Targeted Molecular Testing in Endometrial Carcinoma: Validation of a Clinically Driven Selective ProMisE Testing Protocol
Bibliographic record
Abstract
Incorporation of molecular classification into clinicopathologic assessment of endometrial carcinoma (EC) improves risk stratification. Four EC molecular subtypes, as identified by The Cancer Genome Atlas, can be diagnosed through a validated algorithm Pro active M olecular R is k Classifier for E ndometrial Cancer (ProMisE) using p53 and mismatch repair (MMR) protein immunohistochemistry (IHC), and DNA polymerase epsilon ( POLE) mutational testing. Cost and access are major barriers to universal testing, particularly POLE analysis. We assessed a selective ProMisE algorithm (ProMisE-S): p53 and MMR IHC on all EC's with POLE testing restricted to those with abnormal MMR or p53 IHC (to identify POLEmut EC with secondary abnormalities in MMR and/or p53) and those with high-grade or non-endometrioid morphology, stage >IA or presence of lymphovascular space invasion (so as to avoid testing on the lowest risk tumors). We retrospectively compared the known ProMisE molecular classification to ProMisE-S in 912 EC. We defined a group of "very low-risk" EC (G1/G2, endometrioid, MMR-proficient, p53 wild-type, stage IA, no lymphovascular space invasion) in whom POLE testing will not impact on patient care; using ProMisE-S, POLE testing would not be required in 55% of biopsies and 38% of all EC's, after evaluation of the hysterectomy specimen, in a population-based cohort. "Very low-risk" endometrioid EC with unknown POLE status showed excellent clinical outcomes. Fifteen of 166 (9%) of all p53abn EC showed G1/G2 endometrioid morphology, supporting the potential value of universal p53 IHC. The addition of molecular testing changed the risk category in 89/896 (10%) EC's. In routine practice, POLE testing could be further restricted to only those patients in whom this would alter adjuvant therapy recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".