Graph based fusion of high-dimensional gene- and microRNA expression data

One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs, correlations between miRNA and mRNA expression data as well as target prediction information were used to build a bipartite graph representing the relations between miRNAs and mRNAs. Feature selection is a critical part when fitting prediction models to high- dimensional data. Most methods treat features, in this case genes or miRNAs, as independent, an assumption that does not hold true when dealing with combined gene and miRNA expression data. To improve prediction accuracy, a description of the correlation structure in the data is needed. In this work the bipartite graph was used to guide the feature selection and therewith improve prediction results and find a stable prognostic signature of miRNAs and genes. The method is evaluated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse, an important event in prostate cancer treatment, was used as clinical endpoint. Biochemical relapse coins the renewed rise of the blood level of a prostate marker (PSA) after surgical removal of the prostate. The relapse is a hint for metastases and usually the point in clinical practise to decide for further treatment. A boosting approach was used to predict the biochemical relapse. It could be shown that the bipartite graph in combination with miRNA and mRNA expression data could improve prediction performance. Furthermore the ap- proach improved the stability of the feature selection and therewith yielded more consistent marker sets. Of course, the marker sets produced by this new method contain mRNAs as well as miRNAs. The new approach was compared to two state-of-the-art methods suited for high-dimensional data and showed better prediction performance in both cases

Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification

<p>Abstract</p> <p>Background</p> <p>Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level.</p> <p>Results</p> <p>A new antibody-mediated signal amplification (AMSA) strategy relying on sequential incubation steps with fluorescently-labeled secondary antibodies reactive against each other is introduced here. The signal quantification is performed in the near-infrared range. The RPPA-based analysis of 14 endogenous proteins in seven different cell lines demonstrated a strong correlation (r = 0.89) between AMSA and standard NIR detection. Probing serial dilutions of human cancer cell lines with different primary antibodies demonstrated that the new amplification approach improved the limit of detection especially for low abundant target proteins.</p> <p>Conclusions</p> <p>Antibody-mediated signal amplification is a convenient and cost-effective approach for the robust and specific quantification of low abundant proteins on RPPAs. Contrasting other amplification approaches it allows target protein detection over a large linear range.</p

Real-life data on treatment and outcomes in advanced ovarian cancer : An observational, multinational cohort study (RESPONSE trial)

Background This study aimed to describe the treatment strategies and outcomes for women with newly diagnosed advanced high-grade serous or endometrioid ovarian cancer (OC). Methods This observational study collected real-world medical record data from eight Western countries on the diagnostic workup, clinical outcomes, and treatment of adult women with newly diagnosed advanced (Stage III-IV) high-grade serous or endometrioid OC. Patients were selected backward in time from April 1, 2018 (the index date), with a target of 120 patients set per country, followed for >= 20 months. Results Of the 1119 women included, 66.9% had Stage III disease, 11.7% had a deleterious BRCA mutation, and 26.6% received bevacizumab; 40.8% and 39.3% underwent primary debulking surgery (PDS) and interval debulking surgery (IDS), respectively. Of the patients who underwent PDS, 55.5% had no visible residual disease (VRD); 63.9% of the IDS patients had no VRD. According to physician-assessed responses (at the first assessment after diagnosis and treatment), 53.2% of the total population had a complete response and 25.7% had a partial response to first-line chemotherapy after surgery. After >= 20 months of follow-up, 32.9% of the patients were disease-free, 46.4% had progressive disease, and 20.6% had died. Bevacizumab use had a significant positive effect on overall survival (hazard ratio [HR], 0.62; 95% CI, 0.42-0.91; p = .01). A deleterious BRCA status had a significant positive effect on progression-free survival (HR, 0.60; 95% CI, 0.41-0.84; p < .01). Conclusions Women with advanced high-grade serous or endometrioid OC have a poor prognosis. Bevacizumab use and a deleterious BRCA status were found to improve survival in this real-world population. Lay summary Patients with advanced (Stage III or IV) ovarian cancer (OC) have a poor prognosis. The standard treatment options of surgery and chemotherapy extend life beyond diagnosis for 5 years or more in only approximately 45% of patients. This study was aimed at describing the standard of care in eight Western countries and estimating how many patients who are diagnosed with high-grade serous or endometrioid OC could potentially be eligible for first-line poly(adenosine diphosphate ribose) polymerase inhibitor (PARPi) maintenance therapy. The results highlight the poor prognosis for these patients and suggest that a significant proportion (79%) would potentially be eligible for first-line PARPi maintenance treatment.Peer reviewe

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P &lt; 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P &lt; 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder

Comprehensive Comparison of Various Techniques for the Analysis of Elemental Distributions in Thin Films

The present work shows results on elemental distribution analyses in Cu(In,Ga)Se2 thin films for solar cells performed by use of wavelength-dispersive and energy-dispersive X-ray spectrometry (EDX) in a scanning electron microscope, EDX in a transmission electron microscope, X-ray photoelectron, angle-dependent soft X-ray emission, secondary ion-mass (SIMS), time-of-flight SIMS, sputtered neutral mass, glow-discharge optical emission and glow-discharge mass, Auger electron, and Rutherford backscattering spectrometry, by use of scanning Auger electron microscopy, Raman depth profiling, and Raman mapping, as well as by use of elastic recoil detection analysis, grazing-incidence X-ray and electron backscatter diffraction, and grazing-incidence X-ray fluorescence analysis. The Cu(In,Ga)Se2 thin films used for the present comparison were produced during the same identical deposition run and exhibit thicknesses of about 2 μm. The analysis techniques were compared with respect to their spatial and depth resolutions, measuring speeds, availabilities, and detection limit

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling

<p>Abstract</p> <p>Background</p> <p><it>TMPRSS2-ERG </it>gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although <it>TMPRSS2-ERG </it>fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.</p> <p>Methods</p> <p>We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.</p> <p>Results</p> <p>Comparison of gene expression levels among <it>TMPRSS2-ERG </it>fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like <it>CRISP3 </it>were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in <it>TMPRSS2-ERG </it>fusion-positive tumors.</p> <p>Conclusions</p> <p>The <it>TMPRSS2-ERG </it>gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.</p