Bibliographic record
Abstract
Background: Dendritic cell (DC) vaccines have significant potential in cancer immunotherapy. While autologous DCs can be derived from bone marrow, umbilical cord, and peripheral blood, monocyte-derived DC vaccines are most widely tested in clinical trials. However, producing autologous DC vaccines is labor intensive, has large variations among donors, and may not be feasible for patients with impaired cell function or requiring multiple vaccinations. Human pluripotent stem cells (hPSCs) have unlimited expansion potential while maintaining their pluripotency. They are being tested as a novel cell source to derive DCs for clinical application. Lung cancer is the leading cause for all cancer-related mortality. Efficient treatment of lung cancer by DC vaccines could offer great benefits in cancer immunotherapy. This review uses lung cancer as a case study to discuss the application of DC vaccines. Results: DC derivation from hPSCs has been demonstrated with high purity and comparable in vitro functions to autologous DCs derived from monocytes. The differentiation can be achieved either by co-culturing hPSCs with OP9 stromal cells or by the formation of three-dimensional embryoid bodies. As the scalable culture system is critical for hPSC-derived DC production, progress in the scalable culture systems for other hPSC-derived cells is reviewed and the use of relevant systems for hPSC-derived DCs is proposed. Conclusions: hPSCs provide a new source for DC production and have significant implication in DC-based cancer immunotherapy. Their use in clinical trials requires refinement of the culture expansion and differentiation protocols. Development of scalable culture systems is crucial in truly harnessing the potential of the hPSC-based DC immunotherapy in cancer treatment.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".