Efficacy and Mechanism of a Biomimetic Nanosystem Carrying Doxorubicin and an IDO Inhibitor for Treatment of Advanced Triple-Negative Breast Cancer.

Introduction: Chemotherapy is still the treatment of choice for advanced triple-negative breast cancer. Chemotherapy combined with immunotherapy is being tried in patients with triple-negative breast cancer. As a kind of "cold tumor", triple-negative breast cancer has a bottleneck in immunotherapy. Indoleamine 2, 3-dioxygenase-1 inhibitors can reverse the immunosuppressive state and enhance the immune response. Methods: In this study, mesoporous silica nanoparticles were coated with the chemotherapeutic drug doxorubicin and indoleamine 2, 3-dioxygenase 1 inhibitor 1-Methyl-DL-tryptophan (1-MT), and then encapsulate the surfaces of a triple-negative breast cancer cell membrane to construct the tumor dual-targeted delivery system CDIMSN for chemotherapy and immunotherapy, and to investigate the immunogenic death effect of CDIMSN. Results and discussion: The CDIMSN could target the tumor microenvironment. Doxorubicin induced tumor immunogenic death, while 1-MT reversed immunosuppression. In vivo findings showed that the tumor size in the CDIMSN group was 2.66-fold and 1.56-fold smaller than that in DOX and DIMSN groups, respectively. CDIMSN group was better than naked DIMSN in stimulating CD8+T cells, CD4+T cells and promoting Dendritic Cells(DC) maturation. In addition, blood analysis, biochemical analysis and Hematoxylin staining analysis of mice showed that the bionic nanoparticles had good biological safety.

Senescence-based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies.

Cellular senescence has been listed as a hallmark of cancer, but its role in colorectal cancer (CRC) remains unclear. We comprehensively evaluated the transcriptome, genome, digital pathology, and clinical data from multiple datasets of CRC patients and proposed a novel senescence subtype for CRC. Multi-omics data was used to analyze the biological features, tumor microenvironment, and mutation landscape of senescence subtypes, as well as drug sensitivity and immunotherapy response. The senescence score was constructed to better quantify senescence in each patient for clinical use. Unsupervised learning revealed three transcriptome-based senescence subtypes. Cluster 1, characterized by low senescence and activated proliferative pathways, was sensitive to chemotherapeutic drugs. Cluster 2, characterized by intermediate senescence and high immune infiltration, exhibited significant immunotherapeutic advantages. Cluster 3, characterized by high senescence, high immune, and stroma infiltration, had a worse prognosis and maybe benefit from targeted therapy. We further constructed a senescence scoring system based on seven senescent genes through machine learning. Lower senescence scores were highly predictive of longer disease-free survival, and patients with low senescence scores may benefit from immunotherapy. We proposed the senescence subtypes of CRC and our findings provide potential treatment interventions for each CRC senescence subtype to promote precision treatment.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer.

BACKGROUND: Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT. METHODS: In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored. RESULTS: We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI] = 1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI] = 2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI] = 2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype. CONCLUSION: We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis.

Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature.

Long non-coding RNAs (lncRNAs) remodel the tumor immune microenvironment (TIME) by regulating the functions of tumor-infiltrating immune cells. It remains uncertain the way that TIME-related lncRNAs (TRLs) influence the prognosis and immunotherapy response of colorectal cancer (CRC). Aiming at providing survival and immunotherapy response predictions, a CRC TIME-related lncRNA signature (TRLs signature) was developed and the related potential regulatory mechanisms were explored with a comprehensive analysis on gene expression profiles from 97 immune cell lines, 61 CRC cell lines and 1807 CRC patients. Stratifying CRC patients with the TRLs signature, prolonged survival was observed in the low-risk group, while the patients in the high-risk group had significantly higher pro-tumor immune cells infiltration and higher immunotherapy response rate. Through the complex TRLs-mRNA regulation network, immunoregulation pathways and immunotherapy response pathways were found to be differently activated between the groups. In conclusion, the CRC TRLs signature is capable of making prognosis and immunotherapy response predictions, which may find application in stratifying patients for immunotherapy in the bedside.

