Network-based drugs and biomarkers

Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

Differential Network Analysis: A Statistical Perspective

Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures.

Collective influencers in protein interaction networks

Recent research increasingly shows the relevance of network based approaches for our understanding of biological systems. Analyzing human protein interaction networks, we determined collective influencers (CI), defined as network nodes that damage the integrity of the underlying networks to the utmost degree. We found that CI proteins were enriched with essential, regulatory, signaling and disease genes as well as drug targets, indicating their biological significance. Also by focusing on different organisms, we found that CI proteins had a penchant to be evolutionarily conserved as CI proteins, indicating the fundamental role that collective influencers in protein interaction networks plays for our understanding of regulation, diseases and evolution.

Functional genomics screen with pooled shRNA library and gene expression profiling with extracts of Azadirachta indica identify potential pathways for therapeutic targets in head and neck squamous cell carcinoma

Tumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that neem extracts and their associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell lines treated with crude neem leaf extracts, known for their anti-tumorigenic activity. We analyzed differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing. We found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-β pathway and HSF-1-related gene network. Our results indicate that neem extract affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.

Topological alternate centrality measure capturing drug targets in the network of MAPK pathways

A new centrality of the nodes in the network is proposed called alternate centrality, which can isolate effective drug targets in the complex signalling network. Alternate centrality metric defined over the network substructure (four nodes - motifs). The nodes involving in alternative activation in the motifs gain in metric values. Targeting high alternative centrality nodes hypothesised to be destructive free to the network due to their alternative activation mechanism. Overlapping and crosstalk among the gene products in the conserved network of MAPK pathways selected for the study. In silico knock-out of high alternate centrality nodes causing rewiring in the network is investigated using MCF-7 breast cancer cell line-based data. Degree of top alternate centrality nodes lies between the degree of bridging and pagerank nodes. Node deletion of high alternate centrality on the centralities such as eccentricity, closeness, betweenness, stress, centroid and radiality causes low perturbation. The authors identified the following alternate centrality nodes ERK1, ERK2, MEKK2, MKK5, MKK4, MLK3, MLK2, MLK1, MEKK4, MEKK1, TAK1, P38alpha, ZAK, DLK, LZK, MLTKa/b and P38beta as efficient drug targets for breast cancer. Alternate centrality identifies effective drug targets and is free from intertwined biological processes and lethality.

MTLD, a Database of Multiple Target Ligands, the Updated Version

Polypharmacology plays an important role in drug discovery and polypharmacology drug strategies provide a novel path in drug design. However, to develop a polypharmacology drug with the desired profile remains a challenge. Previously, we developed a free web-accessible database called Multiple Target Ligand Database (MTLD, www.mtdcadd.com). Herein, the MTLD database has been updated, containing 2444 Multiple Target Ligands (MTLs) that bind with 21,424 binding sites from 18,231 crystal structures. Of the MTLs, 304 entries are approved drugs, and 1911 entries are drug-like compounds. Also, we added new functions such as multiple conditional search and linkage visualization. Through querying the updated database, extremely useful information for the development of polypharmacology drugs may be provided.

Controlling Directed Protein Interaction Networks in Cancer

Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.

A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib

A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.

Identification of Pharmacologically Tractable Protein Complexes in Cancer Using the R-Based Network Clustering and Visualization Program MCODER

Current multiomics assay platforms facilitate systematic identification of functional entities that are mappable in a biological network, and computational methods that are better able to detect densely connected clusters of signals within a biological network are considered increasingly important. One of the most famous algorithms for detecting network subclusters is Molecular Complex Detection (MCODE). MCODE, however, is limited in simultaneous analyses of multiple, large-scale data sets, since it runs on the Cytoscape platform, which requires extensive computational resources and has limited coding flexibility. In the present study, we implemented the MCODE algorithm in R programming language and developed a related package, which we called MCODER. We found the MCODER package to be particularly useful in analyzing multiple omics data sets simultaneously within the R framework. Thus, we applied MCODER to detect pharmacologically tractable protein-protein interactions selectively elevated in molecular subtypes of ovarian and colorectal tumors. In doing so, we found that a single molecular subtype representing epithelial-mesenchymal transition in both cancer types exhibited enhanced production of the collagen-integrin protein complex. These results suggest that tumors of this molecular subtype could be susceptible to pharmacological inhibition of integrin signaling.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.

