The immune microenvironment and expression of PD‐L1, PD‐1, PRAME and MHC I in salivary duct carcinoma
Bibliographic record
Abstract
AIMS: Salivary duct carcinoma (SDC) is an aggressive salivary malignancy that results in high mortality rates and is often resistant to chemotherapy. Anti-programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint inhibitors have led to dramatic improvements in patients with various cancers. Other immunotherapeutic approaches, e.g. cancer vaccines, have shown promising results. Cancer testis antigens, e.g. preferentially expressed antigen in melanoma (PRAME), are regarded as promising vaccine targets because of their tumour-specific expression pattern. METHODS AND RESULTS: We analysed the immunoexpression of PD-L1, PD-1, major histocompatibility complex class I (MHC I) and PRAME in 53 SDCs. The immunoexpression levels of PD-L1 in tumour cells (TCs) and immune cells (ICs), PD-1 in ICs, PRAME in TCs and MHC I in TCs were analysed, and were correlated with outcome. PRAME expression was seen in 83% of SDCs. No PRAME staining was present in normal salivary gland tissue. With the three established diagnostic algorithms proposed for head and neck squamous cell carcinoma, the criteria being a combined positive score of ≥1, TC% ≥1%, and TC% ≥25%, 35 (66%), 17 (32%) and three cases (6%), respectively, were deemed to be positive for PD-L1. PD-1-positive ICs were seen in 35 (66%) cases. MHC I down-regulation was seen in 82% of SDCs. There was a significant correlation among PD-L1 expression in ICs, PD-1 expression in ICs, and PRAME expression in TCs. PD-L1 expression in TCs and lack of PD-1 expression in ICs were associated with decreased disease-specific survival in SDC patients. CONCLUSIONS: Alterations of the tumour immune microenvironment are common in SDCs, including expression of PD-1/PD-L1 and PRAME, which opens the way to potential novel immune therapies, such as cancer vaccination and PD-1/PD-L1 blockade, in these tumours.
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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 | 0.000 |
| 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".