Abstract A33: High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets
| Autor: | Angelika B. Riemer, Ashton Omdahl, Dylan Hirsch, Rachel Karchin, Xiaoshan M. Shao, Collin Tokheim, Victor E. Velculescu, Rohit Bhattacharya, Ben Kaminow, Kymberleigh A. Pagel, Lily Zheng, Justin C. Huang, Ashok Sivakumar, Maria Bonsack, Valsamo Anagnostou |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Cancer Immunology Research. 8:A33-A33 |
| ISSN: | 2326-6074 2326-6066 |
| Popis: | Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 52.6 million allele-peptide comparisons in under 2.3 hours, yielding 103,587 candidate immunogenic missense mutations (IMMs). IMM hotspots occurred in 36 genes, including 22 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (p2 patients, with 65% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers. Citation Format: Xiaoshan M. Shao, Rohit Bhattacharya, Justin Huang, Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Ben Kaminow, Ashton Omdahl, Maria Bonsack, Angelika B. Riemer, Victor E. Velculescu, Valsamo Anagnostou, Kymberleigh Pagel, Rachel Karchin. High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A33. |
| Databáze: | OpenAIRE |
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