Inhibition of anti-tumour reactivity of immune cells in the salivary gland cancer: A proteomic approach
Bibliographic record
Abstract
Adenoid cystic carcinoma (ACC), mucoepidermoid carcinoma (MEC), and oral squamous cell carcinoma (OSCC) respond differently to immunotherapy. Pembrolizumab, an immune checkpoint inhibitor, has been approved by the Food and Drug Administration for the treatment of squamous cell carcinomas of the head and neck region. While MEC has shown some response to pembrolizumab; however, ACC is the least responsive. At the molecular level, head and neck cancers produce immunosuppressive molecules, resulting in immune evasion. Therefore, we hypothesised that salivary gland cancer cells produce a higher number of immunosuppressive proteins that cause suppression of the immune system's anti-tumour reactivity. To determine differential protein expressions in OSCC, MEC, and ACC, we constructed cancer–immune cell co-culture models using different oral and salivary gland cancer cells. We performed SWATH, proteome profilers, gene ontology biological function, functional annotation clustering and protein interaction network analysis of all cancer samples in the co-culture models. Analysis of the acquired data showed that the overexpressed proteins in the OSCC cells and participated more in metabolic process, while in the salivary gland cancer cells, overexpressed proteins participated more in immune processes, immune checkpoint pathway. Upon protein function analysis of salivary gland cells, the overexpressed proteins found negatively affecting immune process and checkpoint pathway proteins. Overall, we conclude that salivary gland cancer is less responsive to immunotherapy, possibly because of the high presence of immunosuppressive proteins. However, further analysis is needed to verify the biological functions and interactive partners of each differentially expressed protein in ACC cells.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".