Clinicopathological and molecular characterisation of ‘multiple‐classifier’ endometrial carcinomas
Bibliographic record
Abstract
Endometrial carcinoma (EC) molecular classification based on four molecular subclasses identified in The Cancer Genome Atlas (TCGA) has gained relevance in recent years due to its prognostic utility and potential to predict benefit from adjuvant treatment. While most ECs can be classified based on a single classifier (POLE exonuclease domain mutations - POLEmut, MMR deficiency - MMRd, p53 abnormal - p53abn), a small but clinically relevant group of tumours harbour more than one molecular classifying feature and are referred to as 'multiple-classifier' ECs. We aimed to describe the clinicopathological and molecular features of multiple-classifier ECs with abnormal p53 (p53abn). Within a cohort of 3518 molecularly profiled ECs, 107 (3%) tumours displayed p53abn in addition to another classifier(s), including 64 with MMRd (MMRd-p53abn), 31 with POLEmut (POLEmut-p53abn), and 12 with all three aberrations (MMRd-POLEmut-p53abn). MMRd-p53abn ECs and POLEmut-p53abn ECs were mostly grade 3 endometrioid ECs, early stage, and frequently showed morphological features characteristic of MMRd or POLEmut ECs. 18/28 (60%) MMRd-p53abn ECs and 7/15 (46.7%) POLEmut-p53abn ECs showed subclonal p53 overexpression, suggesting that TP53 mutation was a secondary event acquired during tumour progression. Hierarchical clustering of TCGA ECs by single nucleotide variant (SNV) type and somatic copy number alterations (SCNAs) revealed that MMRd-p53abn tumours mostly clustered with single-classifier MMRd tumours (20/23) rather than single-classifier p53abn tumours (3/23), while POLEmut-p53abn tumours mostly clustered with single-classifier POLEmut tumours (12/13) and seldom with single-classifier p53abn tumours (1/13) (both p ≤ 0.001, chi-squared test). Finally, the clinical outcome of patients with MMRd-p53abn and POLEmut-p53abn ECs [stage I 5-year recurrence-free survival (RFS) of 92.2% and 94.1%, respectively] was significantly different from single-classifier p53abn EC (stage I RFS 70.8%, p = 0.024 and p = 0.050, respectively). Our results support the classification of MMRd-p53abn EC as MMRd and POLEmut-p53abn EC as POLEmut. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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.
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".