SigMA Tool for Low Mutation Count Signature Detection
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Solution Overview
Problem
Current methods for detecting mutational signatures, such as Signature 3, associated with homologous recombination deficiency in tumors are limited by requiring large numbers of mutations, making them unsuitable for clinical use with targeted sequencing panels that have fewer mutations.
Innovation Solution
The development of the SigMA (Signature Multivariate Analysis) tool, which uses a likelihood-based approach to detect mutational signatures, including Signature 3, from low mutation counts, enabling identification in samples with limited sequencing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mutational signature detection methods are used, then detection accuracy is maintained, but the method requires large numbers of mutations making it unsuitable for clinical targeted sequencing panels
Solution Approach 1:
The patent changes the analytical parameters by developing a modified non-negative least squares (NNLS) algorithm specifically optimized for low mutation counts. This algorithmic parameter change enables accurate signature detection with as few as 5-10 mutations, transforming the method's applicability from research-grade whole-genome sequencing to clinically feasible targeted panels while maintaining detection accuracy
Solution Approach 2:
The patent segments the mutation detection process into distinct analytical components: (1) mutation spectrum calculation from targeted panel data, (2) reference signature matrix construction from whole-genome data, and (3) NNLS-based deconvolution. This segmentation allows the method to leverage high-quality reference data while analyzing low-mutation clinical samples, bridging the gap between research and clinical applications
2Measurement precision
If whole-genome sequencing is used to obtain sufficient mutations for signature detection, then detection accuracy is improved, but cost and complexity increase significantly
Solution Approach 1:
The patent introduces a reference signature matrix constructed from whole-genome sequencing data as an intermediary. This reference matrix captures authentic mutational signatures with high precision, which then serves as a template for analyzing simpler, lower-cost targeted panel data. The intermediary reference data bridges the accuracy-gap between complex WGS and simple targeted sequencing
Solution Approach 2:
The patent creates a computational copy of the high-quality signature information obtained from whole-genome sequencing studies. By constructing a reference matrix from WGS data and then using NNLS to decompose targeted panel mutation spectra against this reference, the method copies the essential signature characteristics into a format suitable for low-mutation clinical samples, achieving WGS-level accuracy with targeted panel costs
Data Source
AI summary
Disclosed are systems and methods that can identify mutational signatures relevant to various cancers and/or treatments using genetic data from the tumors. This includes using a likelihood based measure, to compare clusters of tumor spectrums when the sample has sequenced only a sub-set of the genes with a targeted panel. In one example, by enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.