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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Systematic identification of genomic markers of drug sensitivity in cancer cells
|
|---|---|
| Published in |
Nature, March 2012
|
| DOI | 10.1038/nature11005 |
| Pubmed ID | |
| Authors |
Mathew J. Garnett, Elena J. Edelman, Sonja J. Heidorn, Chris D. Greenman, Anahita Dastur, King Wai Lau, Patricia Greninger, I. Richard Thompson, Xi Luo, Jorge Soares, Qingsong Liu, Francesco Iorio, Didier Surdez, Li Chen, Randy J. Milano, Graham R. Bignell, Ah T. Tam, Helen Davies, Jesse A. Stevenson, Syd Barthorpe, Stephen R. Lutz, Fiona Kogera, Karl Lawrence, Anne McLaren-Douglas, Xeni Mitropoulos, Tatiana Mironenko, Helen Thi, Laura Richardson, Wenjun Zhou, Frances Jewitt, Tinghu Zhang, Patrick O’Brien, Jessica L. Boisvert, Stacey Price, Wooyoung Hur, Wanjuan Yang, Xianming Deng, Adam Butler, Hwan Geun Choi, Jae Won Chang, Jose Baselga, Ivan Stamenkovic, Jeffrey A. Engelman, Sreenath V. Sharma, Olivier Delattre, Julio Saez-Rodriguez, Nathanael S. Gray, Jeffrey Settleman, P. Andrew Futreal, Daniel A. Haber, Michael R. Stratton, Sridhar Ramaswamy, Ultan McDermott, Cyril H. Benes |
| Abstract |
Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies. |
X Demographics
The data shown below were collected from the profiles of 38 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 39% |
| United Kingdom | 3 | 8% |
| Germany | 3 | 8% |
| Netherlands | 2 | 5% |
| Australia | 2 | 5% |
| Japan | 2 | 5% |
| Belgium | 1 | 3% |
| Comoros | 1 | 3% |
| Denmark | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 6 | 16% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 55% |
| Scientists | 15 | 39% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 2,316 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 58 | 3% |
| United Kingdom | 25 | 1% |
| Germany | 9 | <1% |
| Spain | 9 | <1% |
| Japan | 8 | <1% |
| Denmark | 5 | <1% |
| China | 5 | <1% |
| Netherlands | 4 | <1% |
| South Africa | 3 | <1% |
| Other | 31 | 1% |
| Unknown | 2159 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 619 | 27% |
| Student > Ph. D. Student | 568 | 25% |
| Student > Master | 203 | 9% |
| Student > Bachelor | 158 | 7% |
| Professor > Associate Professor | 111 | 5% |
| Other | 380 | 16% |
| Unknown | 277 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 815 | 35% |
| Biochemistry, Genetics and Molecular Biology | 484 | 21% |
| Medicine and Dentistry | 275 | 12% |
| Computer Science | 117 | 5% |
| Chemistry | 80 | 3% |
| Other | 229 | 10% |
| Unknown | 316 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 124. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 March 2024.
All research outputs
#343,504
of 26,017,215 outputs
Outputs from Nature
#17,895
of 99,074 outputs
Outputs of similar age
#1,440
of 176,693 outputs
Outputs of similar age from Nature
#150
of 1,038 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 99,074 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 102.3. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 176,693 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 1,038 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.