A Validated Highly Sensitive Microsatellite Instability Assay Accurately Identifies Individuals Harboring Biallelic Germline <i>PMS2</i> Pathogenic Variants in Constitutional Mismatch Repair Deficiency
Bibliographic record
Abstract
BACKGROUND: Constitutional mismatch repair deficiency (CMMRD) is a rare and extraordinarily penetrant childhood-onset cancer predisposition syndrome. Genetic diagnosis is often hampered by the identification of mismatch repair (MMR) variants of unknown significance and difficulties in PMS2 analysis, the most frequently mutated gene in CMMRD. We present the validation of a robust functional tool for CMMRD diagnosis and the characterization of microsatellite instability (MSI) patterns in blood and tumors. METHODS: The highly sensitive assessment of MSI (hs-MSI) was tested on a blinded cohort of 66 blood samples and 24 CMMRD tumor samples. Hs-MSI scores were compared with low-pass genomic instability scores (LOGIC/MMRDness). The correlation of hs-MSI scores in blood with age of cancer onset and the distribution of insertion-deletion (indel) variants in microsatellites were analyzed in a series of 169 individuals (n = 68 CMMRD, n = 124 non-CMMRD). RESULTS: Hs-MSI achieved high accuracy in the identification of CMMRD in blood (sensitivity 98.5% and specificity 100%) and detected MSI in CMMRD-associated tumors. Hs-MSI had a strong positive correlation with whole low-pass genomic instability LOGIC scores (r = 0.89, P = 2.2e-15 in blood and r = 0.82, P = 7e-3 in tumors). Indel distribution identified PMS2 pathogenic variant (PV) carriers from other biallelic MMR gene PV carriers with an accuracy of 0.997. Higher hs-MSI scores correlated with younger age at diagnosis of the first tumor (r = -0.43, P = 0.011). CONCLUSIONS: Our study confirms the accuracy of the hs-MSI assay as ancillary testing for CMMRD diagnosis, which can also characterize MSI patterns in CMMRD-associated cancers. Hs-MSI is a powerful tool to pinpoint PMS2 as the affected germline gene and thus potentially personalize cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".