Deep intronic MSH2 variant confirms Muir-Torre subtype of Lynch syndrome
Bibliographic record
Abstract
Whole genome sequencing (WGS) can uncover clinically significant non-coding variants missed by standard germline testing, as demonstrated here in a patient with Muir-Torre syndrome (MTS), a subtype of Lynch syndrome (LS). In this case, despite a convincing clinical phenotype and immunohistochemical loss of MSH2/MSH6 in one of the patient's tumors, conventional gene panel testing failed to detect a germline pathogenic variant (GPV). WGS identified a deep intronic MSH2 variant, and tumor sequencing revealed somatic MSH2 mutations (second hits) across multiple tumors, confirming mismatch repair deficiency and establishing an LS diagnosis. This report underscores the limitations of routine genetic testing and highlights the clinical utility of WGS in identifying pathogenic variants in non-coding regions. It also emphasizes the role of dermatologists in recognizing cutaneous markers of hereditary cancer syndromes and the importance of interdisciplinary evaluation in guiding both patient care and familial risk assessment.
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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 | 0.001 |
| 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 | 0.001 | 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".