Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

http://dmrocke.ucdavis.edu/Class/BST226.2014.Winter/Nature%20Protocols%20DAVID.pdf

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the

/pdf/regularization-and-variable-selection-via-the-elastic-net-4z8q6j5o0i.pdf

Regularization and variable selection via the elastic net

The original version of this Article contained an error in the author affiliations. Trey Ideker was incorrectly associated with 'Department of Medicine (Oncology), Stanford University School of Medicine, 875 Blake Wilbur Dr, Palo Alto, CA 94304, USA.' This has now been corrected in both the PDF and HTML versions of the Article.

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Publisher Correction: Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

Background/aim R-spondins control WNT signaling and RSPO1 and LGR6, two of its receptors, are uniquely expressed at high levels in high-grade serous ovarian cancer (HGSOC). The aim of this study was to assess the interrelations between the expression of the RSPOs and LGRs in HGSOC and in the ovarian surface (OSE) and fallopian tube surface epithelium (FTSE) from which HGSOC arises. Materials and methods Analysis of TCGA (HGSOC), CCLE (ovary), and other publicly accessed RNA-Seq data using UC San Diego Computational Cancer Analysis Library (CCAL) to perform differential expression analysis, association studies, and gene set inspection using the single-sample GSEA method. Additionally, we employed multiple publicly available databases including StringDB, Human Protein Atlas, and cBioPortal to aid the investigation. Results Among normal tissues, expression of RSPO1, LGR5 and LGR6 was highest in the fallopian tube. The relative levels of expression of the RSPOs and LGRs in the OSE and FTSE matched those in HGSOC. RSPO1 and LGR6 were highly co-expressed in all three tissues. Gene set enrichment analysis (GSEA) showed that expression of RSPO1 was strongly linked to the enrichment of three separate WNT-driven GO pathways. Analysis of genes that impacted overall survival identified two other immediately adjacent genes that control WNT signaling, KREMEN1 and ZNRF3 whose expression and copy number were coordinately linked. Conclusion RSPO1 and LGR6 are coordinately expressed in HGSOC and the two normal tissues from which this tumor arises, and their expression is linked to WNT signaling pathways known the control cell fate and proliferation.

WNT Signaling Driven by R-spondin 1 and LGR6 in High-grade Serous Ovarian Cancer.

Gastrointestinal stromal tumor (GIST) is commonly driven by oncogenic KIT mutations that are effectively targeted by imatinib (IM), a tyrosine kinase inhibitor (TKI). However, IM does not cure GIST, and adjuvant therapy only delays recurrence in high-risk tumors. We hypothesized that GIST contains cells with primary IM resistance that may represent a reservoir for disease persistence. Here, we report a subpopulation of CD34+KITlow human GIST cells that have intrinsic IM resistance. These cells possess cancer stem cell-like expression profiles and behavior, including self-renewal and differentiation into CD34+KIThigh progeny that are sensitive to IM treatment. We also found that TKI treatment of GIST cell lines led to induction of stem cell-associated transcription factors (OCT4 and NANOG) and concomitant enrichment of the CD34+KITlow cell population. Using a data-driven approach, we constructed a transcriptomic-oncogenic map (Onco-GPS) based on the gene expression of 134 GIST samples to define pathway activation during GIST tumorigenesis. Tumors with low KIT expression had overexpression of cancer stem cell gene signatures consistent with our in vitro findings. Additionally, these tumors had activation of the Gas6/AXL pathway and NF-κB signaling gene signatures. We evaluated these targets in vitro and found that primary IM-resistant GIST cells were effectively targeted with either single-agent bemcentinib (AXL inhibitor) or bardoxolone (NF-κB inhibitor), as well as with either agent in combination with IM. Collectively, these findings suggest that CD34+KITlow cells represent a distinct, but targetable, subpopulation in human GIST that may represent a novel mechanism of primary TKI resistance, as well as a target for overcoming disease persistence following TKI therapy.

Pablo Tamayo

Papers

Publisher Correction: Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

WNT Signaling Driven by R-spondin 1 and LGR6 in High-grade Serous Ovarian Cancer.

KITlow Cells Mediate Imatinib Resistance in Gastrointestinal Stromal Tumor

Oracle Data Mining - Data Mining in the Database Environment.

Signatures and Determinants Associated with Cancer and Methods of Use Thereof