IP Library › Granted Patent US 12,456,541
Granted Patent B2
US 12,456,541 · App. 17/077,967 · Granted Oct 28, 2025

Analysis of genetic variants

Inventors: James Xin Sun (Newton, MA); Roman Yelensky (Newton, MA)
Assignee: Foundation Medicine, Inc.
G16B20/20G16B20/00G16B20/10
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Quick Facts
Patent No.
US 12,456,541
App. No.
17/077,967
Granted
Oct 28, 2025
Kind
B2
Abstract

Methods and systems for analyzing genetic variants are disclosed. The methods and systems can be used to classify a variant in a tumor sample as germline, somatic, subclonal, or ambiguous. The variant can be classified by fitting a genome-wide copy number model to a sequence coverage input and a SNP allele frequency input. The fitted model can be used to determine a tumor purity, and a total copy number and a minor allele copy number for each of a plurality of genomic segments. Using the tumor purity, the total copy number and the minor allele copy number for the genomic segment comprising the variant, and the variant allele frequency for the variant in the tumor sample, the variant can be classified as germline, somatic, subclonal or ambiguous.

Claims (59)

1. A system for classifying a variant in a tumor sample from a subject, comprising:

at least one processor operatively connected to a memory that stores one or more program instructions that, when executed by the at least one processor, are configured to:

cause the at least one processor to receive a sequence coverage input for the tumor sample, a SNP allele frequency input for the tumor sample, and a variant allele frequency for the variant in the tumor sample, wherein the sequence coverage input, the SNP allele frequency input, and the variant allele frequency were determined without using a sample-matched normal control;

fit a genome-wide copy number model for the tumor sample to the sequence coverage input and the SNP allele frequency input;

based on the genome-wide copy number model, determine a tumor purity, a total copy number for each of a plurality of genomic segments, and a minor allele copy number for each of the plurality of genomic segments, wherein at least one genomic segment of the plurality of genomic segments includes the variant; and

classify the variant as germline, somatic, subclonal, or ambiguous using the tumor purity, the total copy number for the genomic segment, the minor allele copy number for the genomic segment, and the variant allele frequency for the variant in the tumor sample.

2. The system of claim 1 , wherein the one or more program instructions, when executed by the at least one processor, are configured to determine the variant allele frequency.

3. The system of claim 1 , wherein the one or more program instructions, when executed by the at least one processor, are configured to determine the sequence coverage input.

4. The system of claim 1 , wherein the one or more program instructions, when executed by the at least one processor, are configured to determine the SNP allele frequency input.

5. The system of claim 1 , wherein the genome-wide copy number model associates:

the sequence coverage input with the total copy number for each genomic segment and the tumor purity, and

the SNP allele frequency input with the total copy number for each genomic segment, the minor allele copy number for each genomic segment, and the tumor purity.

6. The system of claim 1 , wherein the variant is classified as germline, somatic, subclonal, or ambiguous using a process comprising:

computing an expected allele frequency for the variant if the variant were a somatic variant, given the tumor purity, the total copy number for the genomic segment comprising the variant, and the minor allele copy number for the genomic segment,

computing an expected allele frequency for the variant if the variant were a germline variant, given the tumor purity, the total copy number for the genomic segment comprising the variant, and the minor allele copy number for the genomic segment, and

classifying the variant as somatic, germline, subclonal, or ambiguous based on the variant allele frequency for the variant in the tumor sample, the expected allele frequency for the variant if the variant were a somatic variant, and the expected allele frequency for the variant if the variant were a germline variant.

7. The system of claim 6 , wherein the variant is classified somatic, germline, subclonal, or ambiguous with a statistical confidence assessed based on read depth and allele frequency variability within the genomic segment comprising the variant.

8. The system of claim 1 , wherein the variant is classified as germline, somatic, subclonal, or ambiguous using a process comprising:

computing a value for mutation type, comprising fitting a somatic/germline status model that associates the value for mutation type with the tumor purity, the total copy number for the genomic segment comprising the variant, the minor allele copy number for the genomic segment, and the variant allele frequency, and

classifying the variant as germline, somatic, subclonal, or ambiguous based on the value for mutation type.

9. The system of claim 1 , further comprising a sequencer configured to generate sequencing data for the tumor sample.

10. The system of claim 9 , wherein the sequencer is configured to generate the sequencing data by next generation sequencing.

11. The system of claim 9 , wherein the sequence coverage input, the SNP allele frequency input, and the variant allele frequency are computed using the sequencing data.

12. The system of claim 1 , wherein the sequence coverage input is computed as a function of a number of reads for a subgenomic interval of a genome of the tumor sample and a number of reads for a control.

