Immunohistochemical Survey of Mismatch Repair Protein Expression in Uterine Sarcomas and Carcinosarcomas
Bibliographic record
Abstract
Uterine sarcomas and carcinosarcomas are an aggressive group of uterine malignancies. The frequency of mismatch repair (MMR) protein loss by immunohistochemical evaluation has not been comprehensively characterized in this group of tumors; hence, the appropriateness of applying an immunohistochemical panel to screen for Lynch syndrome in these tumors remains unclear. We examined for the immunohistochemical loss of 4 MMR proteins (MLH1, MSH2, MSH6, and PMS2) in a series of 67 uterine carcinosarcomas and 51 uterine sarcomas (20 leiomyosarcomas, 11 adenosarcomas, 9 low-grade endometrial stromal sarcomas, 8 high-grade endometrial stromal sarcomas/undifferentiated endometrial sarcomas, and 3 rhabdomyosarcomas) at our institution. Four of the 67 (6.0%) carcinosarcomas demonstrated abnormal MMR protein expression. Two tumors showed concurrent loss of MLH1 and PMS2 in both the carcinomatous and sarcomatous components. One tumor showed the loss of only PMS2 in both components. The remaining tumor showed an isolated loss of MLH1 and PMS2 in only the small cell carcinoma component, whereas the non-small-cell carcinoma and sarcoma components demonstrated normal staining patterns for MMR proteins. Two of 20 leiomyosarcomas (10%) showed the loss of MMR proteins: one with loss of PMS2 and the other with loss of MSH2 and MSH6. All other uterine sarcoma types examined showed intact MMR protein expression. These observations provide a basis for MMR protein screening in uterine carcinosarcomas and leiomyosarcomas but not in other types of uterine mesenchymal or mixed epithelial/mesenchymal malignancies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".