Dendritic cells loaded with exosomes derived from cancer stem cell‐enriched spheroids as a potential immunotherapeutic option
Bibliographic record
Abstract
Abstract Cancer stem cells (CSCs) are responsible for therapeutic resistance and recurrence in colorectal cancer. Despite advances in immunotherapy, the inability to specifically eradicate CSCs has led to treatment failure. Hence, identification of appropriate antigen sources is a major challenge in designing dendritic cell (DC)‐based therapeutic strategies against CSCs. Here, in an in vitro model using the HT‐29 colon cancer cell line, we explored the efficacy of DCs loaded with exosomes derived from CSC‐enriched colonospheres (CSCenr‐EXOs) as an antigen source in activating CSC‐specific T‐cell responses. HT‐29 lysate, HT‐29‐EXOs and CSCenr lysate were independently assessed as separate antigen sources. Having confirmed CSCs enrichment in spheroids, CSCenr‐EXOs were purified and characterized, and their impact on DC maturation was investigated. Finally, the impact of the antigen‐pulsed DCs on the proliferation rate and also spheroid destructive capacity of autologous T cells was assessed. CSCenr‐EXOs similar to other antigen groups had no suppressive/negative impacts on phenotypic maturation of DCs as judged by the expression level of costimulatory molecules. Notably, similar to CSCenr lysate, CSCenr‐EXOs significantly increased the IL‐12/IL‐10 ratio in supernatants of mature DCs. CSCenr‐EXO‐loaded DCs effectively promoted T‐cell proliferation. Importantly, T cells stimulated with CSCenr‐EXOs disrupted spheroids' structure. Thus, CSCenr‐EXOs present a novel and promising antigen source that in combination with conventional tumour bulk‐derived antigens should be further explored in pre‐clinical immunotherapeutic settings for the efficacy in hampering recurrence and metastatic spread.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".