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.

Epigenetic regulation of cell type-specific expression patterns in the human mammary epithelium.

Differentiation is an epigenetic program that involves the gradual loss of pluripotency and acquisition of cell type-specific features. Understanding these processes requires genome-wide analysis of epigenetic and gene expression profiles, which have been challenging in primary tissue samples due to limited numbers of cells available. Here we describe the application of high-throughput sequencing technology for profiling histone and DNA methylation, as well as gene expression patterns of normal human mammary progenitor-enriched and luminal lineage-committed cells. We observed significant differences in histone H3 lysine 27 tri-methylation (H3K27me3) enrichment and DNA methylation of genes expressed in a cell type-specific manner, suggesting their regulation by epigenetic mechanisms and a dynamic interplay between the two processes that together define developmental potential. The technologies we developed and the epigenetically regulated genes we identified will accelerate the characterization of primary cell epigenomes and the dissection of human mammary epithelial lineage-commitment and luminal differentiation.

Gene expression profiling of human breast tissue samples using SAGE-Seq.

We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

Effect of substituents in chlorin e6 derivatives on the loading efficiency of the photosensitizer into the liposome membrane and their biological activity.

In this work, we incorporated the hydrophobic alkylamide and hydroxyalkylamide derivatives of chlorin e6 into the lipid bilayer of liposomes. We obtained the data on the effectiveness of incorporation of studied compounds and have determined the size of liposomes and their stability when stored in liquid form. We also investigated the bioactivity of chlorin photosensitizers and compared the photodynamic activity of studied compounds in free and liposomal forms.

Knowledge-based analysis of proteomics data.

As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.

Putative multifunctional signature of lung metastases in dedifferentiated chondrosarcoma.

Chondrosarcomas are among the most malignant skeletal tumors. Dedifferentiated chondrosarcoma is a highly aggressive subtype of chondrosarcoma, with lung metastases developing within a few months of diagnosis in 90% of patients. In this paper we performed comparative analyses of the transcriptomes of five individual metastatic lung lesions that were surgically resected from a patient with dedifferentiated chondrosarcoma. We document for the first time a high heterogeneity of gene expression profiles among the individual lung metastases. Moreover, we reveal a signature of "multifunctional" genes that are expressed in all metastatic lung lesions. Also, for the first time, we document the occurrence of massive macrophage infiltration in dedifferentiated chondrosarcoma lung metastases.

Cell type-specific DNA methylation patterns in the human breast.

Cellular identity and differentiation are determined by epigenetic programs. The characteristics of these programs in normal human mammary epithelium and their similarity to those in stem cells are unknown. To begin investigating these issues, we analyzed the DNA methylation and gene expression profiles of distinct subpopulations of mammary epithelial cells by using MSDK (methylation-specific digital karyotyping) and SAGE (serial analysis of gene expression). We identified discrete cell-type and differentiation state-specific DNA methylation and gene expression patterns that were maintained in a subset of breast carcinomas and correlated with clinically relevant tumor subtypes. CD44+ cells were the most hypomethylated and highly expressed several transcription factors with known stem cell function including HOXA10 and TCF3. Many of these genes were also hypomethylated in BMP4-treated compared with undifferentiated human embryonic stem (ES) cells that we analyzed by MSDK for comparison. Further highlighting the similarity of epigenetic programs of embryonic and mammary epithelial cells, genes highly expressed in CD44+ relative to more differentiated CD24+ cells were significantly enriched for Suz12 targets in ES cells. The expression of FOXC1, one of the transcription factors hypomethylated and highly expressed in CD44+ cells, induced a progenitor-like phenotype in differentiated mammary epithelial cells. These data suggest that epigenetically controlled transcription factors play a key role in regulating mammary epithelial cell phenotypes and imply similarities among epigenetic programs that define progenitor cell characteristics.

Integrated analysis of homozygous deletions, focal amplifications, and sequence alterations in breast and colorectal cancers.

We have performed a genome-wide analysis of copy number changes in breast and colorectal tumors using approaches that can reliably detect homozygous deletions and amplifications. We found that the number of genes altered by major copy number changes, deletion of all copies or amplification to at least 12 copies per cell, averaged 17 per tumor. We have integrated these data with previous mutation analyses of the Reference Sequence genes in these same tumor types and have identified genes and cellular pathways affected by both copy number changes and point alterations. Pathways enriched for genetic alterations included those controlling cell adhesion, intracellular signaling, DNA topological change, and cell cycle control. These analyses provide an integrated view of copy number and sequencing alterations on a genome-wide scale and identify genes and pathways that could prove useful for cancer diagnosis and therapy.

Bimodal gene expression patterns in breast cancer.

We identified a set of genes with an unexpected bimodal distribution among breast cancer patients in multiple studies. The property of bimodality seems to be common, as these genes were found on multiple microarray platforms and in studies with different end-points and patient cohorts. Bimodal genes tend to cluster into small groups of four to six genes with synchronised expression within the group (but not between the groups), which makes them good candidates for robust conditional descriptors. The groups tend to form concise network modules underlying their function in cancerogenesis of breast neoplasms.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

INTRODUCTION: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. METHODS: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. RESULTS: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. CONCLUSIONS: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Functional analysis of OMICs data and small molecule compounds in an integrated "knowledge-based" platform.

