Tumor-Only Genetic Variant Classification Using Copy Number Signals
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Solution Overview
Problem
Current methods for analyzing genetic variants in cancer tissues require analyzing non-tumor tissue from the subject, which can be invasive and cumbersome.
Innovation Solution
A system and method for analyzing genetic variants in tumor tissues without the need for non-tumor tissue, utilizing sequence coverage, SNP allele frequency, and variant allele frequency inputs to determine variant type and zygosity, employing algorithms like circular binary segmentation and Markov chain Monte Carlo for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-tumor tissue is analyzed to determine variant type and zygosity, then measurement precision is improved, but ease of operation deteriorates due to invasive procedures
Solution Approach 1:
The invention extracts and utilizes specific signals (allele frequency information, copy number data) from tumor tissue sequencing that are sufficient for variant characterization, eliminating the need to analyze non-tumor tissue. This extraction of essential information from a single sample type resolves the contradiction by maintaining measurement precision while removing the invasive requirement for additional tissue collection
Solution Approach 2:
The tumor tissue sample serves multiple functions: it provides both the variant allele frequency data and the copy number/SNP allele frequency data needed for comprehensive variant characterization. This multi-functionality of a single sample type eliminates the need for separate non-tumor tissue analysis, improving ease of operation while maintaining measurement precision
2Ease of operation
If tumor tissue only is analyzed, then ease of operation is improved, but measurement precision deteriorates due to insufficient data for variant classification
Solution Approach 1:
The invention changes the parameters extracted from tumor tissue sequencing to include not only variant allele frequency but also copy number data and SNP allele frequency data. By analyzing multiple parameters from the same tumor sample, the system achieves sufficient measurement precision for variant characterization without requiring additional non-tumor tissue samples
Solution Approach 2:
The sequencing data from tumor tissue acts as an intermediary that contains embedded information about both somatic and germline variants. By using algorithms to extract and interpret multiple parameters (VAF, CN, SAF) from this intermediary data source, the system achieves accurate variant classification using only tumor tissue, thus improving ease of operation while maintaining precision
Data Source
AI summary
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.