A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-11

AUTHORS

Marta Łuksza, 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

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. More... »

PAGES

517

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nature24473

DOI

http://dx.doi.org/10.1038/nature24473

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092574574

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29132144


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