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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
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Accelerating Neoantigen Discovery: A High-Throughput Approach to Immunogenic Target Identification
Lena Pfitzer1, Gitta Boons1, Lien Lybaert1
1myNEO Therapeutics, 9000 Ghent, Belgium.
Vaccines
|August 28, 2025
Summary
A new tool, neoIM (neoantigen immunogenicity), accurately predicts T-cell response to neoantigens, improving cancer immunotherapy target selection. This method surpasses MHC binding affinity predictions, enhancing clinical trial antigen discovery and immunotherapy efficacy.
Area of Science:
- Computational immunology
- Cancer immunotherapy
- Bioinformatics
Background:
- Antigen-targeting immunotherapies rely on identifying immunogenic epitopes for T-cell responses.
- Current methods focusing on MHC binding affinity yield high false positives, limiting clinical utility.
- Accurate prediction of neoantigen immunogenicity is crucial for effective cancer vaccine and therapy design.
Purpose of the Study:
- To develop a novel computational tool, neoIM, for high-precision prediction of neoantigen immunogenicity.
- To evaluate neoIM's performance against existing tools using in vitro and clinical trial data.
- To assess neoIM's potential as a biomarker for predicting response to checkpoint inhibitor therapy.
Main Methods:
- Developed neoIM, a random forest classifier trained on MHC-presented non-self peptides (n=61,829).
- Assessed neoIM performance against existing tools on in vitro immunogenicity datasets (ELISpot assays).
- Conducted retrospective analyses of clinical trial data to evaluate neoIM-based antigen selection and its correlation with overall survival in melanoma patients treated with checkpoint inhibitors (CPI).
Main Results:
- neoIM significantly outperformed existing tools on in vitro benchmarks, increasing predictive power by at least 30% and reducing false positives.
- NeoIM-based antigen selection identified up to 50% more clinically actionable antigens per patient in two clinical trials.
- NeoIM scores showed a correlation with overall survival in melanoma patients treated with CPI, indicating its potential as a predictive biomarker.
Conclusions:
- NeoIM is the first computational tool to accurately predict epitope immunogenicity beyond MHC binding affinity.
- NeoIM enables more precise neoantigen discovery and prioritization, potentially accelerating the development of next-generation immunotherapies.
- The tool's ability to refine response prediction to checkpoint inhibition therapy highlights the importance of evaluating neoantigen immunogenicity.
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