Biased random walk model for the prioritization of drug resistance associated proteins

Multi-drug resistance is the main cause of treatment failure in cancer patients. How to identify molecules underlying drug resistance from multi-omics data remains a great challenge. Here, we introduce a data biased strategy, ProteinRank, to prioritize drug-resistance associated proteins in cancer cells. First, we identified differentially expressed proteins in Adriamycin and Vincristine resistant gastric cancer cells compared to their parental cells using iTRAQ combined with LC-MS/MS experiments, and then mapped them to human protein-protein interaction network; second, we applied ProteinRank to analyze the whole network and rank proteins similar to known drug resistance related proteins. Cross validations demonstrated a better performance of ProteinRank compared to the method without usage of MS data. Further validations confirmed the altered expressions or activities of several top ranked proteins. Functional study showed PIM3 or CAV1 silencing was sufficient to reverse the drug resistance phenotype. These results indicated ProteinRank could prioritize key proteins related to drug resistance in gastric cancer and provided important clues for cancer research.

Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity

Background

Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue.

Results

We propose a strategy based on discriminative area of module activities to identify gene biomarkers which interconnect as a subnetwork or module by integrating gene expression data and protein-protein interactions. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments.

Conclusion

The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0519-y) contains supplementary material, which is available to authorized users.

Array-based proteomic approaches to study signal transduction pathways: prospects, merits and challenges

Very often dysfunctional aspects of various signalling networks are found to be associated with human diseases and disorders. The major characteristics of signal transduction pathways are specificity, amplification of the signal, desensitisation and integration, which is accomplished not solely, but majorly by proteins. Array-based profiling of protein-protein and other biomolecular interactions is a versatile approach, which holds immense potential for multiplex interactome mapping and provides an inclusive representation of the signal transduction pathways and networks. Protein microarrays such as analytical protein microarrays (antigen-antibody interactions, autoantibody screening), RP microarrays (interaction of a particular ligand with all the possible targets in cell), functional protein microarrays (protein-protein or protein-ligand interactions) are implemented for various applications, including analysis of protein interactions and their significance in signalling cascades. Additionally, successful amalgamation of the array-based approaches with different label-free detection techniques allows real-time analysis of interaction kinetics of multiple interaction events simultaneously. This review discusses the prospects, merits and limitations of different variants of array-based techniques and their promising applications for studying the modifications and interactions of biomolecules, and highlights the studies associated with signal transduction pathways and their impact on disease pathobiology.

Integrative analysis of cancer imaging readouts by networks

Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact.

Computational Multiplexing Imaging merges imaging and networks.

Networks show signatures of tumor heterogeneity and phenotypic profiles observed in‐vivo.

A profile ensemble establishes a tumor fingerprint, and this constitutes a novel type of marker.

Personalized treatment is embedded in a systems medicine approach.

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers.

Nanoscale particulate systems for multidrug delivery: towards improved combination chemotherapy

While combination chemotherapy has led to measurable improvements in cancer treatment outcomes, its full potential remains to be realized. Nanoscale particles such as liposomes, nanoparticles and polymer micelles have been shown to increase delivery to the tumor site while bypassing many drug resistance mechanisms that limit the effectiveness of conventional therapies. Recent efforts in drug delivery have focused on coordinated, controlled delivery of multiple anticancer agents encapsulated within a single particle system. In this review, we analyze recent progress made in multidrug delivery in three main areas of interest: co-delivery of antineoplastic agents with drug sensitizers, sequential delivery via temporal release particles and simultaneous delivery of multiple agents. Future directions of the field, in light of recent advances with molecularly targeted agents, are suggested and discussed.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Experimental and computational tools for analysis of signaling networks in primary cells

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow-up in vitro/in vivo validation studies.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

A silk fibroin based hepatocarcinoma model and the assessment of the drug response in hyaluronan-binding protein 1 overexpressed HepG2 cells

