We reported the synthesis and characterization of dual-responsive poly(N-isopropylacrylamide-acrylamide-chitosan) (PAC)-coated magnetic nanoparticles (MNPs) for controlled and targeted drug delivery and imaging applications. The PAC-MNPs size was about 150 nm with 70% iron mass content and excellent superparamagnetic properties. PAC-MNPs loaded with anti-cancer drug doxorubicin showed dual-responsive drug release characteristics with the maximum release of drugs at 40 °C (~78%) than at 37 °C (~33%) and at pH of 6 (~55%) than at pH of 7.4 (~28%) after 21 days. Further, the conjugation of prostate cancer-specific R11 peptides increased the uptake of PAC-MNPs by prostate cancer PC3 cells. The dose-dependent cellular uptake of the nanoparticles was also significantly increased with the presence of 1.3 T magnetic field. The nanoparticles demonstrated cytocompatibility up to concentrations of 500 μg/ ml when incubated over a period of 24 h with human dermal fibroblasts and normal prostate epithelial cells. Finally, pharmacokinetic studies indicated that doxorubicin-loaded PAC-MNPs caused significant prostate cancer cell death at 40 °C than at 37 °C, thereby confirming the temperature-dependent drug release kinetics and in vitro therapeutic efficacy. Future evaluation of in vivo therapeutic efficacy of targeted image-guided cancer therapy using R11-PAC-MNPs will reinforce a significant impact of the multifunctional PAC-MNPs on the future drug delivery systems.
Small cell lung cancer (SCLC) accounts for approximately 15% of all lung cancers and demands effective targeted therapeutic strategies. In this meta-analysis study, we aim to identify significantly mutated genes and regulatory pathways to help us better understand the progression of SCLC and to identify potential biomarkers. Besides ranking genes based on their mutation frequencies, we sought to identify statistically significant mutations in SCLC with the MutSigCV software. Our analysis identified several genes with relatively low mutation frequency, including PTEN, as highly significant (p<0.001), suggesting these genes may play an important role in the progression of SCLC. Our results also indicated mutations in genes involved in the axon guidance pathways likely play an important role in SCLC progression. In addition, we observed that the mutation rate was significantly higher in samples with RB1 gene mutated when compared to samples with wild type RB1, suggesting that RB1 status has significant impact on the mutation profile and disease progression in SCLC.
One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient’s tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs.
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