← Back to cohort
The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma
Caleb Davis, Christopher J. Ricketts, Min Wang, Lixing Yang, Andrew D. Cherniack, Hui Shen, Christian Buhay, Hyo-Jin Kang, Sang Cheol Kim, Catherine C. Fahey, Kathryn E. Hacker, Gyan Bhanot, Dmitry A. Gordenin, Andy Chu, Preethi H. Gunaratne, Michael Biehl, Sahil Seth, Benny Abraham Kaipparettu, Christopher A. Bristow, Lawrence A. Donehower, Eric Wallen, Angela Smith, Satish K. Tickoo, Pheroze Tamboli, Victor E. Reuter, Laura S. Schmidt, James J. Hsieh, Toni K. Choueiri, A. Ari Hakimi, Lynda Chin, Matthew Meyerson, Raju Kucherlapati, Woong‐Yang Park, A. Gordon Robertson, Peter W. Laird, Elizabeth P. Henske, David J. Kwiatkowski, Peter J. Park, Margaret Morgan, Brian Shuch, Donna M. Muzny, David A. Wheeler, W. Marston Linehan, Richard A. Gibbs, W. Kimryn Rathmell, Chad J. Creighton, Sabina Signoretti, Michael Seiler, Hsu Chao, Mike Dahdouli, Xi Liu, Nipun Kakkar, Jeffrey G. Reid, Brittany Downs, Jennifer Drummond, Donna Morton, HarshaVardhan Doddapaneni, Lora Lewis, Adam C. English, Qingchang Meng, Christie Kovar, Qiaoyan Wang, Walker Hale, Alicia Hawes, Divya Kalra, Kimberly Walker, Bradley A. Murray, Carrie Sougnez, Gordon Saksena, Scott L. Carter, Steven E. Schumacher, Barbara Tabak, Travis Zack, Gad Getz, Rameen Beroukhim, Stacey Gabriel, Adrian Ally, Miruna Balasundaram, İnanç Birol, Denise Brooks, Yaron S.N. Butterfield, Eric Chuah, Amanda Clarke, Noreen Dhalla, Ranabir Guin, Robert A. Holt, Darlene Lee, Haiyan I. Li, Emilia L. Lim, Yussanne Ma, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Tina Wong, Steven J.M. Jones, Marco A. Marra, J. Todd Auman, Donghui Tan, Shaowu Meng, Corbin D. Jones, Katherine A. Hoadley, Piotr A. Mieczkowski, Lisle E. Mose, Jeffrey Roach, Umadevi Veluvolu, Matthew D. Wilkerson, Elizabeth Buda, Junyuan Wu, Tom Bodenheimer, Alan P. Hoyle, Janae V. Simons, Mathew G. Soloway, Saianand Balu, D. Neil Hayes, Charles M. Perou, Daniel J. Weisenberger, Timothy J. Triche, Phillip H. Lai, David Van Den Berg, Stephen B. Baylin, Fengju Chen, Cristian Coarfa, Michael S. Noble, Daniel DiCara, Hailei Zhang, Juok Cho, David I. Heiman, Nils Gehlenborg, Doug Voet, Pei Lin, Scott Frazer, Petar Stojanov, Yingchun Liu, Lihua Zou, Michael S. Lawrence, Alexei Protopopov, Xingzhi Song, Jianhua Zhang, Angeliki Pantazi, Angela Hadjipanayis, Eunjung Lee, Lovelace J. Luquette, Semin Lee, Michael Parfenov, Netty Santoso, Jonathan G. Seidman, Andrew Wei Xu, Hyojin Kang, Junehawk Lee, Steven A. Roberts, Leszek J. Klimczak, David C. Fargo, Martin Lang, Yoon‐La Choi, Wenyi Wang, Fan Yu, Jaeil Ahn, Rehan Akbani, John N. Weinstein, David Haussler, Singer Ma, Amie Radenbaugh, Jingchun Zhul, Tara M. Lichtenberg, Erik Zmuda, Aaron D. Black, Benjamin Hanf, Nilsa C. Ramirez, Lisa Wise, Jay Bowen, Kristen Leraas, Tracy Michelle Hall, Julie M. Gastier-Foster, William G. Kaelin, Leigh B. Thorne, Lori Boice, Mei Huang, Cathy D. Vocke, James Peterson, Robert Worrell, María Merino, Bogdan Czerniak, Kenneth D. Aldape, Christopher G. Wood, Paul T. Spellman, Michael B. Atkins, John C. Cheville, R. Houston Thompson, Mark A. Jensen, Todd Pihl, Yunhu Wan, Brenda Ayala, Julien Baboud, Sudhakar Velaga, Jessica Walton, Jia Liu, Sudha Chudamani, Ye Wu, Margi Sheth, Kenna Shaw, John A. Demchok, Tanja M. Davidsen, Liming Yang, Zhining Wang, Roy Tarnuzzer, Jiashan Zhang, Greg Eley, Ina Felau, Jean C. Zenklusen, Carolyn M. Hutter, Mark S. Guyer, Bradley A. Ozenberger, Heidi J. Sofia
Bibliographic record
VenueCancer Cell · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
FundersU.S. National Library of MedicineNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Cancer InstituteNational Institutes of HealthKorea Institute of Science and TechnologyKorea Institute of Science and Technology InformationNational Center for Advancing Translational SciencesFrederick National Laboratory for Cancer Research
KeywordsSomatic cellChromophobe cellRenal cell carcinomaBiologyCancer researchCarcinomaGeneticsMedicinePathologyClear cellGene
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.005
GPT teacher head0.201
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations860
Published2014
Admission routes1
Has abstractno