Microenvironment around tumor cells plays an important role in its malignancy or invasiveness. Hyaluronan (HA), a major component of extracellular matrix is found to be elevated in most of cancerous niche/microenvironment and performs regulatory role in the progression of tumors and metastasis. Overexpression of the hyaladherin, hyaluronan-binding protein 1 (HABP1) in the hepatocarcinoma cells (HepG2) termed as HepR21 leads to enhanced cell proliferation with increased HA 'pool' associated with HA 'cables' indicating elevated tumorous potential under 2D culture conditions. For in vitro experimentation, scaffold based three dimensional niche modeling may have greater acceptance than conventional 2D culture condition. Thus, we have examined the influence of intrinsic properties of non-mulberry tropical tasar silk fibroin on the HepR21 cells in order to develop a 3D hepatocarcinoma construction to act as model. The scaffold of tasar silk fibroin of Antheraea mylitta when efficiently loaded with transformed hepatocarcinoma cells, HepR21; exhibits enhanced adhesiveness, viability, metabolic activity, proliferation and enlarged cellular morphology in 3D compared to its parent cell line HepG2, supporting the earlier observation made in 2D system. In addition, formation of multicellular aggregates, the indicator of tumor progression is also revealed in silk based 3D culture conditions. Further, the use of 4-MU (a hyaluronan synthase inhibitor) on HepR21 cells reduces the HA level and downregulates the expression of growth promoting factors like pAKT and PKC; while upregulating the expression of the tumor suppressor p53. Thus, 4-MU efficiently reduces the tumor potency associated with increased HA pool as well as HA cables and the effect of 4-MU doubling up as an anticancer agent in 2D and 3D are also comparable. The in vitro 3D multicellular model demonstrates the insight of hepatocarcinoma progression and offers the predictability of cellular response to transfection efficacy, drug treatment and therapeutic intervention.

Phosphoproteomics-based network medicine

One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho-signatures containing multiple phosphorylation events within phosphoproteomics-based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics-based network medicine by reviewing the computational (re)construction of phosphorylation-mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho-signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

The scientific drunk and the lamppost: massive sequencing efforts in cancer discovery and treatment

The massive resources devoted to genome sequencing of human tumors have produced important data sets for the cancer biology community. Paradoxically, however, these studies have revealed very little new biology. Despite this, additional resources in the United States are slated to continue such work and to expand similar efforts in genome sequencing to mouse tumors. It may be that scientists are "addicted" to the large amounts of data that can be relatively easily obtained, even though these data seem unlikely, on their own, to unveil new cancer treatment options or result in the ultimate goal of a cancer cure. Rather than using more tumor genetic sequences, a better strategy for identifying new treatment options may be to develop methods for analyzing the signaling networks that underlie cancer development, progression, and therapeutic resistance at both a personal and systems-wide level.

Understanding resistance to combination chemotherapy

The current clinical application of combination chemotherapy is guided by a historically successful set of practices that were developed by basic and clinical researchers 50-60 years ago. Thus, in order to understand how emerging approaches to drug development might aid the creation of new therapeutic combinations, it is critical to understand the defining principles underlying classic combination therapy and the original experimental rationales behind them. One such principle is that the use of combination therapies with independent mechanisms of action can minimize the evolution of drug resistance. Another is that in order to kill sufficient cancer cells to cure a patient, multiple drugs must be delivered at their maximum tolerated dose - a condition that allows for enhanced cancer cell killing with manageable toxicity. In light of these models, we aim to explore recent genomic evidence underlying the mechanisms of resistance to the combination regimens constructed on these principles. Interestingly, we find that emerging genomic evidence contradicts some of the rationales of early practitioners in developing commonly used drug regimens. However, we also find that the addition of recent targeted therapies has yet to change the current principles underlying the construction of anti-cancer combinatorial regimens, nor have they made substantial inroads into the treatment of most cancers. We suggest that emerging systems/network biology approaches have an immense opportunity to impact the rational development of successful drug regimens. Specifically, by examining drug combinations in multivariate ways, next generation combination therapies can be constructed with a clear understanding of how mechanisms of resistance to multi-drug regimens differ from single agent resistance.

