EPM2AIP1 Immunohistochemistry Can Be Used as Surrogate Testing for MLH1 Promoter Methylation in Endometrial Cancer
Bibliographic record
Abstract
Immunohistochemical (IHC) evaluation of DNA mismatch repair proteins (MMR) has become routine practice for Lynch syndrome screening and/or part of diagnostic evaluation in endometrial cancer. Approximately 20% to 30% of endometrial carcinomas demonstrate microsatellite instability due to defective DNA MMR. Vast majority of MLH1/PMS2-deficient tumors are sporadic and show MLH1 promoter methylation. MLH1 methylation testing by quantitative polymerase chain reaction-based technique is time, labor, and tissue intensive with an average institutional turnaround time of 2 weeks. MLH1 and EPM2AIP1 genes share a common promoter whose methylation has been shown to affect both genes. We assessed whether IHC for EPM2AIP1 in combination with MMR proteins can serve as surrogate marker for MLH1 promoter methylation status. We performed a retrospective review of all MLH1/PMS2-deficient endometrial carcinomas that underwent MLH1 promoter methylation testing from January 1 to September 31, 2020, at our institution. Microscopic slides were reviewed and EMP2AIP1 IHC was performed. The results were correlated with MLH1 promoter methylation status (percent methylated rate). A total of 119 cases were identified and successfully tested. Nuclear EPM2AIP1 protein expression was observed in benign endometrial cells and myometrial smooth muscle cells. Loss of nuclear EPM2AIP1 staining was identified in 90/110 (81.8%) methylated tumors with additional 14/110 (12.7%) cases showing aberrant staining patterns. Only 6/110 (5.5%) tumors demonstrated intact EPM2AIP1 nuclear expression in presence of MLH1 promoter methylation. EMP2AIP1 IHC is concordant with MLH1 promoter methylation results in 95% of endometrial carcinomas (94.5% sensitivity, 98.1% positive predictive value) and shows promise as a surrogate marker for methylation 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".