False Negative Rate (2%) of Lynch Syndrome Screening Utilizing A Two-Antibody (PMS2/MSH6) Immunohistochemistry Panel: Failure To Detect a Subset of MSH2-Deficient Endometrial Carcinomas
Bibliographic record
Abstract
Abstract Introduction/Objective Lynch syndrome (LS) is an inherited condition caused by defective DNA mismatch repair (MMR), leading to a higher incidence of cancers of multiple sites. Screening for LS is now recommended for new diagnoses of endometrial cancer (EC) using either two- (PMS2, MSH6) or four-antibody (2/4Ab) (PMS2, MSH6, MSH2, MLH1) immunohistochemical (IHC) panels. The 2Ab panel assumes consistent loss of expression of the minor dimer component, PMS2 or MSH6, when the major component, MLH1 or MSH2, respectively, is lost due to mutation. Recent studies have indicated that 2Ab testing may lead to underdiagnosis of MSH2-deficient tumors in cases where MSH6 staining is weak or focal, potentially leading to underdiagnosis of LS. Methods We conducted a retrospective study using archived slides for 293 cases of EC (identified via LIS search from 2016-2019) that were screened using the 2Ab panel (expanded to 4Ab when PMS2 or MSH6 were negative). MSH6 expression was reviewed; if weak, focal (less than 10% staining), or both, MSH2 IHC was performed. When a previously undetected loss of MSH2 expression was found, the attending clinician was informed such that referral to medical genetics could be arranged. Results Results Overall, 68 (23.2%) tumors were MMR deficient, with 54 (18.4%) showing MLH1/PMS2 loss, 7 (2.4%) with MSH2/MSH6 loss, 2 (0.7%) with isolated PMS2 loss, 4 (1.4%) with isolated MSH6 loss, and 6 (2.0%) with isolated MSH2 loss (i.e. intact but weak/focal MSH6, seen in biopsy and hysterectomy specimens). Interestingly, 1 tumor (1.5%) demonstrated loss of MSH6, MLH1 and PMS2. Two tumors (0.7%) with isolated MSH2 loss were previously unrecognized as MMR-deficient and hence at high risk for LS. Both cases were evaluated by PCR for microsatellite instability (MSI) and confirmed to have high-degree MSI. Conclusion This study identifies the frequency of mismatch repair deficient endometrial cancers in Atlantic Canada, highlights a potential pitfall of using two-stain IHC screening for Lynch syndrome, and supports emerging recommendations for universal Lynch syndrome screening in EC.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".