SOLID TUMOR METHYLATION MARKERS AND USES THEREOF
Disclosed herein are methods and kits for identifying a subject as having esophagus cancer, pancreatic cancer, or stomach cancer. Also provided herein are methods and kits for generating the methylation profile of a biomarker associated with esophagus cancer, pancreatic cancer, or stomach cancer.
1 - 31 . (canceled)
32 : A method of generating a methylation profile of a biomarker in a subject, the method comprising:
(a) detecting a methylation status of the biomarker based on hybridization of a probe to a treated DNA from the subject,
wherein the biomarker is cg10673833,
wherein the treated DNA is processed from a biological sample obtained from the subject to allow for determination of the methylation status of the biomarker; and
(b) generating the methylation profile of the biomarker based on the methylation status of the biomarker.
33 : The method of claim 32 , wherein the generating further comprises generating a pair-wise methylation difference dataset comprising:
(i) a first difference between the methylation profile of the treated DNA with a methylation profile of a first normal sample;
(ii) a second difference between a methylation profile of a second normal sample and a methylation profile of a third normal sample; and
(iii) a third difference between a methylation profile of a first primary cancer sample and a methylation profile of a second primary cancer sample.
34 : The method of claim 33 , wherein the generating further comprises analyzing the pair-wise methylation difference dataset with a control by a machine learning method to generate the methylation profile.
35 : The method of claim 33 , wherein the first primary cancer sample is an esophagus cancer sample, a pancreatic cancer sample, or a stomach cancer sample.
36 : The method of claim 33 , wherein the second primary cancer sample is a non-esophagus cancer sample, non-pancreatic cancer sample, or a non-stomach cancer sample.
37 : The method of claim 34 , wherein the control comprises a set of methylation profiles, wherein each said methylation profile is generated from a biological sample obtained from a known cancer type.
38 : The method of claim 37 , wherein the known cancer type is esophagus cancer, pancreatic cancer, or stomach cancer.
39 : The method of claim 37 , wherein the known cancer type is a relapsed or refractory esophagus cancer, a relapsed or refractory pancreatic cancer, or a relapsed or refractory stomach cancer.
40 : The method of claim 37 , wherein the known cancer type is a metastatic esophagus cancer, a metastatic pancreatic cancer, or a metastatic stomach cancer.
41 : The method of claim 37 , where the known cancer type is esophagus cancer.
42 : The method of claim 41 , wherein esophagus cancer comprises esophageal squamous cell carcinoma, esophageal adenocarcinoma, or undifferentiated esophagus cancer.
43 : The method of claim 37 , where the known cancer type is pancreatic cancer.
44 : The method of claim 43 , wherein pancreatic cancer comprises exocrine pancreatic cancers and pancreatic endocrine tumors.
45 : The method of claim 43 , wherein pancreatic cancer comprises pancreatic adenocarcinoma, pancreatic adenosquamous carcinomas, pancreatic squamous cell carcinomas, signet ring cell carcinomas, undifferentiated pancreatic carcinomas, undifferentiated pancreatic carcinomas with giant cells, ampullary cancer, gastrinomas, insulinomas, glucagonomas, somatostatinomas, VIPomas, PPomas, or carcinoid tumor.
46 : The method of claim 37 , where the known cancer type is stomach cancer.
47 : The method of claim 46 , wherein stomach cancer comprises gastric adenocarcinoma, lymphoma of the stomach, gastrointestinal stromal tumor, carcinoid tumor, primary squamous cell carcinoma of stomach, gastric small-cell carcinoma, or leiomyosarcoma of the stomach.
48 : The method of claim 34 , wherein the machine learning method utilizes an algorithm selected from one or more of the following: a principal component analysis, a logistic regression analysis, a nearest neighbor analysis, a support vector machine, and a neural network model.
49 : The method of claim 32 , further comprising processing an extracted genomic DNA sample from the subject with a deaminating agent to generate the treated DNA.
50 : The method of claim 32 , further comprising performing a DNA sequencing reaction to quantify methylation of the biomarker prior to generating the methylation profile.
51 : The method of claim 50 , wherein the DNA sequencing reaction comprises droplet digital PCR (ddPCR).
52 : The method of claim 50 , wherein the DNA sequencing reaction comprises a next generation sequencing reaction.