Cancer detection and classification using methylome analysis

Patent metadata

Field	Value
Publication number	US-12227737-B2
Application number	US-202218061273-A
Country	US
Kind code	B2
Filing date	Dec 2, 2022
Priority date	Jul 12, 2017
Publication date	Feb 18, 2025
Grant date	Feb 18, 2025

Abstract

There is described herein, a method of capturing cell-free methylated DNA from a sample having less than 100 mg of cell-free DNA, comprising the steps of: subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated; denaturing the sample; and capturing cell-free methylated DNA using a binder selective for methylated polynucleotides.

First claim

The invention claimed is:

1. A method, comprising:
(a) subjecting a plurality of nucleic acid molecules generated from a cell-free deoxynucleic acid (cfDNA) sample of a subject to sequencing to yield a plurality of sequencing reads, wherein said plurality of nucleic acid molecules is generated by subjecting nucleic acid molecules derived from said cfDNA sample to enrichment through immunoprecipitation of one or more methylated regions of said nucleic acid molecules;
(b) computer processing said plurality of sequencing reads to generate a methylation profile for said plurality of nucleic acid molecules; and
(c) computer processing said methylation profile to determine that said subject has or is at risk of having a cancer at an area under a receiver operating characteristic curve (AUROC) of at least about 94%.

2. The method of claim 1 , wherein said cancer is selected from the group consisting of lung cancer, breast cancer, colorectal cancer, acute myelogenous leukemia, and glioblastoma multiform.

3. The method of claim 2 , wherein said cancer is acute myelogenous leukemia.

4. The method of claim 1 , wherein said AUROC is at least about 99%.

5. The method of claim 1 , wherein said determining said subject has or is at risk of having said cancer comprises determining a tissue of origin of said cfDNA.

6. The method of claim 1 , further comprising determining said subject has or is at risk of having a type or a subtype of cancer.

7. The method of claim 1 , when said subject has or is at risk of breast cancer, further comprising determining a subtype of breast cancer, wherein said subtype comprises ER positive, ER negative, HER2 positive, HER2 negative, or triple-negative breast cancer (TNBC).

8. The method of claim 1 , when said subject has or is at risk of acute myelogenous leukemia, further comprising determining a subtype of acute myelogenous leukemia, wherein said subtype comprises FLT3 negative or FLT3 positive.

9. The method of claim 1 , when said subject has or is at risk of glioblastoma multiform, further comprising determining a subtype of glioblastoma multiform, wherein said subtype comprises IDH mutation positive or IDH mutation negative.

10. The method of claim 1 , when said subject has or is at risk of lung cancer, further comprising determining a subtype of lung cancer, wherein said subtype comprises adenocarcinoma, squamous carcinoma, or small cell carcinoma.

11. The method of claim 1 , further comprising generating a report that said subject does or does not have said cancer or is or is not at risk or having said cancer.

12. A method, comprising:
(a) subjecting a plurality of nucleic acid molecules generated from a cell-free deoxynucleic acid (cfDNA) sample of a subject to sequencing to yield a plurality of sequencing reads, wherein said plurality of nucleic acid molecules is generated by subjecting nucleic acid molecules derived from said cfDNA sample to enrichment through immunoprecipitation of one or more methylated regions of said nucleic acid molecules;
(b) computer processing said plurality of sequencing reads to generate a methylation profile for said plurality of nucleic acid molecules; and
(c) computer processing said methylation profile to determine that said subject has or is at risk of having a specific stage of a cancer at an area under a receiver operating characteristic curve (AUROC) of at least about 93%.

13. The method of claim 12 , wherein said cancer is lung cancer.

14. The method of claim 12 , wherein said specific stage is an early stage of lung cancer.

15. The method of claim 12 , wherein said AUROC is at least about 95%.

16. The method of claim 12 , wherein said specific stage is a late stage of lung cancer.

17. The method of claim 12 , wherein said methylation profile comprises methylation levels of a plurality of differentially methylated region (DMR) of said plurality of nucleic acid molecules.

18. The method of claim 17 , wherein said DMR comprises hypermethylation or hypomethylation.

19. The method of claim 1 , further comprising mixing said cfDNA sample with an amount of filler DNA to generate a DNA mixture sample.

20. The method of claim 19 , wherein said DNA mixture sample comprises at least an amount of total DNA that is at least about 50 nanograms (ng).

21. The method of claim 19 , wherein said filler DNA is at least partially methylated and comprises a length of about 50 bp to 800 bp.

22. The method of claim 19 , further comprising incubating said DNA mixture sample to increase a rate of enrichment of at least one or more methylated regions of said plurality of nucleic acid molecules of said cfDNA sample.

23. The method of claim 19 , further comprising incubating said DNA mixture sample with a binder that is configured to bind methylated nucleotides, wherein said binder comprises a protein comprising a methyl-CpG-binding domain.

24. The method of claim 19 , further comprising incubating said DNA mixture sample with a binder that is configured to bind methylated nucleotides, wherein said binder comprises an antibody.

25. The method of claim 1 , wherein computer processing said methylation profile comprises comparing to a methylation profile of a healthy subject or using a trained machine learning algorithm.

26. The method of claim 25 , wherein said trained machine learning algorithm comprises a linear regression.

27. The method of claim 25 , wherein said comparing comprises comparing said methylation profile to said methylation profile of said healthy subject with respect to FANTOM5 enhancers, CpG islands, CpG shores, CpG shelves, or any combination thereof.

Assignees

• Univ Health Network
• Sinai Health Sys

Inventors

• Diniz De Carvalho Daniel
• Shen shu yi
• Singhania Rajat
• Bratman Scott Victor
• Ravinarayana Chakravarthy Ankur

Classifications

• G01N33/5308

  for analytes not provided for elsewhere, e.g. nucleic acids, uric acid, worms, mites · CPC title

• C40B30/04

  by measuring the ability to specifically bind a target molecule, e.g. antibody-antigen binding, receptor-ligand binding · CPC title

• C12Q2600/154

  Methylation markers · CPC title

• C12Q1/6886

  for cancer (immunoassay for cancer G01N33/575) · CPC title

• C12Q1/6806

  Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay (C12Q1/6804 takes precedence) · CPC title