Integrated Epigenetics of Human Breast Cancer: Synoptic Investigation of Targeted Genes, MicroRNAs and Proteins upon Demethylation Treatment

The contribution of aberrant DNA methylation in silencing of tumor suppressor genes (TSGs) and microRNAs has been investigated. Since these epigenetic alterations are reversible, it became of interest to determine the effects of the 5-aza-2'-deoxycytidine (DAC) demethylation therapy in breast cancer at different molecular levels

A microarray analysis of full depth knee cartilage of ovariectomized rats

<p>Abstract</p> <p>Background</p> <p>This short communication focuses the on articular cartilage and the subchondral bone, both of which play important roles in the development of osteoarthritis (OA). There are indications that estrogen-deficiency, as the post-menopausal state, accelerate the development of OA.</p> <p>Findings</p> <p>We investigated, which extracellular matrix (ECM) protein, proteases and different pro-inflammatory factors was up- or down-regulated in the knee joint tissue in response to estrogen-deficiency in rats induced by ovariectomy. These data support previous findings that several metalloproteinases (MMPs) and cysteine proteases are co-regulated with numerous collagens and proteoglycans that are important for cartilage integrity. Furthermore quite a few pro-inflammatory cytokines were regulated by estrogen deprivation.</p> <p>Conclusion</p> <p>We found multiple genes where regulated in the joint by estrogen-deficiency, many of which correspond well with our current knowledge of the pathogenesis of OA. It supports that estrogen-deficiency (e.g. OVX) may accelerate joint deterioration. However, there are also data that draw attention the need for better understanding of the synergy between proteases and tissue turnover.</p

Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition

We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, i.e. deficient GABA transmission, within prefrontal cortex and hippocampus for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically-relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function); moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus, but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support that prefrontal and hippocampal neural disinhibition contributes to clinically relevant cognitive deficits, and we consider pharmacological strategies for ameliorating cognitive deficits by rebalancing disinhibition-induced aberrant neural activity

Oncoplastic breast consortium recommendations for mastectomy and whole breast reconstruction in the setting of post-mastectomy radiation therapy

Aim: Demand for nipple-and skin-sparing mastectomy (NSM/SSM) with immediate breast reconstruction (BR) has increased at the same time as indications for post-mastectomy radiation therapy (PMRT) have broadened. The aim of the Oncoplastic Breast Consortium initiative was to address relevant questions arising with this clinically challenging scenario. Methods: A large global panel of oncologic, oncoplastic and reconstructive breast surgeons, patient advocates and radiation oncologists developed recommendations for clinical practice in an iterative process based on the principles of Delphi methodology. Results: The panel agreed that surgical technique for NSM/SSM should not be formally modified when PMRT is planned with preference for autologous over implant-based BR due to lower risk of long-term complications and support for immediate and delayed-immediate reconstructive approaches. Nevertheless, it was strongly believed that PMRT is not an absolute contraindication for implant-based or other types of BR, but no specific recom-mendations regarding implant positioning, use of mesh or timing were made due to absence of high-quality evidence. The panel endorsed use of patient-reported outcomes in clinical practice. It was acknowledged that the shape and size of reconstructed breasts can hinder radiotherapy planning and attention to details of PMRT techniques is important in determining aesthetic outcomes after immediate BR. Conclusions: The panel endorsed the need for prospective, ideally randomised phase III studies and for surgical and radiation oncology teams to work together for determination of optimal sequencing and techniques for PMRT for each patient in the context of BRPeer reviewe

Robust and tissue-independent gender-specific transcript biomarkers

Context: Correct gender assignment in humans at the molecular level is crucial in many scientific disciplines and applied areas. Materials and methods: Candidate gender markers were identified through supervised statistical analysis of genome wide microarray expression data from human blood samples (N¼123, 58 female, 65 male) as a training set. The potential of the markers to predict undisclosed tissue donor gender was tested on microarray data from 13 healthy and 11 cancerous human tissue collections (internal) and external datasets from samples of varying tissue origin. The abundance of some genes in the marker panel was quantified by RT-PCR as alternative analytical technology. Results: We identified and qualified predictive, gender-specific transcript markers based on a set of five genes (RPS4Y1, EIF1AY, DDX3Y, KDM5D and XIST). Conclusion: Gene expression marker panels can be used as a robust tissue- and platformindependent predictive approach for gender determination

Bioequivalence study of a valsartan tablet and a capsule formulation after single dosing in healthy volunteers using a replicated crossover design.

