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Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction

Nicholas Liao

Cell. 2020 Oct 29;183(3):818-834.e13. doi: 10.1016/j.cell.2020.09.015. | PubMed

Daniel K Wells1, Marit M van Buuren2, Kristen K Dang3, Vanessa M Hubbard-Lucey4, Kathleen C F Sheehan5, Katie M Campbell6, Andrew Lamb3, Jeffrey P Ward7, John Sidney8, Ana B Blazquez9, Andrew J Rech10, Jesse M Zaretsky6, Begonya Comin-Anduix11, Alphonsus H C Ng12, William Chour13, Thomas V Yu3, Hira Rizvi14, Jia M Chen6, Patrice Manning15, Gabriela M Steiner15, Xengie C Doan3, Tumor Neoantigen Selection Alliance, Taha Merghoub16, Justin Guinney17, Adam Kolom18, Cheryl Selinsky15, Antoni Ribas19, Matthew D Hellmann20, Nir Hacohen21, Alessandro Sette22, James R Heath23, Nina Bhardwaj24, Fred Ramsdell15, Robert D Schreiber25, Ton N Schumacher26, Pia Kvistborg27, Nadine A Defranoux28

  1. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Electronic address: dwells@parkerici.org.
  2. Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; T Cell Immunology, Biopharmaceutical New Technologies (BioNTech) Corporation, BioNTech US, Cambridge, MA, USA.
  3. Computational Oncology, Sage Bionetworks, Seattle, WA, USA.
  4. Anna-Maria Kellen Clinical Accelerator, Cancer Research Institute, New York, NY, USA.
  5. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, St. Louis, MO, USA; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, MO, USA.
  6. Division of Hematology and Oncology, Department of Medicine, Johnson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  7. Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
  8. Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.
  9. Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  10. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
  11. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Department of Surgery, David Geffen School of Medicine, Johnson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
  12. Institute for Systems Biology, Seattle, WA, USA.
  13. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
  14. Druckenmiller Center for Lung Cancer Research, MSKCC, New York, NY, USA.
  15. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
  16. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Department of Medicine, MSKCC, New York, NY, USA; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
  17. Computational Oncology, Sage Bionetworks, Seattle, WA, USA; Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
  18. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Anna-Maria Kellen Clinical Accelerator, Cancer Research Institute, New York, NY, USA.
  19. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Division of Hematology and Oncology, Department of Medicine, Johnson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
  20. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Druckenmiller Center for Lung Cancer Research, MSKCC, New York, NY, USA; Department of Medicine, MSKCC, New York, NY, USA; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
  21. Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts General Hospital Cancer Center, Boston, MA, USA.
  22. Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
  23. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Institute for Systems Biology, Seattle, WA, USA.
  24. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  25. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, St. Louis, MO, USA; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, MO, USA.
  26. Division of Molecular Oncology and Immunology, Oncode Institute, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
  27. Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
  28. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA. Electronic address: ndefranoux@parkerici.org.

Abstract

Many 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.

Presented By Daniel Wells | ORCID iD