Exosomes as a tumor immune escape mechanism: possible therapeutic implications
Bibliographic record
Abstract
Advances in cancer therapy have been substantial in terms of molecular understanding of disease mechanisms, however these advances have not translated into increased survival in the majority of cancer types. One unsolved problem in current cancer therapeutics is the substantial immune suppression seen in patients. Conventionally, investigations in this area have focused on antigen-nonspecific immune suppressive molecules such as cytokines and T cell apoptosis inducing molecules such as Fas ligand. More recently, studies have demonstrated nanovesicle particles termed exosomes are involved not only in stimulation but also inhibition of immunity in physiological conditions. Interestingly, exosomes secreted by cancer cells have been demonstrated to express tumor antigens, as well as immune suppressive molecules such as PD-1L and FasL. Concentrations of exosomes from plasma of cancer patients have been associated with spontaneous T cell apoptosis, which is associated in some situations with shortened survival. In this paper we place the "exosome-immune suppression" concept in perspective of other tumor immune evasion mechanisms. We conclude by discussing a novel therapeutic approach to cancer immune suppression by extracorporeal removal of exosomes using hollow fiber filtration technology.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".