13. The system of claim 12 , wherein the control is a process-matched control.

14. The system of claim 1 , wherein the tumor sample is a tissue sample, a blood sample, or a blood constituent sample.

15. A method for classifying a variant in a tumor sample from a subject, comprising:

receiving, at at least one processor, a sequence coverage input for the tumor sample, a SNP allele frequency input for the tumor sample, and a variant allele frequency for the variant in the tumor sample, wherein the sequence coverage input, the SNP allele frequency input, and the variant allele frequency were determined without using a sample-matched normal control;

fitting, using the at least one processor, a genome-wide copy number model for the tumor sample to the sequence coverage input and the SNP allele frequency input;

based on the genome-wide copy number model, determining, using the at least one processor, a tumor purity, a total copy number for each of a plurality of genomic segments, and a minor allele copy number for each of the plurality of genomic segments, wherein at least one genomic segment of the plurality of genomic segments includes the variant; and

classifying, using the at least one processor, the variant as germline, somatic, subclonal or ambiguous using the tumor purity, the total copy number for the genomic segment comprising the variant, the minor allele copy number for the genomic segment, and the variant allele frequency for the variant in the tumor sample.

16. The method of claim 15 , further comprising determining, using the at least one processor, the variant allele frequency.

17. The method of claim 15 , further comprising determining, using the at least one processor, the sequence coverage input.

18. The method of claim 15 , further comprising determining, using the at least one processor, the SNP allele frequency input.

19. The method of claim 15 , wherein the genome-wide copy number model associates:

the sequence coverage input with the total copy number for each genomic segment and the tumor purity, and

the SNP allele frequency input with the total copy number for each genomic segment, the minor allele copy number for each genomic segment, and the tumor purity.

20. The method of claim 15 , wherein classifying the variant as germline, somatic, subclonal, or ambiguous comprises:

computing, using the at least one processor, an expected allele frequency for the variant if the variant were a somatic variant, given the tumor purity, the total copy number for the genomic segment comprising the variant, and the minor allele copy number for the genomic segment,

computing, using the at least one processor, an expected allele frequency for the variant if the variant were a germline variant, given the tumor purity, the total copy number for the genomic segment comprising the variant, and the minor allele copy number for the genomic segment, and

classifying, using the at least one processor, the variant as somatic, germline, subclonal, or ambiguous based on the variant allele frequency for the variant in the tumor sample, the expected allele frequency for the variant if the variant were a somatic variant, and the expected allele frequency for the variant if the variant were a germline variant.

21. The method of claim 20 , wherein the variant is classified somatic, germline, subclonal, or ambiguous with a statistical confidence assessed based on read depth and allele frequency variability within the genomic segment comprising the variant.

22. The method of claim 15 , wherein classifying the variant as germline, somatic, subclonal, or ambiguous comprises:

computing a value for mutation type, comprising fitting a somatic/germline status model that associates the value for mutation type with the tumor purity, the total copy number for the genomic segment comprising the variant, the minor allele copy number for the genomic segment, and the variant allele frequency, and

classifying the variant as germline, somatic, subclonal, or ambiguous based on the value for mutation type.

23. The method of claim 15 , further comprising sequencing nucleic acid molecules in the tumor sample to generate sequencing data for the tumor sample.

24. The method of claim 23 , wherein the nucleic acid molecules are sequenced by next generation sequencing to generate the sequencing data.

25. The method of claim 23 , wherein the sequence coverage input, the SNP allele frequency input, and the variant allele frequency are computed using the sequencing data.

26. The method of claim 15 , wherein the sequence coverage value is computed as a function of a number of reads for a subgenomic interval of a genome of the tumor sample and a number of reads for a control.

27. The method of claim 26 , wherein the control is a process-matched control.

28. The method of claim 15 , wherein the tumor sample is a tissue sample, a blood sample, or a blood constituent sample.

29. A method of treating a subject having a tumor, comprising:

receiving, at at least one processor, a sequence coverage input for a tumor sample from the subject, a SNP allele frequency input for the tumor sample, and a variant allele frequency for a variant in the tumor sample, wherein the sequence coverage input, the SNP allele frequency input, and the variant allele frequency were determined without using a sample-matched normal control;

fitting, using the at least one processor, a genome-wide copy number model for the tumor sample from the subject to a sequence coverage input and a SNP allele frequency input;

based on the genome-wide copy number model, determining, using the at least one processor, a tumor purity, a total copy number for each of a plurality of genomic segments, and a minor allele copy number for each of the plurality of genomic segments, wherein at least one genomic segment of the plurality of genomic segments includes the variant;

classifying, using the at least one processor, the variant as germline, somatic, or subclonal using the tumor purity, the total copy number for the genomic segment comprising the variant, the minor allele copy number for the genomic segment, and a variant allele frequency for the variant in the tumor sample;

generating a recommendation of a selected treatment modality based on at least a characterization of the variant as germline, somatic, or subclonal; and

treating the subject with the selected treatment modality.

30. The method of claim 29 , wherein the selected treatment modality is a drug combination that treats the tumor.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 6, 2020
From: SUN, JAMES XIN; YELENSKY, ROMAN
To: FOUNDATION MEDICINE, INC.
Reel/Frame 054304/0948 →
Continuity (5)
Continuation 15708475 · Sep 19, 2017
Continuation 14274525 · May 9, 2014
Provisional Application 61939936 · Feb 14, 2014
Provisional Application 61821920 · May 10, 2013
Related Publication 20210043274A1 · Feb 11, 2021
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