Neoepitope Identification via Driver Gene Filtering
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Solution Overview
Problem
Current methods for identifying neoepitopes for immune therapy are inefficient, as they fail to effectively filter out unique tumor-specific antigens, leading to a lack of specificity in therapeutic responses, and often result in insufficient immune activation due to variability in antigen processing and presentation.
Innovation Solution
A method involving omics data analysis from tumor and normal tissues to identify expressed missense-based neoepitopes, filtered by HLA type and gene type, specifically targeting cancer driver mutations to enhance immune response and therapeutic efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple filtering methods (mutation type, transcription level, protein expression, HLA binding) are applied to identify neoepitopes, then the number of potential targets is reduced, but the therapeutic efficacy remains uncertain
Solution Approach 1:
The patent segments the neoepitope identification process into multiple independent filtering stages: mutation type filtering, transcription level filtering, protein expression filtering, and HLA binding filtering. Each stage independently evaluates a specific criterion, progressively narrowing down the candidate neoepitopes from the entire mutational landscape to a refined set of high-priority targets.
Solution Approach 2:
The patent performs preliminary computational assessments of multiple neoepitope candidates before actual immunotherapy implementation. By pre-evaluating transcription levels, protein expression, and HLA binding affinity, the system identifies and prioritizes the most promising neoepitopes in advance, reducing the need for extensive experimental validation of low-priority candidates.
2Adaptability or versatility
If all cancer neoepitopes are considered as potential targets, then the coverage of tumor-specific antigens is maximized, but the complexity of target selection increases
Solution Approach 1:
The patent changes multiple parameters simultaneously to evaluate neoepitope quality: mutation type (missense, nonsense, frameshift), transcription level (RNA-seq expression), protein expression (proteomics data), and HLA binding affinity (predictive algorithms). By establishing threshold values for each parameter, the system automatically filters candidates, transforming a complex multi-dimensional selection problem into a series of manageable binary decisions.
3Measurement precision
If HLA variability among patients is considered, then patient-specific neoepitope identification is improved, but the processing complexity increases
Solution Approach 1:
The patent uses computational models and predictive algorithms as virtual copies of the complex HLA binding and antigen processing mechanisms. Instead of performing actual experimental assays for every patient-neoepitope combination, the system employs in silico predictions that replicate the biological processes, significantly reducing processing complexity while maintaining patient-specific accuracy.
Data Source
AI summary
Systems and methods are presented that allow for selection of tumor neoepitopes that are filtered for various criteria. In particularly contemplated aspects, filtering includes a step in which the mutation leading to the neoepitope is ascertained as being located in a cancer driver gene.