DNA Sample Characterisation Using Mutational Signatures for HRD Classification
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Solution Overview
Problem
Existing methods struggle to accurately classify DNA samples from tumors to determine homologous recombination deficiency (HRD) status, which is crucial for personalized cancer treatment strategies, particularly for cancers associated with BRCA1/2 mutations.
Innovation Solution
A method involving cataloguing somatic mutations, determining specific rearrangement and indel signatures, and generating a probabilistic score based on cosine similarity and HRD index to identify HRD status, using a weighted model to predict HR-deficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole genome sequencing is performed to explore all classes of somatic mutation, then comprehensive mutational profiling is achieved, but data complexity and difficulty of clinical interpretation increase
Solution Approach 1:
The patent extracts and focuses on specific rearrangement signatures (RS3 and RS5) from the comprehensive mutational profile, isolating the most clinically relevant features for HRD classification while discarding less informative mutation types. This extraction approach maintains measurement precision for HRD status while reducing data complexity.
Solution Approach 2:
The patent creates a simplified computational model that copies only the essential features (rearrangement signatures RS3 and RS5, indel signatures, and HRD index) needed for HRD classification, rather than processing the complete mutational profile. This copying strategy preserves the critical information needed for accurate classification while eliminating extraneous data complexity.
2Measurement precision
If multiple mutational signatures are analyzed to determine HRD status, then classification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the mutational signature analysis into distinct, independently analyzable components: rearrangement signature RS3, rearrangement signature RS5, indel signatures, and HRD index. Each segment is evaluated separately and then integrated, maintaining high classification accuracy while reducing computational complexity through modular processing.
Solution Approach 2:
The patent transforms multiple mutational signature parameters into a unified probabilistic score through mathematical transformation (cosine similarity calculation and logistic regression). This parameter change converts complex multi-dimensional signature data into a single interpretable HRD probability value, preserving accuracy while simplifying computational output.
3Adaptability or versatility
If comprehensive genomic profiling is performed on all cancer patients, then personalized treatment selection is optimized, but cost and time resources increase
Solution Approach 1:
The patent applies partial action by analyzing only the specific mutational signatures most strongly associated with HRD status (RS3 and RS5), rather than performing exhaustive analysis of all possible mutational types. This selective approach maintains personalized treatment selection accuracy while significantly reducing the time and computational resources required for genomic profiling.
4Measurement precision
If rearrangement mutations are classified into multiple categories, then mutational signature accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by categorizing rearrangement mutations according to their specific characteristics (clustered vs. non-clustered, size categories, specific signature types RS3 and RS5). Each mutation is classified according to its local properties relevant to HRD classification, improving signature accuracy while managing processing complexity through targeted categorization rather than exhaustive classification.
Data Source
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AI summary
The invention provides a method of characterising a DNA sample obtained from a tumour, the method including the steps of: determining the presence or absence of a plurality of base substitution signatures, rearrangement signatures and indel signatures in the sample and copy number profiles for the sample; generating, from the presence or absence of said plurality of base substitution signatures, rearrangement signatures and indel signatures and the copy number profile for the sample, a probabilistic score; and based on said probabilistic score, identifying whether said sample has a high or low likelihood of being homologous recombination (HR) -deficient. Identification of a tumour as HR-deficient may be used to inform treatment choices, for example treatment with a PARP inhibitor or platinum therapy or an anthracycline.