You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer
|
|---|---|
| Published in |
Cell, October 2017
|
| DOI | 10.1016/j.cell.2017.09.007 |
| Pubmed ID | |
| Authors |
A. Gordon Robertson, Jaegil Kim, Hikmat Al-Ahmadie, Joaquim Bellmunt, Guangwu Guo, Andrew D. Cherniack, Toshinori Hinoue, Peter W. Laird, Katherine A. Hoadley, Rehan Akbani, Mauro A.A. Castro, Ewan A. Gibb, Rupa S. Kanchi, Dmitry A. Gordenin, Sachet A. Shukla, Francisco Sanchez-Vega, Donna E. Hansel, Bogdan A. Czerniak, Victor E. Reuter, Xiaoping Su, Benilton de Sa Carvalho, Vinicius S. Chagas, Karen L. Mungall, Sara Sadeghi, Chandra Sekhar Pedamallu, Yiling Lu, Leszek J. Klimczak, Jiexin Zhang, Caleb Choo, Akinyemi I. Ojesina, Susan Bullman, Kristen M. Leraas, Tara M. Lichtenberg, Catherine J. Wu, Nicholaus Schultz, Gad Getz, Matthew Meyerson, Gordon B. Mills, David J. McConkey, TCGA Research Network, Rehan Akbani, Hikmat Al-Ahmadie, Monique Albert, Iakovina Alexopoulou, Adrian Ally, Tatjana Antic, Manju Aron, Miruna Balasundaram, John Bartlett, Stephen B. Baylin, Allison Beaver, Joaquim Bellmunt, Inanc Birol, Lori Boice, Moiz S. Bootwalla, Jay Bowen, Reanne Bowlby, Denise Brooks, Bradley M. Broom, Wiam Bshara, Susan Bullman, Eric Burks, Flavio M. Cárcano, Rebecca Carlsen, Benilton S. Carvalho, Andre L. Carvalho, Eric P. Castle, Mauro A.A. Castro, Patricia Castro, James W. Catto, Vinicius S. Chagas, Andrew D. Cherniack, David W. Chesla, Caleb Choo, Eric Chuah, Sudha Chudamani, Victoria K. Cortessis, Sandra L. Cottingham, Daniel Crain, Erin Curley, Bogdan A. Czerniak, Siamak Daneshmand, John A. Demchok, Noreen Dhalla, Hooman Djaladat, John Eckman, Sophie C. Egea, Jay Engel, Ina Felau, Martin L. Ferguson, Johanna Gardner, Julie M. Gastier-Foster, Mark Gerken, Gad Getz, Ewan A. Gibb, Carmen R. Gomez-Fernandez, Dmitry A. Gordenin, Guangwu Guo, Donna E. Hansel, Jodi Harr, Arndt Hartmann, Lynn M. Herbert, Toshinori Hinoue, Thai H. Ho, Katherine A. Hoadley, Robert A. Holt, Carolyn M. Hutter, Steven J.M. Jones, Merce Jorda, Richard J. Kahnoski, Rupa S. Kanchi, Katayoon Kasaian, Jaegil Kim, Leszek J. Klimczak, David J. Kwiatkowski, Phillip H. Lai, Peter W. Laird, Brian R. Lane, Kristen M. Leraas, Seth P. Lerner, Tara M. Lichtenberg, Jia Liu, Laxmi Lolla, Yair Lotan, Yiling Lu, Fabiano R. Lucchesi, Yussanne Ma, Roberto D. Machado, Dennis T. Maglinte, David Mallery, Marco A. Marra, Sue E. Martin, Michael Mayo, David J. McConkey, Anoop Meraney, Matthew Meyerson, Gordon B. Mills, Alireza Moinzadeh, Richard A. Moore, Edna M. Mora Pinero, Scott Morris, Carl Morrison, Karen L. Mungall, Andrew J. Mungall, Jerome B. Myers, Rashi Naresh, Peter H. O'Donnell, Akinyemi I. Ojesina, Dipen J. Parekh, Jeremy Parfitt, Joseph D. Paulauskis, Chandra Sekhar Pedamallu, Robert J. Penny, Todd Pihl, Sima Porten, Mario E. Quintero-Aguilo, Nilsa C. Ramirez, W. Kimryn Rathmell, Victor E. Reuter, Kimberly Rieger-Christ, A. Gordon Robertson, Sara Sadeghi, Charles Saller, Andrew Salner, Francisco Sanchez-Vega, George Sandusky, Cristovam Scapulatempo-Neto, Jacqueline E. Schein, Anne K. Schuckman, Nikolaus Schultz, Candace Shelton, Troy Shelton, Sachet A. Shukla, Jeff Simko, Parminder Singh, Payal Sipahimalani, Norm D. Smith, Heidi J. Sofia, Andrea Sorcini, Melissa L. Stanton, Gary D. Steinberg, Robert Stoehr, Xiaoping Su, Travis Sullivan, Qiang Sun, Angela Tam, Roy Tarnuzzer, Katherine Tarvin, Helge Taubert, Nina Thiessen, Leigh Thorne, Kane Tse, Kelinda Tucker, David J. Van Den Berg, Kim E. van Kessel, Sven Wach, Yunhu Wan, Zhining Wang, John N. Weinstein, Daniel J. Weisenberger, Lisa Wise, Tina Wong, Ye Wu, Catherine J. Wu, Liming Yang, Leigh Anne Zach, Jean C. Zenklusen, Jiashan Zhang, Jiexin Zhang, Erik Zmuda, Ellen C. Zwarthoff, John N. Weinstein, David J. Kwiatkowski, Seth P. Lerner |
| Abstract |
We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments. |
X Demographics
The data shown below were collected from the profiles of 192 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 | 65 | 34% |
| Spain | 17 | 9% |
| France | 9 | 5% |
| United Kingdom | 8 | 4% |
| Brazil | 4 | 2% |
| Canada | 4 | 2% |
| Netherlands | 3 | 2% |
| Italy | 3 | 2% |
| Germany | 3 | 2% |
| Other | 18 | 9% |
| Unknown | 58 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 92 | 48% |
| Scientists | 66 | 34% |
| Practitioners (doctors, other healthcare professionals) | 29 | 15% |
| Science communicators (journalists, bloggers, editors) | 4 | 2% |
| Unknown | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,002 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1002 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 166 | 17% |
| Student > Ph. D. Student | 151 | 15% |
| Student > Bachelor | 91 | 9% |
| Student > Master | 77 | 8% |
| Student > Doctoral Student | 56 | 6% |
| Other | 165 | 16% |
| Unknown | 296 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 232 | 23% |
| Biochemistry, Genetics and Molecular Biology | 225 | 22% |
| Agricultural and Biological Sciences | 107 | 11% |
| Immunology and Microbiology | 25 | 2% |
| Computer Science | 15 | 1% |
| Other | 64 | 6% |
| Unknown | 334 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 574. 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 03 April 2024.
All research outputs
#41,762
of 25,736,439 outputs
Outputs from Cell
#302
of 17,267 outputs
Outputs of similar age
#827
of 331,953 outputs
Outputs of similar age from Cell
#4
of 164 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 17,267 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 59.6. This one has done particularly well, scoring higher than 98% 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 331,953 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 99% of its contemporaries.
We're also able to compare this research output to 164 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.