Biodegradable Nanoparticles Immunotherapy for the Treatment of Carcinogen-Induced Bladder Cancer
Bibliographic record
Abstract
Abstract Patients with locally advanced and metastatic urothelial carcinoma have a low survival rate (median 15.7 months, 13.1–17.8), with a 23% response rate to monotherapy treatment with anti-PD-L1 immunotherapy. We recently reported the potent effects of i.v. infusions of immune modifying poly(lactic-co-glycolic acid) (PLGA) nanoparticles (IMPs; named ONP-302) to both decrease B16.F10, MC-38, LLC, and 4T1 tumor growth and allow for B16.F10 tumor responsiveness to anti-PD-1 treatment. We next sought to determine if ONP-302 treatment could significantly decrease the more clinically relevant BBN-carcinogen induced bladder cancer. The present data show that ONP-302 treatment significantly decreases bladder cancer stage and overall bladder weight. While minor epithelial hyperplasia remained, ONP-302 treatment inhibited widespread carcinoma in situ and muscle invasive tumor development, as compared to saline treated mice. Mechanistically, ONP-302 infusion decreased tumor growth via the activation of the cGAS/STING pathway within myeloid cells, and subsequently increased CD8+ T cell and NK cell activation via IL-15. In subsequent studies to assess the cellular mechanisms involved in ONP-302 function, we found that in vitro treatment with ONP-302 rapidly induced oxidized DNA within the cells that took up PLGA nanoparticles and oxidized DNA was present within the supernatant as well. Furthermore, co-treatment of ONP-302 with DNase inhibited the STING mediated cytokine production. These findings indicate that ONP-302 allows for tumor control via reprogramming myeloid cells and inducing a STING pathway activation, increases anti-PD-1 response rates, and significantly decreases BBN-carcinogen induced bladder cancer.
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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) | 0.001 | 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".