High prevalence of mismatch repair deficiency in prostate cancers diagnosed in mismatch repair gene mutation carriers from the colon cancer family registry
Bibliographic record
Abstract
The question of whether prostate cancer is part of the Lynch syndrome spectrum of tumors is unresolved. We investigated the mismatch repair (MMR) status and pathologic features of prostate cancers diagnosed in MMR gene mutation carriers. Prostate cancers (mean age at diagnosis = 62 ± SD = 8 years) from 32 MMR mutation carriers (23 MSH2, 5 MLH1 and 4 MSH6) enrolled in the Australasian, Mayo Clinic and Ontario sites of the Colon Cancer Family Registry were examined for clinico-pathologic features and MMR-deficiency (immunohistochemical loss of MMR protein expression and high levels of microsatellite instability; MSI-H). Tumor MMR-deficiency was observed for 22 cases [69 %; 95 % confidence interval (CI) 50–83 %], with the highest prevalence of MMR-deficiency in tumors from MSH2 mutation carriers (19/23, 83 %) compared with MLH1 and MSH6 carriers combined (3/9, 33 %; p = 0.01). MMR-deficient tumors had increased levels of tumor infiltrating lymphocytes compared with tumors without MMR-deficiency (p = 0.04). Under the assumption that tumour MMR-deficiency occurred only because the cancer was caused by the germline mutation, mutation carriers are at 3.2-fold (95 % CI 2.0–6.3) increased risk of prostate cancer, and when assessed by gene, the relative risk was greatest for MSH2 carriers (5.8, 95 % CI 2.6–20.9). Prostate cancer was the first or only diagnosed tumor in 37 % of carriers. MMR gene mutation carriers have at least a twofold or greater increased risk of developing MMR-deficient prostate cancer where the risk is highest for MSH2 mutation carriers. MMR IHC screening of prostate cancers will aid in identifying MMR gene mutation carriers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".