The link between intracellular calcium signaling and exosomal PD-L1 in cancer progression and immunotherapy
Bibliographic record
Abstract
Exosomes are small membrane vesicles containing microRNA, RNA, DNA fragments, and proteins that are transferred from donor cells to recipient cells. Tumor cells release exosomes to reprogram the factors associated with the tumor microenvironment (TME) causing tumor metastasis and immune escape. Emerging evidence revealed that cancer cell-derived exosomes carry immune inhibitory molecule program death ligand 1 (PD-L1) that binds with receptor program death protein 1 (PD-1) and promote tumor progression by escaping immune response. Currently, some FDA-approved monoclonal antibodies are clinically used for cancer treatment by blocking PD-1/PD-L1 interaction. Despite notable treatment outcomes, some patients show poor drug response. Exosomal PD-L1 plays a vital role in lowering the treatment response, showing resistance to PD-1/PD-L1 blockage therapy through recapitulating the effect of cell surface PD-L1. To enhance therapeutic response, inhibition of exosomal PD-L1 is required. Calcium signaling is the central regulator of tumorigenesis and can regulate exosome biogenesis and secretion by modulating Rab GTPase family and membrane fusion factors. Immune checkpoints are also connected with calcium signaling and calcium channel blockers like amlodipine, nifedipine, lercanidipine, diltiazem, and verapamil were also reported to suppress cellular PD-L1 expression. Therefore, to enhance the PD-1/PD-L1 blockage therapy response, the reduction of exosomal PD-L1 secretion from cancer cells is in our therapeutic consideration. In this review, we proposed a therapeutic strategy by targeting calcium signaling to inhibit the expression of PD-L1-containing exosome levels that could reduce the anti-PD-1/PD-L1 therapy resistance and increase the patient's drug response rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".