Tumor-Specific Driver SGA Identification Using Causal Bayesian Networks
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Solution Overview
Problem
Current methods for identifying driver genes in cancer are limited by the need to define baseline mutation rates and struggle to identify genes altered at low frequencies, leading to inconsistent findings and difficulty in determining functional impact.
Innovation Solution
A computer-implemented method, TDI, infers tumor-specific causal relationships between somatic genome alterations (SGAs) and molecular phenotypes using a Bayesian network, integrating frequency and functional-impact oriented approaches to identify driver SGAs in individual tumors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole genome sequencing is performed to comprehensively identify somatic genome alterations, then the completeness of alteration detection is improved, but the cost and complexity of analysis increase significantly
Solution Approach 1:
The patent segments the genome into specific regions of interest (ROS) that are more likely to contain somatic alterations. Instead of analyzing the entire genome, the method focuses on segmented portions (ROS) that are enriched for alteration-containing sequences, thereby reducing complexity while maintaining detection completeness.
Solution Approach 2:
The patent extracts and isolates specific genomic regions (ROS) that are suspected to contain somatic alterations. By taking out only these relevant regions for sequencing and analysis, the method reduces the overall data volume and analysis complexity while preserving the ability to detect alterations comprehensively.
2Measurement precision
If traditional sequencing methods are used to detect somatic alterations, then the ability to detect rare alterations is improved, but the false positive rate increases due to germline heterozygosity
Solution Approach 1:
The patent introduces a statistical model as an intermediary between raw sequencing data and alteration detection. This model incorporates germline heterozygosity information and other covariates to distinguish true somatic alterations from artifacts, thereby reducing false positives while maintaining detection sensitivity.
Solution Approach 2:
The method implements feedback by using control samples and statistical modeling to continuously refine the detection process. Sequencing data from normal and tumor samples are compared, and the statistical model adjusts based on observed patterns of germline variation, improving the reliability of somatic alteration detection.
3Productivity
If focused sequencing of regions of interest is performed to reduce cost and complexity, then the efficiency is improved, but the ability to detect unexpected alterations outside predefined regions is reduced
Solution Approach 1:
The patent implements a dynamic approach where the set of regions of interest is not fixed but can be expanded or modified based on initial findings and specific research questions. The method allows for iterative refinement of ROS definitions, enabling the detection of both expected and unexpected alterations while maintaining sequencing efficiency.
Data Source
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AI summary
The present application provides methods for the identification of somatic genome alterations with functional impact in the genome of a tumor. In several embodiments, the methods comprise generating a bipartite causal Bayesian network with maximal posterior probability including causal edges pointing from genes including somatic mutations and somatic copy number alterations in the genome of the tumor to genes having differential expression in the tumor. The methods can be used, for example, to identify driver somatic genome alterations in the genome of a tumor.