Targeted Treatment of Metastatic Breast Cancer by PLK1 siRNA Delivered by an Antioxidant Nanoparticle Platform
Bibliographic record
Abstract
Abstract Metastatic breast cancer is developed in about 20% to 30% of newly diagnosed patients with early-stage breast cancer despite treatments. Herein, we report a novel nanoparticle platform with intrinsic antimetastatic properties for the targeted delivery of Polo-like kinase 1 siRNA (siPLK1). We first evaluated it in a triple-negative breast cancer (TNBC) model, which shows high metastatic potential. PLK1 was identified as the top therapeutic target for TNBC cells and tumor-initiating cells in a kinome-wide screen. The platform consists of a 50-nm mesoporous silica nanoparticle (MSNP) core coated layer-by-layer with bioreducible cross-linked PEI and PEG polymers, conjugated with an antibody for selective uptake into cancer cells. siRNA is loaded last and fully protected under the PEG layer from blood enzymatic degradation. The material has net neutral charge and low nonspecific cytotoxicity. We have also shown for the first time that the MSNP itself inhibited cancer migration and invasion in TNBC cells owing to its ROS- and NOX4-modulating properties. In vivo, siPLK1 nanoconstructs (six doses of 0.5 mg/kg) knocked down about 80% of human PLK1 mRNA expression in metastatic breast cancer cells residing in mouse lungs and reduced tumor incidence and burden in lungs and other organs of an experimental metastasis mouse model. Long-term treatment significantly delayed the onset of death in mice and improved the overall survival. The platform capable of simultaneously inhibiting the proliferative and metastatic hallmarks of cancer progression is unique and has great therapeutic potential to also target other metastatic cancers beyond TNBC. Mol Cancer Ther; 16(4); 763–72. ©2017 AACR.
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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 | 0.001 | 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".