Fundamental immune-oncogenicity trade-offs define driver mutation fitness.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
23
01
2021
accepted:
28
03
2022
pubmed:
12
5
2022
medline:
7
6
2022
entrez:
11
5
2022
Statut:
ppublish
Résumé
Missense driver mutations in cancer are concentrated in a few hotspots
Identifiants
pubmed: 35545680
doi: 10.1038/s41586-022-04696-z
pii: 10.1038/s41586-022-04696-z
pmc: PMC9159948
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
172-179Subventions
Organisme : NCI NIH HHS
ID : R01 CA240924
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI081848
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA227534
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA221745
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA228963
Pays : United States
Organisme : NCI NIH HHS
ID : K12 CA184746
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA087497
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA224175
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Martínez-Jiménez, F. et al. A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20, 555–572 (2020).
doi: 10.1038/s41568-020-0290-x
Giacomelli, A. O. et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50, 1381–1387 (2018).
doi: 10.1038/s41588-018-0204-y
Baugh, E. H., Ke, H., Levine, A. J., Bonneau, R. A. & Chan, C. S. Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death Differ. 25, 154–160 (2018).
doi: 10.1038/cdd.2017.180
Petitjean, A. et al. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database. Hum. Mutat. 28, 622–629 (2007).
doi: 10.1002/humu.20495
Kato, S. et al. Understanding the function–structure and function–mutation relationships of p53 tumor suppressor protein by high-resolution missense mutation analysis. Proc. Natl Acad. Sci. USA 100, 8424–8429 (2003).
doi: 10.1073/pnas.1431692100
Kotler, E. et al. A systematic p53 mutation library links differential functional impact to cancer mutation pattern and evolutionary conservation. Mol. Cell 71, 178–190 (2018).
doi: 10.1016/j.molcel.2018.06.012
Marty, R. et al. MHC-I genotype restricts the oncogenic mutational landscape. Cell 171, 1272–1283 (2017).
doi: 10.1016/j.cell.2017.09.050
Pyke, R. M. et al. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175, 416–428 (2018).
doi: 10.1016/j.cell.2018.08.048
Ding, J. et al. Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat. Commun. 6, 8554 (2015).
doi: 10.1038/ncomms9554
Huang, N., Shah, P. K. & Li, C. Lessons from a decade of integrating cancer copy number alterations with gene expression profiles. Brief. Bioinform. 13, 305–316 (2012).
doi: 10.1093/bib/bbr056
Fehrmann, R. S. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
doi: 10.1038/ng.3173
Köbel, M. et al. Optimized p53 immunohistochemistry is an accurate predictor of TP53 mutation in ovarian carcinoma. J. Pathol. Clin. Res. 2, 247–258 (2016).
doi: 10.1002/cjp2.53
Murnyák, B. & Hortobágyi, T. Immunohistochemical correlates of TP53 somatic mutations in cancer. Oncotarget 7, 64910 (2016).
doi: 10.18632/oncotarget.11912
Cole, A. J. et al. Assessing mutant p53 in primary high-grade serous ovarian cancer using immunohistochemistry and massively parallel sequencing. Sci. Rep. 6, 26191 (2016).
doi: 10.1038/srep26191
Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).
doi: 10.1056/NEJMoa1609279
Hsiue, E. H. et al. Targeting a neoantigen derived from a common TP53 mutation. Science 371, eabc8697 (2021).
doi: 10.1126/science.abc8697
Eigen, M. Selforganization of matter and the evolution of biological macromolecules. Naturwissenschaften 58, 465–523 (1971).
doi: 10.1007/BF00623322
Gerland, U. & Hwa, T. On the selection and evolution of regulatory DNA motifs. J. Mol. Evol. 55, 386–400 (2002).
doi: 10.1007/s00239-002-2335-z
Łuksza, M. & Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014).
doi: 10.1038/nature13087
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
doi: 10.1038/nature24462
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
doi: 10.1038/nature24473
Ma, L. et al. A plausible model for the digital response of p53 to DNA damage. Proc. Natl Acad. Sci. USA 102, 14266–14271 (2005).
doi: 10.1073/pnas.0501352102
Gaglia, G., Guan, Y., Shah, J. V. & Lahav, G. Activation and control of p53 tetramerization in individual living cells. Proc. Natl Acad. Sci. USA 110, 15497–15501 (2013).
doi: 10.1073/pnas.1311126110
Price, G. R. Fisher’s ‘fundamental theorem’ made clear. Ann. Hum. Genet. 36, 129–140 (1972).
doi: 10.1111/j.1469-1809.1972.tb00764.x
Hunter, J. C. et al. Biochemical and structural analysis of common cancer-associated KRAS mutations. Mol. Cancer Res. 13, 1325–1335 (2015).
doi: 10.1158/1541-7786.MCR-15-0203
Shoval, O. et al. Evolutionary trade-offs, pareto optimality, and the geometry of phenotype space. Science 336, 1157–1160 (2012).
doi: 10.1126/science.1217405
Pinheiro, F., Warsi, O., Andersson, D. I. & Lässig, M. Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nat. Ecol. Evol. 5, 677–687 (2021).
doi: 10.1038/s41559-021-01397-0
Kratz, C. P. et al. Analysis of the Li–Fraumeni spectrum based on an international germline TP53 variant data set: an International Agency for Research on Cancer TP53 database analysis. JAMA Oncol. 7, 1800–1805 (2021).
doi: 10.1001/jamaoncol.2021.4398
De Andrade, K. C. et al. Cancer incidence, patterns, and genotype–phenotype associations in individuals with pathogenic or likely pathogenic germline TP53 variants: an observational cohort study. Lancet Oncol. 22, 1787–1798 (2021).
doi: 10.1016/S1470-2045(21)00580-5
Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483–1489 (2015).
doi: 10.1126/science.aab4082
Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).
doi: 10.1038/s41586-021-03752-4
Bear, A. S. et al. Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting. Nat. Commun. 12, 4365 (2021).
doi: 10.1038/s41467-021-24562-2
Malekzadeh, P. et al. Antigen experienced T cells from peripheral blood recognize p53 neoantigens. Clin. Cancer Res. 26, 1267–1276 (2020).
doi: 10.1158/1078-0432.CCR-19-1874
Colom, B. et al. Mutant clones in normal epithelium outcompete and eliminate emerging tumours. Nature 598, 510–514 (2021).
doi: 10.1038/s41586-021-03965-7
Wylie, A. et al. p53 genes function to restrain mobile elements. Genes Dev. 30, 64–77 (2016).
doi: 10.1101/gad.266098.115
Levine, A. J., Ting, D. T. & Greenbaum, B. D. p53 and the defenses against genome instability caused by transposons and repetitive elements. Bioessays 38, 508–513 (2016).
doi: 10.1002/bies.201600031
McKerrow, W. et al. LINE-1 expression in cancer correlates with p53 mutation, copy number alteration, and S phase checkpoint. Proc. Natl Acad. Sci. USA 119, e2115999119 (2022).
doi: 10.1073/pnas.2115999119
Dayan, P., Hinton, G. E., Neal, R. M. & Zemel, R. S. The Helmholtz machine. Neural Comput. 7, 889–904 (1995).
doi: 10.1162/neco.1995.7.5.889