Mismatch Repair Universal Screening of Endometrial Cancers (MUSE) in a Canadian Cohort
Bibliographic record
Abstract
BACKGROUND: Approximately 2-6% of endometrial cancers (ECs) are due to Lynch Syndrome (LS), a cancer predisposition syndrome caused by germline pathogenic variants (PVs) affecting the DNA mismatch repair (MMR) pathway. Increasingly, universal tissue-based screening of ECs has been proposed as an efficient and cost-effective way to identify families with LS, though few studies have been published on Canadian cohorts. The purpose of this study was to evaluate the feasibility and overall performance of a universal immunohistochemistry (IHC) screening program for women with EC within a single Canadian university hospital centre. METHODS AND RESULTS: From 1 October 2015 to 31 December 2017, all newly diagnosed ECs (n = 261) at our centre were screened for MMR protein deficiency by IHC. MMR deficiency was noted in 69 tumours (26.4%), among which 53 had somatic MLH1 promoter hypermethylation and were considered "screen-negative". The remaining MMR-deficient cases (n = 16) were considered "screen-positive" and were referred for genetic counselling and testing. Germline PVs were identified in 12/16 (75%). One additional PV was identified in a screen-negative individual who was independently referred to the Genetics service. This corresponds to an overall LS frequency of 5.0% among unselected women with EC, and 6.4% among women diagnosed under age 70 years. Our algorithm detected MMR gene pathogenic variants in 4.6% and 6.2% of unselected individuals and individuals under age 70 years, respectively. Four germline PVs (30.8%) were identified in individuals who did not meet any traditional LS screening criteria. CONCLUSIONS: Universal IHC screening for women with EC is an effective and feasible method of identifying individuals with LS in a Canadian context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".