Tumor Microenvironment Multiple Responsive Nanoparticles for Targeted Delivery of Doxorubicin and CpG Against Triple-Negative Breast Cancer.

Introduction: Currently, the main treatment for advanced breast cancer is still chemotherapy. Immunological and chemical combination therapy has a coordinated therapeutic effect and achieves some efficacy. However, the immunosuppressive tumor microenvironment is a major cause for the failure of immunotherapy in breast cancer. CpG oligodeoxynucleotides can activate the tumor immune microenvironment to reverse the failure of immunotherapy. Methods: In this study, we designed an amphiphilic peptide micelle system (Co-LMs), which can targeted delivery of the immune adjuvant CpG and the chemotherapeutic drug doxorubicin to breast cancer tumors simultaneously. The peptide micelle system achieved tumor microenvironment pH and redox-sensitive drug release. Results and Discussion: Co-LMs showed 2.3 times the antitumor efficacy of chemotherapy alone and 5.1 times the antitumor efficacy of immunotherapy alone in triple-negative breast cancer mice. Co-LMs activated cytotoxic CD8+ T lymphocytes and CD4+ T cells in mice to a greater extent than single treatments. We also found that Co-LMs inhibited the metastasis of circulating tumor cells in the bloodstream to some extent. These results indicate that the Co-LMs offer a promising therapeutic strategy against triple-negative breast cancer.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4-90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44-2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8-69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19-2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

DNA Repair-Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients.

Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study.

As a major infectious disease, tuberculosis (TB) still poses a threat to people's health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.

Co-delivery of the autophagy inhibitor si-Beclin1 and the doxorubicin nano-delivery system for advanced prostate cancer treatment.

Resistance to apoptosis is a key mechanism underlying how cancer cells evade tumor therapy. Autophagy can prevent anticancer drug-induced apoptosis and promote tumor resistance. The purpose of this study was to improve the sensitivity and efficacy of chemotherapeutic drugs through the inhibition of autophagy. Hydrophobic doxorubicin-hydrazone-caproyl-maleimide (DOX-EMCH) and autophagy-inhibiting si-Beclin1 were simultaneously delivered via the amphiphilic peptide micelle system (Co-PMs) using poly(L-arginine)-poly(L-histidine)-DOX-EMCH as the copolymer building unit. The constructed micelle system promoted the escape of si-Beclin1 from endosomes and the release of DOX into the nucleus. The Co-PMs exhibited 2.7-fold higher cytotoxicity and proapoptotic ability in PC3 cells than DOX treatment alone, demonstrating that si-Beclin1 could inhibit the autophagic activity of prostate cancer (PCa) cells by targeting the type III PI3K pathway and enhance the sensitivity of the cells to the chemotherapeutic drug DOX. In addition, the peptide micelles successfully passively targeted DOX and si-Beclin1 to the tumor tissue. Compared with DOX or si-Beclin1 treatment alone, the Co-PMs showed a 3.4-fold greater tumor inhibitory potential in vivo, indicative of a significant antiproliferative effect. Our results suggested that the Co-PMs developed in this study have the potential to combine autophagy inhibition and chemotherapy in cancer treatment, especially for PCa.

Codelivery of miR-4638-5p and Docetaxel Based on Redox-Sensitive Polypeptide Micelles as an Improved Strategy for the Treatment of Castration-Resistant Prostate Cancer.

