Tumor Subtype Classification via Tandem Duplication Length Distribution
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Solution Overview
Problem
Current methods fail to accurately classify and target specific subtypes of tumors characterized by tandem duplications, leading to inadequate personalized therapy approaches in cancers such as breast, ovarian, and endometrial carcinomas.
Innovation Solution
A method is developed to classify tumor samples into six subtypes based on the length distribution of tandem duplications using a TDP score calculation, allowing for the identification of specific genetic drivers and therapeutic targets by measuring the length distribution of tandem duplications and assigning subtypes such as Group 1, 2, 1/2mix, 1/3mix, and 2/3mix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current classification methods are used for tumors with tandem duplications, then the classification process is simple, but the classification accuracy and therapeutic targeting are inadequate
Solution Approach 1:
The patent segments tumors with TDP into six distinct subtypes (Group 1, Group 2, Group 3, and their mixtures) based on TD length distribution patterns. This segmentation allows precise identification of specific tumor subtypes while maintaining a systematic classification framework that balances detail with manageability.
Solution Approach 2:
The patent uses TD length distribution parameters (span sizes clustered around 10 kb, 50-600 kb, and >1 Mb) as key discriminators to differentiate between TDP subtypes. By focusing on specific parameter ranges rather than comprehensive genomic analysis, the method achieves high classification accuracy with reduced complexity.
2Reliability
If comprehensive genomic analysis is performed to identify all genetic drivers, then the identification of therapeutic targets is thorough, but the analysis time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary classification of tumors into six TDP subgroups based on TD length distribution before conducting detailed genetic driver analysis. This preliminary action narrows down the search space and allows subsequent focused analysis of subtype-specific drivers, reducing overall analysis time while maintaining comprehensive identification of therapeutic targets.
Solution Approach 2:
The patent applies different analytical approaches to different TDP subtypes based on their specific characteristics. For example, Group 1 tumors (BRCA1-deficient with short-span TDs) are analyzed with specific attention to homologous recombination defects, while Group 2 tumors (BRCA1-wild-type with medium-span TDs) focus on cell cycle and DNA replication genes. This localized approach improves identification accuracy for each subtype while optimizing resource allocation.
3Productivity
If generic therapy approaches are used for all TDP tumors, then the treatment protocol is simple to implement, but the treatment efficacy is reduced due to lack of subtype-specific targeting
Solution Approach 1:
The patent recommends subtype-specific therapeutic strategies tailored to each TDP group's molecular characteristics. Group 1 tumors receive therapies targeting BRCA1 deficiency and homologous recombination defects; Group 2 tumors receive therapies targeting cell cycle and DNA replication pathways; Group 3 tumors receive therapies targeting CDK12 disruptions. This localized therapeutic approach maximizes efficacy for each subtype while maintaining clear, actionable treatment protocols.
Solution Approach 2:
The patent creates a dynamic treatment framework where the therapy selection adapts to the specific TDP subtype identified through TD length distribution analysis. The classification system enables clinicians to dynamically adjust treatment strategies based on the tumor's molecular profile, transitioning from static generic protocols to adaptive subtype-specific therapies, thereby improving productivity while keeping operational complexity manageable through clear decision pathways.
Data Source
AI summary
Provided herein, in some embodiments, are methods for classifying the tandem duplicator phenotype of a tumor.