pVACtools: a computational toolkit to identify and visualize cancer neoantigens

Hundal, Jasreet; Kiwala, Susanna; McMichael, Joshua F.; Miller, Christopher A.; Wollam, Alexander T.; Xia, Huiming; Liu, Connor J.; Zhao, Sidi; Feng, Yangyang; Graubert, Aaron; Wollam, Amber; Neichin, Jonas; Neveau, Megan; Walker, Jason; Gillanders, William E.; Mardis, Elaine R.; Griffith, Obi L.; Griffith, Malachi

doi:10.1101/501817

Cited by 22 publications

(23 citation statements)

References 37 publications

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“…Like T1N, autoimmune diseases with possible involvement of type IV hypersensitivity reactions have been associated with specific MHC-I or MHC-II alleles (Matzaraki et al, 2017). Therefore, we searched potentially cross-reactive MHC-I or MHC-II ligands of SARS-CoV-2 and human proteins for 34 autoimmune-associated MHC alleles (18 MHC-I alleles and 16 MHC-II alleles) (Matzaraki et al, 2017) using pVACtools (Hundal et al, 2020), an immunoinformatic toolkit for predicting MHC ligands using a variety of prediction algorithms. MHC class I molecules are loaded with peptides with 8-12 amino acids largely derived from intracellular proteins digested by the proteasome, while MHC class II molecules are loaded with peptides with 12-18 amino acids derived from extracellular proteins proteolytically digested inside a late endosome of antigen-presenting cells (Khodadoust et al, 2017; Wieczorek et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach

Park

2020

Preprint

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Currently, more than 33 million peoples have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than a million people died from coronavirus disease 2019 (COVID-19), a disease caused by the virus. There have been multiple reports of autoimmune and inflammatory diseases following SARS-CoV-2 infections. There are several suggested mechanisms involved in the development of autoimmune diseases, including cross-reactivity (molecular mimicry). A typical workflow for discovering cross-reactive epitopes (mimotopes) starts with a sequence similarity search between protein sequences of human and a pathogen. However, sequence similarity information alone is not enough to predict cross-reactivity between proteins since proteins can share highly similar conformational epitopes whose amino acid residues are situated far apart in the linear protein sequences. Therefore, we used a hidden Markov model-based tool to identify distant viral homologs of human proteins. Also, we utilized experimentally determined and modeled protein structures of SARS-CoV-2 and human proteins to find homologous protein structures between them. Next, we predicted binding affinity (IC50) of potentially cross-reactive T-cell epitopes to 34 MHC allelic variants that have been associated with autoimmune diseases using multiple prediction algorithms. Overall, from 8,138 SARS-CoV-2 genomes, we identified 3,238 potentially cross-reactive B-cell epitopes covering six human proteins and 1,224 potentially cross-reactive T-cell epitopes covering 285 human proteins. To visualize the predicted cross-reactive T-cell and B-cell epitopes, we developed a web-based application “Molecular Mimicry Map (3M) of SARS-CoV-2” (available at https://ahs2202.github.io/3M/). The web application enables researchers to explore potential cross-reactive SARS-CoV-2 epitopes alongside custom peptide vaccines, allowing researchers to identify potentially suboptimal peptide vaccine candidates or less ideal part of a whole virus vaccine to design a safer vaccine for people with genetic and environmental predispositions to autoimmune diseases. Together, the computational resources and the interactive web application provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune disease following COVID-19.

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Section: Resultsmentioning

confidence: 99%

Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach

Park

2020

Preprint

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“…Human leukocyte antigen class I alleles of each patient were determined from normal DNA FASTQ files using HLA‐HD (v1.2.0.1) 41 . The annotated VCF files were analyzed using pVACseq, a tool of pVACtools (v1.5.9), 42 with the default setting except for turning off the coverage and VAF filters. We used all MHC class I binding algorithms implemented in pVACseq to predict HLA class I (A, B, or C) binding 7‐ to 11‐mer epitopes.…”

Section: Methodsmentioning

confidence: 99%

Neoantigen prediction in human breast cancer using RNA sequencing data

et al. 2020

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“…All networks were implemented with the Keras Python package (TensorFlow back-end) (32,33). Open source software is available at https://github.com/KarchinLab/mhcnuggets, installable via pip or Docker, and has been integrated into the PepVacSeq (34), pvactools (35) and Neoepiscope (36) pipelines.…”

Section: Methods: Implementationmentioning

confidence: 99%

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Shao

Bhattacharya

Huang

et al. 2019

Preprint

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Running title: High-throughput prediction of neoantigens with MHCnuggets Potential Conflicts of Interest: V.A receives research funding from Bristol-Myers Abstract 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 shortterm 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 (p<2e-16) including CD8+ T cells. Notably, only 0.15% of predicted immunogenic missense mutations were observed in >2 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. SynopsisWe developed a new in silico predictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

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pVACtools: a computational toolkit to identify and visualize cancer neoantigens

Cited by 22 publications

References 37 publications

Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach

Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach

Neoantigen prediction in human breast cancer using RNA sequencing data

High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

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