# US 12,006,533 B2 · Detecting cross-contamination in sequencing data using regression techniques

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- Number: US 12,006,533 B2
- Document ID: US12006533B2
- Status: Granted
- Kind: B2
- Application number: 15/900,645
- Filed: 2018-02-20
- Published: 2018-08-23
- Granted: 2024-06-11
- Earliest claimed: 2017-02-17
- Assignees: Grail, LLC
- Applicants: Grail, Inc.
- Inventors: Onur Sakarya; Catalin Barbacioru
- Web page: https://patentgrep.com/patent/US12006533B2

## CPC

- 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
- C12Q 1/6809: Methods for determination or identification of nucleic acids involving differential detection
- C12Q 1/6827: for detection of mutation or polymorphism
- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16B 20/40: Population genetics; Linkage disequilibrium
- G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B 30/10: Sequence alignment; Homology search
- G16B 40/20: Supervised data analysis
- G16B 40/30: Unsupervised data analysis
- C12Q 1/6806: Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay (C12Q1/6804 takes precedence)
- G16B 30/20: Sequence assembly
- 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

Cross-contamination of a test sample used to determine cancer is identified using gene sequencing data. Each test sample includes a number of test sequences that may include a single nucleotide polymorphism (SNP) that can be indicative of cancer. The test sequences are be filtered to remove or negate at least some of the SNPs from the test sequences. Negating the test sequences allows more test sequences to be simultaneously analyzed to determine cross-contamination. Cross-contamination is determined by modeling the variant allele frequency for the test sequences as a function of minor allele frequency, contamination level, and background noise. In some cases, the variant allele frequency is based on a probability function including the minor allele frequency. Cross-contamination of the test sample is determined if the determined contamination level is above a threshold and statistically significant.

## Claims

### Claim 1

A method for identifying contamination in a test sequence using a processor, the method comprising:
accessing one or more physical samples from a first subject including one or more test sequences that are indicative of a cancer presence;
sequencing the one or more physical samples using a next-generation sequencing machine to produce a plurality of test sequences each comprising at least one single nucleotide polymorphism (SNP) from the first subject and collectively forming an initial population;
calling a plurality of variant alleles in the plurality of test sequences, each called variant allele identified as a SNP across the plurality of test sequences having a variant allele frequency: (VAF) indicating contamination of the initial population with test sequences from a second subject;
identifying a plurality of population minor allele frequencies (MAFs) for the plurality of test sequences, each population minor allele frequency (MAF) quantifying a MAF for a SNP at a test site of a plurality of test sites across the plurality of test sequences;
filtering at least some of the SNPs of the plurality of test sequences in the initial population to form a filtered population, the filtering comprising, for each test sequence of the plurality of test sequences in the initial population:
selecting SNPs having a VAF in either a first range or a second range, both ranges indicative of homozygosity, and the first range different from the second range,
for each selected SNP in test sequences whose identified VAF is in the first range, setting a MAF for the selected SNP to the population MAF corresponding to the site of the selected SNP, and
for each selected SNP in test sequences whose identified VAF is in the second range, setting a MAF for the selected SNP to a quantity one minus the population MAF corresponding to the site of the selected SNP;
generating a noise model that estimates a measure of background noise level present in the plurality of test sequences in the filtered population based on measures of background noise levels present in a plurality of test sequences indicative of healthy individuals;
applying a contamination model to a test sequence of the plurality of test sequences in the filtered population using the identified plurality of population MAFs of the plurality of test sequences, the identified VAFs for SNPs across the plurality of test sequences, and the generated noise model based on the plurality of test sequences indicative of healthy individuals to obtain a confidence score representing a likelihood the test sequence originates from the second subject and is contaminating the initial population, wherein the confidence score is below a threshold, indicating the test sequence originates from the second subject; and
responsive to the confidence score indicating the test sequence originates from the second subject, discarding the one or more physical samples due to contamination.

### Claim 2 (depends on 1)

The method of claim 1, wherein the contamination model models each range of homozygous SNPs independently.

### Claim 3 (depends on 1)

The method of claim 1, wherein applying the contamination model further comprises:
regressing the identified VAFs for SNPs of the test sequence of the plurality of test sequences in the filtered population against the noise model and a population MAF of the plurality of population MAFs to determine a p-value of a regression coefficient associated with the population MAF.

### Claim 4 (depends on 3)

The method of claim 3, wherein the confidence score is based on the p-value of the regression coefficient.

### Claim 5 (depends on 1)

The method of claim 1, wherein filtering at least some of the plurality of test sequences to form the filtered population further comprises at least one of, for each test sequence of the plurality of test sequences, removing heterozygous SNPs with a VAF in a range between 0.2 and 0.8.

### Claim 6 (depends on 1)

The method of claim 1, wherein filtering at least some of the plurality of test sequences to form the filtered population further comprises at least one of, for each test sequence of the plurality of test sequences:
removing SNPs including no-calls; and
removing SNPs with a depth less than 1000.

### Claim 7 (depends on 1)

The method of claim 1, wherein the measure of background noise level is a population measure of allele frequency in the plurality of test sequences indicative of healthy individuals.

### Claim 8 (depends on 1)

The method of claim 1, wherein generating the noise model further comprises identifying a background noise that represents static noise generated when sequencing a SNP.

### Claim 9 (depends on 1)

The method of claim 1, wherein generating the noise model further comprises:
determining a noise coefficient for each SNP in the plurality of test sequences indicative of healthy individuals, the noise coefficient predicting the expected noise level for each SNP.

### Claim 10 (depends on 1)

The method of claim 1 wherein the generated noise model is additionally based on a sample type of the plurality of test sequences indicative of healthy individuals.

### Claim 11 (depends on 1)

The method of claim 1, wherein the contamination model additionally includes a random error term.

### Claim 12 (depends on 1)

The method of claim 1, wherein the first range is less than the second range.

### Claim 13 (depends on 1)

The method of claim 1, wherein the first range is between 0.0 and 0.2 and the second range is between 0.8 and 1.0.

### Claim 14 (depends on 1)

The method of claim 1, wherein the first range is below a first cutoff value and the second range is above a second cutoff value.