An innovative plasmacytoid dendritic cell line-based cancer vaccine primes and expands antitumor T-cells in melanoma patients in a first-in-human trial
Bibliographic record
Abstract
The efficacy of immune checkpoint inhibitors has been shown to depend on preexisting antitumor immunity; thus, their combination with cancer vaccines is an attractive therapeutic approach. Plasmacytoid dendritic cells (PDC) are strong inducers of antitumor responses and represent promising vaccine candidates. We developed a cancer vaccine approach based on an allogeneic PDC line that functioned as a very potent antigen-presenting cell in pre-clinical studies. In this phase Ib clinical trial, nine patients with metastatic stage IV melanoma received up to 60 million irradiated PDC line cells loaded with 4 melanoma antigens, injected subcutaneously at weekly intervals. The primary endpoints were safety and tolerability. The vaccine was well tolerated and no serious vaccine-induced side effects were recorded. Strikingly, there was no allogeneic response toward the vaccine, but a significant increase in the frequency of circulating anti-tumor specific T lymphocytes was observed in two patients, accompanied by a switch from a naïve to memory phenotype, thus demonstrating priming of antigen-specific T-cells. Signs of clinical activity were observed, including four stable diseases according to IrRC and vitiligoïd lesions. Four patients were still alive at week 48. We also demonstrate the in vitro enhancement of specific T cell expansion induced by the synergistic combination of peptide-loaded PDC line with anti-PD-1, as compared to peptide-loaded PDC line alone. Taken together, these clinical observations demonstrate the ability of the PDC line based-vaccine to prime and expand antitumor CD8+ responses in cancer patients. Further trials should test the combination of this vaccine with immune checkpoint inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".