Neoantigen Selection via T Cell Recognition and MHC Redundancy
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Solution Overview
Problem
The identification and selection of mutant peptides with immunogenic characteristics (neoantigens) remains complex, hindering the clinical success of neoantigen (personalized) vaccines.
Innovation Solution
The method involves identifying potential neoantigens based on criteria such as potential for linked recognition by CD4 and CD8 T cells, intracellular location of the source protein, and redundancy of presentation by MHC molecules, to select efficacious immune-stimulating antigens for use in vaccines or adoptive cell therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neoantigen identification methods are used, then the process is simple, but the prediction of peptide immunogenicity is inaccurate
Solution Approach 1:
The patent segments the neoantigen identification process into multiple independent evaluation dimensions: MHC binding affinity, peptide immunogenicity prediction, T cell recognition potential, and intracellular location analysis. Each dimension is assessed separately using specialized algorithms and databases, then integrated to produce a comprehensive neoantigen score. This segmentation allows for more precise prediction while managing complexity through modular assessment components.
Solution Approach 2:
The patent introduces new evaluation dimensions beyond traditional MHC binding affinity alone. It incorporates spatial dimension (intracellular location of source protein), temporal dimension (protein degradation rate), and cellular dimension (T cell recognition potential). This multi-dimensional approach enhances prediction accuracy by considering multiple factors simultaneously rather than relying on a single parameter.
2Reliability
If more comprehensive criteria are applied to select neoantigens, then the clinical efficacy is improved, but the selection process becomes more complex
Solution Approach 1:
The patent performs preliminary computational filtering and scoring of potential neoantigens before experimental validation. It pre-evaluates MHC binding affinity, predicts immunogenicity using machine learning models, and assesses T cell recognition potential in silico. This preliminary action identifies high-probability candidates early in the process, reducing the need for extensive subsequent testing and simplifying the overall selection workflow while maintaining high clinical efficacy.
Solution Approach 2:
The patent incorporates feedback loops where clinical outcomes from neoantigen-based therapies are fed back into the selection algorithms. Performance data from patient responses is used to refine and retrain prediction models, continuously improving the accuracy of neoantigen selection. This feedback mechanism ensures that the selection process adapts to real-world clinical data, enhancing reliability while optimizing the selection criteria over time.
Data Source
AI summary
Provided are methods for identifying and selecting an antigen that will be efficacious as an immune-stimulating antigen in vivo, comprising: (i) providing a plurality of potential neoantigens, wherein optionally the plurality of potential neoantigens comprise a plurality of peptides; (ii) identifying if there is a contextual-linked recognition of a candidate neoantigen by a CD4 and/or a CD8 T cell; (iii) identifying if a source protein of the candidate neoantigen, or the neoantigen, is present in a subcellular location source known to contain immunogenic peptides, and the neoantigen is enriched in at least one intracellular compartment or the neoantigen is depleted in at least one intracellular compartment; (iv) identifying if there is a redundancy of presentation of the neoantigen by at least two different major histocompatibility complex (MHC) proteins; and (v) if a potential neoantigen meets the criteria of: (ii) and (iii); (ii) and (iv); (iii) and (iv); or (ii), (iii) and (iv), the potential neoantigen is identified as an antigen that will be efficacious as an immune-stimulating antigen in vivo.