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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries
Ceres Fernández–Rozadilla, Maria Timofeeva, Zhishan Chen, Philip Law, Minta Thomas, Stephanie L. Schmit, Virginia Díez‐Obrero, Li Hsu, Juan Fernández‐Tajes, Claire Palles, Kitty Sherwood, Sarah Briggs, Victoria Svinti, Kevin Donnelly, Susan M. Farrington, James P. Blackmur, P G Vaughan-Shaw, Xiao‐Ou Shu, Jirong Long, Qiuyin Cai, Xingyi Guo, Yingchang Lu, Peter Broderick, James B. Studd, Jeroen R. Huyghe, Tabitha A. Harrison, David V. Conti, Christopher H. Dampier, Mathew Devall, Fredrick R. Schumacher, Marilena Melas, Gad Rennert, Mireia Obón‐Santacana, Vicente Martín, Ferrán Moratalla-Navarro, Jae Hwan Oh, Jeongseon Kim, Sun Ha Jee, Keum Ji Jung, Sun‐Seog Kweon, Min‐Ho Shin, Aesun Shin, Yoon‐Ok Ahn, Dong-Hyun Kim, Isao Oze, Wanqing Wen, Keitaro Matsuo, Koichi Matsuda, Chizu Tanikawa, Zefang Ren, Yu‐Tang Gao, Wei‐Hua Jia, John L. Hopper, Mark A. Jenkins, Aung Ko Win, Rish K. Pai, Jane C. Figueiredo, Robert W. Haile, Steven Gallinger, Michael O. Woods, Polly A. Newcomb, David Duggan, Jeremy P. Cheadle, Richard Kaplan, Tim Maughan, Rachel Kerr, David Kerr, Iva Kirac, Jan Böhm, Lukka-Pekka Mecklin, Pekka Jousilahti, Paul Knekt, Lauri A. Aaltonen, Harri Rissanen, Eero Pukkala, Johan G. Eriksson, Tatiana Cajuso, Ulrika A. Hänninen, Johanna Kondelin, Kimmo Palin, Tomas Tanskanen, Laura Renkonen‐Sinisalo, Brent W. Zanke, Satu Männistö, Demetrius Albanes, Stephanie J. Weinstein, Edward Ruiz-Narváez, Julie R. Palmer, Daniel D. Buchanan, Elizabeth A. Platz, Kala Visvanathan, Cornelia M. Ulrich, Erin M. Siegel, Stefanie Brezina, Andrea Gsur, Peter T. Campbell, Jenny Chang‐Claude, Michael Hoffmeister, Hermann Brenner, Martha L. Slattery, John D. Potter, Konstantinos K. Tsilidis, Matthias B. Schulze, Marc J. Gunter, Neil Murphy, Antoni Castells, Sergi Castellvı́-Bel, Leticia Moreira, Volker Arndt, Anna Shcherbina, Mariana C. Stern, Bens Pardamean, D. Timothy Bishop, Graham G. Giles, Melissa C. Southey, Gregory Idos, Kevin McDonnell, Zomoroda Abu‐Ful, Joel K. Greenson, Katerina Shulman, Flavio Lejbkowicz, Kenneth Offit, Yu‐Ru Su, Robert S. Steinfelder, Temitope O. Keku, Bethany Van Guelpen, Thomas J. Hudson, Heather Hampel, Rachel Pearlman, Sonja I. Berndt, Richard B. Hayes, Marie Elena Martinez, Sushma Thomas, Douglas A. Corley, Paul D.P. Pharoah, Susanna C. Larsson, Yun Yen, Heinz‐Josef Lenz, Emily White, Li Li, Kimberly F. Doheny, Elizabeth Pugh, Tameka Shelford, Andrew T. Chan, Marcia Cruz‐Correa, Annika Lindblom, David J. Hunter, Amit D. Joshi, Clemens Schafmayer, Peter C. Scacheri, Anshul Kundaje, Deborah A. Nickerson, Robert E. Schoen, Jochen Hampe, Zsofia K. Stadler, Pavel Vodička, Ludmila Vodičková, Veronika Vymetálková, Nickolas Papadopoulos, Chistopher Edlund, W. James Gauderman, Duncan C. Thomas, David Shibata, Amanda E. Toland, Sanford D. Markowitz, Andre Kim, Stephen J. Chanock, Fränzel van Duijnhoven, Edith J. M. Feskens, Lori C. Sakoda, Manuela Gago-Domínguez, Alicja Wolk, Alessio Naccarati, Barbara Pardini, Liesel M. FitzGerald, Soo Chin Lee, Shuji Ogino, Stephanie A. Bien, Charles Kooperberg, Christopher Li, Yi Lin, Ross L. Prentice, Conghui Qu, Stéphane Bezieau, Catherine M. Tangen, Elaine R. Mardis, Taiki Yamaji, Norie Sawada, Motoki Iwasaki, Christopher A. Haiman, Loı̈c Le Marchand, Anna H. Wu, Chenxu Qu, Caroline McNeil, Gerhard A. Coetzee, Caroline Hayward, Ian J. Deary, Sarah E. Harris, Evropi Τheodoratou, S. Reid, Marion Walker, Li Yin Ooi, Vı́ctor Moreno, Graham Casey, Stephen B. Gruber, Ian Tomlinson, Wei Zheng, Malcolm G. Dunlop, Richard S. Houlston, Ulrike Peters
Bibliographic record
VenueNature Genetics · 2022
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsMemorial University of NewfoundlandLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer ResearchUniversity of TorontoMount Sinai Hospital
FundersInstitute of GeneticsNational Institute of Environmental Health SciencesNational Heart, Lung, and Blood InstituteMedical Research CouncilSchool of Medicine, Vanderbilt UniversityUniversitat de BarcelonaVanderbilt-Ingram Cancer CenterUniversity of OxfordUSC Norris Comprehensive Cancer CenterUniversity of Southern CaliforniaWorld Health OrganizationCancer Research UKVanderbilt University Medical CenterEuropean CommissionCase Comprehensive Cancer Center, Case Western Reserve UniversityUniversity of WashingtonNational Cancer InstituteCleveland ClinicVanderbilt UniversityFred Hutchinson Cancer Research CenterCancer Research WalesNational Institutes of HealthOffice of Research Infrastructure Programs, National Institutes of HealthSyddansk UniversitetU.S. Department of Health and Human Services
KeywordsBiologyColorectal cancerComputational biologyCancerGenetics
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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 head0.069
GPT teacher head0.369
Teacher spread0.301 · 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
GenreReview
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
Citations242
Published2022
Admission routes2
Has abstractno