Showing papers by "Arnold J. Levine published in 2022"

Fundamental immune–oncogenicity trade-offs define driver mutation fitness

Abstract: Abstract Missense driver mutations in cancer are concentrated in a few hotspots 1 . Various mechanisms have been proposed to explain this skew, including biased mutational processes 2 , phenotypic differences 3–6 and immunoediting of neoantigens 7,8 ; 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 cancer 1 , 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.

Targeting the P53 Protein for Cancer Therapies: The Translational Impact of P53 Research

Abstract: It is only recently that drugs targeting K-RAS and Tp53 missense mutations have been developed, and along with the allele specific nature of some of these drugs comes the possibility of combining them with the immunologic therapies for cancers. It has taken about 40 years since their discoveries to understand the pathways they command, how they function, and how they interact with the environment of the cells they control. This communication focuses on the transfer of some of the hard won information about the p53 protein, its mutations, structures, and activities learned in the basic science laboratory and translated to the clinic.

Fundamental immune–oncogenicity trade-offs define driver mutation fitness

Abstract: Abstract Missense driver mutations in cancer are concentrated in a few hotspots 1 . Various mechanisms have been proposed to explain this skew, including biased mutational processes 2 , phenotypic differences 3–6 and immunoediting of neoantigens 7,8 ; 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 cancer 1 , 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.

The genotypes and phenotypes of missense mutations in the proline domain of the p53 protein

Abstract: The p53 protein is structurally and functionally divided into five domains. The proline-rich domain is localized at amino acids 55-100. 319 missense mutations were identified solely in the proline domain from human cancers. Six hotspot mutations were identified at amino acids 72, 73, 82, 84, 89, and 98. Codon 72 contains a polymorphism that changes from proline (and African descent) to arginine (with Caucasian descent) with increasing latitudes northward and is under natural selection for pigmentation and protection from UV light exposure. Cancers associated with mutations in the proline domain were considerably enriched for melanomas and skin cancers compared to mutations in other p53 domains. These hotspot mutations are enriched at UV mutational signatures disrupting amino acid signals for binding SH-3-containing proteins important for p53 function. Among the protein-protein interaction sites identified by hotspot mutations were MDM-2, a negative regulator of p53, XAF-1, promoting p53 mediated apoptosis, and PIN-1, a proline isomerase essential for structural folding of this domain.