Method and system for determining cancer status

• US 9,984,201 B2
• Filed: 12/31/2015
• Issued: 05/29/2018
• Est. Priority Date: 01/18/2015
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California), Youhealth Oncotech Limited
• Original Assignee: Regents of the University of California (University of California), Youhealth Biotech, Limited
• Inventors: Zhang, Kang, Hou, Rui, Zheng, Lianghong
• Primary Examiner(s): Dejong, Eric S
• Application Number:
  US14/986,520
• Publication Number:
  US 20160210403A1
• Time in Patent Office: 880 Days
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• 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
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures

Abstract

Disclosed herein are methods, systems, platforms, non-transitory computer-readable medium, services, and kits for determining a cancer type in an individual. Also described herein include methods, systems, platforms, non-transitory computer-readable medium, and compositions for generating a CpG methylation profile database.

Claims

1. A computing platform for utilizing CpG cancer methylation data for generation of a cancer CpG methylation profile database, comprising:

(a) a first computing device comprising a processor, a memory module, an operating system, and a computer program including instructions executable by the processor to create a data acquisition application for generating CpG methylation data from a set of biological samples, the data acquisition application comprising;

(1) a sequencing module operating a sequencing device to perform CpG methylation by hybridizing at least one probe sequence selected from SEQ ID NOs;

1-1775 and 1830-2321 to an extracted genomic DNA treated with a deaminating agent, wherein the extracted genomic DNA is obtained from a set of biological samples, 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; and

(2) a data receiving module receiving;

(i) a first pair of CpG methylation datasets generated from the first cancerous biological sample and the first normal biological sample, wherein CpG methylation data generated from the first cancerous biological sample form a first dataset within the first pair of datasets, CpG methylation data generated from the first normal biological sample form 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;

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

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

(b) a second computing device comprising a processor, a memory module, an operating system, and a computer program including instructions executable by the processor to create a data analysis application for generating a cancer CpG methylation profile database, the data analysis application comprising a data analysis module to;

(1) generate a pair-wise methylation difference dataset from the first, second, and third pair of datasets; and

(2) analyze the pair-wise methylation difference dataset with a control dataset by a machine learning method to generate the cancer CpG methylation profile database, wherein

(i) the machine learning method comprises;

identifying a plurality of markers and a plurality of weights based on a top score, and classifying the samples based on the plurality of markers and the plurality of weights; and

(ii) the cancer CpG methylation profile database comprises a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

2. The platform of claim 1, wherein the generating the pair-wise methylation difference dataset comprises:

(a) calculating a difference between the first dataset and the second dataset within the first pair of datasets;

(b) calculating a difference between the third dataset and the fourth dataset within the second pair of datasets; and

(c) calculating a difference between the fifth dataset and the sixth dataset within the third pair of datasets.

3. The platform of claim 1, 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.

4. The platform of claim 1, wherein the sequence device further analyzes the extracted genomic DNA by a next generation sequencing method to generate the CpG methylation data.

5. The platform of claim 1, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200, or more of biomarkers selected from the group consisting of Tables 8-41, and Tables 56-59.

6. The platform of claim 1, wherein the cancer type is a solid cancer type or a hematologic malignant cancer type.

7. The platform of claim 1, wherein the cancer type comprises acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial cancer (BLCA), brain stem glioma, brain lower grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchial tumors, 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 tumors, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC) cancer, childhood cancers, cholangiocarcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myelogenous leukemia, chronic myeloproliferative disorders, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreas islet cell tumors, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), esthesioneuroblastoma, Ewing sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric (stomach) cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblstoma multiforme glioma GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, islet cell tumors, Kaposi sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma [DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medullo epithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin carcinoma, mesothelioma (MESO), metastatic squamous neck cancer with occult primary, mouth cancer, multiple endocrine neoplasia syndromes, multiple myeloma, multiple myeloma/plasma cell neoplasm, mycosis fungoides, myelodysplastic syndromes, myeloproliferative neoplasms, nasal cavity cancer, nasopharyngeal cancer, neuroblastoma, Non-Hodgkin lymphoma, nonmelanoma 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), pineal parenchymal tumors of intermediate differentiation, pineoblastoma, pituitary tumor, plasma cell neoplasm/multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular liver cancer, prostate cancer such as prostate adenocarcinoma (PRAD), rectal cancer, renal cancer, renal cell (kidney) cancer, renal cell cancer, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sezary syndrome, skin cutaneous melanoma (SKCM), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, stomach (gastric) cancer, supratentorial primitive neuroectodermal tumors, T-cell lymphoma, testicular cancer testicular germ cell tumors (TGCT), throat cancer, thymic carcinoma, thymoma (THYM), thyroid cancer (THCA), transitional cell cancer, transitional cell cancer of the renal pelvis and ureter, trophoblastic tumor, ureter cancer, urethral cancer, uterine cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, or Wilm'"'"'s tumor.

