages. In other embodiments, one or more biomarkers are used to determine the prognosis of cancer, predict treatment response, and / or monitor treatment response.

[0305] In some embodiments, methods, systems, platforms, and non-transitory computer-readable media for generating a CpG methylation profile database are also described herein. In some embodiments, one or more biomarkers are used to generate a CpG methylation profile database.

[0306] In some embodiments, the biomarkers described herein include those listed in Table 15. In some embodiments, the biomarkers described herein include those disclosed in Tables 15, 16, 17, and / or 18. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more biomarkers in Tables 15, 16, 17, and / or 18 to determine the type of cancer. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more biomarkers in Tables 15, 16, 17, and / or 18 for early detection of cancer. In other embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more biomarkers in Tables 15, 16, 17, and / or 18 for non-invasive detection of cancer. In yet other embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more biomarkers in Tables 15, 16, 17, and / or 18 to distinguish between different stages of cancer. In other embodiments, a method, system, or non-transitory computer-readable medium described herein uses one or more biomarkers in Tables 15, 16, 17, and / or 18 to determine the prognosis of a cancer, predict a treatment response, and / or monitor a treatment response. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, gastric cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0307] In some embodiments, the biomarkers described herein include: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg030878 97. cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15 797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, c g25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg061539 25. cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg157 59721, cg27023597, cg02782634, cg18942579, cg01409343, cg10530767, c g26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg0414790 6. cg23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg053 04979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg 23093496, cg07641284, cg01681367, cg26769927, cg08480068, cg0291442 7. cg03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg2246 0896, cg07116712, cg10186131, cg06380123, cg18610205, cg12353452, cg1 0590292, cg00037681, cg05596756, cg03569637, cg02522196, cg11655490,cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985、cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, or cg26017930. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more of the following biomarkers to determine cancer type: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683 005, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg2598288 0. cg15797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, c g17864646, cg06153925, cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg1 8942579, cg01409343, cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg0786 0918, cg00206490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg230934 96. cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653601, cg01990910, cg00933696, cg09866569, cg20357538,cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more of the following biomarkers: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706 505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093, cg17518965, cg20695297, cg04 858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg18942579, cg01409 343, cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg0 7860918, cg00206490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934,cg02358862、cg23093496、cg07641284、cg01681367、cg26769927、cg08480068、cg02914427、cg03653601、cg01990910、cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128, cg13821008, cg27405400, cg09366118, cg15341833, cg02233149, cg14247287, cg23824762, cg01604601, cg056 56900, cg08132573, cg24686918, cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055 , cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231 and cg26017930, for early detection of cancer, non-invasive detection of cancer, differentiating different cancer stages, determining the prognosis of cancer, predicting treatment response and / or monitoring treatment response. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more of the following biomarkers to determine cancer type: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08 098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg18384097 , cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg2712 5093, cg26112797, cg24166457, cg19300307, ​​cg17122157, cg13555415, cg11436362, c g10673833, cg09866569, cg08075204, cg05614346, cg02053964, cg27377213, cg244808 10. cg24301930, cg22513455, cg19693177, cg19675731, cg19252956, cg18856478, cg16 509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14083015, cg14058476,cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804, cg 26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16 622899, cg14999168, cg13925432, cg12967050, cg11105292, cg09419005, cg09153080, cg0738 0416, cg06825448, cg05596756, cg03891050, cg01681367, cg01456691, cg00015530, cg274106 01. cg27366280, cg26683005, cg25666403, cg24706505, cg24107852, cg23824762, cg23021796 , cg21122474, cg20336172, cg18610205, cg18456621, cg17518965, cg16748008, cg16191087, c g16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20). In some embodiments, the methods, systems, or non-transitory computer-readable media described herein utilize one or more of the following biomarkers: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25 574765, cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907 272. cg27125093, cg26112797, cg24166457, cg19300307, ​​cg17122157, cg13555415, cg11436362, cg10673833,cg09866569, cg08075204, cg05614346, cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19675731, c g19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14083015, cg14058476, cg12192582, cg1 0590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804, cg26680502, cg23013029, cg22552736, cg21376733, cg208 47580, cg19704755, cg18842353, cg16622899, cg14999168, cg13925432, cg12967050, cg11105292, cg09419005, cg09153080, cg07380 416, cg06825448, cg05596756, cg03891050, cg01681367, cg01456691, cg00015530, cg27410601, cg27366280, cg26683005, cg256664 03. cg24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621, cg17518965, cg16748008 , cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244 and cg00025044 (Table 20), for early detection of cancer, non-invasive detection of cancer, differentiation of different cancer stages, determination of cancer prognosis, prediction of treatment response and / or monitoring of treatment response. In some embodiments, the cancer types comprise acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer,Liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, gastric cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, gastric cancer, or thyroid cancer.

[0308] In some embodiments, the biomarkers described herein include cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a method, system, or non-transitory computer-readable medium described herein uses one or more of the biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555 to determine a cancer type. In some embodiments, the methods, systems, or non-transitory computer-readable media described herein use one or more of the biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555 for early detection of cancer, non-invasive detection of cancer, differentiation of different cancer stages, determination of cancer prognosis, prediction of treatment response, and / or monitoring of treatment response. In some embodiments, the cancer type comprises acute lymphoblastic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphocytic neoplasm, melanoma, myeloid neoplasm, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, stomach cancer, or thyroid cancer. In some embodiments, the cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, stomach cancer, or thyroid cancer.

[0309] In some embodiments, a panel comprises one or more of the biomarkers described herein. In some cases, a panel comprises one or more biomarkers selected from Table 15 or Table 16. In some cases, a panel comprises one or more biomarkers selected from Table 17 or Table 18. In some cases, Tables 15 to 18 represent marker panels for cancer or normal samples. In some cases, Tables 15 and 16 represent marker panels for cancer samples. In some cases, Tables 17 and 18 represent marker panels for cancer samples.

[0310] In some cases, a panel includes one or more of the following biomarkers: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg0303030 087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419 005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg0 6153925, cg27410601, cg03297901, cg06853339, cg12900649, cg27219182 , cg15759721, cg27023597, cg02782634, cg18942579, cg01409343, cg1053 0767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg 04147906, cg23878564, cg07860918, cg00206490, cg07644807, cg0055880 4. cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg023 58862, cg23093496, cg07641284, cg01681367, cg26769927, cg08480068, c g02914427, cg03653601, cg01990910, cg00933696, cg09866569, cg203575 38. cg22460896, cg07116712, cg10186131, cg06380123, cg18610205, cg12 353452, cg10590292, cg00037681, cg05596756, cg03569637, cg02522196,cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0311] In some cases, a panel includes one or more of the following biomarkers: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00 907272, cg27125093, cg26112797, cg24166457, cg19300307, ​​cg17122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204, cg05614 346, cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg1 9693177, cg19675731, cg19252956, cg18856478, cg16509569, cg15797834 , cg15698795, cg15341833, cg14556909, cg14083015, cg14058476, cg1219 2582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg 00558804, cg26680502, cg23013029, cg22552736, cg21376733, cg2084758 0. cg19704755, cg18842353, cg16622899, cg14999168, cg13925432, cg129 67050, cg11105292, cg09419005, cg09153080, cg07380416, cg06825448, c g05596756, cg03891050, cg01681367, cg01456691, cg00015530, cg274106 01, cg27366280, cg26683005, cg25666403, cg24706505, cg24107852, cg23 824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621,cg17518965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20).

[0312] In some embodiments, a panel comprises one or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel comprises two or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel comprises three or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel comprises four or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel comprises biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel consists of one or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel consists of two or more biomarkers: cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel consists of three or more biomarkers: cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel consists of four or more biomarkers: cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel consists of biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some embodiments, a panel comprises biomarker cg25574765. In some embodiments, a panel comprises biomarker cg25490145. In some embodiments, a panel comprises biomarker cg18384097. In some embodiments, a panel comprises biomarker cg17126555. In some embodiments, a panel comprises biomarker cg25922751. In some embodiments, a panel comprises one or more biomarkers cg25574765, cg25490145, cg18384097,cg25922751 and cg17126555, and optionally one or more biomarkers selected from Table 15, Table 16, Table 17, Table 18, and / or biomarkers cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24 166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125 093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg1 1252953, cg18456621, cg07058988, cg17864646, cg06153925, cg27410601 , cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg2702 3597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797, cg 00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387856 4. cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg275 98656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496, c g07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg036536 01, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg07 116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0313] In some embodiments, a panel comprises one or more biomarkers selected from Table 20, and optionally one or more biomarkers selected from Table 15, Table 16, Table 17, Table 18, and / or biomarkers cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg2668300 5. cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg247 06505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg 27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg2549014 5. cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg2741 0601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg 27023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0314] In some embodiments, a group comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000 or more biomarkers. In some cases, a group comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers wherein the biomarkers are selected from Tables 15, 16, 17 and / or 18. In some cases, a panel comprises about 5 or more biomarkers, including 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000

[0315] In some embodiments, a group comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers wherein the biomarkers are selected from Tables 15-16. In some cases, a panel comprises about 5 or more biomarkers, including 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000

[0316] In some embodiments, a group comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers wherein the biomarkers are selected from Tables 17-18. In some cases, a panel comprises about 5 or more biomarkers, including 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers or markers selected from any one of Tables 17-18.

