# US 12,217,830 B2 · Estimating tumor purity from single samples

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- Number: US 12,217,830 B2
- Document ID: US12217830B2
- Status: Granted
- Kind: B2
- Application number: 17/735,904
- Filed: 2022-05-03
- Published: 2022-08-18
- Granted: 2025-02-04
- Earliest claimed: 2019-11-05
- Assignees: Personalis, Inc.
- Applicants: Personalis, Inc.
- Inventors: Nicholas Phillips; Jason Harris
- Web page: https://patentgrep.com/patent/US12217830B2

## CPC

- G16B 40/20: Supervised data analysis
- C12Q 1/6886: for cancer (immunoassay for cancer G01N33/575)
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- C12Q 1/686: Polymerase chain reaction [PCR]
- C12Q 1/6874: involving nucleic acid arrays, e.g. sequencing by hybridisation
- C12Q 2600/156: Polymorphic or mutational markers
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES

## Abstract

The disclosure provides methods for estimating tumor purity from tumor samples without use of matched-normal controls. A set of genomic regions are identified based on a nucleic acid sequence data that is aligned to a reference genome. Each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome. A B-allele frequency distribution for the biological sample is determined based on a B-allele frequency determined for each genomic region of the set of genomic regions. The B-allele frequency distribution is processed using a trained machine-learning model to estimate a metric identifying tumor purity in the biological sample.

## Claims

### Claim 1

A method of determining tumor purity of a biological sample of a subject for informing a cancer feature and evaluating a treatment efficacy for the subject, the method comprising:
obtaining nucleic acid sequence data from one or more sequencers that represent a plurality of nucleic acid molecules of the biological sample of the subject;
aligning the nucleic acid sequence data to a reference genome;
identifying, based on the aligned nucleic acid sequence data, a set of genomic regions, wherein each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome;
determining a B-allele frequency for each genomic region of the set of genomic regions;
determining, based on the B-allele frequencies of the set of genomic regions, a B-allele frequency distribution for the biological sample;
processing the B-allele frequency distribution using a trained machine-learning model to estimate a probability of a true tumor purity as a function of a predicted tumor purity in the biological sample, wherein the trained machine-learning model is trained on a training dataset generated from nucleic acid sequence data derived from one or more tumor cells diluted into normal cells; and
generating a report to inform the cancer feature and evaluate the treatment efficacy for the subject based on the estimated probability of a true tumor purity as a function of a predicted tumor purity in the biological sample.

### Claim 2 (depends on 1)

The method of claim 1, wherein the obtained nucleic acid sequence data of the biological sample of the subject is whole exome sequencing data.

### Claim 3 (depends on 1)

The method of claim 1, wherein the obtained nucleic acid sequence data of the biological sample of the subject is whole genome sequencing data.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
obtaining the biological sample from the subject; and
sequencing the plurality of nucleic acid molecules of the obtained biological sample to generate the nucleic acid sequence data.

### Claim 5 (depends on 4)

The method of claim 4, further comprising isolating the plurality of nucleic acid molecules prior to sequencing.

### Claim 6 (depends on 1)

The method of claim 1, wherein the B-allele frequency distribution is normalized.

### Claim 7 (depends on 1)

The method of claim 1, further comprising outputting a report comprising information identifying the B-allele frequency distribution.

### Claim 8 (depends on 1)

The method of claim 1, further comprising outputting a report comprising estimated metric identifying the tumor purity.

### Claim 9 (depends on 8)

The method of claim 8, wherein the report further comprises information identifying at least one biomarker.

### Claim 10 (depends on 1)

The method of claim 1, wherein said generating the report to inform the cancer feature and evaluate the treatment efficacy for the subject comprises informing at least one of: (i) a cancer stage, or (ii) responsiveness to a cancer treatment.

### Claim 11

A system comprising:
one or more data processors; and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform one or more operations comprising:
obtaining nucleic acid sequence data from one or more sequencers that represent a plurality of nucleic acid molecules of a biological sample of a subject;
aligning the nucleic acid sequence data to a reference genome;
identifying, based on the aligned nucleic acid sequence data, a set of genomic regions, wherein each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome;
determining a B-allele frequency for each genomic region of the set of genomic regions;
determining, based on the B-allele frequencies of the set of genomic regions, a B-allele frequency distribution for the biological sample;
processing the B-allele frequency distribution using a trained machine learning model to estimate a probability of a true tumor purity as a function of a predicted tumor purity in the biological sample, wherein the trained machine-learning model is trained on a training dataset generated from nucleic acid sequence data derived from one or more tumor cells diluted into normal cells; and
generating a report to inform a cancer feature and evaluate the treatment efficacy for the subject based on the estimated probability of a true tumor purity as a function of a predicted tumor purity in the biological sample.

### Claim 12 (depends on 11)

The system of claim 11, wherein the obtained nucleic acid sequence data of the biological sample of the subject is whole exome sequencing data.

### Claim 13 (depends on 11)

The system of claim 11, wherein identifying the set of genomic regions further comprises:
identifying one or more candidate nucleotide-sequence variants in the nucleic acid sequence data; and
calculating reference and alternate read depths for each of the one or more candidate nucleotide-sequence variants.

### Claim 14 (depends on 11)

The system of claim 11, wherein said instructions for generating the report to inform the cancer feature and evaluate the treatment efficacy for the subject comprises instructions for generating the report to inform at least one of: (i) a cancer stage, or (ii) responsiveness to a cancer treatment.