In this work, we have developed a reducible, self-assembling disulfide cross-linked and peptide-based micelle system for codelivery of miR-4638-5p and DTX to improve the efficacy of castration-resistant prostate cancer (CRPC) therapy. The result showed that DTX in micelles (DTX-VPs) inhibited cell growth and induced apoptosis more effectively than free DTX both in vitro and in vivo. In addition, the DTX and miR-4638-5p loaded micelles (Co-VPs) achieved the most pronounced anticancer effect of all groups. Immunohistochemical analysis indicated that miR-4638-5p in micelle system could effectively downregulate the expression of Kidins220 and further improve the anticancer effect by enhancing tumor cell apoptosis and suppressing tumor cell proliferation. Finally, the bioimaging analysis demonstrated that DIR in micelles (DIR-VPs) showed a higher concentration and a longer retention time in tumor tissue than did free DIR, which indicated an excellent tumor-targeting ability of the micelle system. All these results suggest that codelivery of miR-4638-5p and DTX via polypeptide micelle system has a potential for CRPC treatment.

Aptamer-conjugated multi-walled carbon nanotubes as a new targeted ultrasound contrast agent for the diagnosis of prostate cancer.

Early diagnosis is primarily important for the therapeutic and prognostic outcomes of malignancies including prostate cancer (PCa). However, the visuality and veracity of ultrasound imaging for the diagnosis and prognostic prediction of PCa remains poor at present. In this study, we developed a new nanoultrasound contrast agent by modifying multi-walled carbon nanotubes (MWCNTs) with polyethylene glycol (PEG) and anti-PSMA aptamer. The result showed that the modified MWCNTs offered better visuality and veracity and were able to target PCa cells more effectively as compared with the traditional contrast agent. The zeta potential was about - 38 mv. The length of this contrast agent was about 400 nm and the diameter of it was about 30 nm. The zeta potential, TEM, and FT-IR all proved the successful preparation of the agent. The vitro cytological study revealed good cell uptake and biocompatibility of the new contrast agent. The minimum detection concentration in vitro is 10 µg/ml. The earliest stage of the detection was under the parameters of frequency = 6.0 MHz and medical index = 0.06. Both in vitro and in vivo ultrasound imaging demonstrated that the new nanoultrasound contrast agent had a good development effect, distribution, and metabolism, and may prove to be a good targeted ultrasound contrast agent, especially for PCa.

Functionalized Multi-Walled Carbon Nanotubes for Targeting Delivery of Immunostimulatory CpG Oligonucleotides Against Prostate Cancer.

Immuno-based oncotherapy has been successfully implemented for cancer treatment. In the present study, we developed a Oligodeoxynucleotides (ODNs) containing unmethylated CpG motifs (CpG ODNs) nano-delivery system based on Multi-walled carbon nanotubes (MWCNTs) conjugated with H3R6 polypeptide (MHR-CpG) for prostate cancer immunotherapy. The in vitro and in vivo toxicity data revealed that the prepared MHR showed high biocompatibility. Confocal laser scanning microscopy confirmed that MHR-CpG could specifically target the endosomal TLR9. In addition, the use of MHR enhanced the immunogenicity of CpG in both humoral and cellular immune pathways, as evidenced by the increased expression of CD4+ T-cells, CD8+ T-cells, TNF-α, and IL-6. The in vivo anti-cancer efficacy study on RM-1 tumor-bearing mice demonstrated that MHR-CpG could deliver the immunotherapeutics to the tumor site and the tumor-draining lymph node to suppress tumor growth. These results suggested that MHR-CpG was a promising multifunctional nano system for prostate cancer immunotherapy.

A33 antibody-functionalized exosomes for targeted delivery of doxorubicin against colorectal cancer.

Exosomes have emerged as a promising drug carrier with low immunogenicity, high biocompatibility and delivery efficiency. Here in, we isolated exosomes from A33-positive LIM1215 cells (A33-Exo) and loaded them with doxorubicin (Dox). Furthermore, we coated surface-carboxyl superparamagnetic iron oxide nanoparticles (US) with A33 antibodies (A33Ab-US), expecting that these A33 antibodies on the surface of the nanoparticles could bind to A33-positive exosomes and form a complex (A33Ab-US-Exo/Dox) to target A33-positive colon cancer cells. The results showed that A33Ab-US-Exo/Dox had good binding affinity and antiproliferative effect in LIM1215 cells, as shown by increased uptake of the complex. In vivo study showed that A33Ab-US-Exo/Dox had an excellent tumor targeting ability, and was able to inhibit tumor growth and prolong the survival of the mice with reduced cardiotoxicity. In summary, exosomes functionalized by targeting ligands through coating with high-density antibodies may prove to be a novel delivery system for targeted drugs against human cancers.

