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 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 | 0.062 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".