Functional Repair Assay for the Diagnosis of Constitutional Mismatch Repair Deficiency From Non-Neoplastic Tissue
Bibliographic record
Abstract
PURPOSE: Constitutional mismatch repair deficiency (CMMRD) is a highly penetrant cancer predisposition syndrome caused by biallelic mutations in mismatch repair (MMR) genes. As several cancer syndromes are clinically similar, accurate diagnosis is critical to cancer screening and treatment. As genetic diagnosis is confounded by 15 or more pseudogenes and variants of uncertain significance, a robust diagnostic assay is urgently needed. We sought to determine whether an assay that directly measures MMR activity could accurately diagnose CMMRD. PATIENTS AND METHODS: In vitro MMR activity was quantified using a 3'-nicked G-T mismatched DNA substrate, which requires MSH2-MSH6 and MLH1-PMS2 for repair. We quantified MMR activity from 20 Epstein-Barr virus-transformed lymphoblastoid cell lines from patients with confirmed CMMRD. We also tested 20 lymphoblastoid cell lines from patients who were suspected for CMMRD. We also characterized MMR activity from patients with neurofibromatosis type 1, Li-Fraumeni syndrome, polymerase proofreading-associated cancer syndrome, and Lynch syndrome. RESULTS: All CMMRD cell lines had low MMR activity (n = 20; mean, 4.14 ± 1.56%) relative to controls (n = 6; mean, 44.00 ± 8.65%; P < .001). Repair was restored by complementation with the missing protein, which confirmed MMR deficiency. All cases of patients with suspected CMMRD were accurately diagnosed. Individuals with Lynch syndrome (n = 28), neurofibromatosis type 1 (n = 5), Li-Fraumeni syndrome (n = 5), and polymerase proofreading-associated cancer syndrome (n = 3) had MMR activity that was comparable to controls. To accelerate testing, we measured MMR activity directly from fresh lymphocytes, which yielded results in 8 days. CONCLUSION: On the basis of the current data set, the in vitro G-T repair assay was able to diagnose CMMRD with 100% specificity and sensitivity. Rapid diagnosis before surgery in non-neoplastic tissues could speed proper therapeutic management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".