Title |
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies
|
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Published in |
Cancer Cell, July 2017
|
DOI | 10.1016/j.ccell.2017.06.010 |
Pubmed ID | |
Authors |
Marco Mina, Franck Raynaud, Daniele Tavernari, Elena Battistello, Stephanie Sungalee, Sadegh Saghafinia, Titouan Laessle, Francisco Sanchez-Vega, Nikolaus Schultz, Elisa Oricchio, Giovanni Ciriello |
Abstract |
Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response. |
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Geographical breakdown
Country | Count | As % |
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United States | 25 | 23% |
France | 7 | 6% |
Canada | 4 | 4% |
Australia | 4 | 4% |
Saudi Arabia | 4 | 4% |
Germany | 3 | 3% |
India | 3 | 3% |
Spain | 3 | 3% |
China | 2 | 2% |
Other | 17 | 16% |
Unknown | 37 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 51 | 47% |
Scientists | 50 | 46% |
Practitioners (doctors, other healthcare professionals) | 5 | 5% |
Science communicators (journalists, bloggers, editors) | 3 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 329 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 91 | 28% |
Student > Ph. D. Student | 67 | 20% |
Student > Master | 25 | 8% |
Other | 20 | 6% |
Student > Doctoral Student | 16 | 5% |
Other | 49 | 15% |
Unknown | 61 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 102 | 31% |
Agricultural and Biological Sciences | 61 | 19% |
Medicine and Dentistry | 39 | 12% |
Computer Science | 13 | 4% |
Immunology and Microbiology | 12 | 4% |
Other | 32 | 10% |
Unknown | 70 | 21% |