Co-delivery of autophagy inhibitor ATG7 siRNA and docetaxel for breast cancer treatment.

The lysosomal degradation pathway of autophagy has a crucial role in protecting cancer cells from multiple endogenous and exogenous stresses, particularly during the pathogenesis of cancer. Accordingly, agents that inhibit autophagy may have broad therapeutic applications. We have developed a novel strategy based on co-delivery of an autophagy related 7 (ATG7) siRNA and docetaxel (DTX) in a crosslinked, reducible, peptide-based micellar system for breast cancer treatment. Our results show that DTX and siATG7 co-treatment exhibited 2.5- and 1.7-fold higher cytotoxicity and apoptosis, respectively, in MCF-7 cells than DTX treatment alone did, which demonstrates that siATG7 enhances the efficacy and apoptotic effect of DTX. Our study showed that breast cancer cell lines differ greatly in their dependency on autophagy under conditions of normal or stress. Furthermore, siATG7 delivery in a micellar system can effectively silence the ATG7 gene, suppress DTX-induced autophagy, and exhibit improved anticancer effects. In addition, DTX in a co-delivery system showed at least a 1.84-fold greater tumor inhibition compared to that of DTX-loaded micelles in vivo. Finally, a Cy5 indicator that was loaded into crosslinked micelles revealed a remarkably high accumulation in tumors, demonstrating excellent tumor targeting ability of the micellar system. Therefore, our research demonstrated the synergistic efficacy of the combination of autophagy inhibition and chemotherapy delivered by polypeptide micelles for breast cancer therapy.

Tumour microenvironment-responsive lipoic acid nanoparticles for targeted delivery of docetaxel to lung cancer.

In the present study, we developed a novel type of reduction-sensitive nanoparticles (NPs) for docetaxel (DTX) delivery based on cross-linked lipoic acid NPs (LANPs). The physicochemical properties, cellular uptake and in vitro cytotoxicity of DTX loaded LANPs (DTX-LANPs) on A549 cells were investigated. Furthermore, the in vivo distribution and in vivo efficacy of DTX-LANPs was evaluated. The results showed that DTX-LANPs had a particle size of 110 nm and a negative zeta potential of -35 mv with excellent colloidal stability. LANPs efficiently encapsulated DTX with a high drug loading of 4.51% ± 0.49% and showed remarkable reduction-sensitive drug release in vitro. Cellular uptake experiments demonstrated that LANPs significantly increased intracellular DTX uptake by about 10 fold as compared with free DTX. The cytotoxicity of DTX-LANPs showed significantly higher potency in inhibiting A549 cell growth than free DTX, while blank LANPs had a good biocompatibility. In addition, in vivo experiments demonstrated that DTX-LANPs could enhance tumour targeting and anti-tumour efficacy with low systemic toxicity. In conclusion, LANPs may prove to be a potential tumour microenvironment-responsive delivery system for cancer treatment, with the potential for commercialization due to the simple component, controllable synthesis, stability and economy.

Synergistic effect of reduced polypeptide micelle for co-delivery of doxorubicin and TRAIL against drug-resistance in breast cancer.

