Systemic administration of a viral nanoparticle neoadjuvant prevents lung metastasis development through emergency myelopoiesis
Bibliographic record
Abstract
Cancer presents a significant public health concern, particularly in the context of metastatic disease. Surgical removal of primary tumors, while essential, can inadvertently heighten the risk of metastasis. Thus, there is a critical need for innovative neoadjuvant therapies capable of curtailing metastatic progression before or immediately following tumor resection. Addressing this imperative, the papaya mosaic virus nanoparticle (PapMV) has demonstrated potent immunostimulatory capabilities against both viruses and tumors, effectively hindering their proliferation. Our study reveals that PapMV exerts a protective effect against lung metastasis when administered systemically prior to tumor implantation or during the early stages of metastasis in various mouse models of cancer. This anti-tumor effect is initiated by the recruitment of myeloid cells in the lungs. These cells adopt a pro-inflammatory profile, secreting cytokines such as IFN-α, thus fostering a tumor microenvironment inhospitable to tumor progression. Crucially, this protective mechanism hinges on the presence of macrophages before treatment. TLR7 and IFN-I signaling pathways also play pivotal roles in this process. Furthermore, our findings demonstrate that PapMV triggers the activation of the bone marrow emergency response, which accounts for the influx of myeloid cells into the lungs. This study unveils a novel aspect of PapMV's functionality. By bolstering the immune system, PapMV confers robust protection against metastasis at an early stage of disease progression. This discovery holds promise for therapeutic intervention, particularly as a preemptive measure prior to or just after surgical intervention.
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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) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".