TY - JOUR
T1 - High-throughput prediction of MHC Class i and II neoantigens with MH cnuggets
AU - Shao, Xiaoshan M.
AU - Bhattacharya, Rohit
AU - Huang, Justin
AU - Sivakumar, I. K.Ashok
AU - Tokheim, Collin
AU - Zheng, Lily
AU - Hirsch, Dylan
AU - Kaminow, Benjamin
AU - Omdahl, Ashton
AU - Bonsack, Maria
AU - Riemer, Angelika B.
AU - Velculescu, Victor E.
AU - Anagnostou, Valsamo
AU - Pagel, Kymberleigh A.
AU - Karchin, Rachel
N1 - Publisher Copyright:
© 2019 American Association for Cancer Research.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHCbinding affinity and are limited by low 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 that predicts peptide-MHC binding. MHCnuggets can predict binding 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 faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data.We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 ×10-16), including CD8 T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.
AB - Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHCbinding affinity and are limited by low 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 that predicts peptide-MHC binding. MHCnuggets can predict binding 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 faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data.We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 ×10-16), including CD8 T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.
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U2 - 10.1158/2326-6066.CIR-19-0464
DO - 10.1158/2326-6066.CIR-19-0464
M3 - Article
C2 - 31871119
AN - SCOPUS:85081118672
SN - 2326-6066
VL - 8
SP - 396
EP - 408
JO - Cancer Immunology Research
JF - Cancer Immunology Research
IS - 3
ER -