Single molecule genome-wide mutation and fragmentation profiles of cell-free dna

Patent metadata

Field	Value
Publication number	US-2025131982-A1
Application number	US-202218720276-A
Country	US
Kind code	A1
Filing date	Dec 15, 2022
Priority date	Dec 15, 2021
Publication date	Apr 24, 2025
Grant date	—

Abstract

Methods for non-invasive cancer detection use a combination of genome-wide mutation and fragmentation features of cfDNA that facilitate cancer screening.

First claim

1 . A method of determining the frequency of somatic mutations in a subject, comprising:
extracting cell-free DNA (cfDNA) from a subject's biological sample; processing the cfDNA to generate genomic libraries from the extracted cfDNA; sequencing individual cfDNA molecules to obtain mutation profiles; scanning each sequenced cfDNA molecule for single nucleotide changes after removing common germline variants and/or unevaluable regions; determining multiregional differences in mutation profiles; quantifying multiregional differences in mutation profiles by one or more assays wherein the wherein the one or more assays comprise in silico dilution assays and/or downsampling assays; and executing a machine learning model for determining the frequency of somatic mutations in the subject wherein the frequency of somatic mutations is defined as the number of variants per million evaluated positions across all the DNA molecules sequenced.

2 . The method of claim 1 , wherein the determining of genome-wide mutation and fragmentation profiles comprises identifying mutations in sequences of individual cfDNA molecules and changes in fragment lengths.

3 . The method of claim 1 , wherein the mutation profiles comprise mutation frequency and type of mutation across the subject's genome.

4 . The method of claim 3 , wherein the mutation profiles across the subject's genome are determined using non-overlapping bins ranging in size from at least about one thousand bases to at least about twenty million bases.

5 . The method of claim 3 , wherein the mutation profiles across the subject's genome are determined using non-overlapping bins ranging in size from at least about one thousand bases to at least about ten million bases.

6 . The method of claim 3 , wherein the mutation profiles across the subject's genome is determined using non-overlapping bins ranging in size from at least about one thousand bases to at least about five million bases.

7 . The method of claim 3 , wherein mutations for each sequenced molecule are determined after removing common germline variants, and unevaluable regions.

8 . The method of claim 1 , wherein the frequency of single molecule somatic mutations and type of mutation across the subject's genome is diagnostic of cancer as compared to the frequency of single molecule somatic mutations and type of mutation across a normal subject's genome.

9 . The method of claim 1 where such analysis is performed in a subject from whom tumor tissue is unavailable.

10 . A method of treating cancer in a subject, the method comprising:
extracting cell-free DNA (cfDNA) from a subject's biological sample; processing the cfDNA to generate genomic libraries from the extracted cfDNA; sequencing individual cfDNA molecules to obtain mutation profiles; scanning each sequenced cfDNA molecule for single nucleotide changes after removing common germline variants and/or unevaluable regions; determining multiregional differences in mutation profiles and determining the frequency of somatic mutations in the subject; quantifying multiregional differences in mutation profiles by one or more assays wherein the wherein the one or more assays comprise in silico dilution assays and/or downsampling assays; executing a machine learning model for determining the frequency of somatic mutations in the subject wherein the frequency of somatic mutations is defined as the number of variants per million evaluated positions across all the DNA molecules sequenced; and on the basis thereof; administering a cancer treatment to the subject.

11 . The method of claim 10 , wherein the cancer treatment comprises: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.

12 . The method of claim 10 , wherein the cancer comprises colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, brain cancer or ovarian cancer.

13 . The method of claim 12 , wherein the lung cancer is small cell lung cancer (SCLC).

14 . The method of claim 12 , wherein the lung cancer is non-small cell lung cancer (NSCLC).

15 . The method of claim 1 , wherein subjects with cancer comprise altered mutational profiles associated with chromatin organization as compared to healthy individuals.

16 . The method of claim 1 , wherein genome-wide mutation and fragmentation profiles comprises identifying mutations in sequences of individual cfDNA molecules and changes in fragment lengths.

17 . The method of claim 10 , wherein the mutation profiles comprise mutation frequency and type of mutation across the subject's genome.

18 - 21 . (canceled)

22 . A method of determining regional frequency of mutations across a genome comprising:
extracting cell-free DNA (cfDNA) from a subject's biological sample; processing the cfDNA to generate genomic libraries from the extracted cfDNA; sequencing individual cfDNA molecules isolated from a subject, scanning each sequenced cfDNA molecule for single nucleotide changes after removing common germline variants and/or unevaluable regions; estimating mutation frequencies and types of mutations across the genome; determining the mutation types and frequencies in genomic regions altered in cancer to mutation profiles and regions mutated in normal cfDNA to determine multiregional differences in mutation profiles; quantifying multiregional differences in mutation profiles by one or more assays wherein the wherein the one or more assays comprise in silico dilution assays and/or downsampling assays; thereby, executing a machine learning model for determining regional frequency of mutations across a genome.

23 . The method of claim 22 , wherein the estimation of mutation frequencies and types of mutations across the genome comprise using non-overlapping bins ranging in size from thousands to millions of bases.

24 - 30 . (canceled)

31 . A method of determining whether a subject is a responder to a treatment based on the outcome of performing a method of claim 1 .

32 . (canceled)

Assignees

• Univ Johns Hopkins

Inventors

• Velculescu Victor E
• Scharpf Robert B
• Bruhm Daniel C

Classifications

• C12Q2600/106

  Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism · CPC title

• C12Q1/6886 Primary

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

• G16B30/00

  ICT specially adapted for sequence analysis involving nucleotides or amino acids · CPC title

• G16B40/20

  Supervised data analysis · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title