Radiation-guided nanoparticles enhance the efficacy of PARP inhibitors in primary and metastatic BRCA1-deficient tumors via immunotherapy
Bibliographic record
Abstract
Poly (ADP-ribose) polymerase inhibitors (PARPi) have revolutionized the treatment landscape for patients suffering from BRCA1- mutated breast and ovarian cancers. However, responses are not durable. We demonstrate that treatment with the PARPi, niraparib, increases programmed cell death protein-1 ligand (PD-L1) expression in BRCA1 -deficient cancer cells, contributing to immune evasion. To circumvent this, we developed P-selectin-targeted poly (lactic- co -glycolic) acid (PLGA)–poly (ethylene glycol) (PEG)–based nanoparticles (NPs) encapsulating PARP and PD-L1 inhibitors at a synergistic ratio. To further enhance tumor targeting, we leveraged radiation-induced P-selectin upregulation in BRCA1 -deficient cancer cells and their associated angiogenic endothelial cells, improving NP accumulation in the primary tumors and hard-to-target metastatic sites, including brain metastasis. Using a combination of traditional 2-dimensional (2D) cell cultures, advanced 3-dimensional (3D) spheroids, tumor-on-a-chip platforms, and in vivo models, we demonstrate the enhanced accumulation and efficacy of the radiation-guided P-selectin-targeted NPs in primary and brain-metastatic BRCA1 -deficient tumors. Illustration showing a proposed mechanism by which radiation induces P-selectin expression on cancer cells and cancer-associated endothelial cells, resulting in enhanced accumulation of the sulfate-functionalized nanoparticles (sNPs), encapsulating niraparib and PD-L1 inhibitor (PD-L1i), in tumor sites. Created with BioRender.
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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 | 0.001 |
| 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".