Sequential interim analyses of survival data in DNA microarray experiments

<p>Abstract</p> <p>Background</p> <p>Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available.</p> <p>Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple features, particularly of high-dimensional microarray data, where the number of features clearly exceeds the number of samples. Therefore, we examine false discovery rates and power rates in microarray experiments performed during interim analyses of survival studies. In addition, the early stopping based on interim results of such studies is evaluated. As stop criterion we employ the achieved average power rate, i.e. the proportion of detected true positives, for which a new estimator is derived and compared to existing estimators.</p> <p>Results</p> <p>In a simulation study, pre-specified levels of the false discovery rate are maintained in each interim analysis, where reduced levels as used in classical group sequential designs of one single feature are not necessary. Average power rates increase with each interim analysis, and many studies can be stopped prior to their planned end when a certain pre-specified power rate is achieved. The new estimator for the power rate slightly deviates from the true power rate but is comparable to other estimators.</p> <p>Conclusions</p> <p>Interim analyses of microarray experiments can provide evidence for early stopping of long-term survival studies. The developed simulation framework, which we also offer as a new R package 'SurvGenesInterim' available at <url>http://survgenesinter.R-Forge.R-Project.org</url>, can be used for sample size planning of the evaluated study design.</p

Detection of Simultaneous Group Effects in MicroRNA Expression and Related Target Gene Sets

Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, we incorporate expression levels of their related target gene sets. Group effects are determined individually for each microRNA, and by enrichment tests and global tests for target gene sets. The resulting lists of p-values from individual and set-wise testing are combined by means of meta analysis. We propose a new approach to connect microRNA-wise and gene set-wise information by means of p-value combination as often used in meta-analysis. In this context, we evaluate the usefulness of different approaches of gene set tests. In a simulation study we reveal that our combination approach is more powerful than microRNA-wise testing alone. Furthermore, we show that combining microRNA-wise results with ‘competitive’ gene set tests maintains a pre-specified false discovery rate. In contrast, a combination with ‘self-contained’ gene set tests can harm the false discovery rate, particularly when gene sets are not disjunct

Serum peptide reactivities may distinguish neuromyelitis optica subgroups and multiple sclerosis

Objective: To assess in an observational study whether serum peptide antibody reactivities may distinguish aquaporin-4 (AQP4) antibody (Ab)–positive and -negative neuromyelitis optica spectrum disorders (NMOSD) and relapsing-remitting multiple sclerosis (RRMS). Methods: We screened 8,700 peptides that included human and viral antigens of potential relevance for inflammatory demyelinating diseases and random peptides with pooled sera from different patient groups and healthy controls to set up a customized microarray with 700 peptides. With this microarray, we tested sera from 66 patients with AQP4-Ab-positive (n = 16) and AQP4-Ab-negative (n = 19) NMOSD, RRMS (n = 11), and healthy controls (n = 20). Results: Differential peptide reactivities distinguished NMOSD subgroups from RRMS in 80% of patients. However, the 2 NMOSD subgroups were not well-discriminated, although those patients are clearly separated by their antibody reactivities against AQP4 in cell-based assays. Elevated reactivities to myelin and Epstein-Barr virus peptides were present in RRMS and to AQP4 and AQP1 peptides in AQP4-Ab-positive NMOSD. Conclusions: While AQP4-Ab-positive and -negative NMOSD subgroups are not well-discriminated by peptide antibody reactivities, our findings suggest that peptide antibody reactivities may have the potential to distinguish between both NMOSD subgroups and MS. Future studies should thus concentrate on evaluating peptide antibody reactivities for the differentiation of AQP4-Ab-negative NMOSD and MS

Piloting an outcome-based programme evaluation tool in undergraduate medical education

Aims: Different approaches to performance-oriented allocation of resources according to teaching quality are currently being discussed within German medical schools. The implementation of these programmes is impeded by a lack of valid criteria to measure teaching quality. An assessment of teaching quality should include structural and procedural aspects but focus on learning outcome itself. The aim of this study was to implement a novel, outcome-based evaluation tool within the clinical phase of a medical curriculum and address differences between the novel tool and traditional evaluation methods

