Exposure and response analysis of aleglitazar on cardiovascular risk markers and safety outcomes:An analysis of the AleCardio trial

Aims The AleCardio trial aimed to characterize the efficacy and safety of peroxisome proliferator-activated receptor-alpha gamma agonist aleglitazar in patients with type 2 diabetes mellitus and acute coronary syndrome. The trial terminated early because of futility and safety signals. We evaluated whether the safety signals could be attributed to increased exposure to aleglitazar. Materials and Methods The AleCardio trial enrolled 7226 patients to receive aleglitazar 150 mu g or matching placebo on top of standard care. A population pharmacokinetic analysis was conducted in a pharmacokinetic substudy to identify covariates that explained interindividual variability in exposure. Subsequently, the effect of these covariates on surrogate and clinical outcomes was assessed in the full patient population. Results Concomitant administration of clopidogrel was identified as a covariate that influenced the apparent clearance of aleglitazar. Patients using clopidogrel had a mean predicted area under the plasma-concentration-time curve (AUC(0-24)) of 174.7 ng h/mL (SD: +/- 112.9 ng h/mL) versus 142.2 ng h/mL (SD: +/- 92.6 ng h/mL) in patients without clopidogrel. The effect of aleglitazar compared with placebo on HbA1c, haemoglobin, serum creatinine and adiponectin was modified by concomitant clopidogrel use (P for interaction 0.007, 0.002

Who Is at Risk for Diagnostic Discrepancies? Comparison of Pre- and Postmortal Diagnoses in 1800 Patients of 3 Medical Decades in East and West Berlin

<div><h3>Background</h3><p>Autopsy rates in Western countries consistently decline to an average of <5%, although clinical autopsies represent a reasonable tool for quality control in hospitals, medically and economically. Comparing pre- and postmortal diagnoses, diagnostic discrepancies as uncovered by clinical autopsies supply crucial information on how to improve clinical treatment. The study aimed at analyzing current diagnostic discrepancy rates, investigating their influencing factors and identifying risk profiles of patients that could be affected by a diagnostic discrepancy.</p> <h3>Methods and Findings</h3><p>Of all adult autopsy cases of the Charité Institute of Pathology from the years 1988, 1993, 1998, 2003 and 2008, the pre- and postmortal diagnoses and all demographic data were analyzed retrospectively. Based on power analysis, 1,800 cases were randomly selected to perform discrepancy classification (class I-VI) according to modified Goldman criteria. The rate of discrepancies in major diagnoses (class I) was 10.7% (95% CI: 7.7%–14.7%) in 2008 representing a reduction by 15.1%. Subgroup analysis revealed several influencing factors to significantly correlate with the discrepancy rate. Cardiovascular diseases had the highest frequency among class-I-discrepancies. Comparing the 1988-data of East- and West-Berlin, no significant differences were found in diagnostic discrepancies despite an autopsy rate differing by nearly 50%. A risk profile analysis visualized by intuitive heatmaps revealed a significantly high discrepancy rate in patients treated in low or intermediate care units at community hospitals. In this collective, patients with genitourinary/renal or infectious diseases were at particularly high risk.</p> <h3>Conclusions</h3><p>This is the current largest and most comprehensive study on diagnostic discrepancies worldwide. Our well-powered analysis revealed a significant rate of class-I-discrepancies indicating that autopsies are still of value. The identified risk profiles may aid both pathologists and clinicians to identify patients at increased risk for a discrepant diagnosis and possibly suboptimal treatment intra vitam.</p> </div