[0317] In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more than 1000 biomarkers selected from Tables 15, 16, 17, and / or 18 for determining cancer type. In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers for early detection of cancer selected from Tables 15, 16, 17 and / or 18.In other embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein use a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 2829, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80 , 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000 or more biomarkers for non-invasive detection of cancer selected from Tables 15, 16, 17 and / or 18. In yet other embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a combination of: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more than 1000 biomarkers selected from Tables 15, 16, 17, and / or 18 for distinguishing different stages of cancer.In other embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein use a combination of: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 2829, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000 or more biomarkers selected from Tables 15, 16, 17 and / or 18 for determining the prognosis of cancer, predicting a treatment response and / or monitoring a treatment response.

[0318] In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more than 1000 biomarkers selected from Table 17 and / or Table 18 for determining cancer type. In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more biomarkers for early detection of cancer selected from Table 17 and / or Table 18.In other embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a combination of: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers selected from Table 17 and / or Table 18 for non-invasive detection of cancer. In yet other embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a combination of: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more than 1000 biomarkers selected from Table 17 and / or Table 18 for distinguishing different cancer stages.In other embodiments, the methods, systems, or non-transitory computer readable media described herein use a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers selected from Tables 17 and / or 18 for determining the prognosis of cancer, predicting a response to treatment, and / or monitoring a response to treatment.

[0319] In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250 or more biomarkers for determining cancer types: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg2112247 4. cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg2714191 5. cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093 , cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925 , cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg18942579, cg01409343 , cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg07860918, cg00206490 , cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496, cg07641284,cg01681367、cg26769927、cg08480068、cg02914427、cg03653601、cg01990910、cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118, cg15341833, cg02233149, cg14247287, cg23824762, cg01604601, cg05656900, cg081 32573, cg24686918, cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0320] In some embodiments, a method, system, platform, or non-transitory computer-readable medium described herein uses a panel comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250, or more biomarkers cg204689 39. cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12 967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, c g26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg271419 15. cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg139 11392, cg18901104, cg25982880, cg15797834, cg27125093, cg17518965, c g20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg1845662 1. cg07058988, cg17864646, cg06153925, cg27410601, cg03297901, cg068 53339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg 18942579, cg01409343, cg10530767, cg26112797, cg00253248, cg0172229 7. cg22589778, cg07137244, cg04147906, cg23878564, cg07860918, cg0020 6490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg2 2190721, cg01660934, cg02358862, cg23093496, cg07641284, cg01681367,cg26769927、cg08480068、cg02914427、cg03653601、cg01990910、cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833, cg02233149, cg14247287, cg23824762, cg01604601, cg05656900, cg08132573, cg24686918, cg053 52688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035 , cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231 and cg26017930, for early detection of cancer, non-invasive detection of cancer, differentiating different cancer stages, determining the prognosis of cancer, predicting treatment response and / or monitoring treatment response. In some cases, the methods, systems, platforms, or non-transitory computer-readable media described herein utilize a set comprising: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 2829, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45 , 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more biomarkers cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg0287490 8. cg26769927, cg26769700, cg25574765, cg25490145, cg18384097, cg17126555, cg1424728 7. cg07420137, cg05098343, cg01903374, cg00907272, cg27125093, cg26112797, cg24166457 , cg19300307, ​​cg17122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204 , cg05614346, cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177,cg19675731, cg19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14 083015, cg14058476, cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg0055880 4. cg26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622899, cg1 4999168, cg13925432, cg12967050, cg11105292, cg09419005, cg09153080, cg07380416, cg06825448, cg055967 56. cg03891050, cg01681367, cg01456691, cg00015530, cg27410601, cg27366280, cg26683005, cg25666403, c g24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621, cg1751 8965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20).

[0321] In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein use a panel comprising 1, 2, 3, 4, or more than 4 biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555 for determining cancer type. In some embodiments, the methods, systems, platforms, or non-transitory computer-readable media described herein use a panel comprising 1, 2, 3, 4, or more than 4 biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555 for early detection of cancer, non-invasive detection of cancer, distinguishing different cancer stages, determining the prognosis of cancer, predicting treatment response, and / or monitoring treatment response.

[0322] In some embodiments, the CpG methylation profile database comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, or more of the biomarkers selected from Tables 15, 16, 17, and / or 18. In some embodiments, the CpG methylation profile database comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 750, 800, 850, 900, 950, 1000 or more biomarkers selected from Tables 17-18. In some cases, the CpG methylation profile database comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 4 00, 425, 450, 475, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000 or more biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596,cg16927606、cg12967050、cg21122474、cg06064964、cg11779113、cg12042264、cg27377213、cg26680502、cg12504877、cg21913888、cg26683005、cg24166457、cg27141915、cg17122157、cg09844573、cg03087897、cg24706505、cg17126555、cg13911392、cg18901104、cg25982880、cg15797834、cg27125093、cg17518965、cg20695297、cg04858553、cg09419005、cg25490145、cg11252953、cg18456621、cg07058988、cg17864646、cg06153925、cg27410601、cg03297901、cg06853339、cg12900649、cg27219182、cg15759721、cg27023597、cg02782634、cg18942579、cg01409343、cg10530767、cg26112797、cg00253248、cg01722297、cg22589778、cg07137244、cg04147906、cg23878564、cg07860918、cg00206490、cg07644807、cg00558804、cg05304979、cg27598656、cg03549146、cg22190721、cg01660934、cg02358862、cg23093496、cg07641284、cg01681367、cg26769927、cg08480068、cg02914427、cg03653601、cg01990910、cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985、cg18856478、cg23229016、cg21004490、cg24742520、cg23013029、cg19704755、cg07589991、In some cases, the CpG methylation profile database comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more biomarkers selected from the group consisting of: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg267697 00, cg25574765, cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg271250 93. cg26112797, cg24166457, cg19300307, ​​cg17122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204, cg0561434 6. cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19675731, cg19252956, cg18856478, cg1650956 9. cg15797834, cg15698795, cg15341833, cg14556909, cg14083015, cg14058476, cg12192582, cg10590292, cg06787669, cg06439655 , cg02522196, cg02233149, cg00558804, cg26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353 , cg16622899, cg14999168, cg13925432, cg12967050, cg11105292, cg09419005, cg09153080, cg07380416, cg06825448, cg05596756,cg03891050, cg01681367, cg01456691, cg00015530, cg27410601, cg27366280, cg26683005, cg25666403, cg24 706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg18456621, cg175189 65, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20). In some cases, the CpG methylation profile database comprises 1, 2, 3, 4, or more than 4 biomarkers selected from cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555.

[0323] Methylation profile

[0324] A methylation profile as described herein refers to a set of data representing the methylation state or level of one or more biomarkers (or loci) within a DNA molecule. In some cases, a methylation profile as described herein refers to a set of data representing the methylation state or level of one or more biomarkers of Tables 15, 16, 17, and / or 18. In some cases, the methylation profile described herein refers to a set of data representing the methylation status or level of one or more of the following biomarkers: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg266830 05, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg259828 80. cg15797834, cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg0705898 8. cg17864646, cg06153925, cg27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg0278263 4. cg18942579, cg01409343, cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564 , cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862 , cg23093496, cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653601, cg01990910, cg00933696, cg09866569,cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918, cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930. In some cases, the methylation profile described herein refers to a set of data representing the methylation status or level of one or more of the following biomarkers: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg255747 65. cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg01903374, cg00907272, cg2 7125093, cg26112797, cg24166457, cg19300307, ​​cg17122157, cg13555415, cg11436362, cg10673833, cg0986656 9. cg08075204, cg05614346, cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19 675731, cg19252956, cg18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14083015 , cg14058476, cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804, cg266 80502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622899, cg14999168,cg13925432, cg12967050, cg11105292, cg09419005, cg09153080, cg07380416, cg06825448, cg05596756, cg03891050, cg01681367, cg01 456691, cg00015530, cg27410601, cg27366280, cg26683005, cg25666403, cg24706505, cg24107852, cg23824762, cg23021796, cg211224 74, cg20336172, cg18610205, cg18456621, cg17518965, cg16748008, cg16191087, cg16061668, cg14642045, cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20). In some cases, a methylation profile described herein refers to a set of data representing the methylation status or level of one or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555. In some cases, DNA methylation data includes, but is not limited to, a methylation index of a CpG site, a methylation density of a CpG site in a region, a distribution of CpG sites over a contiguous region, a methylation pattern or level of one or more individual CpG sites in a region containing more than one CpG site, a lack of CpG methylation, and / or non-CpG methylation. In some cases, the methylation profile comprises a set of methylation indices for CpG sites, a set of methylation densities for CpG sites in a region, a set of distributions of CpG sites in adjacent regions, a set of methylation patterns or levels for one or more individual CpG sites in a region containing more than one CpG site, a set of absent CpG methylation, a set of non-CpG methylation, or a combination thereof. In some cases, the methylation profile is also referred to herein as a methylation fingerprint or a methylation signature.

[0325] In some embodiments, the methylation profile comprises the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to determine a cancer type. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 for early detection of cancer. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to detect the presence of cancer. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 for non-invasive detection of cancer. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to distinguish between different stages of cancer. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to determine the prognosis of cancer, predict a treatment response, and / or monitor a treatment response.

[0326] In some embodiments, the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 are generated from a tissue sample. In some cases, the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 are generated from a cell-free DNA (cfDNA) sample. In some cases, the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 are generated from a prevalent tumor DNA (ctDNA) sample.

