Abstract 5990: Targeted nanoparticles reduce tumor progression and lung metastasis by limiting DNA damage repair and immune suppression in metastatic triple-negative breast cancer
Bibliographic record
Abstract
Abstract Background: Metastatic triple-negative breast cancer (TNBC), especially with BRCA1/2 mutations, is a deadly subtype of breast cancer with ~12% of 5-year survival. Despite their FDA approval, the therapeutic benefits of poly(adenosine diphosphate-ribose) polymerase inhibitors (PARPi) are limited to BRCA1/2 mutated malignancies. Moreover, the PARPi olaparib has been shown to upregulate the expression of programmed death-ligand 1 (PD-L1) leading to immune suppression. The purpose of this study is to investigate if a novel nanoparticle formulation of oligomer hyaluronic acid (oHA) and doxorubicin (DOX) co-loaded in an iRGD-conjugated polymer-lipid nanocarrier (iRGD-DOX-oHA-PLN) can inhibit DNA damage repair and suppress PD-L1 in both mutant and non-mutant TNBC cells and reduce tumor progression and lung metastasis compared to the PARPi olaparib. We postulate that the co-administered oHA will block the signaling pathways of native hyaluronic acid (HA) receptors, the cluster of differentiation 44 (CD 44) and receptor for HA mediated motility (RHAMM), that regulate DNA damage repair and immunosuppression and enhance DOX efficacy. Methods: In vitro cellular uptake of various DOX formulations by TNBC MDA-MB-231-luc-D3H2LN cells and MDA-MB-436 (BRCA1 mutant) cells was examined using confocal laser scanning microscopy or spectrophotometer. The expression level of RHAMM, PD-L1, and PARP1 parylation (PAR) was evaluated after treatment with iRGD-DOX-oHA-PLN or olaparib in both cell lines using western blot or confocal laser scanning microscopy. The therapeutic efficacy of iRGD-DOX-oHA-PLN compared to olaparib was determined by monitoring the tumor progression and lung metastasis development in orthotopic breast tumor models of MDA-MB-231-luc-D3H2LN or MDA-MB-436 cell line. Results: The expression of native HA receptor RHAMM was profoundly reduced by ~5 folds in vivo and the DNA DSB was significantly increased by the iRGD-DOX-oHA-PLN treatment with 40% of γH2AX positive cells in tumor tissue sections. The iRGD-functionalized PLN enhanced DOX cellular uptake compared to DOX free drug or non-targeted NPs by ~3- and ~1.5-fold, in MDA-MB-231 and MDA-MB-436 cells, respectively. While olaparib upregulated PD-L1 and free DOX increased PAR level, the iRGD-DOX-oHA-PLN reduced the parylation and PD-L1 expression in both TNBC cell lines. The iRGD-DOX-oHA-PLN treatment (two dose of 10 mg/kg i.v. biweekly) outperformed olaparib (twenty doses of 50 mg/kg i.p 5x/week) in preventing tumor progression and lung metastasis in vivo over a 4-week period. Conclusion: The results suggest that the iRGD-DOX-oHA-PLN can effectively inhibit DNA damage repair and immunosuppression of cancer cells and could be a promising multitargeted nanomedicine for the treatment of both BRCA1-mutant and non-mutant metastatic TNBC. Citation Format: Ibrahim Alradwan, Pei Zhi, Tian Zhang, HoYin Lip, Abdulmutalib Zetrini, Chunsheng He, Jeffery Henderson, Andrew Rauth, Xiao Yu Wu. Targeted nanoparticles reduce tumor progression and lung metastasis by limiting DNA damage repair and immune suppression in metastatic triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5990.
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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".