Two formulations of valsartan (Diovan), 320 mg tablets and marketed 160 mg capsules, were evaluated for bioequivalence after single dosing

Optimal deconvolution of transciptional profiling data using quadratic proagramming with application to complex clinical blood samples

Large-scale molecular profiling technologies have enabled measurements of mRNA expression on the scale of whole genomes, which may assist the identification of disease biomarkers and facilitate the basic understanding of cellular processes. Specifically, peripheral blood is the most readily accessible human tissue for the studies of disease association and drug response in clinical trials. However, samples collected from human subjects in clinical trials possess a level of complexity that can hinder or obfuscate the analysis of data derived from them. Solid tissues can vary in composition depending on disease state, anatomical location, and collection method. Even so-called simple samples such as blood represent a complex mixture of circulating cell types of varying origin and functions. Failure to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the following statistical studies. We introduce an approach that explicitly builds upon a linear latent variable model, in which expression from a mixed cell population are modeled as the weighted average of expression from different cell types. We employ quadratic programming to efficiently search for the globally optimal solution in the linear latent model framework that preserves non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell and tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. Our method solves one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment. We have tested our methods to several well controlled benchmark data sets with known mixing fractions. Accurate agreement between predicted and actual mixing fractions was observed as expected, and robust to the experimental system. In addition, we have applied our method to more challenging mRNA expression profiling data from whole blood samples collected in a clinical trial (CFTY720D2201, ClinicalTrials.gov identifier NCT00333138). Our method was able to predict mixing fractions for more than ten species of circulating cells, and was even able to provide accurate estimates for relatively rare cell types ( 0.75). In addition, our method was able to accurately identify changes in leukocyte trafficking associated with FTY720 treatment that is consistent with previous results generated by both Complete Blood Counts and flow cytometry

Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples.

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment

Omalizumab normalizes the gene expression signature of lesional skin in patients with chronic spontaneous urticaria: a randomized, double-blind, placebo-controlled study

Introduction and objectives. Chronic spontaneous urticaria (CSU), including severe and treatment-refractory CSU, shows a strong response to omalizumab, a humanized recombinant monoclonal anti-IgE antibody. The effect of omalizumab on gene expression was assessed in skin biopsies from CSU patients enrolled in a double-blind placebo-controlled study (ClinicalTrials.gov Identifier: NCT01599637). Methods. CSU patients (18-75 years) were randomized to either 300 mg omalizumab (n=20) or placebo (n=10) administered s.c. every 4 weeks for 12 weeks. Lesional and non-lesional skin biopsies were collected from the same body area of consenting subjects and assessed at baseline and on Day 85. Skin biopsies from the same area of 10 untreated healthy volunteers (HV) were also processed as reference. Gene expression data were generated using Affymetrix HG-U133plus2.0 microarrays. Statistical analyses were performed using R packages. In brief, after normalization, low-intensity transcripts (i.e. probesets with intensities less than 100 in ≥50% of the samples) were filtered out. To identify transcriptional changes, linear models were constructed taking into account the type of biopsy (lesional or non-lesional), the study visit and the treatment for each patient. Thresholds for statistical significance and minimal fold change (FC) were defined as P-value ≤0.05 (no multiple testing correction) and absolute FC ≥1.5, respectively. Results. At baseline, 63 transcripts were differentially expressed between lesional and non-lesional skin. Two thirds of this lesional signature was also differentially expressed between lesional and HV skin. Upon treatment with omalizumab, over 75% of this lesional signature changed to reflect non-lesional skin expression levels (different to placebo, P-value 16). Conclusions. Omalizumab, in treatment responders, reverted transcriptional signatures associated with the CSU lesion phenotype to reflect non-lesional/HV expression levels. This result is consistent with observed omalizumab-mediated clinical improvement observed in patients with CSU

Perturbation of microRNAs in rat heart during chronic doxorubicin treatment

Anti-cancer therapy based on anthracyclines (DNA intercalating Topoisomerase II inhibitors) is limited by adverse effects of these compounds on the cardiovascular system, ultimately causing heart failure. Despite extensive investigations into the effects of doxorubicin on the cardiovascular system, the molecular mechanisms of toxicity remain largely unknown. MicroRNAs are endogenously transcribed non-coding 22 nucleotide long RNAs that regulate gene expression by decreasing mRNA stability and translation and play key roles in cardiac physiology and pathologies. Increasing doses of doxorubicin, but not etoposide (a Topoisomerase II inhibitor devoid of cardiovascular toxicity), specifically induced the up-regulation of miR-208b, miR-216b, miR-215, miR-34c and miR-367 in rat hearts. Furthermore, the lowest dosing regime (1 mg/kg/week for 2 weeks) led to a detectable increase of miR-216b in the absence of histopathological findings or alteration of classical cardiac stress biomarkers. In silico microRNA target predictions suggested that a number of doxorubicin-responsive microRNAs may regulate mRNAs involved in cardiac tissue remodeling. In particular miR-34c was able to mediate the DOXinduced changes of Sipa1 mRNA (a mitogen-induced Rap/Ran GTPase activating protein) at the post-transcriptional level and in a seed sequence dependent manner. Our results show that integrated heart tissue microRNA and mRNA profiling can provide valuable early genomic biomarkers of drug-induced cardiac injury as well as novel mechanistic insight into the underlying molecular pathways