[0327] In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 15 to determine a cancer type. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 16 to determine a cancer type. In some cases, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 17 to determine a cancer type. In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 18 to determine a cancer type.

[0328] In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile that comprises the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to detect the presence of cancer in a biological sample. In some cases, the method, system, platform, or non-transitory computer-readable medium then uses at least a second methylation profile that comprises the methylation states or levels of a panel of biomarkers selected from Tables 15, 16, 17, and / or 18 to determine the type of cancer, and optionally distinguish between different cancer stages, determine the prognosis of the cancer, predict a response to treatment, and / or monitor a response to treatment.

[0329] In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005 , cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg2470 6505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg2 7125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145 , cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg27410 601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg2 7023597, cg02782634, cg18942579, cg01409343, cg10530767, cg26112797 , cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg2387 8564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg2 7598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496 , cg07641284, cg01681367, cg26769927, cg08480068, cg02914427, cg03653 601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg0 7116712, cg10186131, cg06380123, cg18610205, cg12353452, cg10590292,cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930. In some cases, the method, system, platform, or non-transitory computer-readable medium then uses at least a second methylation profile comprising the methylation states or levels of one of the following biomarkers to determine the type of cancer, and optionally distinguish between different cancer stages, determine the prognosis of cancer, predict treatment response, and / or monitor treatment response: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg 11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg1712215 7. cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093, cg175 18965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, c g27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg18942579, cg014093 43. cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg07860918, cg00 206490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496,cg07641284、cg01681367、cg26769927、cg08480068、cg02914427、cg03653601、cg01990910、cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400, cg09366118, cg15341833, cg02233149, cg14247287, cg23824762, cg01604601, cg056569 00, cg08132573, cg24686918, cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg257 65104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930. In some cases, the first methylation profile and / or the second methylation profile comprises the methylation state or level of one of the following biomarkers: cg25922751, cg25432518, cg23612220, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg267 69927, cg26769700, cg25574765, cg25490145, cg18384097, cg17126555, cg14247287, cg0742013 7. cg05098343, cg01903374, cg00907272, cg27125093, cg26112797, cg24166457, cg19300307, ​​cg1 7122157, cg13555415, cg11436362, cg10673833, cg09866569, cg08075204, cg05614346, cg02053 964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19675731, cg19252956, c g18856478, cg16509569, cg15797834, cg15698795, cg15341833, cg14556909, cg14083015, cg1405 8476, cg12192582, cg10590292, cg06787669, cg06439655, cg02522196, cg02233149, cg00558804,cg26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg1 8842353, cg16622899, cg14999168, cg13925432, cg12967050, cg11105292, cg0941 9005, cg09153080, cg07380416, cg06825448, cg05596756, cg03891050, cg0168136 7. cg01456691, cg00015530, cg27410601, cg27366280, cg26683005, cg25666403, cg 24706505, cg24107852, cg23824762, cg23021796, cg21122474, cg20336172, cg186 10205, cg18456621, cg17518965, cg16748008, cg16191087, cg16061668, cg1464204 5. cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044 (Table 20).

[0330] In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 20 to detect the presence of cancer in a biological sample. In some cases, the method, system, platform, or non-transitory computer-readable medium then uses at least a second methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 20 to determine the type of cancer, and optionally distinguish between different cancer stages, determine the prognosis of the cancer, predict a response to treatment, and / or monitor a response to treatment.

[0331] In some embodiments, a method, system, platform, or non-transitory computer-readable medium uses a methylation profile comprising the methylation states or levels of a panel of biomarkers selected from Table 20 to detect the presence of cancer in a biological sample. In some cases, the method, system, platform, or non-transitory computer-readable medium then uses at least a second methylation profile comprising the methylation states or levels of a panel of biomarkers selected from the following group to determine the type of cancer, and optionally distinguish between different cancer stages, determine the prognosis of cancer, predict treatment response, and / or monitor treatment response: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg247 06505, cg17126555, cg13911392, cg18901104, cg25982880, cg157978 34. cg27125093, cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg27410601, cg03297901, cg06853339, cg12900649, cg272 19182, cg15759721, cg27023597, cg02782634, cg18942579, cg014093 43. cg10530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg07860918, cg00206490, cg07644807, cg00558804, cg05304979, cg27598656, cg03549146, cg221 90721, cg01660934, cg02358862, cg23093496, cg07641284, cg016813 67. cg26769927, cg08480068, cg02914427, cg03653601, cg01990910,cg00933696、cg09866569、cg20357538、cg22460896、cg07116712、cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900, cg08132573, cg24686918, cg05352688, cg18384097, cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930.

[0332] In some cases, a methylation profile comprising more than 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or more of a genome is considered a methylation profile. In some cases, a methylation profile is generated from a panel of biomarkers selected from Tables 15, 16, 17 and / or 18. In some cases, a method, system, platform or non-transitory computer-readable medium uses the methylation profile described herein to determine the type of cancer. In other cases, a method, system, platform or non-transitory computer-readable medium uses the methylation profile described herein to perform early detection of cancer. In some cases, a method, system, platform or non-transitory computer-readable medium uses the methylation profile described herein to perform non-invasive detection of cancer. In other cases, a method, system or non-transitory computer-readable medium uses the methylation profile described herein to distinguish different cancer stages. In other cases, a method, system, platform or non-transitory computer-readable medium uses the methylation profile described herein to determine the prognosis of cancer, predict treatment response and / or monitor treatment response.

[0333] In some cases, methylation state or methylation degree indicate the presence, lack and / or amount of methylation of the nucleotides in a specific nucleotide or a portion of DNA. In some cases, the methylation state of a specific DNA sequence (e.g., a biomarker or DNA region as described herein) indicates the methylation state of each base in the sequence or can indicate the methylation state of a small class of base pairs in the sequence (e.g., the methylation state of cytosine or the methylation state of one or more specific restriction enzyme recognition sequences), or can indicate information about the regional methylation density in the sequence without providing the exact information of where methylation occurs in the sequence. In some embodiments, methylation state / degree is used to distinguish different subtypes or tumor entities. In some cases, specific DNA methylation patterns distinguish between low and high tumors with potential for metastasis, thereby allowing customized treatment options.

[0334] In some cases, the methylation status of one or more CpG methylation sites within a DNA sequence includes unmethylated, fully methylated and / or hemimethylated sites. In some cases, a collection of methylation profiles is used to generate a methylation map, for example, a methylation profile representing a group of individuals or tumor types or characteristics. In some cases, hypermethylation is the average methylation state corresponding to an increase in the presence of 5-mCyt at one or more CpG dinucleotides within the DNA sequence of the test DNA sample relative to the amount of 5-mCyt found at the corresponding CpG dinucleotide in the normal control DNA sample. In some cases, hypomethylation is the average methylation state corresponding to a decrease in the presence of 5-mCyt at one or more CpG dinucleotides within the DNA sequence of the test DNA sample relative to the amount of 5-mCyt found at the corresponding CpG dinucleotide in the normal control DNA sample.

[0335] In some embodiments, the methylation index of each genomic site (e.g., CpG site) refers to the ratio of sequence reads showing methylation sites to the total number of reads covering the site. In some cases, the regional methylation density is the number of site reads showing methylation in the region divided by the total number of reads covering the sites in the region. In some cases, the CpG methylation density in the region is the number of reads showing CpG methylation divided by the total number of reads covering the CpG sites in the region (e.g., specific CpG sites, CpG islands, or CpG sites in larger regions). For example, the methylation density of each 100 kb segment in the human genome is determined based on the total number of unconverted cytosines (corresponding to methylated cytosines) at the CpG site, which is a portion of all CpG sites covered by the sequence reads mapped to the 100 kb region. In some cases, this analysis is also performed based on other segment sizes (e.g., 50 kb or 1 Mb, etc.). In some cases, the region is the whole genome or a chromosome or a portion of a chromosome (e.g., a chromosome arm). In some cases, when a region includes only CpG sites, the methylation index of the CpG site is the same as the methylation density of the region. In some cases, the ratio of methylated cytosine refers to the number of cytosine sites "C's" showing methylation (e.g., not converted after deamination treatment (such as bisulfite conversion)) relative to the total number of analyzed cytosine residues (i.e., including cytosine outside the CpG situation) in the region. In some cases, the methylation index, methylation density, and methylated cytosine ratio are examples of the degree of methylation.

[0336] In some embodiments, determination of the methylation profile comprises determining the methylation status of more than at least about 1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, or 100, 150, 200, 250, 300, 400, 500, 750, 1000, 2000, 2500, 3000, 4000, 5000, 7500, 10000, 20000, 25000, 30000, 40000, 50000, 75000, 100000, 200000, 300000, 400000, 500000, 600000, and 700000 CpG sites in a DNA sample. In one aspect of this embodiment, a methylation profile is generated based on the methylation status of about 1 to about 500,000 CpG sites.

[0337] In some embodiments, the methylation profile is derived from a biopsy sample. In some cases, the methylation profile is derived from a tissue sample. In some cases, the methylation profile is derived from a cell-free biological sample. In some cases, the methylation profile is derived from a prevalent tumor DNA (ctDNA) sample.

