Gene Combination for Targeted Pan-Cancer HRD, TMB, and MSI Grading
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Solution Overview
Problem
Conventional methods for detecting homologous recombination deficiency (HRD), tumor mutation burden (TMB), and microsatellite instability (MSI) are costly and inefficient when performed simultaneously, necessitating a more economical and accurate method for grading these indicators.
Innovation Solution
A gene combination comprising specific gene sets and fragments is used for targeted sequencing, reducing the number of target genes and sequencing depth while maintaining accuracy, enabling grading and prediction of HRD, TMB, and MSI in a cost-effective manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole exome sequencing is used to detect HRD, TMB, and MSI simultaneously, then detection accuracy is improved, but detection cost increases significantly
Solution Approach 1:
The patent segments the detection task by identifying and sequencing only specific gene regions (gene set A and gene fragment set B) that are most relevant for HRD, TMB, and MSI detection, rather than sequencing the entire exome. This segmentation approach maintains detection accuracy for the target indicators while significantly reducing the quantity of sequencing data required, thereby lowering costs.
Solution Approach 2:
The patent extracts and focuses on specific critical regions within the genome (gene set A containing 45 genes and gene fragment set B containing 22 chromosomal regions) that provide sufficient information for accurate HRD, TMB, and MSI grading. By taking out only the essential sequencing targets rather than the complete exome, the method achieves cost reduction while preserving detection precision for clinical grading purposes.
2Quantity of substance
If targeted sequencing panels are designed for HRD and MSI detection, then detection cost is reduced, but the ability to comprehensively assess TMB is compromised
Solution Approach 1:
The patent designs a universal detection system where the same targeted sequencing panel (gene set A and gene fragment set B) serves multiple functions: HRD grading, TMB assessment, and MSI detection. The panel is constructed to simultaneously capture information relevant to all three indicators, eliminating the need for separate specialized panels and enabling comprehensive multi-parameter assessment at reduced cost.
Solution Approach 2:
The patent merges the detection requirements for HRD, TMB, and MSI into a single integrated targeted sequencing approach. By combining gene set A (45 genes) and gene fragment set B (22 chromosomal regions) into one unified panel, the method achieves synergistic detection of all three indicators simultaneously, avoiding the redundant costs of separate targeted panels while maintaining comprehensive assessment capability.
3Reliability
If conventional sequencing methods are used for comprehensive tumor grading, then detection reliability is improved, but productivity decreases due to higher cost and complexity
Solution Approach 1:
The patent changes the sequencing parameters by transitioning from whole exome sequencing to targeted sequencing of specific gene sets and chromosomal fragments. This parameter change reduces the sequencing depth and coverage requirements while maintaining sufficient reliability for clinical grading. The optimized parameter selection enables faster, more cost-effective detection without sacrificing the reliability needed for accurate HRD, TMB, and MSI assessment.
Data Source
AI summary
The present application relates to the field of tumor grading detection, and in particular to use of a gene combination in the preparation of a product for human tumor homologous recombination deficiency, tumor mutation burden, and microsatellite instability grading detections. The gene combination consists of a gene set A and a gene fragment set B. The gene combination is obtained from actual high-throughput sequencing data by specific pairwise clustering analysis. Data derived from the real world has higher reliability and credibility. Homologous recombination deficiency, tumor mutation burden, and microsatellite instability grading and prediction can be performed accurately for pan-cancer.