Predict drug sensitivity of cancer cells with pathway activity inference.

BACKGROUND: Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells. METHOD: In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE). RESULTS: Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms. CONCLUSION: Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.

"Topological significance" analysis of gene expression and proteomic profiles from prostate cancer cells reveals key mechanisms of androgen response.

BACKGROUND: The problem of prostate cancer progression to androgen independence has been extensively studied. Several studies systematically analyzed gene expression profiles in the context of biological networks and pathways, uncovering novel aspects of prostate cancer. Despite significant research efforts, the mechanisms underlying tumor progression are poorly understood. We applied a novel approach to reconstruct system-wide molecular events following stimulation of LNCaP prostate cancer cells with synthetic androgen and to identify potential mechanisms of androgen-independent progression of prostate cancer. METHODOLOGY/PRINCIPAL FINDINGS: We have performed concurrent measurements of gene expression and protein levels following the treatment using microarrays and iTRAQ proteomics. Sets of up-regulated genes and proteins were analyzed using our novel concept of "topological significance". This method combines high-throughput molecular data with the global network of protein interactions to identify nodes which occupy significant network positions with respect to differentially expressed genes or proteins. Our analysis identified the network of growth factor regulation of cell cycle as the main response module for androgen treatment in LNCap cells. We show that the majority of signaling nodes in this network occupy significant positions with respect to the observed gene expression and proteomic profiles elicited by androgen stimulus. Our results further indicate that growth factor signaling probably represents a "second phase" response, not directly dependent on the initial androgen stimulus. CONCLUSIONS/SIGNIFICANCE: We conclude that in prostate cancer cells the proliferative signals are likely to be transmitted from multiple growth factor receptors by a multitude of signaling pathways converging on several key regulators of cell proliferation such as c-Myc, Cyclin D and CREB1. Moreover, these pathways are not isolated but constitute an interconnected network module containing many alternative routes from inputs to outputs. If the whole network is involved, a precisely formulated combination therapy may be required to fight the tumor growth effectively.

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.

Identifying disease-specific genes based on their topological significance in protein networks.

BACKGROUND: The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest. RESULTS: In this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis. CONCLUSION: The systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

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.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Signature pathways identified from gene expression profiles in the human uterine cervix before and after spontaneous term parturition.

OBJECTIVE: This study aimed to discover "signature pathways" that characterize biologic processes, based on genes differentially expressed in the uterine cervix before and after spontaneous labor. STUDY DESIGN: The cervical transcriptome was characterized previously from biopsy specimens taken before and after term labor. Pathway analysis was used to study the differentially expressed genes, based on 2 gene-to-pathway annotation databases (Kyoto Encyclopedia of Genes and Genomes [Kanehisa Laboratories, Kyoto University, Kyoto, Japan] and Metacore software [GeneGo, Inc, St. Joseph, MI]). Overrepresented and highly impacted pathways and connectivity nodes were identified. RESULTS: Fifty-two pathways in the Metacore database were enriched significantly in differentially expressed genes. Three of the top 5 pathways were known to be involved in cervical remodeling. Two novel pathways were plasmin signaling and plasminogen activator urokinase signaling. The same analysis with the Kyoto Encyclopedia of Genes and Genomes database identified 4 significant pathways that the impact analysis confirmed. Multiple nodes that provide connectivity within the plasmin and plasminogen activator urokinase signaling pathways were identified. CONCLUSION: Three strategies for pathway analysis were consistent in their identification of novel, unexpected, and expected pathways, which suggests that this approach is both valid and effective for the elucidation of biologic mechanisms that are involved in cervical dilation and remodeling.

Identification of interspecies concordance of mechanisms of arsenic-induced bladder cancer.

Exposure to arsenic causes cancer by inducing a variety of responses that affect the expression of genes associated with numerous biological pathways leading to altered cell growth and proliferation, signaling, apoptosis and oxidative stress response. Affymetrix GeneChip arrays were used to detect gene expression changes following dimethylarsinic acid (DMA) exposure to human bladder cells (UROtsa) or rat bladder cells (MYP3) and rat bladder epithelium in vivo at comparable doses. Using different experimental models coupled with transcriptional profiling allowed investigation of the correlation of mechanisms of DMA-induced toxicity between in vitro and in vivo treatment and across species. Our observations suggest that DMA-induced gene expression in UROtsa cells is distinct from that observed in the MYP3 cells. Principal component analysis shows a more distinct separation by treatment and dose in MYP3 cells as compared to UROtsa cells. However, at the level of pathways and biological networks, DMA affects both common and unique processes in the bladder transitional cells of human and rats. Twelve pathways were found common between human in vitro, rat in vitro and rat in vivo systems. These included signaling pathways involved in adhesion, cellular growth and differentiation. Fifty-five genes found to be commonly expressed between rat in vivo and rat in vitro systems were involved in diverse functions such as cell cycle regulation, lipid metabolism and protein degradation. Many of the genes, processes and pathways have previously been associated with arsenic-induced toxicity. Our finding reiterates and also identifies new biological processes that might provide more information regarding the mechanisms of DMA-induced toxicity. The results of our analysis further suggest that gene expression profiles can address pertinent issues of relevance to risk assessment, namely interspecies extrapolation of mechanistic information as well as comparison of in vitro to in vivo response.

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.

A combined approach to drug metabolism and toxicity assessment.

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

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.

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.

Early prediction of drug metabolism and toxicity: systems biology approach and modeling.

Many of the drug candidates that fail in clinical trials are withdrawn because of unforeseen effects of human metabolism, such as toxicity and unfavorable pharmacokinetic profiles. Early pre-clinical elimination of such compounds is important but not yet possible. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound, and incorporate and use multiple pre-clinical experimental data to support such a decision. Currently available resources can be split into three categories: (i). structure-activity relationships (SAR) computational models based on compound structure; (ii). 'pattern' databases of tissue or organ response to drugs, compiled from high-throughput experiments; and (iii). 'systems biology' databases of metabolic pathways, genes and regulatory networks. In this review, we outline the advantages and drawbacks of each of these systems and suggest directions for their integration.

Sperm initiate a Ca2+ wave in frog eggs that is more similar to Ca2+ waves initiated by IP3 than by Ca2+.

We have measured the initial propagation velocity of the sperm-induced Ca(2+) wave in the egg of Xenopus laevis and have compared it with the initial propagation velocities of the inositol triphosphate (IP(3))-induced and Ca(2+)-induced Ca(2+) waves. The initial mean propagation velocity of the sperm-induced wave (13 microm/s) is very similar to that of the IP(3)-induced waves (12.3 microm/s) and two times faster than the mean Ca(2+)-induced wave velocity (6.6 microm/s). We have generated realistic simulations of the fertilization wave in the frog egg using a computational technique based on the finite difference method. Modeling refinements presented here include equations for the production, degradation, and diffusion of IP(3), a description for Ca(2+) dynamics in the endoplasmic reticulum, and a highly concentrated endoplasmic reticulum in the egg cortex. We conclude that models incorporating sperm-induced IP(3) generation fit the data best and those involving the influx of either Ca(2+) or a diffusible sperm factor fit the data poorly. This independence from Ca(2+) influx is also supported by electrophysiological data indicating that Ca(2+) influx is not needed to maintain open Cl(-) channels that generate the fertilization potential.