Should you repeat mismatch repair testing in cases of tumour recurrence? An evaluation of repeat mismatch repair testing by the use of immunohistochemistry in recurrent tumours of the gastrointestinal and gynaecological tracts
Bibliographic record
Abstract
AIMS: The role of mismatch repair (MMR) testing has evolved from identifying Lynch syndrome patients to predicting response to immune checkpoint inhibitors. This has led to requests from clinicians to retest recurrences of MMR-proficient primary tumours in the hope that the recurrence may show a different MMR status and qualify the patient for treatment. We aimed to determine whether repeat testing is warranted. METHODS AND RESULTS: We evaluated recurrent tumours (local recurrences or metastases) from 137 patients with MMR-proficient primary tumours of the gastrointestinal and gynaecological tracts. The local recurrences and metastases all occurred at least 30 days after resection of the primary tumour. We used a combination of a tissue microarray and whole slide staining to perform immunohistochemistry (IHC) for PMS2, MLH1, MSH2, and MSH6, and compared the results with the MMR status of the primary tumour. Three of 137 (2%) initially showed a discordant staining pattern. However, further investigation showed that these discordances were attributable to some of the known pitfalls associated with MMR IHC interpretation - post-radiotherapy loss of MSH6 expression and subclonal loss of MLH1 staining. We did not identify any cases with a genuine discordance in MMR status. CONCLUSION: We conclude that repeat MMR IHC testing of recurrences is not warranted, as MMR status does not change relative to that of the primary tumour.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".