Tumor-Only Mutation Classification Using Context-Specific Likelihoods
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Solution Overview
Problem
Existing methods for classifying germline and somatic mutations in cancer genomes are labor-intensive, time-consuming, and lack robustness, generalizability, and scalability, often leading to inaccurate classification due to the complexity of tumor samples containing mixed cell types and copy number alterations.
Innovation Solution
A statistical method using germline and somatic mutation models to classify mutations based on context-specific parameters such as tumor purity, ploidy, and copy number alterations, employing a likelihood ratio to determine the most fitting model for accurate classification without a matched normal sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to classify germline and somatic mutations, then the process can be performed, but it is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual, labor-intensive classification methods with an automated computational system that uses statistical models (germline and somatic mutation models) and likelihood ratio calculations to automatically classify mutations, thereby increasing productivity and reducing time requirements
Solution Approach 2:
The system performs self-service classification by automatically evaluating mutations against established models and criteria without requiring manual intervention, allowing the classification process to be conducted independently and efficiently
2Measurement precision
If traditional classification methods are used, then the basic classification can be performed, but accuracy is reduced due to complexity of tumor samples
Solution Approach 1:
The patent changes the approach by introducing context-specific parameters (tumor purity, ploidy, copy number alterations) that modify the classification criteria, allowing the system to adapt to the complexity of tumor samples and improve accuracy through contextual adjustment
Solution Approach 2:
The system applies local quality analysis by evaluating different aspects of mutation context (such as specific genomic locations, copy number states, and tumor purity levels) individually to determine the most appropriate classification, thereby handling complexity through localized assessment
3Loss of information
If comprehensive analysis of tumor samples is performed, then more information is obtained, but the process becomes more complex and less scalable
Solution Approach 1:
The patent segments the complex analysis process into distinct components: identifying mutations, determining context parameters (purity, ploidy, copy number), calculating likelihood ratios, and making classifications. This segmentation maintains information completeness while reducing overall complexity through modular processing
Solution Approach 2:
The system achieves universality by creating a scalable framework that can handle various tumor sample types and complexities through a unified approach, allowing the same basic process to be applied across different contexts without requiring separate specialized analyses for each scenario
Data Source
AI summary
Context-specific tumor-only mutation classification is described. A mutation classification module may classify a mutation identified in sequencing data from a tumor sample as germline or somatic based on a likelihood ratio relative to a threshold, the likelihood ratio comparing a germline model likelihood of a germline model of the mutation to a somatic model likelihood of a somatic model of the mutation and the threshold calculated based on a context of the mutation. The mutation classification module may output the classification of the mutation.