Nanoparticles-mediated intratumoral gene editing targeting PD-L1 and Galectin-9 for improved cancer immunotherapy
Bibliographic record
Abstract
PD-L1, a typical immune checkpoint expressed on tumor cells, reduces the effectiveness of T cell-mediated killing, which is further aggravated by Galectin-9 (Gal-9) co-expression through the TIM3/Gal-9 pathway. Although immune checkpoint inhibitors have shown promise in cancer therapy, limitations remain including low response rate, systemic toxicities, and the need of frequent treatments. Here, we described a dual knockout approach targeting PD-L1 and Gal-9 in tumor cells, achieved by nanoparticle-assisted CRISPR-Cas9 delivery, aimed at improved cancer immunotherapy. A calcium phosphate nanoparticle (CaP NP) was engineered for co-delivery of CRISPR-Cas9/sgRNA ribonucleoprotein (RNP) and initiation of anti-tumor immunity. Intratumoral administration of RNP-loaded CaP NPs effectively knocked out PD-L1 and Gal-9 in tumor cells, evoking robust anti-tumor immunity. Additionally, Ca 2+ overload due to the degradation of CaP NPs led to release of damage-associated molecular patterns (DAMPs) signals, further enhancing T-cell-mediated antitumor immune responses. Our results demonstrated that this treatment effectively evoked both local and systemic anti-tumor immune responses, significantly inhibiting the growth of primary and distant tumors in mouse models. Importantly, local treatment also altered the phenotypes of circulating tumor cells, as a substantial of circulating tumor cells originated from RNP-CaP-treated primary tumors and exhibited dual knockouts, which led to reduced lung metastasis.
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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) | 0.000 | 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".