Recurrence and variability of germline <i>EPCAM</i> deletions in Lynch syndrome
Bibliographic record
Abstract
Recently, we identified 3' end deletions in the EPCAM gene as a novel cause of Lynch syndrome. These truncating EPCAM deletions cause allele-specific epigenetic silencing of the neighboring DNA mismatch repair gene MSH2 in tissues expressing EPCAM. Here we screened a cohort of unexplained Lynch-like families for the presence of EPCAM deletions. We identified 27 novel independent MSH2-deficient families from multiple geographical origins with varying deletions all encompassing the 3' end of EPCAM, but leaving the MSH2 gene intact. Within The Netherlands and Germany, EPCAM deletions appeared to represent at least 2.8% and 1.1% of the confirmed Lynch syndrome families, respectively. MSH2 promoter methylation was observed in epithelial tissues of all deletion carriers tested, thus confirming silencing of MSH2 as the causative defect. In a total of 45 families, 19 different deletions were found, all including the last two exons and the transcription termination signal of EPCAM. All deletions appeared to originate from Alu-repeat mediated recombination events. In 17 cases regions of microhomology around the breakpoints were found, suggesting nonallelic homologous recombination as the most likely mechanism. We conclude that 3' end EPCAM deletions are a recurrent cause of Lynch syndrome, which should be implemented in routine Lynch syndrome diagnostics.
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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 | 0.003 |
| 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".