Delivery of sTRAIL variants by MSCs in combination with cytotoxic drug treatment leads to p53-independent enhanced antitumor effects

Mesenchymal stem cells (MSCs) are able to infiltrate tumor tissues and thereby effectively deliver gene therapeutic payloads. Here, we engineered murine MSCs (mMSCs) to express a secreted form of the TNF-related apoptosis-inducing ligand (TRAIL), which is a potent inducer of apoptosis in tumor cells, and tested these MSCs, termed MSC.sTRAIL, in combination with conventional chemotherapeutic drug treatment in colon cancer models. When we pretreated human colorectal cancer HCT116 cells with low doses of 5-fluorouracil (5-FU) and added MSC.sTRAIL, we found significantly increased apoptosis as compared with single-agent treatment. Moreover, HCT116 xenografts, which were cotreated with 5-FU and systemically delivered MSC.sTRAIL, went into remission. Noteworthy, this effect was protein 53 (p53) independent and was mediated by TRAIL-receptor 2 (TRAIL-R2) upregulation, demonstrating the applicability of this approach in p53-defective tumors. Consequently, when we generated MSCs that secreted TRAIL-R2-specific variants of soluble TRAIL (sTRAIL), we found that such engineered MSCs, labeled MSC.sTRAIL DR5, had enhanced antitumor activity in combination with 5-FU when compared with MSC.sTRAIL. In contrast, TRAIL-resistant pancreatic carcinoma PancTu1 cells responded better to MSC.sTRAIL DR4 when the antiapoptotic protein XIAP (X-linked inhibitor of apoptosis protein) was silenced concomitantly. Taken together, our results demonstrate that TRAIL-receptor selective variants can potentially enhance the therapeutic efficacy of MSC-delivered TRAIL as part of individualized and tumor-specific combination treatments. © 2013 Macmillan Publishers Limited All rights reserved

A Randomized Double-Blind Study Comparing the Efficacy and Safety of Orlistat Versus Placebo in Obese Patients with Mild to Moderate Hypercholesterolemia

INTRODUCTION: Obesity is a chronic disease and a serious health problem that leads to increased prevalence of diabetes, hypertension, dyslipidemia and gallbladder disease. OBJECTIVE: To evaluate the efficacy of orlistat for weight loss and improved lipid profile compared to placebo in obese patients with hypercholesterolemia, treated over a period of 6 months. METHODOLOGY: In a 6-month, multicenter (10 centers in Portugal), double-blind, parallel, placebo-controlled study, 166 patients, aged 18-65 years, body mass index (BMI) > or = 27 kg/m2, LDL cholesterol > 155 mg/dl, were randomized to a reduced calorie diet (600 kcal/day deficit) plus orlistat three times a day or placebo. Exclusion criteria included triglycerides > 400 mg/dl, severe cardiovascular disease, uncontrolled hypertension, type 1 or 2 diabetes under pharmacological treatment, and gastrointestinal or pancreatic disease. RESULTS: The mean difference in weight from baseline was 5.9% (5.6 kg) in the orlistat group vs. 2.3% (2.2 kg) in the placebo group. In the orlistat group 49% of patients achieved 5-10% weight loss and 8.8% achieved > 10%. The orlistat group showed a significant reduction in total and LDL cholesterol, with similar changes for HDL in both treatment groups. The frequency of gastrointestinal adverse events was slightly higher in the orlistat group than in the placebo group, leading to discontinuation in 7 patients. CONCLUSION: Treatment with orlistat plus a reduced calorie diet for 6 months achieved significant reductions in weight, BMI and lipid parameters

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Triple-negative breast cancer with brain metastases: a comparison between basal-like and non-basal-like biological subtypes

