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- Daniel K Wells, Marit M van Buuren, Kristen K Dang, Vanessa M Hubbard-Lucey, Kathleen C F Sheehan, Katie M Campbell, Andrew Lamb, Jeffrey P Ward, John Sidney, Ana B Blazquez, Andrew J Rech, Jesse M Zaretsky, Begonya Comin-Anduix, Alphonsus H C Ng, William Chour, Thomas V Yu, Hira Rizvi, Jia M Chen, Patrice Manning, Gabriela M Steiner, Xengie C Doan, Tumor Neoantigen Selection Alliance, Taha Merghoub, Justin Guinney, Adam Kolom, Cheryl Selinsky, Antoni Ribas, Matthew D Hellmann, Nir Hacohen, Alessandro Sette, James R Heath, Nina Bhardwaj, Fred Ramsdell, Robert D Schreiber, Ton N Schumacher, Pia Kvistborg, and Nadine A Defranoux.
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Electronic address: dwells@parkerici.org.
- Cell. 2020 Oct 29; 183 (3): 818-834.e13.
AbstractMany approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.Copyright © 2020 Elsevier Inc. All rights reserved.
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