Methods for detecting biallelic loss of function in next-generation sequencing genomic data
Inventors
Pozzorini, Christian • XU, Zhenyu
Assignees
Interested in licensing this patent?
MTEC can help explore whether this patent might be available for licensing for your application.
Publication Number
US-11830579-B2
Publication Date
2023-11-28
Expiration Date
Abstract
A genomic data analyzer maybe configured to detect and characterize biallelic genomic alterations for at least one gene in next generation sequencing variant calling information for patient tumor samples characterized by different purity ratios of somatic genomic material. The variant analysis module may compare the observed variant fraction distributions of putative heterozygous germline mutations to the theoretical distributions corresponding to different chromosomal aberration events to detect a combination of genomic alteration events. The variant analysis module maybe used in next-generation-sequencing oncogenomics testing to identify biallelic loss of function on tumor suppressor genes to facilitate the biological understanding and choice of a personalized oncology treatment targeting the analyzed patient tumor solely from next generation sequencing data variant information, without requiring complementary germline analysis or biological assays. The proposed genomic data analyzed may help determine whether PARP inhibitors such as Olaparib are a recommended chemotherapy treatment to target ovarian or breast cancers.
Core Innovation
The invention describes an automated next generation sequencing (NGS) genomic analysis method that identifies the presence of a chromosomal aberration in tumor sample cells in at least one gene from next generation sequencing data analysis of a pool of patient tumor samples only. The method uses variant calling information comprising SNPs and INDELs genomic variants in at least two genes and includes a variant classification data processor module for biallelic loss of function caused by a combination of at least two genomic alterations. This tumor-only workflow is designed to detect and characterize chromosomal aberrations without acquiring germline reference samples.
The method selects a set of putative germline heterozygous SNPs by comparing the genomic variants to a reference human genome variant database. For each possible value sample purity p, where sample purity is a ratio of somatic DNA material in an overall formal-in-fixed paraffin-embedded (FFPE) sample, a mixture model is fitted to the observed variant fraction distribution of the selected SNP set, with modeled variant fraction distribution values calculated as a function of the sample purity p. The method infers an optimal sample purity p as the possible sample purity value for which the mixture model best fits the observed variant fraction distribution.
For each gene to be analyzed, a subset of putative germline heterozygous SNPs located in the gene is selected and the presence of a chromosomal aberration is inferred by comparing the observed variant fraction distribution with a predicted variant fraction distribution for each possible chromosomal aberration. The predicted variant fraction distribution values are calculated as a function of the optimal sample purity p. The method further determines responsiveness of cancer in a specified patient to a PARP inhibitor by detecting biallelic genomic alteration in BRCA1 or BRCA2 genes from the variant calling information and then treating the cancer, precancerous lesions, or benign tumors if responsiveness is determined.
Claims Coverage
The provided claims coverage centers on one independent claim combining tumor-only chromosomal aberration identification from NGS with sample-purity inference via a mixture model and a PARP inhibitor treatment determination focused on BRCA1/BRCA2 biallelic loss of function.
Tumor-only chromosomal aberration identification from NGS variant calling
Acquiring variant calling information of the next generation sequencing of multiple patient tumor samples only, where the variant calling information comprises for each patient a list of SNPs and INDELs genomic variants in at least two genes to be analyzed; selecting putative germline heterozygous SNPs by comparing to a reference human genome variant database; and, for each gene to be analyzed, selecting a subset of the putative germline heterozygous SNPs located in the gene and inferring the presence of a chromosomal aberration in the tumor sample cells by comparing observed and predicted variant fraction distributions.
Mixture-model-based optimal sample purity inference for FFPE
For each possible value sample purity p, fitting a mixture model to the observed variant fraction distribution of the set, with modeled variant fraction distribution values calculated as a function of the sample purity p; and inferring an optimal sample purity p as the possible sample purity p for which the mixture model best fits the observed variant fraction distribution.
Gene-by-gene aberration inference using predicted variant fraction distributions
Inferring the presence of a chromosomal aberration for each gene by comparing, on at least the subset, the observed variant fraction distribution with the predicted variant fraction distribution for each possible chromosomal aberration, where the predicted variant fraction distribution values are calculated as a function of the optimal sample purity p.
PARP inhibitor responsiveness determination from BRCA1/BRCA2 biallelic alterations
Determining if cancer or precancerous lesions or benign tumors in the specified patient will be responsive to treatment with a PARP inhibitor by detecting genomic alterations in BRCA1 and BRCA2 genes from the variant calling information; and, if the specified patient sample cells carry a biallelic genomic alteration in the BRCA1 gene or in the BRCA2 gene, determining that the specified patient cancer will respond to treatment with the PARP inhibitor; and treating the cancer in the specified patient with the PARP inhibitor if responsiveness is determined.
The claim coverage is centered on a tumor-only NGS analysis that infers sample purity using a mixture model of variant-fraction distributions, then performs gene-by-gene chromosomal aberration inference by comparing observed and predicted variant fraction distributions, and finally uses BRCA1/BRCA2 biallelic genomic alteration status to determine PARP inhibitor responsiveness for treating cancer, precancerous lesions, or benign tumors.
Stated Advantages
Provides stated advantages over prior art requiring germline reference samples and relies primarily on variant-fraction distributions to detect copy-neutral LOH.
Documented Applications
Identifying presence of chromosomal aberrations in tumor sample cells in at least one gene from NGS data analysis of a pool of patient tumor samples only, with classification of biallelic loss-of-function caused by combinations of genomic alterations.
Determining if cancer or precancerous lesions or benign tumors will be responsive to treatment with a PARP inhibitor, specifically based on detecting biallelic genomic alterations in BRCA1 and BRCA2 genes, and treating responsive cases with a PARP inhibitor.
Targeted FFPE ovarian cancer analysis using BRCA1/BRCA2/TP53 and reported gene-level LOH type and variant classification outcomes, including estimated candidates likely benefiting from PARP inhibition.
Integration of coverage-based intragenic CNV inference consistent with BRCA1 exon-level loss for a documented example.
Interested in licensing this patent?