A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy

Marta Luksza, Nadeem Riaz, Vladimir Makarov, Vinod P. Balachandran, Matthew D. Hellmann, Alexander Solovyov, Naiyer A. Rizvi, Taha Merghoub, Arnold J. Levine, Timothy A. Chan, Jedd D. Wolchok, Benjamin D. Greenbaum

Research output: Contribution to journalArticlepeer-review

451 Scopus citations

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.

Original languageEnglish
Pages (from-to)517-520
Number of pages4
JournalNature
Volume551
Issue number7681
DOIs
StatePublished - 23 Nov 2017

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