The molecular cartography of malignant and benign sebaceous tumours
Bibliographic record
Abstract
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.
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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.
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 | 0.004 | 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) | 0.005 | 0.002 |
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.
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.
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".