1
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Budczies J, Kazdal D, Menzel M, Beck S, Kluck K, Altbürger C, Schwab C, Allgäuer M, Ahadova A, Kloor M, Schirmacher P, Peters S, Krämer A, Christopoulos P, Stenzinger A. Tumour mutational burden: clinical utility, challenges and emerging improvements. Nat Rev Clin Oncol 2024; 21:725-742. [PMID: 39192001 DOI: 10.1038/s41571-024-00932-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2024] [Indexed: 08/29/2024]
Abstract
Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.
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Affiliation(s)
- Jan Budczies
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
- Center for Personalized Medicine (ZPM), Heidelberg, Germany.
| | - Daniel Kazdal
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Michael Menzel
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Susanne Beck
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Klaus Kluck
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Christian Altbürger
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Constantin Schwab
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Michael Allgäuer
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Aysel Ahadova
- Department of Applied Tumour Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumour Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias Kloor
- Department of Applied Tumour Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Applied Tumour Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Center for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Solange Peters
- Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University, Lausanne, Switzerland
| | - Alwin Krämer
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany
| | - Petros Christopoulos
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Thoracic Oncology, Thoraxklinik and National Center for Tumour Diseases at Heidelberg University Hospital, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
- Center for Personalized Medicine (ZPM), Heidelberg, Germany.
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2
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Huang X, Gan Z, Cui H, Lan T, Liu Y, Caron E, Shao W. The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics. Nucleic Acids Res 2024; 52:D1062-D1071. [PMID: 38000392 PMCID: PMC10767952 DOI: 10.1093/nar/gkad1068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/16/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.
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Affiliation(s)
- Xiaoxiang Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziao Gan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haowei Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian Lan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Etienne Caron
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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3
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Schrader M. Origins, Technological Advancement, and Applications of Peptidomics. Methods Mol Biol 2024; 2758:3-47. [PMID: 38549006 DOI: 10.1007/978-1-0716-3646-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.
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Affiliation(s)
- Michael Schrader
- Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany.
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4
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Yarmarkovich M, Marshall QF, Warrington JM, Premaratne R, Farrel A, Groff D, Li W, di Marco M, Runbeck E, Truong H, Toor JS, Tripathi S, Nguyen S, Shen H, Noel T, Church NL, Weiner A, Kendsersky N, Martinez D, Weisberg R, Christie M, Eisenlohr L, Bosse KR, Dimitrov DS, Stevanovic S, Sgourakis NG, Kiefel BR, Maris JM. Targeting of intracellular oncoproteins with peptide-centric CARs. Nature 2023; 623:820-827. [PMID: 37938771 PMCID: PMC10665195 DOI: 10.1038/s41586-023-06706-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/03/2023] [Indexed: 11/09/2023]
Abstract
The majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.
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Affiliation(s)
- Mark Yarmarkovich
- Perlmutter Cancer Center, New York University Grossman School of Medicine, New York, NY, USA.
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Quinlen F Marshall
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John M Warrington
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Alvin Farrel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David Groff
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei Li
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Erin Runbeck
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hau Truong
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jugmohit S Toor
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Sarvind Tripathi
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Son Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Helena Shen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tiffany Noel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Amber Weiner
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nathan Kendsersky
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dan Martinez
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rebecca Weisberg
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Molly Christie
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Laurence Eisenlohr
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kristopher R Bosse
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Nikolaos G Sgourakis
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - John M Maris
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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5
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Munday PR, Fehring J, Revote J, Pandey K, Shahbazy M, Scull KE, Ramarathinam SH, Faridi P, Croft NP, Braun A, Li C, Purcell AW. Immunolyser: A web-based computational pipeline for analysing and mining immunopeptidomic data. Comput Struct Biotechnol J 2023; 21:1678-1687. [PMID: 36890882 PMCID: PMC9988424 DOI: 10.1016/j.csbj.2023.02.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 02/01/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
Immunopeptidomics has made tremendous contributions to our understanding of antigen processing and presentation, by identifying and quantifying antigenic peptides presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. Large and complex immunopeptidomics datasets can now be routinely generated using Liquid Chromatography-Mass Spectrometry techniques. The analysis of this data - often consisting of multiple replicates/conditions - rarely follows a standard data processing pipeline, hindering the reproducibility and depth of analysis of immunopeptidomic data. Here, we present Immunolyser, an automated pipeline designed to facilitate computational analysis of immunopeptidomic data with a minimal initial setup. Immunolyser brings together routine analyses, including peptide length distribution, peptide motif analysis, sequence clustering, peptide-MHC binding affinity prediction, and source protein analysis. Immunolyser provides a user-friendly and interactive interface via its webserver and is freely available for academic purposes at https://immunolyser.erc.monash.edu/. The open-access source code can be downloaded at our GitHub repository: https://github.com/prmunday/Immunolyser. We anticipate that Immunolyser will serve as a prominent computational pipeline to facilitate effortless and reproducible analysis of immunopeptidomic data.
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Affiliation(s)
- Prithvi Raj Munday
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Joshua Fehring
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Jerico Revote
- Monash eResearch Centre, Monash University, Melbourne Clayton, VIC, 3800, Australia
| | - Kirti Pandey
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Mohammad Shahbazy
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Katherine E. Scull
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Sri H. Ramarathinam
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Pouya Faridi
- Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Nathan P. Croft
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Asolina Braun
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Chen Li
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Anthony W. Purcell
- Department of Biochemistry and Molecular Biology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
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6
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Johnson AC, Silva JAF, Kim SC, Larsen CP. Progress in kidney transplantation: The role for systems immunology. Front Med (Lausanne) 2022; 9:1070385. [PMID: 36590970 PMCID: PMC9800623 DOI: 10.3389/fmed.2022.1070385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/16/2022] [Indexed: 12/23/2022] Open
Abstract
The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.
