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

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

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

BITES: Balanced Individual Treatment Effect for Survival data

Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e.~data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e.~we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM). We show in simulation studies that this approach outperforms the state of the art. Further, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort. BITES is provided as an easy-to-use python implementation

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

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus

Intralesional TLR4 agonist treatment strengthens the organ defense against colonizing cancer cells in the brain

Brain metastasis in breast cancer remains difficult to treat and its incidence is increasing. Therefore, the development of new therapies is of utmost clinical relevance. Recently, toll-like receptor (TLR) 4 was correlated with IL6 expression and poor prognosis in 1 215 breast cancer primaries. In contrast, we demonstrated that TLR4 stimulation reduces microglia-assisted breast cancer cell invasion. However, the expression, prognostic value, or therapeutic potential of TLR signaling in breast cancer brain metastasis have not been investigated. We thus tested the prognostic value of various TLRs in two brain-metastasis gene sets. Furthermore, we investigated different TLR agonists, as well as MyD88 and TRIF-deficient microenvironments in organotypic brain-slice ex vivo co-cultures and in vivo colonization experiments. These experiments underline the ambiguous roles of TLR4, its adapter MyD88, and the target nitric oxide (NO) during brain colonization. Moreover, analysis of the gene expression datasets of breast cancer brain metastasis patients revealed associations of TLR1 and IL6 with poor overall survival. Finally, our finding that a single LPS application at the onset of colonization shapes the later microglia/macrophage reaction at the macro-metastasis brain-parenchyma interface (MMPI) and reduces metastatic infiltration into the brain parenchyma may prove useful in immunotherapeutic considerations