Neoantigen Selection for Heterogeneous Malignancies
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Solution Overview
Problem
Current cancer immunotherapies face challenges in efficiently and accurately predicting and selecting neoantigens for immunogenic compositions due to the complex mutational landscape of cancer, leading to ineffective treatments with significant side effects and high costs.
Innovation Solution
A novel method for selecting tumor-specific peptides that provides optimal coverage across heterogeneous malignancies by using a list of peptides and subclones, with an objective function to maximize subclone scores, ensuring peptides are immunogenic, have high manufacture feasibility, and are presented on the cell surface, thereby forming a personalized immunogenic composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cancer vaccines targeting tumor-associated antigens are used, then immune activation is attempted, but immune tolerance prevents effective immune response
Solution Approach 1:
The patent extracts and isolates neoantigens from the complex tumor mutational landscape through multi-step bioinformatics filtering and prediction algorithms. This extraction process separates the immunogenic neoantigens from non-immunogenic mutations, creating a focused vaccine composition that avoids the immune tolerance problem associated with traditional tumor-associated antigens
Solution Approach 2:
The patent changes the fundamental parameter of antigen selection from tumor-associated antigens to neoantigens derived from somatic mutations. This parameter change transforms the vaccine target from self-antigens subject to immune tolerance to non-self antigens that can effectively activate the immune system
2Adaptability or versatility
If neoantigens are selected from complex mutational landscape, then tumor-specific coverage is improved, but prediction accuracy and selection efficiency deteriorate
Solution Approach 1:
The patent segments the complex neoantigen selection process into distinct computational modules: mutation filtering, peptide prediction, MHC binding affinity calculation, immunogenicity prediction, and subclone coverage optimization. Each module handles a specific aspect of the problem, improving overall accuracy while maintaining efficiency
Solution Approach 2:
The patent adds the dimension of subclone coverage to the neoantigen selection process by incorporating tumor heterogeneity data and selecting peptides that collectively cover multiple subclones. This multi-dimensional approach simultaneously optimizes for both prediction accuracy and comprehensive tumor coverage
3Reliability
If comprehensive neoantigen screening is performed, then immunogenic composition effectiveness is improved, but manufacturing cost and time increase
Solution Approach 1:
The patent applies partial action by selecting a focused subset of high-probability neoantigens rather than including all predicted neoantigens. The bioinformatics pipeline ranks candidates by multiple criteria and selects only the top candidates most likely to be immunogenic and effective, reducing manufacturing complexity while maintaining vaccine effectiveness
Data Source
AI summary
Disclosed herein are methods for selecting tumor-specific neoantigens from a tumor of a subject that are suitable for subject-specific immunogenic compositions.