Cationic peptides as a non-viral gene vector have become a hotspot of research because of their high transfection efficcacy and safety. Based on our previous study, we synthesized a cationic reduction-responsive vector based on disulfide cross-linked L-arginine, L-histidine and lipoic acid (LHRss) as the co-carrier of both doxorubicin (DOX) and the necrosis factor-related apoptosis-inducing ligand (pTRAIL). The LHRss/DOX/TRAIL construct has reduction-sensitive behavior and an enhanced endosomal escape ability to increase the cytotoxicity of DOX and the transfection efficiency. Further, the LHRss/DOX/TRAIL construct increased the accumulation of DOX and promoted the expression of pTRAIL, thus increasing cellular apoptosis by 83.7% in MCF-7/ADR cells. In addition, the in vivo biodistribution results showed that the LHRss/DOX/TRAIL construct could target tumors well. The in vivo anti-tumor effect study demonstrated that the LHRss/DOX/TRAIL construct inhibited tumor growth markedly, with a tumor inhibitory rate of 94.0%. The co-delivery system showed a significant synergistic anti-tumor effect. The LHRss/DOX/TRAIL construct may prove to be a promising co-delivery vector for the effective treatment of drug resistant breast cancer.

Aptamer-mediated delivery of docetaxel to prostate cancer through polymeric nanoparticles for enhancement of antitumor efficacy.

Treatment of aggressive prostate cancer remains a great challenge due to inadequate drug distribution into the cancerous lesions after administration. This study aimed to develop aptamer-anchored nanoparticles (apt-NPs) for systemic delivery of docetaxel (DTX) and to evaluate the tumoricidal activity against the prostate cancer in vitro and in vivo. DTX-loaded apt-NPs (DTX-apt-NPs) were prepared by a solvent diffusion technique using functional PLGA-b-PEG and sodium oleate. DTX-apt-NPs were characterized by in vitro release, antitumor activity, cellular uptake and cytotoxic mechanisms. Pharmacokinetics and tissue distribution studies were performed in rats to investigate the biofate of DTX-apt-NPs. Finally, the in vivo antitumor efficacy was examined on the LNCaP cells xenograft tumor model. The resulting DTX-apt-NPs were 93.6nm in particle size with narrow distribution and possessed a high entrapment efficiency (97.62%) and acceptable drug loading (8.91%). DTX-apt-NPs demonstrated an enhanced in vitro antitumor effect and marked cellular uptake compared with the solution formulation or conventional nanoparticles. The intracellular trafficking of DTX-apt-NPs was shown to be an active transport process involving the clathrin-dependent endocytosis. Anti-PSMA aptamer-mediated delivery was assumed mainly responsible for the enhanced antitumor efficacy. DTX-apt-NPs that can target to PSMA-overexpressed prostate cancer provide a feasible approach for systemic delivery of DTX to the cancerous prostate to achieve a fine prognosis.

Reducible self-assembling cationic polypeptide-based micelles mediate co-delivery of doxorubicin and microRNA-34a for androgen-independent prostate cancer therapy.

The co-delivery of chemotherapeutic drugs and microRNAs (miR) represents a promising strategy for tumor therapy due to the synergistic effect achieved. In the present study, hydrophobic doxorubicin (DOX) and negatively charged miR-34a were simultaneously delivered via a reducible self-assembling disulfide cross-linked stearyl-peptide-based micellar system (SHRss) using poly(l-arginine)-poly(l-histidine)-stearoyl as the copolymer building unit. The nanoscale SHRss micelles exhibited a low critical micelle concentration (CMC) with positive surface charge. In addition, the present micellar system facilitated the escape of miR-34a from the endosome and release of DOX into the cell nucleus, leading to the downregulation of silent information regulator 1 (SIRT1) expression and inhibition of DU145 and PC3 androgen-independent prostate cancer cell proliferation. In addition, DOX and miR-34a, delivered by SHRss micelles, passively targeted tumor tissue. Furthermore, a synergistic anti-proliferative effect was observed compared with DOX or miR-34a treatment alone in vivo. Our results demonstrate that the SHRss micelles developed in the present study represent a promising approach for combined delivery of gene agents and hydrophobic chemotherapeutic drugs in cancer therapy.