[0338] comparison

[0339] The various methods described herein include steps of comparing values, levels, characteristics, features, properties, etc. to suitable controls (interchangeably referred to herein as appropriate controls), control samples, or controls. In some embodiments, the control is a value, level, characteristic, feature, property, etc. measured in a cell, tissue, organ, or sample obtained from a patient. In some cases, the cell, tissue, organ, or sample is a normal cell, tissue, organ, or sample. In some cases, the cell tissue, organ, or sample is a cancer cell, tissue, organ, or sample. For example, the methylation level of the biomarkers of the present invention in a sample from an uninfected individual or a normal control individual or an uninfected family member of an individual is analyzed. In another embodiment, the control is a value, level, characteristic, feature, property, etc. measured before starting treatment (e.g., cancer treatment) for a patient, or a treatment regimen therebetween. In other embodiments, the control is a predefined value, level, characteristic, feature, property, etc.

[0340] In some embodiments, the control is a methylation profile of one or more biomarkers of the invention associated with a cancer type, compared to a patient sample. In some cases, the control is a methylation profile of one or more biomarkers of Tables 15, 16, 17, 18, and / or 20. In some cases, the control is the methylation profile of one or more of the following biomarkers: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg271419 15. cg17122157, cg09844573, cg03087897, cg24706505, cg17126555, cg13911392, cg18901104, cg25982880, cg15797834, cg27125093 , cg17518965, cg20695297, cg04858553, cg09419005, cg25490145, cg11252953, cg18456621, cg07058988, cg17864646, cg06153925, cg 27410601, cg03297901, cg06853339, cg12900649, cg27219182, cg15759721, cg27023597, cg02782634, cg18942579, cg01409343, cg10 530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244, cg04147906, cg23878564, cg07860918, cg00206490, cg0764 4807, cg00558804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg02358862, cg23093496, cg07641284, cg016813 67. cg26769927, cg08480068, cg02914427, cg03653601, cg01990910, cg00933696, cg09866569, cg20357538, cg22460896, cg07116712,cg10186131、cg06380123、cg18610205、cg12353452、cg10590292、cg00037681、cg05596756、cg03569637、cg02522196、cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg25574765、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25922751、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg10673833、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg18384097、cg16266227, cg19675731, cg21461981, cg25765104, cg26394055, cg20685713, cg23589035, cg01903374, cg23612220, cg26315985, cg18856478, cg23229016, cg21004490, cg24742520, cg23013029, cg19704755, cg07589991, cg10055231, and cg26017930. In some cases, the control is the methylation profile of one or more biomarkers cg25574765, cg25490145, cg18384097, cg25922751, and cg17126555.

[0341] In some cases, the control is a positive control, such as a methylation profile obtained from a cancer sample; or a negative control, such as a methylation profile obtained from a normal sample. In some cases, the control is also referred to as a training set or training data set.

[0342] Detection method

[0343] In some embodiments, various methods are utilized to measure, detect, determine, identify, and characterize the methylation status / level of a biomarker (i.e., a region / fragment of DNA or a region / fragment of genomic DNA (e.g., a region / fragment containing a CpG island)) in the development of a disease or condition (e.g., cancer) and thereby diagnose the onset, presence, or status of the disease or condition.

[0344] In some cases, the methylation profile is generated from a biological sample isolated from an individual. In some embodiments, the biological sample is a biopsy. In some cases, the biological sample is a tissue sample. In other cases, the biological sample is a cell-free biological sample. In other cases, the biological sample is a prevalent tumor DNA sample. In one embodiment, the biological sample is a cell-free biological sample containing prevalent tumor DNA.

[0345] In some embodiments, biomarkers (also referred to herein as markers) are obtained from tissue samples. In some cases, tissues correspond to any cells. Different types of tissues correspond to different types of cells (e.g., liver, lung, blood, connective tissue, and the like), and correspond to healthy cells relative to tumor cells or tumor cells at different neoplasia stages, or correspond to replaced malignant tumor cells. In some embodiments, tissue samples further comprise clinical samples, and also include cells, cell supernatants, organs, and the like of cultures. Samples also include fresh frozen tissue blocks and / or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, which are prepared for pathological analysis or immunohistochemical studies.

[0346] In some embodiments, the biomarkers are 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, serum, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, earwax, breast milk, bronchoalveolar lavage fluid, semen, prostatic fluid, cowper's fluid or pre-ejaculatory fluid, female pre-ejaculatory fluid, sweat, tears, cystic fluid, pleural and peritoneal fluid, pericardial fluid, ascites, lymph, chyme, chyle, bile, interstitial fluid, menstruation, pus, sebum, vomitus, vaginal secretions / washings, synovial fluid, mucosal secretions, aqueous feces, pancreatic juice, sinus lavage fluid, bronchial and lung aspirates, cystic fluid, or umbilical cord blood). In some embodiments, the biological fluid is blood, a blood derivative, or a blood fraction, such as serum or plasma. In a specific embodiment, the sample comprises a blood sample. In another embodiment, a serum sample is used. In another embodiment, the sample comprises urine. In some embodiments, liquid samples also include samples that have been manipulated in any way after their acquisition, such as centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washing, or enrichment according to a specific cell population.

[0347] In some embodiments, the biomarkers in normal samples (e.g., normal or control tissue without disease, or normal or control body fluids, feces, blood, serum, amniotic fluid), most importantly, biomarkers in healthy feces, blood, serum, amniotic fluid or other body fluids, are methylated or not methylated. In other embodiments, the biomarkers in samples from patients with or at risk of cancer are hypomethylated or hypermethylated, for example, the methylation frequency is reduced or increased (respectively) by 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% compared to normal samples. In one embodiment, the sample is also hypomethylated or hypermethylated compared to a previous analysis of a sample from the same patient with or at risk of cancer (particularly for comparison of disease progression).

[0348] In some embodiments, the methylation gene panel comprises a group of biomarkers, such as the above-mentioned biomarkers. In some cases, the methylation gene panel corresponding to the methylation gene panel of a tumor of an organism (e.g., a human being) is classified as a tumor methylation gene panel. In some cases, the tumor methylation gene panel is determined using cell-free (or protein-free) tumor DNA in a tumor tissue or biological sample. Other examples of the methylation gene panel of interest include methylation gene panels in organs that help DNA enter body fluids (e.g., methylation gene panels of tissues (e.g., brain, breast, lung, prostate, kidney, plasma, etc.)).

[0349] In some embodiments, the plasma methylation genome is a methylation genome measured in the plasma or serum of an animal (e.g., human). In some cases, since plasma and serum include cell-free DNA, the plasma methylation genome is an example of a cell-free or protein-free methylation genome. Since the plasma methylation genome is a mixture of a tumor and other methylation genomes of interest, the plasma methylation genome is also an example of a mixed methylation genome. In some cases, the urine methylation genome is measured from an individual's urine sample. In some cases, the cell methylation genome corresponds to a methylation genome measured from a patient's cell (e.g., a tissue cell from an organ (such as brain, lung, breast, and the like). The methylation genome of blood cells is referred to as a blood cell methylation genome (or blood methylation genome).

[0350] In some embodiments, DNA (e.g., genomic DNA, such as extracted genomic DNA or treated genomic DNA) is isolated by any standard method in the art, including the use of commercially available kits. In short, in cases where the DNA of interest is encapsulated by cell membranes, the biological sample must be broken and lysed by enzymatic, chemical, or mechanical means. In some cases, proteins and other contaminants are then removed from the DNA solution, for example by digestion with proteinase K. The DNA is then recovered from the solution. In these cases, this is performed by a variety of methods, including salting out, organic extraction, or binding the DNA to a solid support. In some cases, the method of choice is influenced by several factors, including time, cost, and the required amount of DNA.

[0351] In the case where the sample DNA (e.g., circulating DNA from a cell-free sample such as blood or urine) is not enclosed in a membrane, standard methods in the art for isolating and / or purifying DNA are optionally used (see, e.g., 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 protein denaturing agents, such as chaotropic salts, such as guanidine hydrochloride or urea; or detergents, such as sodium dodecyl sulfate (SDS), cyanogen bromide. Alternative methods include, but are not limited to, ethanol precipitation or propanol precipitation, vacuum concentration, especially vacuum concentration with the aid of a centrifuge. In some cases, those skilled in the art also utilize devices such as: filtration devices, such as ultrafiltration, silica surfaces or membranes, magnetic particles, polystyrene particles, polystyrene surfaces, positively charged surfaces and positively charged membranes, charged membranes, charged surfaces, charged exchange membranes, charged exchange surfaces.

[0352] In some cases, once the nucleic acid is extracted, methylation analysis is performed by any means known in the art. A variety of methylation analysis procedures are known in the art and can be used to implement the present invention. Such analyses allow the determination of the methylation status of one or more CpG sites within a tissue sample. In addition, such methods can be used for absolute or relative quantification of methylated nucleic acids. Such methylation analysis includes two main steps and other techniques. 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 main step includes (i) amplification and detection, or (ii) direct detection by various 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 (including circular ligation and cleavage) of dye-modified probes; (d) pyrosequencing; (e) single molecule sequencing; (f) mass spectrometry; or (g) Southern blot analysis.

