Prediction of Platinum Resistance from Expression Levels of Genes Related to Homologous Recombination

International audienceWe aimed to predict platinum resistance in High grade serous ovarian cancer (HGSOC) patients from mRNA measurements. Based on the known correlation between the activity of the Homologous Recombination (HR) pathway and primary platinum sensitivity we hypothesized that HR- and Fanconi Anemia (FA)-related genes could be used to classify new patients as either resistant or sensitive.We performed deconvolution on HGSOC mRNA expression data obtained from HERCULES and TCGA consortia and used the epithelial ovarian cancer component to evaluate our hypothesis. In the HERCULES dataset, we had suitable data from 7 resistant and 17 sensitive patients, while TCGA provided data from 35 resistant and 35 sensitive patients. We built our classifier using genes from KEGG's HR and FA pathways and the Nearest Centroid Classification method. In the HERCULES dataset, the balanced accuracy score was 0.791, while it was 0.586 in the TCGA dataset. We explored whether any of the HR+FA genes differed between resistant and sensitive patients in the HERCULES dataset. We noticed that ABRAXAS1 significantly differed (padj <0.05 after Benjamini-Hochberg correction, Mann-Whitney U test). Our subsequent accuracy estimate of an ABRAXAS1-only classifier was 0.842. We also found a subgroup of HR-related genes (BRCA1, ABRAXAS1, FANCC, PMS2, RAD50) that led to an accuracy estimate of 0.964

BaSysBio: Towards an understanding of dynamic transcriptional regulation at global scale in bacteria: a systems biology approach

9th International Conference on Systems Biology, ICSB 2008, 22-28 August, Goteborg, SwedenBaSysBio adopts a systems biology approach in which quantitative experimental data will be generated for each step of the information flow and will fuel computational modelling. High throughput technologies (living cell arrays, tiling DNA microarrays, MDLC proteomics and quantitative metabolomics) will be developed in conjunction with new computational modelling concepts to facilitate the understanding of biological complexity. Models will simulate the cellular transcriptional responses to environmental changes and their impact on metabolism and proteome dynamics. The iterative process of simulations and model-driven targeted experiments will generate novel hypotheses about the mechanistic nature of dynamic cellular responses, unravel emerging systems properties and ultimately provide an efficient roadmap to tackle novel, pathogenic organismsN

Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses

SummarySystems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses

The Cytoscape platform for network analysis and visualization

International audienceCytoscape is an open source software platform that supports the visualization and analysis of molecular profiling data in the context of functional interaction networks. It is developed by several research groups that are actively involved in the development of technologies around the generation and integrative analysis of molecular profiling data in the context of biological and biomedical research. Here, we outline the rationale behind the use of functional interaction networks, introduce the Cytoscape platform, and present an example in which data analysis and visualization using Cytoscape has led to a discovery of previously unknown disease biology

Weighted sequence graphs: boosting iterated dynamic programming using locally suboptimal solutions

We present a novel technique for improving a fundamental aspect of iterated dynamic programming procedures on sequences, such as progressive sequence alignment. Instead of relying on the unrealistic assumption that each iteration can be performed accurately without including information from other sequences, our technique employs the combinatorial data structure of weighted sequence graphs to represent an exponential number of optimal and suboptimal sequences. The usual dynamic programming algorithm on linear sequences can be generalized to weighted sequence graphs, and therefore allows to align sequence graphs instead of individual sequences in subsequent stages. Thus, locally suboptimal, but globally correct solutions can for the first time be identified through iterated sequence alignment. We demonstrate the utility of our technique by applying it to the benchmark alignment problem of Sankoff et al. (J. Mol. Evol. 7 (1976) 133). Although a recent effort could improve on the original solution from 1976 slightly, our technique leads to even more significant improvements

The Deferred Path Heuristic for the Generalized Tree Alignment Problem

Many multiple alignment methods implicitly or explicitly try to minimize the amount of biological change implied by an alignment. At the level of sequences, biological change is measured along a phylogenetic tree, a structure frequently being predicted only after the multiple alignment instead of together with it. The Generalized Tree Alignment problem addresses both questions simultaneously. It can formally be viewed as a Steiner tree problem in sequence space and our approach merges a path heuristic for the construction of a Steiner tree with a clustering method as usually applied only to distance data. This combination is achieved using sequence graphs, a data structure for efficient representation of similar sequences. Although somewhat slower in practice than an earlier method by Hein (Mol. Biol. Evol., 6:649--668,1989) the current approach achieves significantly better results in terms of the underlying scoring function. Furthermore, a variant of the algorithm is intr..