Low expression of aldehyde deyhdrogenase 1A1 (ALDH1A1) is a prognostic marker for poor survival in pancreatic cancer

<p>Abstract</p> <p>Background</p> <p>Aldehyde deyhdrogenase 1 (ALDH1) has been characterised as a cancer stem cell marker in different types of tumours. Additionally, it plays a pivotal role in gene regulation and endows tumour cells with augmented chemoresistance. Recently, ALDH1A1 has been described as a prognostic marker in a pancreatic cancer tissue microarray. The aim of this study was to reevaluate the expression of ALDH1A1 as a prognostic marker on whole-mount tissue sections.</p> <p>Methods</p> <p>Real-time-quantitative-PCR (qRT-PCR) and Western blotting were used to evaluate the expression profile of ALDH1A1 in seven pancreatic cancer cell lines and one non-malignant pancreatic cell line. Immunostaining against ALDH1A1 and Ki-67 was performed on paraffin-embedded samples from 97 patients with pancreatic cancer. The immunohistochemical results were correlated to histopathological and clinical data.</p> <p>Results</p> <p>qRT-PCR and Western blotting revealed a different expression pattern of ALDH1A1 in different malignant and non-malignant pancreatic cell lines. Immunohistochemical analysis demonstrated that ALDH1A1 was confined to the cellular cytoplasm and occurred in 72 cases (74%), whereas it was negative in 25 cases (26%). High expression of ALDH1A1 was significantly correlated to an increased proliferation rate (Spearman correlation, p = 0.01). Univariate and multivariate analyses showed that decreased expression of ALDH1A1 is an independent adverse prognostic factor for overall survival.</p> <p>Conclusions</p> <p>Immunonhistochemical analysis on whole-mount tissue slides revealed that ALDH1A1 is more abundantly expressed in pancreatic cancer than initially reported by a tissue microarray analysis. Moreover, high expression of ALDH1A1 correlated significantly with the proliferation of tumour cells. Intriguingly, this study is the first which identifies low expression of ALDH1A1 as an independent adverse prognostic marker for overall survival in pancreatic cancer.</p

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

A randomised controlled trial of losartan as an anti-fibrotic agent in non-alcoholic steatohepatitis.

Introduction: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease worldwide. Experimental and small clinical trials have demonstrated that angiotensin II blockers (ARB) may be anti-fibrotic in the liver. The aim of this randomised controlled trial was to assess whether treatment with Losartan for 96 weeks slowed, halted or reversed the progression of fibrosis in patients with non-alcoholic steatohepatitis (NASH). Methods: Double-blind randomised-controlled trial of Losartan 50 mg once a day versus placebo for 96 weeks in patients with histological evidence of NASH. The primary outcome for the study was change in histological fibrosis stage from pre-treatment to end-of-treatment. Results: The study planned to recruit 214 patients. However, recruitment was slower than expected, and after 45 patients were randomised (median age 55; 56% male; 60% diabetic; median fibrosis stage 2), enrolment was suspended. Thirty-two patients (15 losartan and 17 placebo) completed follow up period: one patient (6.7%) treated with losartan and 4 patients (23.5%) in the placebo group were “responders” (lower fibrosis stage at follow up compared with baseline). The major reason for slow recruitment was that 39% of potentially eligible patients were already taking an ARB or angiotensin converting enzyme inhibitor (ACEI), and 15% were taking other prohibited medications. Conclusions: Due to the widespread use of ACEI and ARB in patients with NASH this trial failed to recruit sufficient patients to determine whether losartan has anti-fibrotic effects in the liver