Antibody signatures in patients with histopathologically defined multiple sclerosis patterns

Early active multiple sclerosis (MS) lesions can be classified histologically into three main immunopathological patterns of demyelination (patterns I-III), which suggest pathogenic heterogeneity and may predict therapy response. Patterns I and II show signs of immune-mediated demyelination, but only pattern II is associated with antibody/complement deposition. In pattern III lesions, which include Baló's concentric sclerosis, primary oligodendrocyte damage was proposed. Serum antibody reactivities could reflect disease pathogenesis and thus distinguish histopathologically defined MS patterns. We established a customized microarray with more than 700 peptides that represent human and viral antigens potentially relevant for inflammatory demyelinating CNS diseases, and tested sera from 66 patients (pattern I n = 12; II n = 29; III n = 25, including 8 with Baló's), healthy controls, patients with Sjögren's syndrome and stroke patients. Cell-based assays were performed for aquaporin 1 (AQP1) and AQP4 antibody detection. No single peptide showed differential binding among study cohorts. Because antibodies can react with different peptides from one protein, we also analyzed groups of peptides. Patients with pattern II showed significantly higher reactivities to Nogo-A peptides as compared to patterns I (p = 0.02) and III (p = 0.02). Pattern III patients showed higher reactivities to AQP1 (compared to pattern I p = 0.002, pattern II p = 0.001) and varicella zoster virus (VZV, compared to pattern II p = 0.05). In patients with Baló's, AQP1 reactivity was also significantly higher compared to patients without Baló's (p = 0.04), and the former revealed distinct antibody signatures. Histologically, Baló's patients showed loss of AQP1 and AQP4 in demyelinating lesions, but no antibodies binding conformational AQP1 or AQP4 were detected. In summary, higher reactivities to Nogo-A peptides in pattern II patients could be relevant for enhanced axonal repair and remyelination. Higher reactivities to AQP1 peptides in pattern III patients and its subgroup of Baló's patients possibly reflect astrocytic damage. Finally, latent VZV infection may cause peripheral immune activation

Predicting pathway membership via domain signatures

Motivation: Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database

GOSim – an R-package for computation of information theoretic GO similarities between terms and gene products

<p>Abstract</p> <p>Background</p> <p>With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).</p> <p>Results</p> <p>We present the freely available software package <it>GOSim</it>, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. <it>GOSim </it>extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. <it>GOSim </it>hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.</p> <p>Conclusion</p> <p><it>GOSim </it>is implemented as a package for the statistical computing environment <it>R </it>and is distributed under GPL within the CRAN project.</p

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

<p>Abstract</p> <p>Background</p> <p>Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.</p> <p>Results</p> <p>We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property.</p> <p>Conclusions</p> <p>The package 'ddepn' is freely available on R-Forge and CRAN <url>http://ddepn.r-forge.r-project.org</url>, <url>http://cran.r-project.org</url>. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.</p

MicroRNAs preferentially target the genes with high transcriptional regulation complexity

Over the past few years, microRNAs (miRNAs) have emerged as a new prominent class of gene regulatory factors that negatively regulate expression of approximately one-third of the genes in animal genomes at post-transcriptional level. However, it is still unclear why some genes are regulated by miRNAs but others are not, i.e. what principles govern miRNA regulation in animal genomes. In this study, we systematically analyzed the relationship between transcription factors (TFs) and miRNAs in gene regulation. We found that the genes with more TF-binding sites have a higher probability of being targeted by miRNAs and have more miRNA-binding sites on average. This observation reveals that the genes with higher cis-regulation complexity are more coordinately regulated by TFs at the transcriptional level and by miRNAs at the post-transcriptional level. This is a potentially novel discovery of mechanism for coordinated regulation of gene expression. Gene ontology analysis further demonstrated that such coordinated regulation is more popular in the developmental genes.Comment: supplementary data available at http://www.bri.nrc.ca/wan