Potentiating Cancer Immunotherapy Using Papaya Mosaic Virus-Derived Nanoparticles
Bibliographic record
Abstract
The recent development of novel immunotherapies is revolutionizing cancer treatment. These include, for example, immune checkpoint blockade, immunomodulation, or therapeutic vaccination. Although effective on their own, combining multiple approaches will most likely be required in order to achieve the maximal therapeutic benefit. In this regard, the papaya mosaic virus nanoparticle (PapMV) has shown tremendous potential as (i) an immunostimulatory molecule, (ii) an adjuvant, and (iii) a vaccine platform through its intrinsic capacity to activate the innate immune response in an IFN-α-dependent manner. Here, we demonstrate that intratumor administration of PapMV significantly slows down melanoma progression and prolongs survival. This correlates with enhanced chemokine and pro-inflammatory-cytokine production in the tumor and increased immune-cell infiltration. Proportions of total and tumor-specific CD8(+) T cells dramatically increase following PapMV treatment whereas those of myeloid-derived suppressor cells (MDSC) concomitantly decrease. Moreover, systemic PapMV administration prevents metastatic tumor-implantation in the lungs. Importantly, PapMV also synergistically improves the therapeutic benefit of dendritic cell (DC)-based vaccination and PD-1 blockade by potentiating antitumor immune responses. This study illustrates the immunostimulatory potential of a plant virus-derived nanoparticle for cancer therapy either alone or in conjunction with other promising immunotherapies in clinical development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".