Florian Huber, Marion Arnaud, Brian J. Stevenson, Justine Michaux, Fabrizio Benedetti, Jonathan Thevenet, Sara Bobisse, Johanna Chiffelle, Talita Gehert, Markus Müller, HuiSong Pak, Anne I. Krämer, Emma Ricart Altimiras, Julien Racle, Marie Taillandier-Coindard, Katja Muehlethaler, Aymeric Auger, Damien Saugy, Baptiste Murgues, Abdelkader Benyagoub, David Gfeller, Denarda Dangaj Laniti, Lana Kandalaft, Blanca Navarro Rodrigo, Hasna Bouchaab, Stephanie Tissot, George Coukos, Alexandre Harari, Michal Bassani-Sternberg
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引用次数: 0
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.
期刊介绍:
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