[0353] Alternatively, restriction enzyme digestion can be used to amplify PCR products from bisulfite-converted DNA, such as those 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 analysis suitable for determining the degree of DNA methylation at a specific locus in a small amount of genomic DNA. Briefly, restriction enzyme digestion is used to reveal methylation-associated sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-associated sequence differences are first introduced into 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). The bisulfite-converted DNA is then PCR-amplified using primers specific for the CpG sites of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection with a labeled, specifically hybridized probe. The degree of methylation in the original DNA sample is expressed as a linear, quantitative measure of the relative amounts of digested and undigested PCR products over a wide range of DNA methylation levels. Furthermore, this technique is reliably applied to DNA obtained from microdissected, paraffin-embedded tissue samples. Typical reagents for COBRA analysis (e.g., as might be found in a typical COBRA-based kit) may include, but are not limited to: a specific gene (or methylation-altered DNA sequence or CpG island); a restriction enzyme and appropriate buffer; a gene-hybridizing oligonucleotide; a control hybridizing oligonucleotide; a kinase labeling kit for oligonucleotide probes; and radioactive nucleotides. Additionally, bisulfite conversion reagents may include: a DNA denaturation buffer; a sulfonation buffer; a DNA recovery reagent or kit (e.g., precipitation, ultrafiltration, affinity columns); a desulfonation buffer; and a DNA recovery component.

[0354] In one embodiment, the methylation profile of selected CpG sites is determined using methylation-specific PCR (MSP). MSP allows assessment of the methylation status of virtually any group of CpG sites within a CpG island without relying on 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. Patent Publication No. 2010 / 0144836 (Van Engeland et al); herein incorporated by reference in their entirety). Briefly, DNA is modified with a deaminating agent such as sodium bisulfite to convert unmethylated cytosine (but not methylated cytosine) to uracil, and then amplified with primers specific for methylated versus unmethylated DNA. Typical reagents for MSP analysis (e.g., as might be found in a typical MSP-based kit) may include, but are not limited to: methylated and unmethylated PCR primers for a specific gene (or methylation-altered DNA sequence or CpG island), optimized PCR buffers and deoxynucleotides, and specific probes. ColoSure TM The test is a commercially available test for colon cancer based on MSP technology and measurement of vimentin gene methylation (Itzkowitz et al., 2007, Clin Gastroenterol. Hepatol. 5(1), 111-117). Alternatively, quantitative multiplexed methylation-specific PCR (QM-PCR) as described in Fackler et al., 2004, Cancer Res. 64(13): 4442-4452 or Fackler et al., 2006, Clin. Cancer Res. 12(11 Pt 1): 3306-3310 can be used.

[0355] In one embodiment, the methylation profile of the selected CpG sites is determined using MethyLight and / or Heavy Methyl Methods. MethyLight and Heavy Methyl analysis is a high-throughput quantitative methylation analysis using fluorescence-based real-time PCR (Taq Man (R)) technology that does not require further manipulation after the PCR step (Eads, CA et al., 2000, Nucleic Acid Res. 28, e 32; Cottrell et al., 2007, J. Urology 177, 1753; U.S. Patent No. 6,331,393 (Laird et al.), the contents of which are incorporated herein by reference in their entirety). Briefly, the MethyLight method starts with a mixed sample of genomic DNA and is converted into a mixed pool of methylation-related sequence differences using a sodium bisulfite reaction according to standard procedures (the bisulfite method converts unmethylated cytosine residues into uracil). Fluorescence-based PCR is then performed using either an "unbiased" (using primers that do not overlap with known CpG methylation sites) PCR reaction or a "biased" (using PCR primers that overlap with known CpG dinucleotides) reaction. In some cases, sequence discrimination is performed at both the amplification method level and the fluorescence detection method level. In some cases, MethyLight analysis is used as a quantitative test of methylation patterns in genomic DNA samples, where sequence discrimination occurs at the probe hybridization level. In this quantitative format, the PCR reaction provides unbiased amplification in the presence of a fluorescent probe that overlaps with a specific putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primer nor the probe overlaps with any CpG dinucleotide. Alternatively, a qualitative test of genomic methylation can be achieved by probing a biased PCR pool with a control oligonucleotide that does not "cover" known methylation sites or with an oligonucleotide that covers potential methylation sites. Typical reagents for a MethyLight assay (e.g., as might be found in a typical MethyLight-based kit) may include, but are not limited to: PCR primers for a specific gene (or DNA sequence with altered methylation or a CpG island); TaqMan® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase. MethyLight technology is commercially available as a test for lung cancer (epi proLung BL Reflex assay); and colon cancer (epi proColon assay and mSEPT9 assay) (Epigenomics, Berlin, Germany), PCT Publication No. WO 2003 / 064701 (Schweikhardt and Sledziewski), the contents of which are incorporated herein by reference in their entirety.

[0356] Quantitative MethyLight uses bisulfite to convert genomic DNA and uses PCR and methylation-independent primers to amplify methylation sites. Detection probes with two different fluorophores specific for methylated and unmethylated sites can simultaneously and quantitatively measure methylation. The heavy methylation technique begins with bisulfite conversion of DNA. Next, a specific blocker is used to prevent amplification of unmethylated DNA. Methylated genomic DNA does not bind to the blocker and its sequence will be amplified. The amplified sequence is detected with a methylation-specific probe. (Cottrell et al., 2004, Nuc. Acids Res. 32: e10, the contents of which are incorporated herein by reference in their entirety).

[0357] Ms-SNuPE technology is a quantitative method for assessing methylation differences at specific CpG sites. It is based on bisulfite treatment of DNA, followed by single nucleotide primer extension (Gonzalgo and Jones, 1997, Nucleic Acids Res. 25, 2529-2531). In short, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine into uracil while leaving 5-methylcytosine unchanged. The desired target sequence is then amplified using PCR primers specific for bisulfite-converted DNA, and the resulting product is separated and used as a template for methylation analysis of the CpG site of interest. In some cases, a small amount of DNA (e.g., microdissected lesion sections) is analyzed, and this method avoids the use of restriction enzymes to determine the methylation status of CpG sites. Typical reagents for Ms-SNuPE analysis (e.g., as found in a typical kit based on Ms-SNuPE) include, but are not limited to, PCR primers for a specific gene (or DNA sequence with altered methylation or CpG island); optimized PCR buffer and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPE reaction); and radionucleotides. In addition, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

[0358] In another embodiment, the methylation status of selected CpG sites is determined using a differential binding-based methylation detection method. To identify differentially methylated regions, one approach is to capture methylated DNA. This method 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 Publication No. WO 2006 / 056480A2 (Relhi), the contents of which are incorporated herein by reference in their entirety). This fusion protein offers several advantages over conventional methylation-specific antibodies. MBD-FC has a higher affinity for methylated DNA and binds double-stranded DNA. Most importantly, the two proteins differ in how they bind DNA. Methylation-specific antibodies bind DNA randomly, meaning only a binary answer can be obtained. On the other hand, the methyl-binding domain of MBD-FC binds to DNA molecules regardless of their methylation status. The strength of this protein-DNA interaction is defined by the degree of DNA methylation. After binding of genomic DNA, non-methylated and methylated DNA can be fractionated using an eluent with increasing salt concentration, making the separation more controllable (Gebhard et al., 2006, Nucleic Acids Res. 34:e82). Thus, this method (called methyl-CpG immunoprecipitation (MCIP)) not only enriches genomic DNA but also fractionates it according to the degree of methylation, which is particularly helpful when the fraction of unmethylated DNA is also to be investigated.

[0359] In an alternative embodiment, 5-methylcytidine antibodies bind to and precipitate methylated DNA. Antibodies were obtained from Abeam (Cambridge, MA), Diagenode (Sparta, NJ), or Eurogentec (c / o AnaSpec, Fremont, CA). Once the methylated fragments are isolated, they can be sequenced using microarray-based techniques such as methylated CpG island recovery analysis (MIRA) or methylated DNA immunoprecipitation (MeDIP) (Pelizzola et al., 2008, Genome Res. 18, 1652-1659; O'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 / partially melted molecule separation (MBD / SPM, Shiraishi et al., 1999, Proc. Natl. Acad. Sci. USA 96(6):2913-2918).

[0360] In some embodiments, the methylation detection method includes random shearing or random fragmentation of genomic DNA, cutting the DNA using methylation-dependent or methylation-sensitive restriction enzymes, and then selectively identifying and / or analyzing the cut or uncut DNA. Selective identification can include, for example, separating cut and uncut DNA (e.g., based on size) and quantifying the cut or uncut sequence of interest. See, for example, U.S. Patent No. 7,186,512. Alternatively, the method can encompass amplifying intact DNA after restriction enzyme digestion, thereby amplifying only DNA that is not cleaved by the restriction enzyme in the amplified region. See, for example, U.S. Patent Nos. 7,910,296; 7,901,880; and 7,459,274. In some embodiments, amplification can be performed using gene-specific primers.

[0361] For example, a methyl-sensitive enzyme preferentially or substantially cleaves or digests its DNA recognition sequence when it is unmethylated. Therefore, the fragments cut into by an unmethylated DNA sample are smaller than those of a methylated DNA sample. Similarly, a highly methylated DNA sample does not undergo cleavage. In contrast, a methyl-sensitive enzyme only cleaves its DNA recognition sequence when it is methylated. Methyl-sensitive enzymes suitable for digesting unmethylated DNA in technical methods include, but are not limited to, Hpall, Hhal, Maell, BstUI, and Acil. In some cases, the enzyme used is Hpall, which only cuts the unmethylated sequence CCGG. In other cases, another enzyme used is Hhal, which only cuts the unmethylated sequence GCGC. Both enzymes are available from New England BioLabs (R), Inc. Two or more methyl-sensitive enzymes that only digest unmethylated DNA are also used in combination. Suitable enzymes that digest only methylated DNA include, but are not limited to, Dpnl, which cuts only fully methylated 5'-GATC sequences; and McrBC (endonuclease), which cuts DNA containing modified cytosines (5-methylcytosine, 5-hydroxymethylcytosine, or N4-methylcytosine) and cuts at the recognition site 5'...PumC(N4o-3ooo)PumC...3' (New England BioLabs, Inc., Beverly, MA). The cleavage methods and procedures for selected restriction enzymes that cut DNA at specific sites are well known to those skilled in the art. For example, many restriction enzyme suppliers provide information on the conditions and types of DNA sequences cut by specific restriction enzymes, including New England BioLabs, Pro-Mega Biochems, Boehringer-Mannheim, and similar suppliers. Sambrook et al. (See Sambrook et al., Molecular Biology: A Laboratory Approach, Cold Spring Harbor, NY 1989) provide a general description of methods using restriction and other enzymes.