Analysis of microarray, SNPs, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high-fidelity annotated knowledge base of protein interactions, pathways, and functional ontologies. This knowledge base has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here we present MetaDiscovery, an integrated platform for functional data analysis which is being developed at GeneGo for the past 8 years. On the content side, MetaDiscovery encompasses a comprehensive database of protein interactions of different types, pathways, network models and 10 functional ontologies covering human, mouse, and rat proteins. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for identification of over- and under-connected proteins in the data set, and a network module made up of network generation algorithms and filters. The suite also features MetaSearch, an application for combinatorial search of the database content, as well as a Java-based tool called MapEditor for drawing and editing custom pathway maps. Applications of MetaDiscovery include identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds, and clinical applications (analysis of large cohorts of patients and translational and personalized medicine).

A comprehensive functional analysis of tissue specificity of human gene expression.

BACKGROUND: In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues. RESULTS: We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases. CONCLUSION: A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.

Pathway mapping tools for analysis of high content data.

The complexity of human biology requires a systems approach that uses computational approaches to integrate different data types. Systems biology encompasses the complete biological system of metabolic and signaling pathways, which can be assessed by measuring global gene expression, protein content, metabolic profiles, and individual genetic, clinical, and phenotypic data. High content screening assays can also be used to generate systems biology knowledge. In this review, we will summarize the pathway databases and describe biological network tools used predominantly with this genomics, proteomics, and metabolomics data but which are equally as applicable for high content screening data analysis. We describe in detail the integrated data-mining tools applicable to building biological networks developed by GeneGo, namely, MetaCore and MetaDrug.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity.

It is widely recognized that either predicting or determining the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties of molecules helps to prevent the failure of some compounds before they reach the clinic. Consequently, there has been considerable research into developing better in silico, in vitro and in vivo methods and models. Toxicogenomics, proteomics, metabonomics and pharmacogenomics represent the latest experimental approaches that can be combined with high-throughput molecular screening of targets to provide a view of the complete biological system that is modulated by a compound. The functional interpretation and relevance of these complex multidimensional data to the phenotype observed in humans is the focus of current research in toxicology. Multiple content databases, data mining and predictive modeling algorithms, visualization tools, and high-throughput data-analysis solutions are being integrated to form systems-ADME/Tox. In this review, we focus on the most recent advances and applications in this area.

Biological networks and analysis of experimental data in drug discovery.

Cellular life can be represented and studied as the 'interactome'--a dynamic network of biochemical reactions and signaling interactions between active proteins. Systemic networks analysis can be used for the integration and functional interpretation of high-throughput experimental data, which are abundant in drug discovery but currently poorly utilized. The composition and topology of complex networks are closely associated with vital cellular functions, which have important implications for life science research. Here we outline recent advances in the field, available tools and applications of network analysis in drug discovery.

Using proteomics and network analysis to elucidate the consequences of synaptic protein oxidation in a PS1 + AbetaPP mouse model of Alzheimer's disease.

Increasing evidence suggests that oxidative injury is involved in the pathogenesis of many age-related neurodegenerative disorders, including Alzheimer's disease (AD). Identifying the protein targets of oxidative stress is critical to determine which proteins may be responsible for the neuronal impairments and subsequent cell death that occurs in AD. In this study, we have applied a high-throughput shotgun proteomic approach to identify the targets of protein carbonylation in both aged and PS1 + AbetaPP transgenic mice. However, because of the inherent difficulties associated with proteomic database searching algorithms, several newly developed bioinformatic tools were implemented to ascertain a probability-based discernment between correct protein assignments and false identifications to improve the accuracy of protein identification. Assigning a probability to each identified peptide/protein allows one to objectively monitor the expression and relative abundance of particular proteins from diverse samples, including tissue from transgenic mice of mixed genetic backgrounds. This robust bioinformatic approach also permits the comparison of proteomic data generated by different laboratories since it is instrument- and database-independent. Applying these statistical models to our initial studies, we detected a total of 117 oxidatively modified (carbonylated) proteins, 59 of which were specifically associated with PS1 + AbetaPP mice. Pathways and network component analyses suggest that there are three major protein networks that could be potentially altered in PS1 + AbetaPP mice as a result of oxidative modifications. These pathways are 1) iNOS-integrin signaling pathway, 2) CRE/CBP transcription regulation and 3) rab-lyst vesicular trafficking. We believe the results of these studies will help establish an initial AD database of oxidatively modified proteins and provide a foundation for the design of future hypothesis driven research in the areas of aging and neurodegeneration.

A novel method for generation of signature networks as biomarkers from complex high throughput data.

Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment. We have developed and applied the approach of "signature networks" which overcomes some of the drawbacks of clustering methods. We have demonstrated signature network assembly, functional analysis and logical operations on the networks that can be generated. In addition, we have used this technique in a proof of concept study to compare the effect of differential drug treatment using 4-hydroxytamoxifen and estrogen on the MCF-7 breast cancer cell line from a previously published study. We have shown that the two compounds can be differentiated by the networks of interacting genes. Both networks consist of a core module of genes including c-Fos as part of c-Fos/c-Jun heterodimer and c-Myc which is clearly visible. Using algorithms in our MetaCore software we are able to subtract the 4-hydroxytamoxifen and estrogen networks to further understand differences between these two treatments and show that the estrogen network is assembled around the core with other modules essential for all phases of the cell cycle. For example, Cyclin D1 is present in networks for the estrogen treated cells from two separate studies. These signature networks represent an approach to identify biomarkers and a general approach for discovering new relationships in complex high throughput toxicology data.

Computational prediction of human drug metabolism.

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

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.