MétaCan
Menu
Back to cohort
Record W4412569680 · doi:10.1016/j.isci.2025.113176

Multi-omics whole-genome characterization of the copy number landscape of metastatic pancreatic ductal adenocarcinoma

2025· article· en· W4412569680 on OpenAlexafffund
James T. Topham, Joanna M. Karasinska, Andrew Metcalfe, Hassan A. Ali, Steve E. Kalloger, Maya Kevorkova, Emma Titmuss, Gian Luca Negri, Sandra E. Spencer Miko, Gun Ho Jang, Grainne M. O’Kane, Richard A. Moore, Andrew J. Mungall, Jonathan M. Loree, Faiyaz Notta, Julie M. Wilson, Oliver F. Bathe, Patricia A. Tang, Rachel Goodwin, Gregg B. Morin, Jennifer J. Knox, Steven Gallinger, Janessa Laskin, Marco A. Marra, Steven J.M. Jones, Daniel J. Renouf, David F. Schaeffer

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsVancouver General HospitalUniversity of TorontoUniversity Health NetworkUniversity of British ColumbiaOttawa HospitalUniversity of CalgaryOntario Institute for Cancer ResearchCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia HospitalPancreas Centre (Canada)
FundersHartwig Medical FoundationNational Cancer InstituteOntario Institute for Cancer ResearchBC Cancer FoundationNational Human Genome Research InstituteTerry Fox Research InstituteGenome British Columbia
KeywordsPancreatic ductal adenocarcinomaOmicsCopy-number variationGenomeComputational biologyBiologyAdenocarcinomaMedicineBioinformaticsGenePancreatic cancerCancerGenetics

Abstract

fetched live from OpenAlex

amplification versus expression is strongest in PDAC among 23 other cancer types. Taken together, these data provide a detailed overview of the copy number landscape in PDAC while highlighting chr7q21/22 amplification as a recurrent somatic event impacting both gene expression and patient survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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)0.001
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.331
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicPancreatic and Hepatic Oncology ResearchFrench-language works237,207