8. The platform of claim 1, wherein the control dataset comprises a set of methylation profiles, wherein each said methylation profile is generated from a biological sample obtained from a known cancer type.

9. The platform of claim 1, wherein the biological samples comprise a circulating tumor DNA sample or a tissue sample.

10. The platform of claim 1, wherein the at least one probe comprises a sequence selected from SEQ ID NOs:

1-1775.

11. The platform of claim 1, wherein the at least one probe comprises a sequence selected from SEQ ID NOs:

1830-2321.

12. A computer-implemented method for generating a cancer CpG methylation profile database, comprising:

a) hybridizing at least one probe sequence selected from SEQ ID NOs;

1-1775 and 1830-2321 to an extracted genomic DNA treated with a deaminating agent to generate CpG methylation data, wherein the extracted genomic DNA is obtained from a set of biological samples, 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;

b) obtaining a first pair of CpG methylation datasets, with a first processor, generated from the first cancerous biological sample and the first normal biological sample, wherein CpG methylation data generated from the first cancerous biological sample form a first dataset within the first pair of datasets, CpG methylation data generated from the first normal biological sample form 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;

c) obtaining a second pair of CpG methylation datasets, with the first computing device, generated from the second normal biological sample and the third normal biological sample, wherein CpG methylation data generated from the second normal biological sample form a third dataset within the second pair of datasets, CpG methylation data generated from the third normal biological sample form a fourth dataset within the second pair of datasets, and the first, second, and third normal biological samples are different;

d) obtaining a third pair of CpG methylation datasets, with the first computing device, generated from the second cancerous biological sample and the third cancerous biological sample, wherein CpG methylation data generated from the second cancerous biological sample form a fifth dataset within the third pair of datasets, CpG methylation data generated from the third cancerous biological sample form a sixth dataset within the third pair of datasets, and the first, second, and third cancerous biological samples are different;

e) generating a pair-wise methylation difference dataset, with a second processor, from the first, second, and third pair of datasets; and

f) analyzing the pair-wise methylation difference dataset with a control dataset by a machine learning method to generate the cancer CpG methylation profile database, wherein

(1) the machine learning method comprises;

identifying a plurality of markers and a plurality of weights based on a top score, and classifying the samples based on the plurality of markers and the plurality of weights; and

(2) the cancer CpG methylation profile database comprises a set of CpG methylation profiles and each CpG methylation profile represents a cancer type.

13. The computer-implemented method of claim 12, wherein step e) further comprises

a) calculating a difference between the first dataset and the second dataset within the first pair of datasets;

b) calculating a difference between the third dataset and the fourth dataset within the second pair of datasets; and

c) calculating a difference between the fifth dataset and the sixth dataset within the third pair of datasets.

14. The computer-implemented method of claim 12, 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.

15. The computer-implemented method of claim 12, wherein the methylation profile comprises at least 10, 20, 30, 40, 50, 100, 200, or more of biomarkers selected from the group consisting of Tables 8-41 or Tables 56-59.

16. The computer-implemented method of claim 12, wherein the cancer type is a solid cancer type or a hematologic malignant cancer type.

17. The computer-implemented method of claim 12, wherein the biological samples comprise a circulating tumor DNA sample or a tissue sample.