Solid tumor methylation markers and uses thereof

• US 10,544,467 B2
• Filed: 12/12/2017
• Issued: 01/28/2020
• Est. Priority Date: 07/06/2016
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California), Youhealth Oncotech Limited
• Original Assignee: Youhealth Oncotech Limited
• Inventors: Zhang, Kang, Hou, Rui, Zheng, Lianghong
• Primary Examiner(s): Johannsen, Diana B
• Application Number:
  US15/839,318
• Publication Number:
  US 20180094325A1
• Time in Patent Office: 777 Days
• Field of Search: None
• CPC Class Codes: A61K 31/711 Natural deoxyribonucleic acids, i.e. containing only 2'-deoxyriboses attached to adenine, guanine, cytosine or thymine and having 3'-5' phosphodiester links
• CPC Class Codes: A61K 45/06 Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
• CPC Class Codes: C12Q 1/6832 Enhancement of hybridisation reaction
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/154 Methylation markers
• CPC Class Codes: G01N 33/574 for cancer
• CPC Class Codes: G01N 33/57438 of liver, pancreas or kidney
• CPC Class Codes: G01N 33/57446 of stomach or intestine
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/30 Detection of binding sites or motifs
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis

Abstract

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.

Claims

1. A method of generating a methylation profile of a biomarker in a subject in need thereof, comprising:

a) processing an extracted genomic DNA with a deaminating agent to generate a genomic DNA sample comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample from the subject;

b) detecting a hybridization between the extracted genomic DNA and a probe, wherein the probe hybridizes to cg10673833 (myosin IG); and

c) generating a methylation profile based on the detected hybridization between the extracted genomic DNA and the probe;

wherein the probe comprises a structure of Formula I;

2. The method of claim 1, wherein the probe comprises a structure of Formula Ia:

3. The method of claim 1, wherein A comprises at least 40 contiguous nucleotides starting at position 1 from the 5′

terminus of SEQ ID NO;

1.

4. The method of claim 1, wherein A comprises at least 50 contiguous nucleotides starting at position 1 from the 5′

terminus of SEQ ID NO;

1.

5. The method of claim 1, wherein B comprises at least 15 contiguous nucleotides starting at position 1′

from the 3′

terminus of SEQ ID NO;

1.

6. The method of claim 1, wherein B comprises at least 20 contiguous nucleotides starting at position 1′

from the 3′

terminus of SEQ ID NO;

1.

7. The method of claim 1, wherein L is about 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 nucleotides in length.

8. The method of claim 1, wherein the probe comprises SEQ ID NO:

1.

9. The method of claim 1, wherein the generating further comprises generating a pair-wise methylation difference dataset comprising:

(i) a first difference between the methylation profile of the treated genomic 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.

10. The method of claim 9, 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.

11. The method of claim 9, wherein the first primary cancer sample is an esophagus cancer sample, a pancreatic cancer sample, or a stomach cancer sample.

12. The method of claim 9, wherein the second primary cancer sample is a non-esophagus cancer sample, non-pancreatic cancer sample, or a non-stomach cancer sample.

13. The method of claim 10, 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.

14. The method of claim 13, wherein the known cancer type is esophagus cancer, pancreatic cancer, or stomach cancer.

15. The method of claim 13, 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.

16. The method of claim 13, wherein the known cancer type is a metastatic esophagus cancer, a metastatic pancreatic cancer, or a metastatic stomach cancer.

17. The method of claim 13, where the known cancer type is esophagus cancer.

18. The method of claim 17, wherein esophagus cancer comprises esophageal squamous cell carcinoma, esophageal adenocarcinoma, or undifferentiated esophagus cancer.

19. The method of claim 13, where the known cancer type is pancreatic cancer.

20. The method of claim 19, wherein pancreatic cancer comprises exocrine pancreatic cancers and pancreatic endocrine tumors.

21. The method of claim 19, 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.

22. The method of claim 13, where the known cancer type is stomach cancer.

23. The method of claim 22, 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.

24. The method of claim 10, 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.

25. The method of claim 1, wherein the method further comprises performing a DNA sequencing reaction to quantify the methylation of the biomarker prior to generating the methylation profile.

26. A method of selecting a subject suspected of having a solid tumor for treatment, the method comprising:

a) processing an extracted genomic DNA with a deaminating agent to generate a genomic DNA sample comprising deaminated nucleotides, wherein the extracted genomic DNA is obtained from a biological sample from the subject suspected of having a solid tumor;

b) generating a methylation profile comprising cg10673833 (myosin IG) from the extracted genomic DNA, wherein the generating comprises detecting a hybridization between the extracted genomic DNA and a probe that hybridizes to cg10673833 (myosin IG);

c) comparing the methylation profile comprising cg10673833 with a control;

d) identifying the subject as having the solid tumor if the methylation profile correlates to the control; and

e) administering an effective amount of a therapeutic agent to the subject if the subject is identified as having the solid tumor;

wherein the solid tumor is selected from esophagus cancer, pancreatic cancer, or stomach cancer; and

wherein the probe comprises a structure of Formula I;