The Day-To-Day Practice of MMR and MSI Assessment in Colorectal Adenocarcinoma: What We Know and What We Still Need to Explore
Bibliographic record
Abstract
BACKGROUND: The DNA mismatch repair (MMR) system is a highly preserved protein complex recognizing short insertions, short deletions, and single base mismatches during DNA replication and recombination. MMR protein status is identified using immunohistochemistry. Deficit in one or more MMR proteins, configuring deficient MMR status (dMMR), leads to frameshift mutations particularly clustered in microsatellite repeats. Thus, microsatellite instability (MSI) is the epiphenomenon of dMMR. In colorectal cancer (CRC), MMR/MSI status is a biomarker with prognostic and predictive value of resistance to 5-fluorouracil and response to immune checkpoint inhibitor therapy. SUMMARY: In this Review, we describe the challenges the practicing pathologist may face in relation to the assessment of MMR/MSI status and any open issues which still need to be addressed, focusing on pre-analytic issues, pitfalls in the interpretation, and technical aspects of the different assays. KEY MESSAGES: The current methods of detecting dMMR/MSI status have been optimized for CRCs, and whether these techniques can be applied to all tumor and specimen types is still not fully understood. Following the Food and Drug Administration (FDA), tissue/site agnostic drug approval of pembrolizumab for advanced/metastatic MSI tumors, MMR/MSI status in gastrointestinal tract is a common request from the oncologist. In this setting, several issues still need to be addressed, including criteria for sample adequacy.
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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 | 0.021 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".