Reconstitution of the ERG Gene Expression Network Reveals New Biomarkers and Therapeutic Targets in ERG Positive Prostate Tumors.

BACKGROUND: Despite a growing number of studies evaluating cancer of prostate (CaP) specific gene alterations, oncogenic activation of the ETS Related Gene (ERG) by gene fusions remains the most validated cancer gene alteration in CaP. Prevalent gene fusions have been described between the ERG gene and promoter upstream sequences of androgen-inducible genes, predominantly TMPRSS2 (transmembrane protease serine 2). Despite the extensive evaluations of ERG genomic rearrangements, fusion transcripts and the ERG oncoprotein, the prognostic value of ERG remains to be better understood. Using gene expression dataset from matched prostate tumor and normal epithelial cells from an 80 GeneChip experiment examining 40 tumors and their matching normal pairs in 40 patients with known ERG status, we conducted a cancer signaling-focused functional analysis of prostatic carcinoma representing moderate and aggressive cancers stratified by ERG expression. RESULTS: In the present study of matched pairs of laser capture microdissected normal epithelial cells and well-to-moderately differentiated tumor epithelial cells with known ERG gene expression status from 20 patients with localized prostate cancer, we have discovered novel ERG associated biochemical networks. CONCLUSIONS: Using causal network reconstruction methods, we have identified three major signaling pathways related to MAPK/PI3K cascade that may indeed contribute synergistically to the ERG dependent tumor development. Moreover, the key components of these pathways have potential as biomarkers and therapeutic target for ERG positive prostate tumors.

Manual annotation of protein interactions.

Protein interactions are the basic building blocks for assembly of pathways and networks. Almost any biologically meaningful functionality (for instance, linear signaling pathways, chains of metabolic reactions, transcription factor dimmers, protein complexes of transcriptosome, gene-disease associations) can be represented as a combination of binary relationships between "network objects" (genes, proteins, RNA species, bioactive compounds). Naturally, the assembled pathways and networks are only as good as their "weakest" link (i.e., a wrongly assigned interaction), and the errors multiply in multi-step pathways. Therefore, the utility of "systems biology" is fundamentally dependent on quality and relevance of protein interactions. The second important parameter is the sheer number of interactions assembled in the database. One needs a "critical mass" of species-specific interactions in order to build cohesive networks for a gene list, not a constellation of non-connected proteins and protein pairs. The third issue is semantic consistency between interactions of different types. Transient physical signal transduction interactions, reactions of endogenous metabolism, transcription factor-promoter binding, and kinetic drug-target interactions are all very different in nature. Yet, they have to fit well into one database format and be consistent in order to be useful in reconstruction of cellular processes.High-quality protein interactions are available in peer-reviewed "small experiment" literature and, to a much smaller extent, patents. However, it is very challenging to find the interactions, annotate with searchable (and computable) parameters, catalogue in the database format in computer readable form, and assemble into a database. There are hundreds of thousands of mammalian interactions scattered in tens of thousands of papers in a few thousands of scientific journals. There are no widely used standards for reporting the interactions in scientific texts and, therefore, text-mining tools have only limited applicability. In order to generate a meaningful database of protein interactions, one needs a well-developed technology of manual curation, equipped with computational solutions, managerial procedures, quality control, and users' feedback. Here we describe our ever-evolving annotation approach, the important annotation issues and our solutions, and the mammalian protein interactions database MetaBase which we have been working on for over 8 years.

MetaMiner (CF): a disease-oriented bioinformatics analysis environment.

MetaMiner (CF) is a data analysis platform for a broad range of CF researchers including wet lab biologists, bioinformaticians, clinicians, and chemists. To understand disease mechanisms and gain insight into complex biological actions, analysis of even simple gene interactions often requires integration of a variety of separate data resources such as literature, 3D molecular models, metabolic pathways, ontologies, small molecules, and drugs. Large-scale data sets from high-throughput screening assays, microarrays, and other data intensive procedures present an even greater challenge in data handling and analysis which now requires interdisciplinary teams of scientists with strikingly diverse backgrounds including computer scientists, statisticians, biologists, and clinicians. To address the issues raised by the complexity of analysis and resource limitations of many research laboratories, MetaMiner (CF) was developed by GeneGo under direction and funding of Cystic Fibrosis Foundation Therapeutics. The platform was designed to provide the CF community with a single tool for analyzing experimental data in a disease-centered environment. To that end, the most important biological and chemical experimental data available today in cystic fibrosis research have been assembled and integrated with data analysis and visualization tools to highlight the key pathways leading to and perturbed by the disease. GeneGo developers assembled and edited CF-specific content and designed the disease-specific interface under the guidance and review of a team of leading cystic fibrosis experts. Updates and revisions will be processed quarterly under the direction of the CF Foundation Therapeutics.

Network analysis of human glaucomatous optic nerve head astrocytes.

BACKGROUND: Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive. METHODS: To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs). RESULTS: Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome. CONCLUSION: Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.

Genome-wide functional synergy between amplified and mutated genes in human breast cancer.

A single cancer cell contains large numbers of genetic alterations that in combination create the malignant phenotype. However, whether amplified and mutated genes form functional and physical interaction networks that could explain the selection for cells with combined alterations is unknown. To investigate this issue, we characterized copy number alterations in 191 breast tumors using dense single nucleotide polymorphism arrays and identified 1,747 genes with copy number gain organized into 30 amplicons. Amplicons were distributed unequally throughout the genome. Each amplicon had distinct enrichment pattern in pathways, networks, and molecular functions, but genes within individual amplicons did not form coherent functional units. Genes in amplicons included all major tumorigenic pathways and were highly enriched in breast cancer-causative genes. In contrast, 1,188 genes with somatic mutations in breast cancer were distributed randomly over the genome, did not represent a functionally cohesive gene set, and were relatively less enriched in breast cancer marker genes. Mutated and gained genes did not show statistically significant overlap but were highly synergistic in populating key tumorigenic pathways including transforming growth factor beta, WNT, fibroblast growth factor, and PIP3 signaling. In general, mutated genes were more frequently upstream of gained genes in transcription regulation signaling than vice versa, suggesting that mutated genes are mainly regulators, whereas gained genes are mostly regulated. ESR1 was the major transcription factor regulating amplified but not mutated genes. Our results support the hypothesis that multiple genetic events, including copy number gains and somatic mutations, are necessary for establishing the malignant cell phenotype.

Molecular definition of breast tumor heterogeneity.

Cells with distinct phenotypes including stem-cell-like properties have been proposed to exist in normal human mammary epithelium and breast carcinomas, but their detailed molecular characteristics and clinical significance are unclear. We determined gene expression and genetic profiles of cells purified from cancerous and normal breast tissue using markers previously associated with stem-cell-like properties. CD24+ and CD44+ cells from individual tumors were clonally related but not always identical. CD44+ cell-specific genes included many known stem-cell markers and correlated with decreased patient survival. The TGF-beta pathway was specifically active in CD44+ cancer cells, where its inhibition induced a more epithelial phenotype. Our data suggest prognostic relevance of CD44+ cells and therapeutic targeting of distinct tumor cell populations.