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7
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Nagel R, Pataskar A, Champagne J, Agami R. Boosting Antitumor Immunity with an Expanded Neoepitope Landscape. Cancer Res 2022; 82:3637-3649. [PMID: 35904353 PMCID: PMC9574376 DOI: 10.1158/0008-5472.can-22-1525] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/07/2022] [Accepted: 07/21/2022] [Indexed: 01/07/2023]
Abstract
Immune-checkpoint blockade therapy has been successfully applied to many cancers, particularly tumors that harbor a high mutational burden and consequently express a high abundance of neoantigens. However, novel approaches are needed to improve the efficacy of immunotherapy for treating tumors that lack a high load of classic genetically derived neoantigens. Recent discoveries of broad classes of nongenetically encoded and inducible neoepitopes open up new avenues for therapeutic development to enhance sensitivity to immunotherapies. In this review, we discuss recent work on neoantigen discovery, with an emphasis on novel classes of noncanonical neoepitopes.
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Affiliation(s)
- Remco Nagel
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Abhijeet Pataskar
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Julien Champagne
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Reuven Agami
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Erasmus MC, Rotterdam University, Rotterdam, the Netherlands
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8
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Kovalchik KA, Ma Q, Wessling L, Saab F, Despault J, Kubiniok P, Hamelin DJ, Faridi P, Li C, Purcell AW, Jang A, Paramithiotis E, Tognetti M, Reiter L, Bruderer R, Lanoix J, Bonneil É, Courcelles M, Thibault P, Caron E, Sirois I. MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets. Mol Cell Proteomics 2021; 21:100178. [PMID: 34798331 PMCID: PMC8717601 DOI: 10.1016/j.mcpro.2021.100178] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.
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Affiliation(s)
| | - Qing Ma
- School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, ON K1N 6N5, Canada
| | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Frederic Saab
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Jérôme Despault
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - David J Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Pouya Faridi
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Chen Li
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anthony W Purcell
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anne Jang
- CellCarta, Montreal, QC H2X 3Y7, Canada
| | | | | | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | | | - Joël Lanoix
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Éric Bonneil
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Mathieu Courcelles
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Pierre Thibault
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada; Department of Chemistry, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada.
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada.
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9
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Yarmarkovich M, Marshall QF, Warrington JM, Premaratne R, Farrel A, Groff D, Li W, di Marco M, Runbeck E, Truong H, Toor JS, Tripathi S, Nguyen S, Shen H, Noel T, Church NL, Weiner A, Kendsersky N, Martinez D, Weisberg R, Christie M, Eisenlohr L, Bosse KR, Dimitrov DS, Stevanovic S, Sgourakis NG, Kiefel BR, Maris JM. Cross-HLA targeting of intracellular oncoproteins with peptide-centric CARs. Nature 2021; 599:477-484. [PMID: 34732890 PMCID: PMC8599005 DOI: 10.1038/s41586-021-04061-6] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 09/23/2021] [Indexed: 12/27/2022]
Abstract
The majority of oncogenic drivers are intracellular proteins, thus constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient to generate responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins that are essential for tumourigenesis and focus on targeting the unmutated peptide QYNPIRTTF, discovered on HLA-A*24:02, which is derived from the neuroblastoma dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (CARs) using a counter-panning strategy with predicted potentially cross-reactive peptides. We further hypothesized that peptide-centric CARs could recognize peptides on additional HLA allotypes when presented in a similar manner. Informed by computational modelling, we showed that PHOX2B peptide-centric CARs also recognize QYNPIRTTF presented by HLA-A*23:01 and the highly divergent HLA-B*14:02. Finally, we demonstrated potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that peptide-centric CARs have the potential to vastly expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and widen the population of patients who would benefit from such therapy by breaking conventional HLA restriction.
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Affiliation(s)
- Mark Yarmarkovich
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Quinlen F Marshall
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John M Warrington
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Alvin Farrel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David Groff
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei Li
- University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Erin Runbeck
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hau Truong
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jugmohit S Toor
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Sarvind Tripathi
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Son Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Helena Shen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tiffany Noel
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Amber Weiner
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nathan Kendsersky
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dan Martinez
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rebecca Weisberg
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Molly Christie
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Laurence Eisenlohr
- Department of Pathology and Lab Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kristopher R Bosse
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Nikolaos G Sgourakis
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - John M Maris
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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10
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Wen B, Zeng W, Liao Y, Shi Z, Savage SR, Jiang W, Zhang B. Deep Learning in Proteomics. Proteomics 2020; 20:e1900335. [PMID: 32939979 PMCID: PMC7757195 DOI: 10.1002/pmic.201900335] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/14/2020] [Indexed: 12/17/2022]
Abstract
Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen‐Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Chinese Academy of SciencesInstitute of Computing TechnologyBeijing100190China
| | - Yuxing Liao
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Zhiao Shi
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Sara R. Savage
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen Jiang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Bing Zhang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
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11
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Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process. Cancers (Basel) 2020; 12:cancers12061660. [PMID: 32585818 PMCID: PMC7352969 DOI: 10.3390/cancers12061660] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/11/2020] [Accepted: 06/20/2020] [Indexed: 12/14/2022] Open
Abstract
According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.
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