Fundamental immune–oncogenicity trade-offs define driver mutation fitness

Abstract

Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3–6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical ‘free fitness’ framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.

Immune evasion impacts the selective landscape of driver genes during tumorigenesis

“…Interestingly, a recent model explains the presence of hotspots events in driver genes in terms of a trade-off fitness advantage of oncogenicity versus immunogenicity, possibly confirming the differences we observe in the distribution of mutations between the escaped and non-escaped cohorts [15]. As we could expect from our findings with the distribution of mutations, the proportion of mutational signatures also varies between escape+ and escape-.…”

Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

“…Neoantigen-based vaccines showed limited clinical response in previous trials (5,53). This might have been due to poor candidate selection or because of a dysregulated T cell state in the treated patients.…”

Calreticulin mutant myeloproliferative neoplasms induce MHC-I skewing, which can be overcome by an optimized peptide cancer vaccine

“…It has long been postulated that the immune system shapes tumors by eliminating clones expressing immunogenic antigens in a process called immunoediting ( 39 – 41 ). We have also recently shown that there is a trade-off between tumorigenicity of a mutation and its immunogenicity ( 42 ). Clones that evade elimination would either enter a state of equilibrium with the immune system or manage to evade it completely, such that it manifests clinically.…”