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A Transcriptome-Wide Association Study Among 97,898 Women to Identify Candidate Susceptibility Genes for Epithelial Ovarian Cancer Risk
Yingchang Lu, Alicia Beeghly‐Fadiel, Lang Wu, Xingyi Guo, Bingshan Li, Joellen M. Schildkraut, Hae Kyung Im, Y. Ann Chen, Jennifer B. Permuth, Brett M. Reid, Jamie K. Teer, Kirsten B. Moysich, Irene L. Andrulis, Hoda Anton‐Culver, Banu Arun, Elisa V. Bandera, Rósa B. Barkardóttir, Daniel R. Barnes, Javier Benı́tez, Line Bjørge, James D. Brenton, Ralf Bützow, Trinidad Caldés, Maria A. Caligo, Ian Campbell, Jenny Chang‐Claude, Kathleen Claes, Fergus J. Couch, Daniel W. Cramer, Mary B. Daly, Anna DeFazio, Joe Dennis, Orland Dı́ez, Susan M. Domchek, Thilo Dörk, Douglas F. Easton, Peter A. Fasching, Renée T. Fortner, George Fountzilas, Eitan Friedman, Patricia A. Ganz, Judy E. Garber, Graham G. Giles, Andrew K. Godwin, David E. Goldgar, Marc T. Goodman, Mark H. Greene, Jacek Gronwald, Ute Hamann, Florian Heitz, Michelle A.T. Hildebrandt, Claus Høgdall, Antoinette Hollestelle, Peter J. Hulick, David G. Huntsman, Evgeny N. Imyanitov, Claudine Isaacs, Anna Jakubowska, Paul A. James, Beth Y. Karlan, Linda E. Kelemen, Lambertus A. Kiemeney, Susanne K. Kjær, Ava Kwong, Nhu D. Le, Goska Leslie, Fabienne Lesueur, Douglas A. Levine, Amalia Mattiello, Taymaa May, Lesley McGuffog, Iain A. McNeish, Melissa A. Merritt, Francesmary Modugno, Marco Montagna, Susan L. Neuhausen, Heli Nevanlinna, Finn Cilius Nielsen, Liene Ņikitina-Zaķe, Robert L. Nussbaum, Kenneth Offit, Edith Oláh, Olufunmilayo I. Olopade, Sara H. Olson, Håkan Olsson, Ana Osório, Sue K. Park, Michael T. Parsons, Petra H.M. Peeters, Tanja Pejović, Paolo Peterlongo, Catherine M. Phelan, Miguel Ángel Pujana, Susan J. Ramus, Gad Rennert, Harvey A. Risch, Gustavo C. Rodriguez, Cristina Rodríguez‐Antona, Isabelle Romieu, Matti A. Rookus, Mary Anne Rossing, Iwona K. Rzepecka, Dale P. Sandler, Rita K. Schmutzler, Veronica Wendy Setiawan, Priyanka Sharma, Weiva Sieh, Jacques Simard, Christian F. Singer, Honglin Song, Melissa C. Southey, Amanda B. Spurdle, Rebecca Sutphen, Anthony J. Swerdlow, Manuel R. Teixeira, Soo‐Hwang Teo, Mads Thomassen, Marc Tischkowitz, Amanda E. Toland, Antonia Trichopoulou, Nadine Tung, Shelley S. Tworoger, Elizabeth J. van Rensburg, Adriaan Vanderstichele, Ana Vega, Digna Velez Edwards, Penelope M. Webb, Jeffrey N. Weitzel, Nicolas Wentzensen, Emily White, Alicja Wolk, Anna H. Wu, Drakoulis Yannoukakos, Kristin K. Zorn, Simon A. Gayther, Antonis C. Antoniou, Andrew Berchuck, Ellen L. Goode, Georgia Chenevix‐Trench, Thomas A. Sellers, Paul D.P. Pharoah, Wei Zheng, Jirong Long
Bibliographic record
VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsCentre hospitalier universitaire de QuébecBC Cancer AgencyPrincess Margaret Cancer CentreUniversity of British ColumbiaLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersMedical Research and Materiel CommandNational Center for Advancing Translational SciencesNational Center for Research ResourcesNIHR Cambridge Biomedical Research CentreInstituto de Salud Carlos IIINational Health and Medical Research CouncilWorld Cancer Research FundMedical Research CouncilCanadian Institutes of Health ResearchHealth CanadaNational Institutes of HealthBC Cancer FoundationHebei Medical UniversityPomorski Uniwersytet Medyczny W SzczecinieMutuelle Générale de l'Education NationaleDeutsche KrebshilfeInstitut Gustave-RoussyNorges ForskningsrådHelse VestJapan Society for the Promotion of ScienceHellenic Health FoundationMinistry of Health, Labour and WelfareOvarian Cancer Research FundBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadAssociazione Italiana per la Ricerca sul CancroCanadian HIV Trials Network, Canadian Institutes of Health ResearchInstitut National de la Santé et de la Recherche MédicaleCancerfondenNational Cancer InstitutePeter MacCallum FoundationUniversity College LondonCancer Institute NSWEuropean CommissionFred C. and Katherine B. Andersen FoundationUniversity of CambridgeRoswell Park Cancer InstituteNational Institute for Health and Care ResearchSwedish Cancer FoundationOvarian Cancer AustraliaU.S. Department of DefenseCancer Research UKWorld Health OrganizationWellcome TrustKreftforeningenCancer Research SocietyCentre International de Recherche sur le CancerLigue Contre le CancerVanderbilt University Medical CenterDeutsches KrebsforschungszentrumCancer Council VictoriaKræftens BekæmpelseMayo Foundation for Medical Education and ResearchRadboud UniversiteitLon V. Smith FoundationEuropean Social FundState of Connecticut Department of Public HealthKorea Health Industry Development InstituteOregon Health and Science UniversityMinnesota Ovarian Cancer AllianceMoffitt Cancer CenterVanderbilt UniversityRutgers Cancer Institute of New JerseyCancer AustraliaOak FoundationNordForskVetenskapsrådetGeorgia Clinical and Translational Science Alliance
KeywordsTranscriptomeOvarian cancerCandidate geneGeneBiologyEpithelial ovarian cancerCancerOncologyGeneticsInternal medicineBioinformaticsComputational biologyMedicineGene expression
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 head0.068
GPT teacher head0.435
Teacher spread0.367 · 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 teacher head, not a consensus.
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
Citations70
Published2018
Admission routes2
Has abstractyes