[0362] In some cases, a methylation-dependent restriction enzyme is a restriction enzyme that cleaves or digests DNA at or near a methylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is unmethylated. Methylation-dependent restriction enzymes include those that cleave methylated recognition sequences (e.g., Dpnl) and enzymes that cleave sequences near the recognition sequence, rather than the recognition sequence (e.g., McrBC). For example, the McrBC recognition sequence is 5'RmC(N40-3000)RmC3', where "R" is a purine and "mC" is a methylated cytosine, and "N40-3000" indicates the distance between the two RmC half-sites where a restriction event has been observed. McrBC typically cleaves near one half-site or the other, but the cleavage positions are typically distributed over several base pairs (approximately 30 base pairs relative to the methylated base). McrBC sometimes cleaves 3' of the two half-sites, sometimes cleaves 5' of the two half-sites, and sometimes cleaves between the two sites. Exemplary methylation-dependent restriction enzymes include, for example, McrBC, McrA, MrrA, Bisl, Gla1, and Dpnl. One skilled in the art will appreciate that any methylation-dependent restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention.

[0363] In some cases, a methylation-sensitive restriction enzyme is one that cleaves DNA at or near an unmethylated recognition sequence, but does not cleave DNA at or near the same sequence when the recognition sequence is methylated. Exemplary methylation-sensitive restriction enzymes are described, for example, in 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 sequences when the cytosine within the recognition sequence is methylated at position C5 include, for example, 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, PstI, 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 sequences when adenosine within the recognition sequence is methylated at position N6 include, for example, Mbo I. Those skilled in the art will appreciate that any methylation-sensitive restriction enzyme, including homologs and orthologs of the restriction enzymes described herein, is also suitable for use in the present invention. Those skilled in the art will further appreciate that a methylation-sensitive restriction enzyme that fails to cut at or near its recognition sequence in the presence of cytosine methylation may be insensitive to the presence of adenosine methylation at or near its recognition sequence. Similarly, a methylation-sensitive restriction enzyme that fails to cut at or near its recognition sequence in the presence of adenosine methylation may be insensitive to the presence of cytosine methylation at or near its recognition sequence. For example, Sau3AI is sensitive to the presence of methylated cytosine at or near its recognition sequence (i.e., fails to cut), but is insensitive to the presence of methylated adenosine at or near its recognition sequence (i.e., cuts). Those skilled 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 (comprising their recognition sequence), while other methylation-sensitive restriction enzymes are blocked by methylation of only both strands but can cleave when the recognition site is hemimethylated.

[0364] In an alternative embodiment, adapters are optionally added to the ends of the randomly fragmented DNA, followed by digestion of the DNA with a methylation-dependent or methylation-sensitive restriction enzyme, and then amplification of the intact DNA using primers that hybridize to the adapter sequence. In this case, a second step is performed to determine the presence, absence, or amount of a specific gene in the DNA amplification pool. In some embodiments, the DNA is amplified using real-time quantitative PCR.

[0365] In other embodiments, the method comprises quantifying the average methylation density of a target sequence within a population of genomic DNA. In some embodiments, the method comprises contacting the genomic DNA with a methylation-dependent restriction enzyme or a methylation-sensitive restriction enzyme under conditions that allow at least some copies of potential restriction enzyme cleavage sites in the locus to remain uncleaved; quantifying the intact copies of the locus; and comparing the amount of amplified product to a control value representing the amount of methylation of a control DNA, thereby quantifying the average methylation density in the locus relative to the methylation density of the control DNA.

[0366] In some cases, the amount of methylation of the locus of DNA is determined as follows: a sample of genomic DNA comprising the locus is provided, the DNA is cleaved with a methylation-sensitive or methylation-dependent restriction enzyme, and then the amount of intact DNA is quantified or the amount of cut DNA of the DNA locus of interest is quantified. The amount of intact or cut DNA will depend on the following: the initial amount of genomic DNA containing the locus, the amount of locus methylation, and the number of nucleotides (i.e., fraction) in the locus where methylation occurs in the genomic DNA. The amount of methylation in the DNA locus can be determined by comparing the amount of intact DNA or cut DNA with a control value, which represents the amount 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 amount of intact or cut DNA in the same locus or a second locus in another (e.g., normal, non-lesioned) cell.

[0367] By using at least one methylation-sensitive or methylation-dependent restriction enzyme under conditions that allow at least some copies of the potential restriction enzyme cleavage site in the locus to remain uncleaved and then quantifying the remaining complete copies and comparing the amount with the control value, the average methylation density of the locus can be determined. If the methylation-sensitive restriction enzyme contacts the copy of the DNA locus under conditions that allow at least some copies of the potential restriction enzyme cleavage site in the locus to remain uncleaved, the remaining intact DNA will be proportional to the methylation density, and thus can be compared with the control value to determine the relative methylation density of the locus in the sample. Similarly, if the methylation-dependent restriction enzyme contacts the copy of the DNA locus under conditions that allow at least some copies of the potential restriction enzyme cleavage site in the locus to remain uncleaved, the remaining intact DNA will be inversely proportional to the methylation density, and thus can be compared with the control value to determine the relative methylation density of the locus in the sample. Such analysis is disclosed in, for example, U.S. Patent No. 7,910,296.

[0368] Methylated CpG island amplification (MCA) technology 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 incorporated herein by reference in their entirety). Briefly, genomic DNA from primary tumors, cell lines, and normal tissues is digested with restriction enzymes that have different sensitivities to cytosine methylation in their recognition sites, followed by arbitrary pre-sensitized PCR amplification. After the PCR products are resolved on high-resolution polyacrylamide gels, fragments showing differential methylation are cloned and sequenced. The cloned fragments are then used as probes in Southern analysis to confirm differential methylation in these regions. Typical reagents for MCA analysis (e.g., as might be found in a typical MCA-based kit) may include, but are not limited to, PCR primers for any pre-primed genomic DNA; PCR buffer and nucleotides, restriction enzymes and appropriate buffers; gene hybridization oligonucleotides or probes; and control hybridization oligonucleotides or probes.

[0369] Other methylation detection methods include those described in, for example, U.S. Pat. Nos. 7,553,627; 6,331,393; U.S. Pat. No. 12 / 476,981; U.S. Pat. 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).

[0370] 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 for assessing 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. 11102-108; and U.S. Patent Publication No. 2009 / 0155791 (Wojdacz et al.), the contents of which are incorporated herein by reference in their entirety). Several commercially available real-time PCR machines have HRM systems, including the Roche LightCycler 480, the Corbett Research RotorGene 6000, and the Applied Biosystems 7500. HRM can 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).

[0371] In another embodiment, the methylation status of the selected CpG loci is determined using a primer extension assay, comprising an optimized PCR amplification reaction that produces an amplified target for analysis using mass spectrometry. The analysis can also be performed in a multitasking format. Mass spectrometry is a particularly effective method for detecting polynucleotides associated with differential methylation regulatory elements. The presence of a polynucleotide sequence is tested by comparing the mass of the detected signal with the expected mass of the polynucleotide of interest. The relative signal intensity of a specific polynucleotide sequence (e.g., a mass peak on the spectrum) indicates the relative population of a specific allele, making it possible to directly calculate the allele ratio based on this data. This method is described in detail in PCT Publication No. WO 2005 / 012578A1 (Beaulieu et al.), which is incorporated herein by reference in its entirety. In methylation analysis, this analysis can be used to detect methylation-dependent C to T sequence changes introduced by bisulfite. These methods are particularly suitable for performing multiplexed amplification reactions and multiplexed primer extension reactions (such as multiplexed homogeneous primer mass extension (hME) assays) in a single well, thereby further improving throughput and reducing the unit reaction cost of primer extension reactions.

[0372] 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, 117-130). Comprehensive high-throughput arrays for relative methylation (CHARM) technology are described in WO 2009 / 021141 (Feinberg and Irizarry). Roche (R) NimbleGen (R) microarrays include chromatin immunoprecipitation-on-chip (ChIP-chip) or methylated DNA immunoprecipitation-on-chip (MeDIP-chip). These tools have been used in 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 the use of bisulfite 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. Patent No. 7,611,869 (Fan)). As an alternative to bisulfate oxidation, Bayeyt et al. have reported oxidation of 5-methylcytosine with a selective oxidant instead of reaction with thymidine, followed by PCR or pyrophosphate sequencing (WO 2009 / 049916 (Bayeyt et al.). These references for these techniques are incorporated herein by reference in their entirety.

[0373] In some cases, the amount of intact DNA within the locus flanked by amplification primers is quantified using a quantitative amplification method (e.g., quantitative PCR or quantitative linear amplification) followed by restriction digestion. Quantitative amplification methods are disclosed, for example, in U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, and in, for example, 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).

