Prevalence of Mismatch Repair Deficiency in Primary Prostate Cancer in a Large Prospective Cohort
Bibliographic record
Abstract
PURPOSE: Mismatch repair (MMR) deficiency and microsatellite instability are predictive biomarkers for immunotherapy response. The best approach to identify patients with such tumors is unclear in prostate cancer. EXPERIMENTAL DESIGN: This study included 1,016 men diagnosed with primary prostate cancer during prospective follow-up of the Health Professionals Follow-up Study and Physicians' Health Study. The highest-grade/index lesions from radical prostatectomy (95%) or transurethral resections of the prostate were mounted on tissue microarrays. Scoring of immunohistochemistry for the MMR proteins MLH1, MSH2, MSH6, and PMS2 required a nontumor internal positive control for designating deficiency. Validation was done on full sections and with PCR-based quantification of microsatellite repeats. RESULTS: Tumor stage was predominantly pathologically localized with a full distribution of Gleason scores. MMR tumor scoring could be performed with available internal positive control tissue in 75% to 90% of cases, depending on the MMR protein. Of the 903 tumors evaluable for MSH2 protein loss, 4 tumors had loss of MSH2 (prevalence, 0.4%; 95% confidence interval, 0.2%-1.1%), and 3 of 708 evaluable tumors had concomitant loss of MSH6 (prevalence, 0.4%; 95% confidence interval, 0.1%-1.2%). No tumor had loss of MLH1 or PMS2. The four MMR-deficient cases had higher Gleason scores, and three had non-zero microsatellite repeats. CONCLUSIONS: In this nationwide prospective study, MMR deficiency was rare in primary, surgically treated prostate cancer. The low prevalence and the need for an internal positive control for this assay are feasibility concerns for unselected routine immunohistochemistry-based screening for MMR deficiency on limited tissue specimens, such as prostate biopsies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".