Title |
Phenotypic Characterization of a Comprehensive Set of MAPK1/ERK2 Missense Mutants
|
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Published in |
Cell Reports, October 2016
|
DOI | 10.1016/j.celrep.2016.09.061 |
Pubmed ID | |
Authors |
Lisa Brenan, Aleksandr Andreev, Ofir Cohen, Sasha Pantel, Atanas Kamburov, Davide Cacchiarelli, Nicole S. Persky, Cong Zhu, Mukta Bagul, Eva M. Goetz, Alex B. Burgin, Levi A. Garraway, Gad Getz, Tarjei S. Mikkelsen, Federica Piccioni, David E. Root, Cory M. Johannessen |
Abstract |
Tumor-specific genomic information has the potential to guide therapeutic strategies and revolutionize patient treatment. Currently, this approach is limited by an abundance of disease-associated mutants whose biological functions and impacts on therapeutic response are uncharacterized. To begin to address this limitation, we functionally characterized nearly all (99.84%) missense mutants of MAPK1/ERK2, an essential effector of oncogenic RAS and RAF. Using this approach, we discovered rare gain- and loss-of-function ERK2 mutants found in human tumors, revealing that, in the context of this assay, mutational frequency alone cannot identify all functionally impactful mutants. Gain-of-function ERK2 mutants induced variable responses to RAF-, MEK-, and ERK-directed therapies, providing a reference for future treatment decisions. Tumor-associated mutations spatially clustered in two ERK2 effector-recruitment domains yet produced mutants with opposite phenotypes. This approach articulates an allele-characterization framework that can be scaled to meet the goals of genome-guided oncology. |
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Geographical breakdown
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United States | 9 | 39% |
Switzerland | 3 | 13% |
France | 1 | 4% |
Belgium | 1 | 4% |
Canada | 1 | 4% |
Germany | 1 | 4% |
Unknown | 7 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 13 | 57% |
Members of the public | 9 | 39% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | <1% |
Sweden | 1 | <1% |
Australia | 1 | <1% |
Unknown | 150 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 46 | 30% |
Researcher | 42 | 27% |
Student > Master | 9 | 6% |
Student > Bachelor | 7 | 5% |
Other | 7 | 5% |
Other | 12 | 8% |
Unknown | 30 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 54 | 35% |
Agricultural and Biological Sciences | 29 | 19% |
Medicine and Dentistry | 10 | 7% |
Chemistry | 8 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 4 | 3% |
Other | 15 | 10% |
Unknown | 33 | 22% |