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Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma
Siyuan Zheng, Andrew D. Cherniack, Ninad Dewal, Richard A. Moffitt, Ludmila Danilova, Bradley A. Murray, Antônio Marcondes Lerário, Tobias Else, Theo Knijnenburg, Giovanni Ciriello, Guillaume Assié, Olena Morozova, Rehan Akbani, Juliann Shih, Katherine A. Hoadley, Toni K. Choueiri, Jens Waldmann, Özgür Mete, A. Gordon Robertson, Hsin-Ta Wu, Benjamin J. Raphael, Lina Shao, Matthew Meyerson, Michael J. Demeure, Felix Beuschlein, Anthony J. Gill, Stan B. Sidhu, Madson Q. Almeida, Maria Candida Barisson Villares Fragoso, Leslie Cope, Electron Kebebew, Mouhammed Amir Habra, Timothy G. Whitsett, Kimberly J. Bussey, William E. Rainey, L. Sylvia, Jérôme Bertherat, Martin Faßnacht, David A. Wheeler, Roel G.W. Verhaak, Thomas J. Giordano, Gary D. Hammer, Hsin-Tu Wu, Christopher C. Benz, Adrian Ally, Miruna Balasundaram, Reanne Bowlby, Denise Brooks, Yaron S.N. Butterfield, Rebecca Carlsen, Noreen Dhalla, Ranabir Guin, Robert A. Holt, Steven J.M. Jones, Darlene Lee, Haiyan I. Li, Lynette Lim, Yussanne Ma, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Karen Mungall, Sara Sadeghi, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Peter J. Park, Matthias Kroiß, Jianjiong Gao, Chris Sander, Nikolaus Schultz, Corbin D. Jones, Raju Kucherlapati, Piotr A. Mieczkowski, Charles M. Perou, Donghui Tan, Umadevi Veluvolu, Matthew D. Wilkerson, D. Neil Hayes, Marc Ladanyi, Marcus Quinkler, J. Todd Auman, Ana Cláudia Latronico, Berenice B. Mendonça, Mathilde Sibony, Zack Sanborn, Michelle Bellair, Christian Buhay, Kyle R. Covington, Mahmoud Dahdouli, Huyen Dinh, HarshaVardhan Doddapaneni, Brittany Downs, Jennifer Drummond, Richard A. Gibbs, Walker Hale, Yi Han, Alicia Hawes, Hai Hu, Nipun Kakkar, Divya Kalra, Ziad U. Khan, Christine Kovar, Sandy Lee, Lora Lewis, Donna Morton, Donna M. Muzny, Jireh Santibanez, Xi Liu, B. Dousset, Lionel Groussin, Rossella Libé, Lynda Chin, Sheila M. Reynolds, Ilya Shmulevich, Sudha Chudamani, Jia Liu, Laxmi Lolla, Ye Wu, Jen Jen Yeh, Saianand Balu, Tom Bodenheimer, Alan P. Hoyle, Shaowu Meng, Lisle E. Mose, Yan Shi, Janae V. Simons, Matthew G. Soloway, Junyuan Wu, Wei Zhang, Kenna R. Mills Shaw, John A. Demchok, Ina Felau, Margi Sheth, Roy Tarnuzzer, Liming Yang, Jean C. Zenklusen, Jiashan Zhang, Tanja M. Davidsen, Catherine Crawford, Carolyn M. Hutter, Heidi J. Sofia, Jeffrey Roach, Wiam Bshara, Carmelo Gaudioso, Quaid Morris, Patsy Soon, Shelley Alonso, Julien Baboud, Todd Pihl, Rohini Raman, Qiang Sun, Yunhu Wan, Rashi Naresh, Harindra Arachchi, Rameen Beroukhim, Scott L. Carter, Juok Cho, Scott Frazer, Stacey Gabriel, Gad Getz, David I. Heiman, Michael S. Lawrence, Pei Lin, Michael S. Noble, Gordon Saksena, Steven E. Schumacher, Carrie Sougnez, Doug Voet, Hailei Zhang, Jay Bowen, Sara E. Coppens, Julie M. Gastier‐Foster, Mark Gerken, Carmen Helsel, Kristen M. Leraas, Tara M. Lichtenberg, Nilsa C. Ramirez, Lisa Wise, Erik Zmuda, Stephen B. Baylin, James G. Herman, Janine LoBello, Aprill Watanabe, David Haussler, Amie Radenbaugh, Arjun A. Rao, Jingchun Zhu, Detlef K. Bartsch, Silviu Sbiera, Bruno Allolio, Timo Deutschbein, Cristina L. Ronchi, Victoria M. Raymond, Michelle Vinco, Linda Amble, Phillip H. Lai, David Van Den Berg, Daniel J. Weisenberger, Bruce Robinson, Zhenlin Ju, Hoon Kim, Shiyun Ling, Wenbin Liu, Yiling Lu, Gordon B. Mills, Kanishka Sircar, Qianghu Wang, Kosuke Yoshihara, Peter W. Laird, Fan Yu, Wenyi Wang, Eve Shinbrot, Martín Reincke, John N. Weinstein, Timothy Defreitas
Bibliographic record
VenueCancer Cell · 2016
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyUniversity Health Network
FundersNational Cancer InstituteNational Human Genome Research InstituteNational Institutes of HealthNovartisBayer
KeywordsAdrenocortical carcinomaBiologyDNA methylationgenomic DNAGenomeCpG siteCancer researchGeneGenome instabilityDNAGeneticsComputational biologyGene expressionDNA damageEndocrinology
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 score0.002
Threshold uncertainty score0.006
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 | 0.001 |
| 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) | 0.002 | 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 head0.020
GPT teacher head0.257
Teacher spread0.236 · 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
Citations658
Published2016
Admission routes1
Has abstractno