Molecular Characterization of MSI-H Colorectal Cancer by <i>MLHI</i> Promoter Methylation, Immunohistochemistry, and Mismatch Repair Germline Mutation Screening
Bibliographic record
Abstract
Microsatellite instability (MSI) occurs in 10% to 20% of colorectal cancers (CRC) and has been attributed to both MLH1 promoter hypermethylation and germline mutation in the mismatch repair (MMR) genes. We present results from a large population- and clinic-based study of MLH1 methylation, immunohistochemistry, and MMR germline mutations that enabled us to (a) estimate the prevalence of MMR germline mutations and MLH1 methylation among MSI-H cases and help us understand if all MSI-H CRC is explained by these mechanisms and (b) estimate the associations between MLH1 methylation and sex, age, and tumor location within the colon. MLH1 methylation was measured in 1,061 population-based and 172 clinic-based cases of CRC. Overall, we observed MLH1 methylation in 60% of population-based MSI-H cases and in 13% of clinic-based MSI-H cases. Within the population-based cases with MMR mutation screening and conclusive immunohistochemistry results, we identified a molecular event in MMR in 91% of MSI-H cases: 54% had MLH1 methylation, 14% had a germline mutation in a MMR gene, and 23% had immunohistochemistry evidence for loss of a MMR protein. We observed a striking age difference, with the prevalence of a MMR germline mutation more than 4-fold lower and the prevalence of MLH1 methylation more than 4-fold higher in cases diagnosed after the age of 50 years than in cases diagnosed before that age. We also determined that female sex is an independent predictor of MLH1 methylation within the MSI-H subgroup. These results reinforce the importance of distinguishing between the underlying causes of MSI in studies of etiology and prognosis.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".