Molecular-based classification algorithm for endometrial carcinoma to categorize ovarian endometrioid carcinoma into prognostically significant groups.
Bibliographic record
Abstract
e17081 Background: The Cancer Genome Atlas classification divides endometrial carcinoma in biologically distinct groups, and testing for p53, mismatch repair proteins (MMR) and polymerase ɛ (POLE) exonuclease domain mutations has been shown to predict the molecular subgroup and clinical outcome. While abnormalities in these markers have been described in ovarian endometrioid carcinoma (OEC), their role in predicting its molecular profile and prognosis is still not fully explored. Methods: OECs resected in a 14 year period were retrieved. Only tumors with confirmed endometrioid histology and negative WT1 were included. POLE mutational analysis and immunohistochemistry for p53, MLH1, MSH2, MSH6 and PMS2 was performed in formalin-fixed, paraffin-embedded tissue. Following the molecular classifier proposed for endometrial carcinoma ( B J Cancer 2015;113:299-310), cases were classified as: POLE mutated, MMR abnormal, p53 abnormal and p53 wild type. Clinicopathologic information was recorded including patient outcome. Results: 73 tumors were successfully reviewed and tested. Of these, 8 (11%) were POLE mutated, 6 (8%) were MMR abnormal and 17 (23%) were p53 abnormal; the remaining 42 cases (58%) were p53 wild type (no POLE, p53 or MMR abnormalities). Mean follow-up period was 69 months (median 62, range 1-179). The molecular classification was an independent predictor for disease free and overall survival on multivariate analysis (p = 0.005 and 0.045 respectively, Cox proportional hazard model). POLE mutated and MMR abnormal groups had 100% 5 year DFS and OS. p53 wild type cases had intermediate survival rates (86% DFS and OS). Conversely, the p53 abnormal group had worse outcomes with 42% DFS and 76% OS at 5 years. Conclusions: OEC can be classified into prognostically distinct subgroups by testing for molecular surrogates, akin to endometrial cancer. MMR and POLE alterations seem to identify a subset of ovarian endometrioid carcinomas with excellent outcome; conversely, abnormal p53 expression carries a worse prognosis. In the era of personalized medicine, the use of these markers in the routine evaluation of ovarian endometrioid tumors should be considered.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".