The SysteMHC Atlas project

Abstract Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

Roman A. Zubarev | Emanuel Schmid | Witold Wolski | Juan Antonio Vizcaíno | Eric W. Deutsch | Qi Wang | Stefan Stevanovic | Morten Nielsen | Alexey I. Nesvizhskii | Björn Peters | Miguel Marcilla | Alberto Paradela | Catherine E. Costello | Ralph Schlapbach | Anthony W. Purcell | Etienne Caron | Cecilia S. Lindestam Arlehamn | David S. Campbell | Robert L. Moritz | Hans-Georg Rammensee | Stefan H. E. Kaufmann | Heiko Schuster | Ruedi Aebersold | Albert J. R. Heck | Nicola Ternette | Tom H. M. Ottenhoff | Pierre Thibault | Claude Perreault | Arie Admon | Gustavo A. de Souza | Wenguang Shao | Patrick G. A. Pedrioli | Christian Scurtescu | Mathieu Courcelles | Daniel Kowalewski | Fabio Marino | Kerrie Vaughan | Alessandro Sette | Krista E. Meijgaarden | Natalie Nieuwenhuizen | John C. Castle | Anders Jimmy Ytterberg | Peter A. van Veelen | Cécile A. C. M. van Els | Ludvig M. Sollid | Michal Bassani-Sternberg | J. Castle | R. Aebersold | A. Nesvizhskii | H. Rammensee | S. Stevanović | R. Moritz | M. Nielsen | Bjoern Peters | A. Sette | E. Deutsch | A. Heck | D. Campbell | R. Schlapbach | E. Caron | C. Perreault | R. Zubarev | P. Thibault | J. Vizcaíno | W. Wolski | L. Sollid | A. Purcell | A. Admon | T. Ottenhoff | A. Ytterberg | Wenguang Shao | E. Schmid | M. Bassani-Sternberg | C. Costello | K. Vaughan | F. Marino | M. Marcilla | N. Ternette | N. Nieuwenhuizen | D. Kowalewski | C. L. Arlehamn | A. Paradela | K. Meijgaarden | P. Veelen | Qi Wang | C. Els | G. D. Souza | H. Schuster | M. Courcelles | S. Kaufmann | Christian Scurtescu | Miguel Marcilla | Natalie E. Nieuwenhuizen

[1]  Maxim N. Artyomov,et al.  Checkpoint Blockade Cancer Immunotherapy Targets Tumour-Specific Mutant Antigens , 2014, Nature.

[2]  J. Vizcaíno,et al.  Exploring the potential of public proteomics data , 2015, Proteomics.

[3]  Natalie I. Tasman,et al.  iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* , 2011, Molecular & Cellular Proteomics.

[4]  Albert J R Heck,et al.  Expanding the detectable HLA peptide repertoire using electron-transfer/higher-energy collision dissociation (EThcD) , 2014, Proceedings of the National Academy of Sciences.

[5]  Arie Admon,et al.  The Human Immunopeptidome Project, a Suggestion for yet another Postgenome Next Big Thing , 2011, Molecular & Cellular Proteomics.

[6]  J. Leunissen,et al.  The Human Leukocyte Antigen–presented Ligandome of B Lymphocytes* , 2013, Molecular & Cellular Proteomics.

[7]  Darren R. Flower,et al.  Mycobacterium tuberculosis Peptides Presented by HLA-E Molecules Are Targets for Human CD8+ T-Cells with Cytotoxic as well as Regulatory Activity , 2010, PLoS pathogens.

[8]  Sébastien Lemieux,et al.  The MHC I immunopeptidome conveys to the cell surface an integrative view of cellular regulation , 2011, Molecular systems biology.

[9]  J. Neefjes,et al.  Towards a systems understanding of MHC class I and MHC class II antigen presentation , 2011, Nature Reviews Immunology.

[10]  Drew M. Pardoll,et al.  The blockade of immune checkpoints in cancer immunotherapy , 2012, Nature Reviews Cancer.

[11]  Etienne Caron,et al.  Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry* , 2015, Molecular & Cellular Proteomics.

[12]  Deborah Hix,et al.  The immune epitope database (IEDB) 3.0 , 2014, Nucleic Acids Res..

[13]  Catherine E Costello,et al.  Immunogenic HLA-DR-Presented Self-Peptides Identified Directly from Clinical Samples of Synovial Tissue, Synovial Fluid, or Peripheral Blood in Patients with Rheumatoid Arthritis or Lyme Arthritis. , 2017, Journal of proteome research.

[14]  Ruedi Aebersold,et al.  Highlights of the Biology and Disease-driven Human Proteome Project, 2015-2016. , 2016, Journal of proteome research.

[15]  Morten Nielsen,et al.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.

[16]  S. Lemieux,et al.  MHC class I-associated peptides derive from selective regions of the human genome. , 2016, The Journal of clinical investigation.

[17]  E. Lundberg,et al.  Towards a knowledge-based Human Protein Atlas , 2010, Nature Biotechnology.

[18]  D. Cole,et al.  The ultimate mix and match: making sense of HLA alleles and peptide repertoires , 2015, Immunology and cell biology.

