An integrated approach for inference and mechanistic modeling for advancing drug development

An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.     

Model‐free feedback design for a mixed cancer therapy

In this article, a model‐free feedback control design is proposed for the drug administration in mixed cancer therapy. This strategy is very attractive because of the important issue of parameter uncertainties unavoidable when dealing with biological models. The proposed feedback scheme use past measurements to update an on‐line simplified model. The control design is then based on model predictive control in which a suitable switching is performed between two different cost functions. The effectiveness of the proposed model‐free control strategy is validated using a recently developed model (unknown to the controller) governing the cancer growth on a cells population level under combined immune and chemotherapy and using real human data. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Stability analysis of mixtures of mutagenetic trees

Background  

Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.     

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.     

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.     

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.     

Correlation between Oncogenic Mutations and Parameter Sensitivity of the Apoptosis Pathway Model

One of the major breakthroughs in oncogenesis research in recent years is the discovery that, in most patients, oncogenic mutations are concentrated in a few core biological functional pathways. This discovery indicates that oncogenic mechanisms are highly related to the dynamics of biologic regulatory networks, which govern the behaviour of functional pathways. Here, we propose that oncogenic mutations found in different biological functional pathways are closely related to parameter sensitivity of the corresponding networks. To test this hypothesis, we focus on the DNA damage-induced apoptotic pathway—the most important safeguard against oncogenesis. We first built the regulatory network that governs the apoptosis pathway, and then translated the network into dynamics equations. Using sensitivity analysis of the network parameters and comparing the results with cancer gene mutation spectra, we found that parameters that significantly affect the bifurcation point correspond to high-frequency oncogenic mutations. This result shows that the position of the bifurcation point is a better measure of the functionality of a biological network than gene expression levels of certain key proteins. It further demonstrates the suitability of applying systems-level analysis to biological networks as opposed to studying genes or proteins in isolation.     

MicroRNAs: key players of taxane resistance and their therapeutic potential in human cancers

The successful long‐term use of taxane for cancer therapy is often prevented by the development of drug resistance in clinic. Thus, exploring the mechanisms involved is a first step towards rational strategies to overcome taxane resistance. Taxane resistance‐related microRNA (miRNAs) are under investigation and miRNAs could induce the taxane resistance of tumour cells by regulating cell cycle distribution, survival and/or apoptosis pathways, drug transports, epithelial–mesenchymal transition and cancer stem cell. This article summarizes current research involving miRNAs as regulators of key target genes for tanxanxe chemoresistance and discusses the complex regulatory networks of miRNAs. Also, the authors will envisage future developments towards the potential use of targeting miRNAs as a novel strategy for improving response of tumour patients to taxane. miRNAs play critical roles in taxane chemoresistance and the miRNA‐based therapies will be helpful for overcoming drug resistance and developing more effective personalized anti‐cancer treatment strategies. Further research studies should be performed to promote therapeutic–clinical use of taxane resistance‐related miRNAs in cancer patients, especially in those patients with taxane‐resistant cancers.     

A Predictive Framework for Integrating Disparate Genomic Data Types Using Sample-Specific Gene Set Enrichment Analysis and Multi-Task Learning

Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits.     

Limited evidence that cancer susceptibility regions are preferential targets for somatic mutation

BackgroundGenome wide-association studies have successfully identified several hundred independent loci harboring common cancer susceptibility alleles that are distinct from the more than 110 cancer predisposition genes. The latter are generally characterized by disruptive mutations in coding genes that have been established as ‘drivers’ of cancer in large somatic sequencing studies. We set out to determine whether, similarly, common cancer susceptibility loci map to genes that have altered frequencies of mutation.ResultsIn our analysis of the intervals defined by the cancer susceptibility markers, we observed that cancer susceptibility regions have gene mutation frequencies comparable to background mutation frequencies. Restricting analyses to genes that have been determined to be pleiotropic across cancer types, genes affected by expression quantitative trait loci, or functional genes indicates that most cancer susceptibility genes classified into these subgroups do not display mutation frequencies that deviate from those expected. We observed limited evidence that cancer susceptibility regions that harbor common alleles with small estimated effect sizes are preferential targets for altered somatic mutation frequencies.ConclusionsOur findings suggest a complex interplay between germline susceptibility and somatic mutation, underscoring the cumulative effect of common variants on redundant pathways as opposed to driver genes. Complex biological pathways and networks likely link these genetic features of carcinogenesis, particularly as they relate to distinct polygenic models for each cancer type.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Interplay between HTRA1 and classical signalling pathways in organogenesis and diseases

