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A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy.

Authors
  • Łuksza, Marta1
  • Riaz, Nadeem2, 3
  • Makarov, Vladimir3, 4
  • Balachandran, Vinod P5, 6, 7
  • Hellmann, Matthew D7, 8, 9
  • Solovyov, Alexander10, 11, 12, 13
  • Rizvi, Naiyer A14
  • Merghoub, Taha7, 15, 16
  • Levine, Arnold J1
  • Chan, Timothy A2, 3, 4, 7
  • Wolchok, Jedd D7, 8, 15, 16
  • Greenbaum, Benjamin D10, 11, 12, 13
  • 1 The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, USA. , (Jersey)
  • 2 Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 3 Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 4 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 5 Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 6 David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 7 Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 8 Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 9 Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, USA.
  • 10 Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • 11 Department of Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • 12 Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • 13 Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • 14 Department of Medicine, Columbia University Medical Center, New York, New York, USA.
  • 15 Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • 16 Melanoma and Immunotherapeutics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Type
Published Article
Journal
Nature
Publisher
Springer Nature
Publication Date
Nov 23, 2017
Volume
551
Issue
7681
Pages
517–520
Identifiers
DOI: 10.1038/nature24473
PMID: 29132144
Source
Medline
License
Unknown

Abstract

Checkpoint blockade immunotherapies enable the host immune system to recognize and destroy tumour cells. Their clinical activity has been correlated with activated T-cell recognition of neoantigens, which are tumour-specific, mutated peptides presented on the surface of cancer cells. Here we present a fitness model for tumours based on immune interactions of neoantigens that predicts response to immunotherapy. Two main factors determine neoantigen fitness: the likelihood of neoantigen presentation by the major histocompatibility complex (MHC) and subsequent recognition by T cells. We estimate these components using the relative MHC binding affinity of each neoantigen to its wild type and a nonlinear dependence on sequence similarity of neoantigens to known antigens. To describe the evolution of a heterogeneous tumour, we evaluate its fitness as a weighted effect of dominant neoantigens in the subclones of the tumour. Our model predicts survival in anti-CTLA-4-treated patients with melanoma and anti-PD-1-treated patients with lung cancer. Importantly, low-fitness neoantigens identified by our method may be leveraged for developing novel immunotherapies. By using an immune fitness model to study immunotherapy, we reveal broad similarities between the evolution of tumours and rapidly evolving pathogens.

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