Gritstone bio Presents Improvements to EDGE™ Platform at AACR 2024
-- State-of-the-art neoantigen prediction platform (EDGE™) now predicts HLA Class I presentation of epitopes with >80% accuracy --
-- Newly developed EDGE-II model achieves superior predictive performance of HLA Class II presentation and CD4+ immunogenicity over publicly available models --
-- Combined with Gritstone's vaccine vectors, EDGE has demonstrated best-in-class potential in identifying neoantigens capable of eliciting T cell immune responses for robust and durable immunity --
“Identifying which of the hundreds of tumor mutations are most likely to serve as neoantigens, key targets of tumor-specific T cells, is critical to the development of effective neoantigen-directed vaccines,” said
“Today, EDGE is able to predict HLA Class I presentation of epitopes with >80% accuracy, a significant increase since 2018 when we initially published the model,” said
Abstract 904: EDGE™ enables state-of-the-art identification of peptide-HLAs for the development of T cell inducing vaccines in oncology and infectious diseases
EDGE for Oncology:
- Class I antigens – predicted using allele-specific and pan-specific models
- Allele-specific model is an improved version of published 2018 EDGE model that predicts for 116 HLA alleles and achieves an Average Precision (AP) of 0.63 and Positive Predictive Value at 40% Recall (PPV40) of 0.79
- Pan-specific model trains using the HLA allele sequence and is applicable to any Class I allele with known sequence, achieving an AP of 0.65 and PPV40 of 0.81
- 2-fold better performance vs MHCFlurry 2.0 when ranking mutations from 80 cancer patients based on immunogenicity
- Detectable CD8 responses to over half of the 20 administered candidate neoantigens per patient (n = 5) after treatment with Gritstone’s personalized cancer vaccines
- Class II antigens – predicted using EDGE-II model
- Uses a pretrained protein language model, a novel learned HLA allele-deconvolution strategy, and in-house immunopeptidomics training data
- Achieves a test set AP of 0.92 and outperforms NetMHCIIpan and BERTMHC on an externally curated validation set with an AP = 0.71
- CD4 immunogenicity in a personalized cancer vaccine context is better predicted by EDGE-II than NetMHCIIpan and MARIA
EDGE for Infectious Disease:
- Class I antigens – predicted using EDGE-ID model
- Optimizing EDGE for use on infectious diseases results in improved performance
- Trained using both human immunopeptidomics and infectious disease binding affinity datasets and tested on publicly available infectious disease datasets (HIV, Influenza A, and SARS-CoV-2)
- Better performance on HIV and Influenza A datasets vs. MHCFlurry 2.0; comparable SARS-CoV-2 performance
The poster has been added to the ‘Scientific Publications’ page of the Gritstone bio website.
About EDGE™ (Epitope Discovery for GEnomes)
Gritstone bio believes effective identification of the mutations that are most likely to serve as neoantigens is critical to developing effective neoantigen-directed vaccines. For this reason, we developed EDGE™, a proprietary platform technology that leverages artificial intelligence to identify which of the hundreds of mutations within a tumor are most likely to serve as targets for a patients’ immune system. A key strategic asset, Gritstone leverages EDGE’s capabilities to identify T cell targets for oncology and infectious disease.
About Gritstone bio
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