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Record W4417501125 · doi:10.1038/s41467-025-66584-0

The molecular cartography of malignant and benign sebaceous tumours

2025· article· en· W4417501125 on OpenAlexaff
Ingrid Ferreira, Oscar M. Rueda, Louise van der Weyden, Sahil Sahni, Oliver Cast, Kim Wong, Martin Del Castillo Velasco‐Herrera, Helen Caldwell, Jacqueline Marcia Boccacino, Tobi Alegbe, Ishan Mehta, Ashray Gunjur, Prashant Gupta, Victoria Harle, Kaori Koga, I. Matzusaki, Masakazu Fujimoto, Katharina Wiedemeyer, A. Stratigos, Anca Oniscu, Kun Wang, Eytan Ruppin, Pieter Demetter, Ian M. Frayling, Mark J. Arends, Thomas Brenn, David J. Adams

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of Calgary
FundersWellcome TrustWellcome
KeywordsSebaceous carcinomaContext (archaeology)MutationGene duplicationGeneFusion geneCarcinoma

Abstract

fetched live from OpenAlex

Sebaceous tumours (STs) are rare skin appendage tumours and include benign sebaceous adenoma (SA) and sebaceoma (SM), malignant extra-ocular sebaceous carcinoma (SC-E) and peri-ocular sebaceous carcinoma (SC-O). Here, an extensive worldwide collection of 286 tumours is deeply characterised, revealing a propensity to develop in the context of a high tumour mutational burden (except in SC-O) which is most frequently associated with mismatch repair deficiency (dMMR), followed by UV-induced damage, POLE/POLD1 mutations, and AID/APOBEC activation signatures. Biallelic TP53 inactivation with concomitant ZNF750 and/or RB1 mutation is seen in SC-E/SC-O. Amplification of 8q (including MYC) is related to SC-O, while amplification of 1q21.3 (including HRNR) and chromosome 20 are shared by SC-O and SC-E, as is deletion of 13q14.3 (where RB1 resides). The most frequently mutated gene is NOTCH1. Extensive fusion gene, expression and molecular cluster analyses provide a molecular portrait of this rare and enigmatic tumour type.

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 distilled prediction

Teacher imitation

Not 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.305
Teacher spread0.297 · 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 teacher head, 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 routes1
Has abstractyes

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