Significant frequency of MSH2/MSH6 abnormality in ovarian endometrioid carcinoma supports histotype‐specific Lynch syndrome screening in ovarian carcinomas
Bibliographic record
Abstract
AIMS: Lynch syndrome screening in ovarian carcinoma is controversial. The aim of this study was to assess the frequency of deficient mismatch repair (dMMR) protein in a retrospective cohort enriched for non-high-grade serous carcinomas and its association with outcome within histological types. METHODS AND RESULTS: Tissue microarrays representing 612 ovarian carcinomas were tested for mismatch repair proteins (MLH1, PMS2, MSH2, and MSH6) by immunohistochemistry. dMMR was detected in 13.8% of endometrioid and 2.4% of clear cell carcinomas, but not in other histological types. Within endometrioid carcinomas, 11 of 25 dMMR cases showed abnormal MLH1/PMS2, 10 cases showed abnormal MSH2/MSH6, and four cases showed only abnormal MSH6, indicating that at least 7.7% of endometrioid carcinomas have dMMR probably related to Lynch syndrome. The four dMMR clear cell carcinomas showed abnormal MSH2/MSH6 in three cases and only abnormal MSH6 in one case, all probably related to Lynch syndrome. Within endometrioid carcinomas, dMMR was significantly associated with age <50 years, synchronous endometrial endometrioid carcinoma, a higher CA125 level at diagnosis, higher FIGO grade, absence of ARID1A, and at least 20 CD8-positive intraepithelial lymphocytes per high-power field, but was not associated with cancer-specific death. Age <50 years, higher CA125 levels at diagnosis and at least 20 CD8-positive intraepithelial lymphocytes per high-power field remained significant after adjustment for multiple testing, but their sensitivity for identifying dMMR remained insufficient. CONCLUSION: Our data support the policy of histotype-specific Lynch syndrome screening in ovarian carcinoma confined to endometrioid and clear cell carcinomas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".