Jeff Gentry

Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

Network structures and algorithms in Bioconductor

UNLABELLED In this paper, we review the central concepts and implementations of tools for working with network structures in Bioconductor. Interfaces to open source resources for visualization (AT&T Graphviz) and network algorithms (Boost) have been developed to support analysis of graphical structures in genomics and computational biology. AVAILABILITY Packages graph, Rgraphviz, RBGL of Bioconductor (www.bioconductor.org).

Bioconductor Software for Graphs

We describe software tools for creating, manipulating, and visualizing graphs in the Bioconductor project. We give the rationale for our design decisions and provide brief outlines of how to make use of these tools. The discussion mirrors that of Chapter 20 where the different mathematical constructs were described. It is worth differentiating between packages that are mainly infrastructure (sets of tools that can be used to create other pieces of software) and packages that are designed to provide an end-user application. The packages graph, RBGL, and Rgraphviz are infrastructure packages. Software developers may use these packages to construct tools aimed at specific applications areas, such as the GOstats package.

SGDI: SYSTEM FOR GENOMIC DATA INTEGRATION

This paper describes a framework for collecting, annotating, and archiving high-throughput assays from multiple experiments conducted on one or more series of samples. Specific applications include support for large-scale surveys of related transcriptional profiling studies, for investigations of the genetics of gene expression and for joint analysis of copy number variation and mRNA abundance. Our approach consists of data capture and modeling processes rooted in R/Bioconductor, sample annotation and sequence constituent ontology management based in R, secure data archiving in PostgreSQL, and browser-based workspace creation and management rooted in Zope. This effort has generated a completely transparent, extensible, and customizable interface to large archives of high-throughput assays. Sources and prototype interfaces are accessible at www.sgdi.org/software.

Abstract 181: Therapeutic approaches to metastasis induced by mesenchymal stem cells in the tumor microenvironment

Metastasis is the primary cause of death in non-hematological cancers yet there are no specific therapeutics against it because of a lack of validated targets. The Weinberg lab demonstrated that bone-marrow derived mesenchymal stem cells (MSCs) home to the stroma of breast tumors and induce metastasis. We verified these findings in MDA-MB-231 (MDA) xenografts in which co-injection of human MSCs increased MDA thoracic metastasis by 5-fold. In vitro, MSC co-culture induced GFP-labeled MDA cells to migrate 3-fold faster in a modified ‘wound-healing’ assay, mirroring metastasis in vivo. To identify therapeutic targets within MSC-induced metastasis, we performed gene-expression analysis of MSC-MDA co-cultures separated by flow cytometry compared with cells grown alone. The interferon pathway was found to be the most activated pathway upon co-culture, increasing mainly in MSCs. We studied the relevance of these genes in human cancers by first analyzing 3 different gene-expression datasets comparing human breast cancer stroma with normal stroma. Genes upregulated in breast cancer stroma were then studied in a meta-analysis of 19 whole tumor gene-expression datasets correlating gene-expression changes with survival. We identified 103 genes that are upregulated in MSCs by MSC-MDA interactions, are increased in human breast cancer stroma and are significantly associated with poor survival. To determine if they are necessary for MSC-induced metastatic behavior, we performed shRNA knockdown in MSC-MDA co-cultures measuring effects on in vitro migration. Knockdown of a number of interferon-associated genes significantly reduced migration supporting an unexpected functional role of interferons in metastasis. The top interferon gene, ISG15, is an attractive candidate for therapeutic targeting because it is a secreted ubiquitin-like factor that conjugates a number of cytoskeletal proteins involved in motility. In parallel, we conducted a small-molecule screen with 1600 compounds on the migration of MSC-MDA co-cultures to identify small-molecule inhibitors of metastasis. Counter screens on highly motile endothelial cells excluded compounds that non-specifically inhibit normal cell migration. Only 1 compound, RSL3, specifically blocked MSC-induced MDA migration with cytotoxicity at >10-fold higher concentrations. RSL3 inhibits the glutathione peroxidase 4 (GPX4) enzyme that metabolizes lipid peroxides including arachidonic acid metabolites participating in inflammatory cascades like interferon gamma signaling. RSL3 activity was completely abrogated by co-treatment with the 5-lipoxygenase inhibitor zileuton consistent with the role of arachidonic acid metabolites in MSC-MDA migration. In summary, targeting components of interferon and arachidonic acid pathways have been discovered as novel therapeutic approaches against microenvironment-induced breast cancer metastasis. Citation Format: Shrikanta Chattopadhyay, Cherrie Huang, Siddhartha Mukherjee, Rushdia Z. Yusuf, Vasanthi Viswanathan, Ben S. Wittner, Jeff Gentry, Alykhan Shamji, Sridhar Ramaswamy, David T. Scadden, Stuart L. Schreiber. Therapeutic approaches to metastasis induced by mesenchymal stem cells in the tumor microenvironment. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 181. doi:10.1158/1538-7445.AM2014-181

Abstract 2203: Integrative analysis for efficient candidate oncogene discovery in cancer cell lines

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC A fundamental challenge in cancer genomics is to efficiently identify biologically relevant candidate oncogenes from the large amount of molecular information generated in human cancer studies from various high throughput modalities. METHODS. We developed the DELTA (Difference in Expression Lined to Amplification) measurement to systematically identify candidate oncogenes in human cancer cell lines. We applied this metric to a large, diverse collection of 365 cancer cell lines densely profiled at both the expression and genomic level using the Affymetrix U133X3P and 500K Mapping arrays. RESULTS. We first identified recurrent cancer-driving amplification events using a GISTIC-inspired approach. This algorithmic analysis revealed 170 amplicons across 18 different cancer types. The majority of these recurrent amplicons do not contain known oncogenes. We then used the DELTA metric to further prioritize candidate oncogenes within these recurrent amplicons. Systematic shRNA knock down of these candidates was then used to identify SOX2 and S100A8 as candidate oncogenes in esophageal and lung cancer cell lines, respectively. CONCLUSION. Differential gene expression analysis with the DELTA metric may be useful for the functional analysis of recurrent genomic amplicons across human cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2203.

Querying On-line Resources

Many different meta-data resources are available on-line, and several of these provide a Web services model for interactions. R and Bioconductor support the use of different technologies (including HTTP, SOAP, and XML-RPC) for accessing different Web services. In this chapter we describe the tools for accessing Web services and demonstrate their use in a number of examples.