Background: We performed a phase II trial of pembrolizumab in patients with NSCLC or melanoma with untreated brain metastases to determine the activity of PD-1 blockade in the CNS. Interim results were previously published, and we now report an updated analysis of the full NSCLC cohort. Methods: This was an open-label, single-institution, phase 2 study. Eligible patients were ≥ 18 years of age with advanced NSCLC with ≥1 brain metastasis 5-20mm not previously treated or progressing after prior radiation, no neurologic symptoms or corticosteroid requirement, and performance status <2. Patients were treated with pembrolizumab 10 mg/kg IV every 2 weeks. Cohort 1 was for patients with PD-L1 ≥1% and cohort 2 PD-L1 <1% or unevaluable. The primary endpoint was the proportion of patients achieving a brain metastasis response. All treated patients were analyzed for response and safety endpoints. This study is closed to accrual and is registered with Clinicaltrials.gov, number NCT02085070. Here we report the updated results of the NSCLC cohort. Findings: Between March 31, 2014 and May 21, 2018, 42 patients were treated. Median followup was 8.3 months (IQR 4.5 to 26.2 months). Eleven of 37 patients in cohort 1 had a brain metastasis response (29.7% [95% CI, 15•9-47•0%]). There were no responses in cohort 2. Grade 3-4 AEs related to treatment included 2 patients with pneumonitis, and 1 each with constitutional symptoms, colitis, adrenal insufficiency, hyperglycemia, and hypokalemia. Treatment-related serious adverse events occurred in 6 (14%) patients and included pneumonitis acute kidney injury, colitis, hypokalemia, and adrenal insufficiency. There were no treatment-related deaths. Interpretation: Pembrolizumab has activity in brain metastases from NSCLC with PD-L1 expression ≥1% and is safe in select patients with untreated brain metastases. Further investigation of immunotherapy in patients with CNS disease from NSCLC is warranted.

Cell-derived exosomes have been demonstrated to be efficient carriers of small RNAs to neighbouring or distant cells, highlighting the preponderance of exosomes as carriers for gene therapy over other artificial delivery tools. In the present study, we employed modified exosomes expressing the neuron-specific rabies viral glycoprotein (RVG) peptide on the membrane surface to deliver opioid receptor mu (MOR) siRNA into the brain to treat morphine addiction. We found that MOR siRNA could be efficiently packaged into RVG exosomes and was associated with argonaute 2 (AGO2) in exosomes. These exosomes efficiently and specifically delivered MOR siRNA into Neuro2A cells and the mouse brain. Functionally, siRNA-loaded RVG exosomes significantly reduced MOR mRNA and protein levels. Surprisingly, MOR siRNA delivered by the RVG exosomes strongly inhibited morphine relapse via the down-regulation of MOR expression levels. In conclusion, our results demonstrate that targeted RVG exosomes can efficiently transfer siRNA to the central nervous system and mediate the treatment of morphine relapse by down-regulating MOR expression levels. Our study provides a brand new strategy to treat drug relapse and diseases of the central nervous system.

Colorectal cancer (CRC) is among the most frequently occurring cancers worldwide. Baicalin is isolated from the roots of Scutellaria baicalensis and is its dominant flavonoid. Anticancer activity of baicalin has been evaluated in different types of cancers, especially in CRC. However, the molecular mechanisms underlying the contribution of baicalin to the treatment of CRC are still unknown. Here, we confirmed that baicalin can effectively induce and enhance apoptosis in HT-29 cells in a dose-dependent manner and suppress tumour growth in xenografted nude mice. We further performed a miRNA microarray analysis of baicalin-treated and untreated HT-29 cells. The results showed that a large number of oncomiRs, including miR-10a, miR-23a, miR-30c, miR-31, miR-151a and miR-205, were significantly suppressed in baicalin-treated HT-29 cells. Furthermore, our in vitro and in vivo studies showed that baicalin suppressed oncomiRs by reducing the expression of c-Myc. Taken together, our study shows a novel mechanism for anti-cancer action of baicalin, that it induces apoptosis in colon cancer cells and suppresses tumour growth by reducing the expression of c-Myc and oncomiRs.

Small RNAs, including small interfering RNAs (siRNA) and microRNAs (miRNA), are emerging as promising therapeutic drugs against a wide array of diseases. The key obstacle for the successful clinical application of small RNAs is to develop a safe delivery system directed at the target tissues only. Current small RNA transfer techniques use viruses or synthetic agents as delivery vehicles. The replacement of these delivery vehicles with a low toxicity and high target-specific approach is essential for making small RNA therapy feasible. Because exosomes have the intrinsic ability to traverse biological barriers and to naturally transport functional small RNAs between cells, they represent a novel and exciting delivery vehicle for the field of small RNA therapy. As therapeutic delivery agents, exosomes will potentially be better tolerated by the immune system because they are natural nanocarriers derived from endogenous cells. Furthermore, exosomes derived from genetically engineered cells can deliver small RNAs to target tissues and cells. Thus, exosome-based delivery of small RNAs may provide an untapped, effective delivery strategy to overcome impediments such as inefficiency, nonspecificity, and immunogenic reactions. In this review, we briefly describe how exosomal small RNAs function in recipient cells. Furthermore, we provide an update and overview of new findings that reveal the potential applications of exosome-based small RNA delivery as therapeutics in clinical settings. WIREs RNA 2016, 7:758-771. doi: 10.1002/wrna.1363 For further resources related to this article, please visit the WIREs website.

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.