The high temperature requirement factor A1 (HTRA1) is a serine protease which modulates an array of signalling pathways driving basal biological processes. HTRA1 plays a significant role in cell proliferation, migration and fate determination, in addition to controlling protein aggregates through refolding, translocation or degradation. The mutation of HTRA1 has been implicated in a plethora of disorders and this has also led to its growing interest as drug therapy target. This review details the involvement of HTRA1 in certain signalling pathways, namely the transforming growth factor beta (TGF-β), canonical Wingless/Integrated (WNT) and NOTCH signalling pathways during organogenesis and various disease pathogenesis such as preeclampsia, age-related macular degeneration (AMD), small vessel disease and cancer. We have also explored possible avenues of exploiting the serine proteases for therapeutic management of these disorders.     

Protein interaction networks in medicine and disease

The physical interaction of proteins is subject to intense investigation that has revealed that proteins are assembled into large densely connected networks. In this review, we will examine how signaling pathways can be combined to form higher order protein interaction networks. By using network graph theory, these interaction networks can be further analyzed for global organization, which has revealed unique aspects of the relationships between protein networks and complex biological phenotypes. Moreover, several studies have shown that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer progression. These relationships suggest a novel paradigm for treatment of complex multigenic disease where the protein interaction network is the target of therapy more so than individual molecules within the network.     

Therapeutic resistance resulting from mutations in Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR signaling pathways

Chemotherapy remains a commonly used therapeutic approach for many cancers. Indeed chemotherapy is relatively effective for treatment of certain cancers and it may be the only therapy (besides radiotherapy) that is appropriate for certain cancers. However, a common problem with chemotherapy is the development of drug resistance. Many studies on the mechanisms of drug resistance concentrated on the expression of membrane transporters and how they could be aberrantly regulated in drug resistant cells. Attempts were made to isolate specific inhibitors which could be used to treat drug resistant patients. Unfortunately most of these drug transporter inhibitors have not proven effective for therapy. Recently the possibilities of more specific, targeted therapies have sparked the interest of clinical and basic researchers as approaches to kill cancer cells. However, there are also problems associated with these targeted therapies. Two key signaling pathways involved in the regulation of cell growth are the Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways. Dysregulated signaling through these pathways is often the result of genetic alterations in critical components in these pathways as well as mutations in upstream growth factor receptors. Furthermore, these pathways may be activated by chemotherapeutic drugs and ionizing radiation. This review documents how their abnormal expression can contribute to drug resistance as well as resistance to targeted therapy. This review will discuss in detail PTEN regulation as this is a critical tumor suppressor gene frequently dysregulated in human cancer which contributes to therapy resistance. Controlling the expression of these pathways could improve cancer therapy and ameliorate human health.     

Potential and challenges of a human cytome project

BACKGROUND: The elucidation of the molecular pathways from the 20-40.000 genes of the sequenced human genome via investigation of genetic networks and molecular pathways up to the cellular and organismal phenotypes is highly complex and time consuming. STRATEGY AND GOALS: The proposed upside-down research strategy of a human cytome project accesses the expressed molecular cell phenotypes by differential screening, for example of diseased versus healthy, or undifferentiated versus differentiated cells to obtain information on disease or differentiation related molecular hotspots at the single cell level. The genome serves as inventory of the biomolecular capacities of organisms while the mechanisms of genome realisation are initially entirely bypassed. Detected molecular hotspots are further investigated by backward directed systems biology, including molecular pathway modelling to elucidate disease related molecular pathways. New drug targets may be identified to specifically influence such pathways. Differential screening provides, in addition, individualized disease course predictions for everyday medicine, in form of "predictive medicine by cytomics." The early recognition of future disease complications enables an immediate application of preventive therapies. This is likely to lower disease related irreversible tissue destruction and adverse drug reactions and will allow to individually optimize patient therapy. OUTLOOK: Immediate medical use, facilitated access to the detection of new drug targets, increased research speed and the stimulation for advanced technological developments represent major driving forces for the efforts to establish a human cytome project.     

Cancer: a Systems Biology disease

Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets? Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.     

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.