<scp>PD</scp>‐L1, <scp>RB</scp>1 and mismatch repair protein immunohistochemical expression in neuroendocrine carcinoma, small cell type, of the uterine cervix
Bibliographic record
Abstract
AIMS: Neuroendocrine carcinoma, small cell type, of the uterine cervix (SmCC-Cx) is a rare human papilloma virus (HPV) related tumour with limited therapeutic options. Merkel cell carcinoma, another virus-associated neuroendocrine malignancy, has significant programmed death ligand 1 (PD-L1) expression rates. PD-L1 expression has been reported in other malignancies of the cervix. We aimed to determine the prevalence of PD-L1 in the context of mismatch repair protein (MMR) and RB1 expression status in SmCC-Cx. METHODS AND RESULTS: Ten cases of SmCC-Cx were tested by immunohistochemistry for expression of PD-L1, MLH1, MSH2, MSH6, PMS2, RB1, CD3, CD20 and for HPV by in-situ hybridisation (ISH). PD-L1 expression was scored quantitatively (H-score) in tumour cells and lymphocytes (tumoral/peritumoral). PD-L1 positivity was seen in seven cases, focal in most (H-score range 3-140). Three of nine cases showed MMR deficiency. PD-L1 expression levels correlated with MMR expression status: all three MLH1/PMS2-deficient cases had a ≥5% PD-L1 staining and an H-score ≥10 (P = 0.01). RB1 was lost in four of nine cases, all PD-L1 positive, but this correlation was not statistically significant. Seven of nine tumours were positive for HPV-ISH; two of these had MLH1/PMS2 loss. Of the two HPV-ISH negative tumours, one had MLS1/PMS2 loss. CONCLUSIONS: PD-L1 expression, predominantly focal, is seen in 70% of SmCC-Cx, while loss of MMR expression is seen in 33% of SmCC-Cx in our cohort. PD-L1 expression in more than 10% of tumour cells is seen in a subset of tumours in association with loss of MMR expression. These patients may be amenable to immune checkpoint inhibitor therapy as a promising alternative for this aggressive disease.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".