An Integrative DNA Sequencing and Methylation Panel to Assess Mismatch Repair Deficiency
Bibliographic record
Abstract
Clinical testing for mismatch repair (MMR) deficiency often entails serial testing of tumor and constitutional DNA using multiple assays. To minimize cost and specimen requirements of MMR testing, we developed an integrated targeted sequencing protocol (termed MultiMMR) that tests for promoter methylation, mutations, copy number alterations, copy neutral loss of heterozygosity, and microsatellite instability from a single aliquot of DNA. Hybrid capture of DNA-sequencing libraries constructed with methylated adapters was performed on 142 samples (60 tumors and 82 constitutional samples) from 82 patients with MMR-associated colorectal, endometrial, and brain cancers as well as a synthetic DNA mix with 11 known mutations. The captured material was split to enable parallel bisulfite and conventional sequence analysis. The panel targeted microsatellite regions and 13 genes associated with MMR, hypermutation, and hereditary colorectal cancer. MultiMMR recapitulated clinical testing results in 23 of 24 cases, was able to explain MMR loss in an additional 29 of 48 patients with incomplete or inconclusive testing, and identified all 11 MMR variants within the synthetic DNA mix. Promoter methylation and microsatellite instability analysis found 95% and 97% concordance with clinical testing, respectively. We report the feasibility for amalgamation of the current stepwise and complex clinical testing workflow into an integrated test for hereditary and somatic causes of MMR deficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".