Periostin is up-regulated in high grade and high stage prostate cancer

BACKGROUND: Expression of periostin is an indicator of epithelial-mesenchymal transition in cancer but a detailed analysis of periostin expression in prostate cancer has not been conducted so far. METHODS: Here, we evaluated periostin expression in prostate cancer cells and peritumoural stroma immunohistochemically in two independent prostate cancer cohorts, including a training cohort (n = 93) and a test cohort (n = 325). Metastatic prostate cancers (n = 20), hormone refractory prostate cancers (n = 19) and benign prostatic tissues (n = 38) were also analyzed. RESULTS: In total, strong epithelial periostin expression was detectable in 142 of 418 (34.0%) of prostate carcinomas and in 11 of 38 benign prostate glands (28.9%). Increased periostin expression in carcinoma cells was significantly associated with high Gleason score (p < 0.01) and advanced tumour stage (p < 0.05) in the test cohort. Whereas periostin expression was weak or absent in the stroma around normal prostate glands, strong periostin expression in tumour stroma was found in most primary and metastatic prostate cancers. High stromal periostin expression was associated with higher Gleason scores (p < 0.001). There was a relationship between stromal periostin expression and shortened PSA relapse free survival times in the training cohort (p < 0.05). CONCLUSIONS: Our data indicate that periostin up-regulation is related to increased tumour aggressiveness in prostate cancer and might be a promising target for therapeutical interventions in primary and metastatic prostate cancer

Technology generation to dissemination:lessons learned from the tef improvement project

Indigenous crops also known as orphan crops are key contributors to food security, which is becoming increasingly vulnerable with the current trend of population growth and climate change. They have the major advantage that they fit well into the general socio-economic and ecological context of developing world agriculture. However, most indigenous crops did not benefit from the Green Revolution, which dramatically increased the yield of major crops such as wheat and rice. Here, we describe the Tef Improvement Project, which employs both conventional- and molecular-breeding techniques to improve tef\u2014an orphan crop important to the food security in the Horn of Africa, a region of the world with recurring devastating famines. We have established an efficient pipeline to bring improved tef lines from the laboratory to the farmers of Ethiopia. Of critical importance to the long-term success of this project is the cooperation among participants in Ethiopia and Switzerland, including donors, policy makers, research institutions, and farmers. Together, European and African scientists have developed a pipeline using breeding and genomic tools to improve the orphan crop tef and bring new cultivars to the farmers in Ethiopia. We highlight a new variety, Tesfa, developed in this pipeline and possessing a novel and desirable combination of traits. Tesfa\u2019s recent approval for release illustrates the success of the project and marks a milestone as it is the first variety (of many in the pipeline) to be released

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

Downregulation of Homologous Recombination DNA Repair Genes by HDAC Inhibition in Prostate Cancer Is Mediated through the E2F1 Transcription Factor

Histone deacetylase inhibitors (HDACis) re-express silenced tumor suppressor genes and are currently undergoing clinical trials. Although HDACis have been known to induce gene expression, an equal number of genes are downregulated upon HDAC inhibition. The mechanism behind this downregulation remains unclear. Here we provide evidence that several DNA repair genes are downregulated by HDAC inhibition and provide a mechanism involving the E2F1 transcription factor in the process.Applying Analysis of Functional Annotation (AFA) on microarray data of prostate cancer cells treated with HDACis, we found a number of genes of the DNA damage response and repair pathways are downregulated by HDACis. AFA revealed enrichment of homologous recombination (HR) DNA repair genes of the BRCA1 pathway, as well as genes regulated by the E2F1 transcription factor. Prostate cancer cells demonstrated a decreased DNA repair capacity and an increased sensitization to chemical- and radio-DNA damaging agents upon HDAC inhibition. Recruitment of key HR repair proteins to the site of DNA damage, as well as HR repair capacity was compromised upon HDACi treatment. Based on our AFA data, we hypothesized that the E2F transcription factors may play a role in the downregulation of key repair genes upon HDAC inhibition in prostate cancer cells. ChIP analysis and luciferase assays reveal that the downregulation of key repair genes is mediated through decreased recruitment of the E2F1 transcription factor and not through active repression by repressive E2Fs.Our study indicates that several genes in the DNA repair pathway are affected upon HDAC inhibition. Downregulation of the repair genes is on account of a decrease in amount and promoter recruitment of the E2F1 transcription factor. Since HDAC inhibition affects several pathways that could potentially have an impact on DNA repair, compromised DNA repair upon HDAC inhibition could also be attributed to several other pathways besides the ones investigated in this study. However, our study does provide insights into the mechanism that governs downregulation of HR DNA repair genes upon HDAC inhibition, which can lead to rationale usage of HDACis in the clinics

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe