Fundamental immune-oncogenicity trade-offs define driver mutation fitness. - OpacWWW

Tytuł:: Fundamental immune-oncogenicity trade-offs define driver mutation fitness.
Autorzy:: Hoyos D; Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Zappasodi R; Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .; Department of Medicine, Weill Cornell Medical College, New York, NY, USA. .; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .; Immunology and Microbial Pathogenesis Program, Weill Cornell Graduate School of Medical Sciences, New York, NY, USA. .
Schulze I; Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Sethna Z; Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
de Andrade KC; Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Bajorin DF; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Bandlamudi C; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Callahan MK; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Funt SA; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Hadrup SR; Experimental and Translational Immunology, Health Technology, Technical University of Denmark, Lyngby, Denmark.
Holm JS; Experimental and Translational Immunology, Health Technology, Technical University of Denmark, Lyngby, Denmark.
Rosenberg JE; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Shah SP; Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA.
Vázquez-García I; Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Weigelt B; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Wu M; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Zamarin D; Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Campitelli LF; Adaptive Biotechnologies, Seattle, WA, USA.
Osborne EJ; Adaptive Biotechnologies, Seattle, WA, USA.
Klinger M; Adaptive Biotechnologies, Seattle, WA, USA.
Robins HS; Adaptive Biotechnologies, Seattle, WA, USA.
Khincha PP; Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Savage SA; Division of Cancer Epidemiology and Genetics, Clinical Genetics Branch, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
Balachandran VP; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Wolchok JD; Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Hellmann MD; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Merghoub T; Swim Across America Laboratory and Ludwig Collaborative, Immunology Program, Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .; Department of Medicine, Weill Cornell Medical College, New York, NY, USA. .; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .
Levine AJ; Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA.
Łuksza M; Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Greenbaum BD; Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .; Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY, USA. .
Źródło:: Nature [Nature] 2022 Jun; Vol. 606 (7912), pp. 172-179. Date of Electronic Publication: 2022 May 11.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.
MeSH Terms:: Carcinogenesis*/genetics
Carcinogenesis*/immunology
Evolution, Molecular*
Lung Neoplasms*/genetics
Lung Neoplasms*/therapy
Mutation*/genetics
Datasets as Topic ; Genes, p53 ; Genetic Fitness ; Genomics ; Healthy Volunteers ; Humans ; Immunotherapy ; Mutation, Missense ; Reproducibility of Results
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Grant Information:: R01 CA240924 United States CA NCI NIH HHS; R01 AI081848 United States AI NIAID NIH HHS; R01 CA227534 United States CA NCI NIH HHS; P30 CA008748 United States CA NCI NIH HHS; P50 CA221745 United States CA NCI NIH HHS; U01 CA228963 United States CA NCI NIH HHS; K12 CA184746 United States CA NCI NIH HHS; P01 CA087497 United States CA NCI NIH HHS; U01 CA224175 United States CA NCI NIH HHS
Entry Date(s):: Date Created: 20220511 Date Completed: 20220603 Latest Revision: 20230921
Update Code:: 20240105
PubMed Central ID:: PMC9159948
DOI:: 10.1038/s41586-022-04696-z
PMID:: 35545680
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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.
(© 2022. The Author(s).)

Erratum in: Nature. 2022 May 31;:. (PMID: 35641605)

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Fundamental immune-oncogenicity trade-offs define driver mutation fitness.