The aim of this study was to divide the group of triple-negative breast cancer patients with brain metastases into basal-like and non-basal-like biological subtypes in order to compare clinical features and survival rates in those two groups. A comprehensive analysis of 111 consecutive triple-negative breast cancer patients with brain metastases treated in the years 2003–2009 was performed. In 75 patients, immunohistochemistry was used as a surrogate of microarray in order to evaluate the expression of three basal markers: cytokeratin 5/6 (CK 5/6), EGFR/HER1 and c-KIT. The basal-like (ER/PgR/HER2-negative, CK5/6positive and/or HER1-positive) and non-basal-like (ER/PgR/HER2-negative, CK5/6-negative, HER1-negative) subsets were selected. Clinical features and survivals were compared in both groups. In the group of 111 triple-negative breast cancer patients, median DFS, OS and survival from brain metastases were 20, 29 and 4 months, respectively. In 75 patients who were evaluable for basal markers, median DFS, OS and survival from brain metastases were 18, 26 and 3.2 months, respectively. In the basal-like subtype, the survival rates were 15, 26 and 3 months, respectively, and in the non-basal-like subtypes, they were 20, 30 and 2.8 months, respectively. No statistically significant differences in survivals were detected between the basal-like and non-basal-like biological subtypes. Factors influencing survival from brain metastases were: Karnofsky performance status (KPS), the status of extracranial disease and age. Biological markers differentiating triple-negative group into basal-like and non-basal-like subtype (CK 5/6, HER1, c-KIT) had no influence on survival. In patients with triple-negative breast cancer and brain metastases, well-known clinical, but not molecular, features correlated with survival

Time series modeling for syndromic surveillance

BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization

Basal keratin expression in breast cancer by quantification of mRNA and by immunohistochemistry

Definitions of basal-like breast cancer phenotype vary, and microarray-based expression profiling analysis remains the gold standard for the identification of these tumors. Immunohistochemical identification of basal-like carcinomas is hindered with a fact, that on microarray level not all of them express basal-type cytokeratin 5/6, 14 and 17. We compared expression of cytokeratin 5, 14 and 17 in 115 patients with operable breast cancer estimated by real-time RT-PCR and immunohistochemistry

Type I IFN and TNFα cross-regulation in immune-mediated inflammatory disease: basic concepts and clinical relevance

A cross-regulation between type I IFN and TNFα has been proposed recently, where both cytokines are hypothesized to counteract each other. According to this model, different autoimmune diseases can be viewed as disequilibrium between both cytokines. As this model may have important clinical implications, the present review summarizes and discusses the currently available clinical evidence arguing for or against the proposed cross-regulation between TNFα and type I IFN. In addition, we review how this cross-regulation works at the cellular and molecular levels. Finally, we discuss the clinical relevance of this proposed cross-regulation for biological therapies such as type I IFN or anti-TNFα treatment

TRAIL-receptor preferences in pancreatic cancer cells revisited: Both TRAIL-R1 and TRAIL-R2 have a licence to kill

Background TRAIL is a potent and specific inducer of apoptosis in tumour cells and therefore is a possible new cancer treatment. It triggers apoptosis by binding to its cognate, death-inducing receptors, TRAIL-R1 and TRAIL-R2. In order to increase its activity, receptor-specific ligands and agonistic antibodies have been developed and some cancer types, including pancreatic cancer, have been reported to respond preferentially to TRAIL-R1 triggering. The aim of the present study was to examine an array of TRAIL-receptor specific variants on a number of pancreatic cancer cells and test the generality of the concept of TRAIL-R1 preference in these cells. Methods TRAIL-R1 and TRAIL-R2 specific sTRAIL variants were designed and tested on a number of pancreatic cancer cells for their TRAIL-receptor preference. These sTRAIL variants were produced in HEK293 cells and were secreted into the medium. After having measured and normalised the different sTRAIL variant concentrations, they were applied to pancreatic and control cancer cells. Twenty-four hours later apoptosis was measured by DNA hypodiploidy assays. Furthermore, the specificities of the sTRAIL variants were validated in HCT116 cells that were silenced either for TRAIL-R1 or TRAIL-R2. Results Our results show that some pancreatic cancer cells use TRAIL-R1 to induce cell death, whereas other pancreatic carcinoma cells such as AsPC-1 and BxPC-3 cells trigger apoptosis via TRAIL-R2. This observation extended to cells that were naturally TRAIL-resistant and had to be sensitised by silencing of XIAP (Panc1 cells). The measurement of TRAIL-receptor expression by FACS revealed no correlation between receptor preferences and the relative levels of TRAIL-R1 and TRAIL-R2 on the cellular surface. Conclusions These results demonstrate that TRAIL-receptor preferences in pancreatic cancer cells are variable and that predictions according to cancer type are difficult and that determining factors to inform the optimal TRAIL-based treatments still have to be identified