Phylogenetic Analysis of Metastases Using SSNV Classification
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Solution Overview
Problem
Current methods fail to provide detailed insights into tumor dissemination and phylogeny, especially in cases of multiple metastases, as they are not applicable to omics data from tumors and their metastases at different sites or time points, limiting the understanding of genetic origin and differentiation of sub-populations in metastases.
Innovation Solution
The system and method analyze omics data from tumors and metastases to determine phylogeny by classifying somatic single nucleotide variants (SSNVs) as fully shared, partially shared, private, or absent, and calculating a phylogenetic profile for the primary tumor and its metastases using an error probability model and filtering criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current methods are used to analyze tumor data, then general tumor clonality can be determined, but detailed insights into tumor dissemination and phylogeny of multiple metastases cannot be obtained
Solution Approach 1:
The method segments the analysis by classifying SSNVs into distinct categories (fully shared, partially shared, private, absent) based on their presence across different tumor samples. This segmentation allows detailed phylogenetic reconstruction by tracking which variants are inherited from the primary tumor versus which arise de novo in metastases, thereby resolving the technical contradiction between obtaining detailed phylogenetic information and maintaining applicability across diverse sample types.
2Loss of information
If phylogenetic analysis of multiple metastases is performed, then understanding of metastatic heterogeneity improves, but complexity of data analysis increases
Solution Approach 1:
The method applies local quality by using filtering criteria tailored to specific variant types and sample characteristics. Different filtering thresholds and parameters are applied based on the local data quality, variant frequency, and sample type, allowing complex phylogenetic analysis while managing computational complexity through adaptive, context-specific filtering strategies.
3Measurement precision
If stringent filtering criteria are applied to determine SSNVs, then accuracy of phylogenetic profiling increases, but quantity of identified variants decreases
Solution Approach 1:
The method employs parameter changes by adjusting filtering thresholds and confidence levels based on the specific analysis context, sample quality, and variant frequency. This allows the system to optimize between accuracy and quantity of identified variants dynamically, applying stricter filters when high precision is critical and more permissive filters when broader variant detection is needed, thereby resolving the contradiction between measurement precision and quantity of identified variants.
Data Source
AI summary
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.