Utility of MLH1 Methylation Analysis in the Clinical Evaluation of Lynch Syndrome in Women with Endometrial Cancer
Bibliographic record
Abstract
Clinical screening criteria, such as young age of endometrial cancer diagnosis and family history of signature cancers, have traditionally been used to identify women with Lynch Syndrome, which is caused by mutation of a DNA mismatch repair gene. Immunohistochemistry and microsatellite instability analysis have evolved as important screening tools to evaluate endometrial cancer patients for Lynch Syndrome. A complicating factor is that 15-20% of sporadic endometrial cancers have immunohistochemical loss of the DNA mismatch repair protein MLH1 and high levels of microsatellite instability due to methylation of MLH1. The PCR-based MLH1 methylation assay potentially resolves this issue, yet many clinical laboratories do not perform this assay. The objective of this study was to determine if clinical and pathologic features help to distinguish sporadic endometrial carcinomas with MLH1 loss secondary to MLH1 methylation from Lynch Syndrome-associated endometrial carcinomas with MLH1 loss and absence of MLH1 methylation. Of 337 endometrial carcinomas examined, 54 had immunohistochemical loss of MLH1. 40/54 had MLH1 methylation and were designated as sporadic, while 14/54 lacked MLH1 methylation and were designated as Lynch Syndrome. Diabetes and deep myometrial invasion were associated with Lynch Syndrome; no other clinical or pathological variable distinguished the 2 groups. Combining Society of Gynecologic Oncology screening criteria with these 2 features accurately captured all Lynch Syndrome cases, but with low specificity. In summary, no single clinical/pathologic feature or screening criteria tool accurately identified all Lynch Syndrome-associated endometrial carcinomas, highlighting the importance of the MLH1 methylation assay in the clinical evaluation of these patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.000 | 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 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".