Dataset Information

Mutational processes shape the landscape of TP53 mutations in human cancer.

ABSTRACT: Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3-8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

SUBMITTER: Giacomelli AO

PROVIDER: S-EPMC6168352 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Mutational processes shape the landscape of TP53 mutations in human cancer.

Giacomelli Andrew O AO Yang Xiaoping X Lintner Robert E RE McFarland James M JM Duby Marc M Kim Jaegil J Howard Thomas P TP Takeda David Y DY Ly Seav Huong SH Kim Eejung E Gannon Hugh S HS Hurhula Brian B Sharpe Ted T Goodale Amy A Fritchman Briana B Steelman Scott S Vazquez Francisca F Tsherniak Aviad A Aguirre Andrew J AJ Doench John G JG Piccioni Federica F Roberts Charles W M CWM Meyerson Matthew M Getz Gad G Johannessen Cory M CM Root David E DE Hahn William C WC

Nature genetics 20180917 10

Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations<sup>1,2</sup>. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models<sup>3-8</sup>. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis sc ...[more]

PMID: 30224644

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Project description:Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

Dataset Information

Mutational processes shape the landscape of TP53 mutations in human cancer.

Publications

Mutational processes shape the landscape of TP53 mutations in human cancer.

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