[19]  Hans-Georg Rammensee,et al.  Isolation and analysis of naturally processed viral peptides as recognized by cytotoxic T cells , 1990, Nature.

[20]  Eric W. Deutsch,et al.  Combining Results of Multiple Search Engines in Proteomics* , 2013, Molecular & Cellular Proteomics.

[21]  D. Harlan,et al.  Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion , 2016, Science.

[22]  Richard D Smith,et al.  Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)* , 2011, Molecular & Cellular Proteomics.

[23]  George Coukos,et al.  Mass spectrometry-based antigen discovery for cancer immunotherapy. , 2016, Current opinion in immunology.

[24]  Nathan P Croft,et al.  Quantifying epitope presentation using mass spectrometry. , 2015, Molecular immunology.

[25]  Alessandro Sette,et al.  An open-source computational and data resource to analyze digital maps of immunopeptidomes , 2015, eLife.

[26]  Brendan MacLean,et al.  CPTAC Assay Portal: a repository of targeted proteomic assays , 2014, Nature Methods.

[27]  P. Kloetzel,et al.  A large fraction of HLA class I ligands are proteasome-generated spliced peptides , 2016, Science.

[28]  Jennifer G. Abelin,et al.  Mass Spectrometry Profiling of HLA‐Associated Peptidomes in Mono‐allelic Cells Enables More Accurate Epitope Prediction , 2017, Immunity.

[29]  Matthias Mann,et al.  Origins of mass spectrometry-based proteomics , 2016, Nature Reviews Molecular Cell Biology.

[30]  Morten Nielsen,et al.  Different binding motifs of the celiac disease-associated HLA molecules DQ2.5, DQ2.2, and DQ7.5 revealed by relative quantitative proteomics of endogenous peptide repertoires , 2014, Immunogenetics.

[31]  Alessandro Sette,et al.  Deciphering the human antigenome , 2016, Expert review of vaccines.

[32]  Ash A. Alizadeh,et al.  Antigen Presentation Profiling Reveals Recognition of Lymphoma Immunoglobulin Neoantigens , 2017, Nature.

[33]  Hans-Georg Rammensee,et al.  HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL) , 2014, Proceedings of the National Academy of Sciences.

[34]  Ben C. Collins,et al.  OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data , 2014, Nature Biotechnology.

[35]  Sorin Istrail,et al.  Comparative immunopeptidomics of humans and their pathogens. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Ruedi Aebersold,et al.  A first dataset toward a standardized community-driven global mapping of the human immunopeptidome , 2016, Data in brief.

[37]  John Chilton,et al.  Using iRT, a normalized retention time for more targeted measurement of peptides , 2012, Proteomics.

[38]  H. Rammensee,et al.  Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules , 1991, Nature.

[39]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[40]  Jennifer G. Abelin,et al.  MHC Class I–Associated Phosphopeptides Are the Targets of Memory-like Immunity in Leukemia , 2013, Science Translational Medicine.

[41]  Ole Lund,et al.  Genome-Based In Silico Identification of New Mycobacterium tuberculosis Antigens Activating Polyfunctional CD8+ T Cells in Human Tuberculosis , 2011, The Journal of Immunology.

[42]  Albert J R Heck,et al.  Arginine (Di)methylated Human Leukocyte Antigen Class I Peptides Are Favorably Presented by HLA-B*07. , 2017, Journal of proteome research.

[43]  Bjoern Peters,et al.  Memory T Cells in Latent Mycobacterium tuberculosis Infection Are Directed against Three Antigenic Islands and Largely Contained in a CXCR3+CCR6+ Th1 Subset , 2013, PLoS pathogens.

[44]  M. Mann,et al.  Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry , 2016, Nature Communications.

[45]  Chris Sander,et al.  Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome , 2016, Cell.

[46]  Shabaz Mohammed,et al.  Sampling From the Proteome to the Human Leukocyte Antigen-DR (HLA-DR) Ligandome Proceeds Via High Specificity* , 2016, Molecular & Cellular Proteomics.

[47]  K. Rock,et al.  Present Yourself! By MHC Class I and MHC Class II Molecules. , 2016, Trends in immunology.

[48]  Ilka Hoof,et al.  Comprehensive Analysis of the Naturally Processed Peptide Repertoire: Differences between HLA-A and B in the Immunopeptidome , 2015, PloS one.

[49]  David Gfeller,et al.  Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide–HLA Interactions , 2016, The Journal of Immunology.

[50]  Eric W. Deutsch,et al.  The PeptideAtlas project , 2005, Nucleic Acids Res..

[51]  Brian A. Nosek,et al.  How open science helps researchers succeed , 2016, eLife.

[52]  R. Henderson,et al.  Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. , 1992, Science.

[53]  Lennart Martens,et al.  A Golden Age for Working with Public Proteomics Data , 2017, Trends in biochemical sciences.

[54]  Gary D Bader,et al.  The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. , 2013, Journal of proteome research.

[55]  Anne M Evans,et al.  Identification of class I MHC-associated phosphopeptides as targets for cancer immunotherapy , 2006, Proceedings of the National Academy of Sciences.

[56]  Ruedi Aebersold,et al.  Building consensus spectral libraries for peptide identification in proteomics , 2008, Nature Methods.