Cancer Driver Gene Detection Using Gene-Specific Mutation Rates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting cancer driver genes suffer from low concordance and high rates of false positives and negatives due to the heterogeneity of mutational patterns across cancer samples.
Innovation Solution
A statistical model is used to determine gene-specific background mutation rates by optimizing parameters through negative binomial regression and Bayesian inference, allowing for the identification of genes and pathways with significantly more mutations than expected, thereby distinguishing tumor suppressors and oncogenes based on spatial distribution of non-silent mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods utilize signs of positive selection (recurrent mutations, spatial patterns, functional impact bias) to detect driver genes, then potential driver genes can be identified, but the low concordance across methods results in high false positives and negatives
Solution Approach 1:
The patent transforms the detection approach by changing from multiple separate detection parameters (recurrent mutations, spatial patterns, functional impact) to a single unified statistical parameter (gene-specific background mutation rate). This allows for more accurate comparison and reduces false positives/negatives by establishing a consistent baseline for what constitutes abnormal mutation accumulation.
Solution Approach 2:
The patent introduces gene-specific background mutation rates as an intermediary parameter that mediates between observed mutations and driver gene identification. This intermediary allows for normalization and comparison across different genes and samples, resolving the low concordance issue by providing a common reference framework.
2Measurement precision
If gene-specific background mutation rates are determined using statistical models with optimized parameters, then the accuracy of identifying candidate driver genes is enhanced, but the computational complexity and time required for analysis increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing gene-specific background mutation rates from large cohorts of cancer samples. These pre-computed rates serve as reference values that can be quickly applied to new samples without requiring re-optimization, significantly reducing analysis time while maintaining high accuracy.
Solution Approach 2:
The patent applies partial action by focusing computational resources on optimizing parameters for genes showing elevated mutation rates, rather than uniformly processing all genes. This selective approach reduces overall computational time while maintaining detection accuracy for the most promising candidate genes.
Data Source
AI summary
Described herein are methods, systems, and apparatuses for detecting significantly mutated genes/pathways in a cancer cohort. A driver gene detection technique taking into account the heterogeneous mutational context in a cancer cohort is disclosed. A statistical model of a gene-specific mutation rate distribution (e.g., using an optimized gene specific mean estimation and/or a gene-specific dispersion estimation) is used to model a sample/gene-specific background mutation rate. The statistical model may then be used to detect gene/pathway enrichment and distinguish tumor suppressors and oncogenes based on the spatial distribution of non-silent mutations, loss-of-function mutations, and/or gain-of-function mutations.