[0374] After the nucleic acid is reacted or separated in a methylation-specific manner, in some cases, the nucleic acid is subjected to sequence-based analysis. For example, after determining that a specific melanoma genomic sequence is hypermethylated or hypomethylated compared to a benign counterpart, the amount of this genomic sequence can be determined. Subsequently, this amount can be compared with a standard control value and serve as an indication of melanoma. In many cases, it is necessary to amplify the nucleic acid sequence using any of several nucleic acid amplification procedures well known in the art. Specifically, nucleic acid amplification is the chemical or enzymatic synthesis of a nucleic acid copy containing a sequence complementary to the amplified nucleic acid sequence (template). The methods and kits of the present invention can use any nucleic acid amplification or detection method known to those skilled in the art, such as those described in U.S. Pat. Nos. 5,525,462 (Takarada et al.); 6,114,117 (Hepp et al.); 6,127,120 (Graham et al.); 6,344,317 (Urnovitz); 6,448,001 (Oku); 6,528,632 (Catanzariti et al.); and PCT Publication No. WO 2005 / 111209 (Nakajima et al.); all of which are incorporated herein by reference in their entirety.

[0375] In some embodiments, nucleic acids are amplified by PCR amplification, using methods known to those skilled in the art. However, those skilled in the art will recognize that amplification can be accomplished by any known method, such as ligase chain reaction (LCR), Q-replica amplification, rolling circle amplification, transcription amplification, self-sustaining sequence replication, nucleic acid sequence-based amplification (NASBA), each of which provides sufficient amplification. Branched DNA technology is also used, as appropriate, to qualitatively demonstrate the presence of technical sequences representing specific methylation patterns, or to quantitatively determine the amount of this specific genomic sequence in a sample. Nolte reviewed the use of branched DNA signal amplification for directly quantifying nucleic acid sequences in clinical samples (Nolte, 1998, Adv. Clin. Chem. 33: 201-235).

[0376] PCR methods are 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, for example, Innis et al., eds., PCR Protocols, A Guide to Methods and Application, Academic Press, Inc., San Diego, Calif. 1990; U.S. Patent No. 4,683,202 (Mullis). PCR reagents and protocols are also available from commercial suppliers, such as Roche Molecular Systems. In some cases, PCR is performed as an automated method using thermostable enzymes. In this method, the temperature of the reaction mixture is automatically cycled through the denaturation zone, primer bonding zone, and extension reaction zone. Machines particularly suitable for this purpose are commercially available.

[0377] In some embodiments, the amplified sequence is also measured using an invasive cleavage reaction, such as the Invader(R) technology (Zou et al., 2010, Association of Clinical Chemistry (AACC) poster presentation, "Sensitive Quantification of Methylated Markers with a Novel Methylation Specific Technology" on July 28, 2010; and U.S. Patent No. 7,011,944 (Prudent et al.)).

[0378] Suitable next-generation sequencing technologies are widely available. Examples include the 454 Life Sciences platform (Roche, Branford, CT) (Margulies et al., 2005 Nature, 437, 376-380); Illumina's Genome Analyzer, GoldenGate Methylation Assay, or Infinium Methylation Assay, i.e., Infinium Human Methylation 27KBeadArray or VeraCode GoldenGate Methylation Array (Illumina, San Diego, CA; Bibkova et al., 2006, Genome Res. 16, 383-393; U.S. Patents 6,306,597 and 7,598,035 (Macevicz); 7,232,656 (Balasubramanian et al.); Bio-Rad's QX200 TM Droplet Digital TM PCR system; or the SOLiD system for DNA sequencing by ligation (Applied Biosystems / Life Technologies; U.S. Patents 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.)); Helicos True single-molecule DNA sequencing technology (Harris et al., 2008 Science, 320, 106-109; U.S. Patents 7,037,687 and 7,645,596 (Williams et al.); 7,169,560 (Lapidus et al.); 7,769,400 (Harris)); Pacific Biosciences' Single Molecule Real-Time (SMRT) system. TM) technology, and sequencing (Soni and Meller, 2007, Clin. Chem., 1996-2001); semiconductor sequencing (Ion Torrent; Personal Genome Machine); DNA nanosphere sequencing; sequencing using Dover Systems' technology (Polonator), and technologies that do not require amplification or other transformation of 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 for the sequencing of multiple nucleic acid molecules isolated from a sample in parallel with high-order multiplexing. Each of these platforms allows for the sequencing of a single nucleic acid fragment molecule, either homologously amplified or not. Specific platforms include, for example, (i) sequencing by ligation of dye-modified probes (including circular ligation and cleavage), (ii) pyrosequencing, and (iii) single molecule sequencing.

[0379] Pyrosequencing is a method for sequencing nucleic acids by synthesis that relies on the detection of pyrophosphate released when nucleotides are incorporated. Generally speaking, synthesis sequencing involves synthesizing one nucleotide at a time (a DNA strand whose complementary strand is being sequenced). The nucleic acid under investigation can be fixed to a solid support, hybridized with a sequencing primer, and incubated with DNA polymerase, ATP sulfurylase, luciferase, apyrase, adenosine 5' phosphosulfate, and luciferin. Nucleotide solutions are added and removed sequentially. The correct incorporation of nucleotides releases pyrophosphate, which interacts with ATP sulfurylase and produces ATP in the presence of adenosine 5' phosphosulfate, promoting the luciferin reaction and generating a chemiluminescent signal, thereby enabling sequence determination. Pyrosequencing machines and methylation-specific reagents were obtained from Qiagen, Inc. (Valencia, CA). See also Tost and Gut, 2007, Nat. Prot. 2 2265-2275. Examples of systems that can be used by those skilled in the art based on pyrosequencing generally include the following steps: ligating an adapter nucleic acid to a study nucleic acid and hybridizing the study nucleic acid to beads; amplifying the nucleotide sequence in the study nucleic acid in an emulsion; sorting the beads using a picoliter porous solid support; and sequencing the amplified nucleotide sequence by pyrosequencing methods (e.g., Nakano et al., 2003, J. Biotech. 102, 117-124). Such systems can be used to exponentially amplify amplification products generated by the methods described herein, for example, by ligating a heterologous nucleic acid to a first amplification product generated by the methods described herein.

[0380] probe

[0381] In some cases, one or more probes in a probe set are used in the above sequencing methods. In some cases, one or more probes in a probe set comprise a probe of Formula I:

[0382]

[0383] in:

[0384] A is the first target binding region;

[0385] B is the second target binding region; and

[0386] L is the linker region;

[0387] wherein A has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 30 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775; B has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 12 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775; L is linked to A; and B is linked to A or L.

[0388] In some cases, L is connected to A and B is connected to L. In some cases, A, B, and L are connected as illustrated in Formula Ia:

[0389]

[0390] In some cases, the plurality of probes comprises at least 10, 20, 30, 50, 100, 200, 500, 1000, 1500, 1775, 1800, 2000 or more than 2000 probes. In some cases, the plurality of probes comprises 10, 20, 30, 50, 100 or more than 100 probes.

[0391] In some embodiments, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 35 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 40 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 45 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 50 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 55 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 60 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 65 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 70 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 80 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775. In some cases, A has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 90 contiguous nucleotides starting at position 1 from the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775.

[0392] In some embodiments, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 14 contiguous nucleotides starting at position 1' from the 3' terminus of the identical sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 15 contiguous nucleotides starting at position 1' from the 3' terminus of the identical sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 18 contiguous nucleotides starting at position 1' from the 3' terminus of the identical sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 20 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 22 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 25 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 28 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 30 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 35 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 40 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 45 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775.In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 50 contiguous nucleotides starting at position 1' from the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775. In some cases, B has at least 70%, 80%, 90%, 95%, or 99% sequence identity to at least 55 contiguous ...

Claims

1. A method for generating a methylation profile of a biomarker of an individual, the method comprising: a. treating the extracted genomic DNA with a deaminating agent to produce a treated genomic DNA comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample of the individual; b. detecting hybridization between the extracted genomic DNA and a probe, wherein the probe hybridizes to cg10673833 and has at least 90% sequence identity to SEQ ID NO: 1876 or 1877; and c. generating a methylation profile based on the detected hybridization between the extracted genomic DNA and the probe.

2. The method according to claim 1, wherein the probe is SEQ ID NO: 1876 and / or 1877.

3. The method of claim 1 or 2, further comprising comparing the methylation profile of the biomarker with a reference CpG methylation profile obtained from a cancer CpG methylation profile database, wherein a correlation between the methylation profile of the biomarker and the reference CpG methylation profile determines the presence of cancer in the individual.

4. The method of claim 1 or 2, wherein the methylation profile of the biomarker is a methylation index, wherein the methylation index is the ratio of sequence reads showing methylation at the biomarker site divided by the total number of reads covering the site.

