Neoantigen Epitope Ranking for Patient-Specific Immune Response
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Solution Overview
Problem
Existing immunotherapy approaches struggle to accurately predict which neoantigens will elicit an immune response in individual patients, leading to potential off-target effects and reduced therapeutic efficacy.
Innovation Solution
A method and system for ranking neoantigens based on personalized patient data, incorporating HLA binding, T-cell response, and RNA-sequence data to calculate a single score for each epitope, and using vector space embeddings to prioritize neoepitopes for targeted immunotherapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional immunotherapy approaches are used to target neoantigens, then the treatment can be administered, but the accuracy of predicting which neoantigens will elicit an immune response is low, leading to off-target effects and reduced therapeutic efficacy
Solution Approach 1:
The patent segments the prediction process into multiple independent scoring components: HLA binding affinity score, T-cell response score, and neoantigen expression score. Each component evaluates a specific aspect of immune response potential, allowing for precise, multi-dimensional assessment rather than a single crude prediction metric.
Solution Approach 2:
The patent transforms the prediction approach by changing from qualitative assessment to quantitative scoring. Multiple numerical parameters (binding affinity, response likelihood, expression levels) are calculated and integrated to produce a comprehensive priority score, enabling precise ranking of neoantigens by their immune response potential.
2Measurement precision
If multiple scoring parameters are calculated and integrated for each epitope, then the prediction accuracy of immune response increases, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal computational framework that handles multiple scoring parameters through a single integrated priority score calculation. The system is designed to process diverse data types (HLA binding, T-cell response, expression levels) through a unified algorithmic approach, reducing operational complexity despite the multi-parameter nature of the analysis.
Solution Approach 2:
The patent uses vector space embeddings to create simplified representations of complex neoantigen data. By mapping high-dimensional scoring parameters into embedded vector spaces, the system preserves the essential relationships and prediction accuracy while reducing the computational burden of processing and comparing multiple parameters.
Data Source
AI summary
A method of ranking epitopes derived from neoantigens as targets for personalized immunotherapy includes collecting candidate epitopes based on patient data of a cancer patient. A set of scores are calculated for each of the candidate epitopes, each of the scores in a respective one of the sets for a respective one of the candidate epitopes representing an independent measure of a likelihood of the respective one of candidate epitopes to elicit an immune response in the cancer patient. The scores in each of the sets of scores are combined into a single score for each of the candidate epitopes. The single scores for the candidate epitopes in each case reflect an overall likelihood of eliciting the immune response in the patient. The candidate epitopes are ranked using the single scores for the immunotherapy.