Genomic Microsatellite Signatures Identify Germline Mismatch Repair Deficiency and Risk of Cancer Onset
Bibliographic record
Abstract
PURPOSE Diagnosis of Mismatch Repair Deficiency (MMRD) is crucial for tumor management and early detection in patients with the cancer predisposition syndrome constitutional mismatch repair deficiency (CMMRD). Current diagnostic tools are cumbersome and inconsistent both in childhood cancers and in determining germline MMRD. PATIENTS AND METHODS We developed and analyzed a functional Low-pass Genomic Instability Characterization (LOGIC) assay to detect MMRD. The diagnostic performance of LOGIC was compared with that of current established assays including tumor mutational burden, immunohistochemistry, and the microsatellite instability panel. LOGIC was then applied to various normal tissues of patients with CMMRD with comprehensive clinical data including age of cancer presentation. RESULTS Overall, LOGIC was 100% sensitive and specific in detecting MMRD in childhood cancers (N = 376). It was more sensitive than the microsatellite instability panel (14%, P = 4.3 × 10 −12 ), immunohistochemistry (86%, P = 4.6 × 10 −3 ), or tumor mutational burden (80%, P = 9.1 × 10 −4 ). LOGIC was able to distinguish CMMRD from other cancer predisposition syndromes using blood and saliva DNA ( P < .0001, n = 277). In normal cells, MMRDness scores differed between tissues (GI > blood > brain), increased over time in the same individual, and revealed genotype-phenotype associations within the mismatch repair genes. Importantly, increased MMRDness score was associated with younger age of first cancer presentation in individuals with CMMRD ( P = 2.2 × 10 −5 ). CONCLUSION LOGIC was a robust tool for the diagnosis of MMRD in multiple cancer types and in normal tissues. LOGIC may inform therapeutic cancer decisions, provide rapid diagnosis of germline MMRD, and support tailored surveillance for individuals with CMMRD.
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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 | 0.002 |
| 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) | 0.002 | 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".