A neoantigen fitness model predicts tumor response to checkpoint blockade immunotherapy

By M. Łuksza, N. Riaz, V. Makarov, V. Balachandran, M. Hellmann, Alexander Solovyov, N. Rizvi, T. Merghoub, Arnold J. Levine, Timothy A. Chan and 2 more. Nature, 2017

598 citations recorded by the source corpus.

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.

Subfields: Immunology, Oncology

View Paper: https://www.semanticscholar.org/paper/96e124ee2e492fe4ccf99e7bb06112aba183c906

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

1. Sandra Hervás-Stubbs
2. I. Melero
3. Charles Swanton
4. M. Hellmann
5. Nicholas McGranahan
6. J. Wolchok

In Oncology

1. Nicholas McGranahan
2. M. Hellmann
3. J. Wolchok
4. M. Callahan
5. I. Melero
6. Charles Swanton

Citations in the corpus

• Next generation of immune checkpoint therapy in cancer: new developments and challenges (2018, methodology): “A recently developed model using malignancy-specific neoantigens appears to predict tumor response to ICI therapy in patients with melanoma and lung cancer receiving anti-CTLA-4 and anti-PD-1 therapy, respectively [152].”
• Genomic features of response to combination immunotherapy in patients with advanced non-small cell lung cancer (2018, background): “, 2016), fitness (Luksza et al., 2017), and interactions with T cell receptor binding (Glanville et al.”
• Advances in immunotherapy for hepatocellular carcinoma (2021, background): “I.M. receives funding from MINECO SAF2014-52361-R and SAF 2017- 83267-C2-1R; Worldwide Cancer Research Grant (15-1146); the Asociacion Española Contra el Cancer (AECC) Foundation under grant GCB15152947MELE; and the European Union’s Horizon 2020 Program (grant agreement no. 635122 PROCROP).”