The development of drug resistance in cancer cells is one of the major obstacles to achieving effective chemotherapy. We hypothesized that the combination of a doxorubicin (Dox) prodrug and microRNA (miR)21 inhibitor might show synergistic antitumor effects on drug-resistant breast cancer cells. In this study, we aimed to develop new high-density lipoprotein-mimicking nanoparticles (HMNs) for coencapsulation and codelivery of this potential combination. Dox was coupled with a nuclear localization signal (NLS) peptide to construct a prodrug (NLS-Dox), thereby electrostatically condensing miR21 inhibitor (anti-miR21) to form cationic complexes. The HMNs were formulated by shielding these complexes with anionic lipids and Apo AI proteins. We have characterized that the coloaded HMNs had uniformly dispersed distribution, favorable negatively charged surface, and high coencapsulation efficiency. The HMN formulation effectively codelivered NLS-Dox and anti-miR21 into Dox-resistant breast cancer MCF7/ADR cells and wild-type MCF7 cells via a high-density-lipoprotein receptor-mediated pathway, which facilitated the escape of Pgp drug efflux. The coloaded HMNs consisting of NLS-Dox/anti-miR21 demonstrated greater cytotoxicity with enhanced intracellular accumulation in resistant MCF7/ADR cells compared with free Dox solution. The reversal of drug resistance by coloaded HMNs might be attributed to the suppression of miR21 expression and the related antiapoptosis network. Furthermore, the codelivery of anti-miR21 and NLS-Dox by HMNs showed synergistic antiproliferative effects in MCF7/ADR-bearing nude mice, and was more effective in tumor inhibition than other drug formulations. These data suggested that codelivery of anti-miR21 and chemotherapeutic agents by HMNs might be a promising strategy for antitumor therapy, and could restore the drug sensitivity of cancer cells, alter intracellular drug distribution, and ultimately enhance chemotherapeutic effects.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

Notch signaling is a key parameter in regulating cell fate during tissue homeostasis, and an aberrant Notch pathway can result in mammary gland carcinoma and has been associated with poor breast cancer diagnosis. Although inhibiting Notch signaling would be advantageous in the treatment of breast cancer, the currently available Notch inhibitors have a variety of side effects and their clinical trials have been discontinued. Thus, in search of a more effective and safer Notch inhibitor, inhibiting recombinant signal binding protein for immunoglobin kappaJ region (RBPJ) specifically makes sense, as RBPJ forms a transcriptional complex that activates Notch signaling. From our established database of more than 10,527 compounds, a drug repurposing strategy-combined docking study and molecular dynamic simulation were used to identify novel RBPJ-specific inhibitors. The compounds with the best performance were examined using an in vitro cellular assay and an in vivo anticancer investigation. Finally, an FDA-approved antibiotic, fidaxomicin, was identified as a potential RBPJ inhibitor, and its ability to block RBPJ-dependent transcription and thereby inhibit breast cancer growth was experimentally verified. Our study demonstrated that fidaxomicin suppressed Notch signaling and may be repurposed for the treatment of breast cancer.