Title |
Mutational processes shape the landscape of TP53 mutations in human cancer
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Published in |
Nature Genetics, September 2018
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DOI | 10.1038/s41588-018-0204-y |
Pubmed ID | |
Authors |
Andrew O. Giacomelli, Xiaoping Yang, Robert E. Lintner, James M. McFarland, Marc Duby, Jaegil Kim, Thomas P. Howard, David Y. Takeda, Seav Huong Ly, Eejung Kim, Hugh S. Gannon, Brian Hurhula, Ted Sharpe, Amy Goodale, Briana Fritchman, Scott Steelman, Francisca Vazquez, Aviad Tsherniak, Andrew J. Aguirre, John G. Doench, Federica Piccioni, Charles W. M. Roberts, Matthew Meyerson, Gad Getz, Cory M. Johannessen, David E. Root, William C. Hahn |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 65 | 41% |
United Kingdom | 15 | 9% |
France | 7 | 4% |
Australia | 5 | 3% |
Austria | 3 | 2% |
Netherlands | 2 | 1% |
Germany | 2 | 1% |
Saudi Arabia | 2 | 1% |
Finland | 2 | 1% |
Other | 13 | 8% |
Unknown | 44 | 28% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 80 | 50% |
Members of the public | 74 | 46% |
Practitioners (doctors, other healthcare professionals) | 4 | 3% |
Science communicators (journalists, bloggers, editors) | 2 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 425 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 86 | 20% |
Researcher | 72 | 17% |
Student > Bachelor | 39 | 9% |
Student > Master | 30 | 7% |
Student > Doctoral Student | 20 | 5% |
Other | 53 | 12% |
Unknown | 125 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 129 | 30% |
Agricultural and Biological Sciences | 68 | 16% |
Medicine and Dentistry | 54 | 13% |
Computer Science | 10 | 2% |
Immunology and Microbiology | 7 | 2% |
Other | 30 | 7% |
Unknown | 127 | 30% |