A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

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Publication Details

Journal
Nature Biotechnology
Published
2024-10-11
DOI
https://doi.org/10.1038/s41587-024-02420-y
Citations
69
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
16.13
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article

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

Jonathan Thévenet, Fabrizio Benedetti, Lana E. Kandalaft, Katja Muehlethaler et al.
69 citations
Nature Biotechnology
vaccines and immunoinformatics approaches
16.13
article

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy

Jonathan Thévenet, Fabrizio Benedetti, Lana E. Kandalaft, Katja Muehlethaler, David Gfeller, George Coukos, Marion Arnaud, Emma Ricart Altimiras, Justine Michaux, Johanna Chiffelle, Alexandre Harari, Hasna Bouchaab, Marie Taillandier-Coindard, Julien Racle, Damien Saugy, Sara Bobisse, Abdelkader Benyagoub, Denarda Dangaj Laniti, Brian J. Stevenson, HuiSong Pak, Anne I. Krämer, Michal Bassani‐Sternberg, Aymeric Auger, Florian Huber, Baptiste Murgues, Markus Müller, Blanca Navarro Rodrigo, Talita Gehert, Stéphanie Tissot
article en
69 citations

Abstract

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Nature BiotechnologyVol. 43(8)
SIB Swiss Institute of Bioinformatics (CH), Centre Hospitalier Universitaire Vaudois (CH), Ludwig Cancer Research (CH), Swiss Cancer Center Léman (CH), University of Lausanne (CH)
Good health and well-being
Openalex Percentile: Top 0%
vaccines and immunoinformatics approaches
16.13
DOIOpen Access PDFOpenAlex
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