5. The method according to claim 1 or 2, wherein the methylation profile is a methylation profile of one or more other biomarkers selected from the following: cg25922751, cg25432518, cg23130731, cg13911392, cg11334870, cg11252953, cg10542975, cg08098128, cg02874908, cg26769927, cg26769700, cg25574765, cg25490145, cg18384097, cg17126555, cg14247287, cg07420137, cg05098343, cg g01903374, cg00907272, cg27125093, cg26112797, cg24166457, cg193003 07, cg17122157, cg13555415, cg11436362, cg09866569, cg08075204, cg05 614346, cg02053964, cg27377213, cg24480810, cg24301930, cg22513455, cg19693177, cg19675731, cg18856478, cg15797834, cg15698795, cg153418 33. cg14556909, cg14083015, cg14058476, cg12192582, cg06787669, cg06 439655, cg02522196, cg02233149, cg00558804, cg26680502, cg23013029, cg22552736, cg21376733, cg20847580, cg19704755, cg18842353, cg16622 899, cg14999168, cg13925432, cg12967050, cg11105292, cg09419005, cg09 153080, cg07380416, cg06825448, cg03891050, cg01681367, cg01456691, cg00015530, cg27410601, cg27366280, cg25666403, cg24706505, cg24107 852, cg23824762, cg23021796, cg21122474, cg20336172, cg18610205, cg1 8456621, cg17518965, cg16748008, cg16191087, cg16061668, cg14642045,cg13924996, cg12353452, cg09335715, cg08858130, cg08480068, cg08052292, cg07428959, cg06153925, cg04147906, cg03431741, cg00282244, and cg00025044.

6. The method of claim 5, wherein the comparing further comprises generating a pairwise methylation difference dataset, the pairwise methylation difference dataset comprising: (i) a first difference between the methylation profile of the processed genomic DNA and the methylation profile of a first normal sample; (ii) a second difference between the methylation profile of the second normal sample and the methylation profile of the third normal sample; and (iii) a third difference between the methylation profile of the first primary cancer sample and the methylation profile of the second primary cancer sample.

7. The method of claim 6, wherein the comparing further comprises analyzing the pairwise methylation difference dataset using a control by a machine learning method to generate the methylation profile.

8. The method of claim 7, wherein the machine learning method utilizes one or more algorithms selected from the group consisting of principal component analysis, logistic regression analysis, nearest neighbor analysis, support vector machine, and neural network model.

9. The method of claim 3, wherein the comparing further comprises determining the cancer type of the individual.

10. The method according to claim 1 or 2, wherein the methylation profile is a methylation profile of one or more other biomarkers selected from the group consisting of: cg20468939, cg24790419, cg26836479, cg16911583, cg15139596, cg16927606, cg12967050, cg21122474, cg06064964, cg11779113, cg12042264, cg27377213, cg26680502, cg12504877, cg21913888, cg26683005, cg24166457, cg27141915, cg17122157, cg09844573, cg03087897, cg24706505, cg13911392, cg18901 104. cg25982880, cg15797834, cg27125093, cg17518965, cg20695297, cg0 4858553, cg09419005, cg11252953, cg18456621, cg07058988, cg17864646 , cg06153925, cg27410601, cg03297901, cg06853339, cg12900649, cg27219 182. cg15759721, cg27023597, cg02782634, cg18942579, cg01409343, cg1 0530767, cg26112797, cg00253248, cg01722297, cg22589778, cg07137244 , cg04147906, cg23878564, cg07860918, cg00206490, cg07644807, cg0055 8804, cg05304979, cg27598656, cg03549146, cg22190721, cg01660934, cg0 2358862, cg23093496, cg07641284, cg01681367, cg26769927, cg08480068 , cg02914427, cg03653601, cg01990910, cg00933696, cg09866569, cg20357 538, cg22460896, cg07116712, cg10186131, cg06380123, cg18610205, cg1 2353452, cg10590292, cg00037681, cg05596756, cg03569637, cg02522196,cg11655490、cg19693177、cg26363363、cg21249754、cg23147227、cg01657186、cg23764129、cg04514998、cg07332880、cg16061668、cg14088196、cg03758697、cg05398700、cg14058476、cg18158859、cg19300307、cg18842353、cg10732611、cg24480810、cg02053964、cg25954028、cg14642045、cg24165921、cg18215449、cg16402452、cg21376733、cg16509569、cg08075204、cg14556909、cg07119472、cg14999168、cg09399878、cg02874908、cg10542975、cg15698795、cg11791526、cg00862408、cg16260696、cg00220455、cg20826709、cg11436362、cg13924996、cg07420137、cg24301930、cg13395086、cg20136100、cg09153080、cg09902130、cg07380416、cg27284288、cg13912307、cg10511890、cg00242035、cg04314978、cg25225070、cg20411756、cg24247537、cg04330884、cg23130731、cg04888360、cg00907272、cg05979232、cg00025044、cg04441857、cg09684112、cg27388962、cg05931497、cg13408086、cg13555415、cg22552736、cg16191087、cg13925432、cg13464240、cg14633252、cg19252956、cg00015530、cg08632810、cg12737392、cg26769700、cg03218479、cg02609337、cg10351284、cg23554164、cg19021985、cg21031128、cg19421584、cg17984956、cg05177060、cg24107852、cg25652701、cg00282244、cg18887230、cg08486903、cg09335715、cg12629796、cg16454130、cg26433975、cg06787669、cg12192582、cg05098343、cg07573366、cg11105292、cg05287480、cg16748008、cg16644023、cg06488150、cg09450197、cg20336172、cg08858130、cg12098228、cg26811313、cg25432518、cg16622899、cg12359001、cg01209642、cg14564351、cg23429794、cg26401541、cg20046343、cg20847580、cg03431741、cg07417146、cg09001226、cg06482498、cg03891050、cg00899907、cg13597051、cg18113826、cg04859102、cg01620360、cg14083015、cg15046123、cg03190513、cg01456691、cg17207512、cg20510285、cg01149192、cg05614346、cg06439655、cg11334870、cg08912922、cg23021796、cg24835948、cg10393744、cg07428959、cg17694130、cg03956042、cg19266387、cg13512830、cg19982684、cg22513455、cg07186032、cg08052292、cg27366280、cg06825448、cg25451702、cg08098128、cg13821008、cg27405400、cg09366118、cg15341833、cg02233149、cg14247287、cg23824762、cg01604601、cg05656900、cg08132573、cg24686918、cg05352688、cg16266227、cg19675731、cg21461981、cg25765104、cg26394055、cg20685713、cg23589035、cg01903374、cg23612220、cg26315985、cg18856478、cg23229016、cg21004490、cg24742520, cg23013029, cg19704755, cg07589991, cg10055231 and cg26017930.

11. The method of any one of claims 1-2 and 6-9, wherein the generating further comprises hybridizing each of the one or more biomarkers with a probe, and performing a DNA sequencing reaction to quantify the methylation of each of the one or more biomarkers.

12. The method of claim 11, wherein the probe comprises a probe of Formula Ia: in: A is the first target binding region; B is the second target binding region; and L is the linker region; wherein A has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 30 contiguous nucleotides starting from position 1 at the 5' terminus of a sequence selected from SEQ ID NOs: 1-1775; and B has at least 70%, 80%, 90%, 95% or 99% sequence identity with at least 12 contiguous nucleotides starting from position 1' at the 3' terminus of the same sequence selected from SEQ ID NOs: 1-1775.

13. The method of claim 11, wherein the probe comprises a sequence selected from SEQ ID NOs: 1830-2321.

14. The method according to any one of claims 9 and 12-13, wherein the cancer type is a solid cancer type or a hematological malignancy cancer type.

15. The method according to any one of claims 9 and 12-13, wherein the cancer type is a metastatic cancer type or a relapsed or refractory cancer type.

16. The method of any one of claims 9 and 12-13, wherein the cancer type comprises acute myeloid leukemia, acute lymphoblastic leukemia, adrenocortical carcinoma, bladder urothelial carcinoma, brain stem glioma, brain low grade glioma, brain tumor, breast cancer, bronchial tumor, Burkitt's lymphoma, cancer of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, atypical teratoid / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, squamous cell carcinoma of the cervix, endocervical adenocarcinoma, childhood cancer, bile duct cancer, chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disease, colon adenocarcinoma, colorectal cancer, craniopharyngioma, cutaneous T cell lymphoma, endocrine pancreatic Pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer, olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor, gestational trophoblastic tumor, glioblastoma multiforme glioma, hairy cell leukemia, head and neck cancer, heart cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphocytic neoplasm diffuse large B-cell lymphoma, malignant fibrous histiocytoma bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma, metastatic squamous neck cancer of unknown primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasm, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papilloma, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma, moderately differentiated pineal parenchymal tumor, pineal mother cell pleural blastoma, primary central nervous system lymphoma, primary hepatocellular carcinoma; prostate cancer; colorectal cancer; kidney cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, Sézare syndrome, epidermal melanoma, small cell lung cancer, small intestinal cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor, pharyngeal cancer, thymic cancer, thymoma, thyroid cancer, transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, ureteral cancer, urethral cancer, uterine cancer, uveal melanoma, vaginal cancer, vulvar cancer, Waldenstrom's macroglobulinemia, or Wilm's tumor.

17. The method of any one of claims 1-2, 6-9, and 12-13, wherein the biological sample comprises a circulating tumor DNA sample or a tissue sample.

18. A probe set comprising a plurality of probes, the probe set comprising probes having at least 90% sequence identity to SEQ ID NO: 1876 or 1877.

19. The probe set as claimed in claim 18, comprising probes of SEQ ID NO: 1876 and / or 1877.

20. The probe set of claim 18 or 19, wherein the plurality of probes are used in a solution-based next generation sequencing reaction to generate CpG methylation data.

21. The probe set of claim 20, wherein the solution-based next-generation sequencing reaction is a droplet digital PCR sequencing method.

Images