# US 11,908,588 B2 · Systems and methods for genetic analysis of metastases

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- Number: US 11,908,588 B2
- Document ID: US11908588B2
- Status: Granted
- Kind: B2
- Application number: 15/755,096
- Filed: 2016-08-25
- Published: 2020-07-16
- Granted: 2024-02-20
- Earliest claimed: 2015-08-25
- Assignees: NantOmics, LLC
- Applicants: Nantomics, LLC
- Inventors: John Zachary Sanborn
- Web page: https://patentgrep.com/patent/US11908588B2

## CPC

- G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16H 70/60: relating to pathologies
- G16B 5/20: Probabilistic models
- G16B 10/00: ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
- G16B 20/10: Ploidy or copy number detection
- G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
- G16B 20/40: Population genetics; Linkage disequilibrium
- 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
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G01N 33/575: for cancer
- G16B 45/00: ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)

## Abstract

Contemplated systems and methods use identification and classification of somatic single nucleotide variants found in a primary tumor and metastases to determine phylogeny of the metastases.

## Claims

### Claim 1

A method of determining phylogeny of a metastasis and treating a melanoma patient, the method comprising:
obtaining a plurality of respective nucleic acid sequences from a primary tumor, a first metastasis, a second metastasis, and non-tumor tissue collected from a melanoma patient;
informationally coupling an analysis engine to a sequence database, wherein the sequence database stores the plurality of respective nucleic acid sequences;
determining, by the analysis engine, somatic single nucleotide variants (SSNVs) for the primary tumor, the first metastasis, and the second metastasis relative to the nucleic acid sequences from non-tumor tissue of the patient by using an error probability model,
wherein the error probability model comprises the steps of mapping short reads of the tumor sequences to the reference genome and determining error probability by using a statistical model that incorporates the mapping qualities, error probabilities from raw sequence quality scores, sampling of two haplotypes, and an empirical model for correlated errors at a site;
filtering, by the analysis engine, the determined SSNVs;
determining, by the analysis engine, a status for each SSNV with respect to each of the nucleic acid sequences from the primary tumor, the first metastasis, and the second metastasis;
using, by the analysis engine, the status to classify each SSNV into a class selected form the group consisting of as a fully shared SSNV, a partially shared SSNV, a private SSNV, or an absent SSNV;
using, by the analysis engine, the classifications of the SSNVs, to calculate a phylogenetic profile for the primary tumor, the first metastasis, and the second metastasis; and
generating a targeted therapy for the melanoma, prepared for administration, wherein the targeted therapy recognizes or binds to the fully shared SSNV, wherein the fully shared SSNV is one that is present in the primary tumor, first metastasis and second metastasis.

### Claim 2 (depends on 1)

The method of claim 1, wherein the step of determining SSNVs comprises a step of computing likelihoods of all possible genotypes for the primary tumor, the first metastasis, and the second metastasis.

### Claim 3 (depends on 1)

The method of claim 1, wherein the step of determining SSNVs uses an error probability model.

### Claim 4 (depends on 1)

The method of claim 1, further comprising a step of filtering the determined SSNV where the determined SSNV is heterozygous.

### Claim 5 (depends on 4)

The method of claim 4, wherein the step of filtering comprises at least one of filtering by genotype quality, filtering by somatic score, filtering by mapping quality, filtering by quality sum of mismatches, filtering by average number of mismatches, filtering by fractional distance to 3′-end, and filtering by mutant allele depth.

### Claim 6 (depends on 1)

The method of claim 1, wherein the status is ‘present’ or ‘absent’.

### Claim 7 (depends on 1)

The method of claim 1, wherein each class requires presence of at least three somatic single nucleotide variants, and wherein each of the somatic single nucleotide variants must have sufficient sequencing coverage to establish a tumor variant percentage with a 99% confidence interval of <1.

### Claim 8

A method of determining phylogeny of a metastasis and treating a melanoma patient, the method comprising:
obtaining a plurality of respective nucleic acid sequences from a primary tumor, a first metastasis, a second metastasis, and non-tumor tissue collected from a melanoma patient;
informationally coupling an analysis engine to a sequence database, wherein the sequence database stores the plurality of nucleic acid sequences;
determining, by the analysis engine somatic single nucleotide variants (SSNVs) for the primary tumor, the first metastasis, and the second metastasis relative to the nucleic acid sequences from non-tumor tissue of the patient, wherein the step includes a determination of likelihood for a possible genotype using an error probability model;
wherein the error probability model comprises the steps of mapping short reads of the tumor sequences to the reference genome and determining error probability by using a statistical model that incorporates the mapping qualities, error probabilities from raw sequence quality scores, sampling of two haplotypes, and an empirical model for correlated errors at a site;
filtering, by the analysis engine, the determined SSNVs;
identifying, by the analysis engine, for each of the primary tumor, the first metastasis, and the second metastasis fully shared SSNVs, partially shared SSNVs, private SSNVs, and absent SSNVs;
calculating, by the analysis engine, a phylogenetic profile for the primary tumor, the first metastasis, and the second metastasis based on the fully shared SSNVs, partially shared SSNVs, private SSNVs, and absent SSNVs; and
generating a targeted therapy for the melanoma, prepared for administration, wherein the targeted therapy recognizes or binds to the fully shared SSNV, wherein the fully shared SSNV is one that is present in the primary tumor, first metastasis and second metastasis.

### Claim 9 (depends on 8)

The method of claim 8, wherein the plurality of nucleic acid sequences are exome nucleic acid sequences or whole genome nucleic acid sequences.

### Claim 10 (depends on 8)

The method of claim 8, wherein the plurality of nucleic acid sequences are in SAM or BAM format.

### Claim 11 (depends on 8)

The method of claim 8, wherein the step of determining SSNVs comprises a step of computing likelihoods of all possible genotypes for the primary tumor, the first metastasis, and the second metastasis.

### Claim 12 (depends on 8)

The method of claim 8, further comprising a step of filtering the determined SSNV where the determined SSNV is heterozygous.

### Claim 13 (depends on 12)

The method of claim 12, wherein the step of filtering comprises at least one of filtering by genotype quality, filtering by somatic score, filtering by mapping quality, filtering by quality sum of mismatches, filtering by average number of mismatches, filtering by fractional distance to 3′-end, and filtering by mutant allele depth.

### Claim 14 (depends on 8)

The method of claim 8, further comprising a step of determining allelic state for the SSNVs.

### Claim 15 (depends on 8)

The method of claim 8, further comprising a step of determining a copy number aberration.