Specific Pathology Features Enrich Selection of Endometrial Carcinomas for POLE Testing
Bibliographic record
Abstract
Identification of ultramutated/ POLE -mutated endometrial carcinomas ( POLEM ECs) has important implications given its association with better prognosis. However, POLE mutation testing is not widely available. Our objective was to evaluate POLEM ECs versus POLE wild-type ( POLEWT ) ECs, within a cohort of consultation cases with features suggestive of an ultramutated phenotype. Consultation cases of EC that had undergone POLE hotspot mutation testing over a 3.5-year period were included. Tumor morphology and immunohistochemistry were reviewed for both groups. Chi-square test and t test were used for statistical analysis. Of 25 consultation cases, 12 harbored a POLE mutation (48%) and 13 were wild-type (52%). Patients with POLEM ECs were younger (59 vs. 71.3 y; P =0.01). Ambiguous histomorphology (5/12 vs. 1/13; P =0.04) and the presence of more than rare bizarre nuclei (8/12 vs. 2/12; P =0.01) differed significantly between POLEM and POLEWT ECs, respectively. In the POLEM group, one case (1/12) demonstrated PMS2 loss, and one (1/12) showed subclonal MLH1/PMS2 loss. Among POLEWT ECs, 3/13 (23%) showed MLH1/PMS2 loss. p53 was subclonally overexpressed in 4/10 POLEM and 1/13 POLEWT cases ( P =0.06). Mutant p53 patterns were seen in 1/10 POLEM versus 6/13 of POLEWT ECs, respectively ( P =0.06). Within our cohort, the specificity of ambiguous histomorphology, bizarre nuclei, subclonal biomarker expression, and marked tumor-infiltrating lymphocytes for POLEM EC was 83%, 80%, 80%, and 71%, respectively. Where universal POLE testing is not available, these data suggest that morphologic screening (particularly ambiguous histomorphology and the presence of more than rare bizarre nuclei) can be useful for selective enrichment of ECs for POLE testing.
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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 | 0.001 |
| 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.
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".