Abstract 4339: RGD-conjugated nanoparticles for targeted inhibition of adhesion and migration of integrin αvβ3-overexpressing breast cancer cells.
Bibliographic record
Abstract
Abstract Background: Tumor cell adhesion and migration are critical in the establishment of metastasis. This study aims to develop a RGD-conjugated nanoparticle system that is able to target αvβ3 integrin receptor-overexpressing breast tumor cells while evading the liver uptake, thus enhancing treatment of cancer metastasis through inhibition of adhesion and migration of cancer cells. Methods: The nanoparticle formulation was first optimized with a suitable concentration of RGD targeting moiety on the nanoparticle surface to maximize nanoparticle accumulation in tumor and minimize liver uptake. RGD-conjugated solid lipid nanoparticles (RGD-SLNs) were synthesized with a fatty acid lipid core and a poly(ethylene glycol) corona and decorated with varying concentrations of RGD peptides (0%, 0.5%, 1%, 5% and 10% mol/mol ratio). The quantity and kinetics of the nanoparticle binding to αvβ3 integrin receptor and in vitro cellular uptake on αvβ3 positive MDA-MB-231 cells were investigated by fluorescence microscopy and compared with the results with αvβ3 negative MCF-7 cells. The influence of RGD-SLNs on cell adhesion and migration via binding to αvβ3 integrin receptor was examined using standard adhesion and transwell migration assays. Whole animal biodistribution, tumor uptake and intratumoral distribution of quantum dot-loaded nanoparticles were investigated to identify optimized RGD-concentrations. Results: RGD-SLNs showed specific binding for αvβ3 integrin receptors on MDA-MB-231 cells as compared to the negative control (MCF-7 cells). The RGD-SLN formed clusters at the cellular membrane and then entered the cells at a lower pace than SLN. The RGD-SLNs also exhibited higher cellular uptake compared to SLN in MDA-MB-231 cells while there was no detectable difference in uptake in αvβ3 negative MCF-7 cells. RGD concentration of 1% on the SLN surface was found to have most tumor retention and low liver uptake among all formulations. Receptor mediated uptake of RGD-SLNs reduced cell adhesion and migration towards fibronectin gradient, attributable to the occupation of the receptors by the RGD-SLN and slow recycling of the receptors to the cell surface. Conclusions: We have optimized the RGD-SLN formulation to maximize tumor accumulation and minimize liver uptake. The RGD-SLN formulation has the potential to prevent metastasis through interference with cell adhesion and migration. Citation Format: Dan Shan, Ping Cai, Preethy Prasad, Andrew Michael Rauth, Xiao Yu Wu. RGD-conjugated nanoparticles for targeted inhibition of adhesion and migration of integrin αvβ3-overexpressing breast cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4339. doi:10.1158/1538-7445.AM2013-4339
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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".