Tumor-Derived Exosome and Immune Modulation
Bibliographic record
Abstract
Tumor cells, like most other cells, release exosomes called tumor-derived exosomes (TEX) and are vital for intercellular communication. TEX are membrane-bound extracellular vesicles (EVs), containing unique cargo reminiscent of the parent tumor cells and possess immunomodulatory functions. TEX carries factors that directly promote immunosuppression in the tumor microenvironment and indirectly attract immunosuppressive T-regulatory (Treg) cells. The tumor-secreted exosomes can transfer their cargo by multiple mechanisms like fusion, phagocytosis, and receptor-mediated endocytosis, activating the recipient cells. TEX directly engages and releases cytokines, inactivating natural killer (NK) cells and T-cells and activating apoptosis. Tumor-derived exosomes also release soluble factors to suppress dendritic cell (DC) maturation while activating the expansion of immune-suppressive cells like Myeloid-derived suppressor cells (MDSCs) and Regulatory T (Treg) cells. Several studies have shown the relevance of TEX containing tumor-associated antigens (TAA) in reducing the efficacy of cancer immunotherapy and adoptive cell therapy. Hence understanding the basic biology and mechanism of TEX-mediated immunosuppression is critical in discovering cancer biomarkers and finding better immunotherapy and cell therapy approaches. In this chapter, we have discussed TEX biogenesis, TEX’s structural and molecular features, TEX-mediated immunosuppression, and its relation to immunotherapy.
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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 | 0.001 |
| 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) | 0.006 | 0.004 |
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".