Cassette Sequence Design for Neoantigen Junction Epitopes
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Solution Overview
Problem
Current methods for identifying neoantigens for cancer vaccines have low positive predictive value, failing to model the entire epitope generation process and often miss somatic mutations, leading to inefficient vaccine design and potential auto-immunity risks.
Innovation Solution
An optimized approach using next-generation sequencing and nonlinear deep learning models to identify and select neoantigens, considering peptide-allele mappings and presentation likelihoods, and designing cassette sequences to minimize junction epitope presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard methods are used to identify neoantigens, then the process is simpler and faster, but the positive predictive value is low and many false positives occur
Solution Approach 1:
The epitope generation process is divided into multiple discrete steps including TAP transport, proteasomal cleavage, MHC binding, transport to cell surface, and TCR recognition. Each step is modeled separately with specific parameters, allowing precise prediction of which neoantigens will be presented and which will not, thereby improving positive predictive value while maintaining computational efficiency
Solution Approach 2:
The method performs preliminary filtering by modeling each step of epitope generation in advance, predicting which candidate neoantigens will successfully navigate the entire process. This preliminary action eliminates false positives before vaccine design, improving precision without requiring complex post-filtering steps
2Measurement precision
If more comprehensive modeling of epitope generation is performed, then prediction accuracy improves, but computational time and complexity increase
Solution Approach 1:
The method uses parameter thresholds and weighted scoring to efficiently evaluate each modeling step. By assigning different weights to different steps based on their importance and using threshold-based filtering, the system achieves high prediction accuracy while minimizing computational time through early elimination of unlikely candidates
3Reliability
If vaccines include more neoantigens to ensure coverage, then therapeutic effectiveness improves, but the risk of presenting unwanted junction epitopes increases
Solution Approach 1:
The method extracts and identifies junction epitopes that span the boundaries of concatenated neoantigen sequences. By detecting these junction epitopes in advance and removing or modifying them from the vaccine cassette, the system maintains comprehensive neoantigen coverage while eliminating the harmful unwanted immune responses they would otherwise trigger
Data Source
AI summary
Given a set of therapeutic epitopes, a cassette sequence is designed to reduce the likelihood that junction epitopes are presented in the patient. The cassette sequence is designed by taking into account presentation of junction epitopes that span the junction between a pair of therapeutic epitopes in the cassette. The cassette sequence may be designed based on a set of distance metrics each associated with a junction of the cassette. The distance metric may specify a likelihood that one or more of the junction epitopes spanning between a pair of adjacent epitopes will be presented.