Biological network analysis: insights into structure and functions

In the past two decades, great efforts have been devoted to extract the dependence and interplay between structure and functions in biological networks because they have strong relevance to biological processes. In this article, we reviewed the recent development in the biological network analysis. In detail, we first reviewed the interactome topological properties of biological networks, the methods for structure and functional patterns.

Exploring the human diseasome: the human disease network

Advances in genome-scale molecular biology and molecular genetics have greatly elevated our knowledge on the basic components of human biology and diseases. At the same time, the importance of cellular networks between those biological components is increasingly appreciated. Built upon these recent technological and conceptual advances, a new discipline called the network medicine, an approach to understand human diseases from a network point-of-view, is about to emerge. In this review article, we will survey some recent endeavours along this direction, centred on the concept and applications of the human diseasome and the human disease network. Questions, and partial answers thereof, such as how the connectivity between molecular parts translates into the relationships between the related disorders on a global scale and how central the disease-causing genetic components are in the cellular network, will be discussed. The use of the diseasome in combination with various interactome networks and other disease-related factors is also reviewed.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.

Genome-environment interactions that modulate aging: powerful targets for drug discovery

Aging is the major biomedical challenge of this century. The percentage of elderly people, and consequently the incidence of age-related diseases such as heart disease, cancer, and neurodegenerative diseases, is projected to increase considerably in the coming decades. Findings from model organisms have revealed that aging is a surprisingly plastic process that can be manipulated by both genetic and environmental factors. Here we review a broad range of findings in model organisms, from environmental to genetic manipulations of aging, with a focus on those with underlying gene-environment interactions with potential for drug discovery and development. One well-studied dietary manipulation of aging is caloric restriction, which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard aging in model organisms. Caloric restriction is already being used as a paradigm for developing compounds that mimic its life-extension effects and might therefore have therapeutic value. The potential for further advances in this field is immense; hundreds of genes in several pathways have recently emerged as regulators of aging and caloric restriction in model organisms. Some of these genes, such as IGF1R and FOXO3, have also been associated with human longevity in genetic association studies. The parallel emergence of network approaches offers prospects to develop multitarget drugs and combinatorial therapies. Understanding how the environment modulates aging-related genes may lead to human applications and disease therapies through diet, lifestyle, or pharmacological interventions. Unlocking the capacity to manipulate human aging would result in unprecedented health benefits.

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression

BACKGROUND

Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.

RESULTS

This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.

CONCLUSIONS

Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

Integrative network biology: graph prototyping for co-expression cancer networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure.

A personalized systems medicine approach to refractory rumination

RATIONALE, AIMS AND OBJECTIVES

As with many functional disorders, rumination syndrome poses a great dilemma when approached via standard of care. This case report illustrates how rumination syndrome may be effectively approached using the systems medicine.

METHOD

The patient's treatment involved two distinctively different treatment cycles. Initially she was treated in an academic tertiary inpatient and outpatient multidisciplinary program with a primary symptom-based focus with little improvement. She subsequently sought care at a systems-based integrative medicine clinic within an academic family medicine centre, which identified the inciting events, diagnosed the current pathology and developed a stepwise treatment plan.

RESULTS

The patient is now rumination free.

CONCLUSION

Chronic or refractory diseases, especially when regarded as 'functional' may be approached by a systems medicine methodology, which allows physicians to fine-tune the vast amount of specific pieces of knowledge to achieve an integrated approach to managing the whole person.

Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer

Dynamic remodeling of the extracellular matrix (ECM) is essential for development, wound healing and normal organ homeostasis. Life-threatening pathological conditions arise when ECM remodeling becomes excessive or uncontrolled. In this Perspective, we focus on how ECM remodeling contributes to fibrotic diseases and cancer, which both present challenging obstacles with respect to clinical treatment, to illustrate the importance and complexity of cell-ECM interactions in the pathogenesis of these conditions. Fibrotic diseases, which include pulmonary fibrosis, systemic sclerosis, liver cirrhosis and cardiovascular disease, account for over 45% of deaths in the developed world. ECM remodeling is also crucial for tumor malignancy and metastatic progression, which ultimately cause over 90% of deaths from cancer. Here, we discuss current methodologies and models for understanding and quantifying the impact of environmental cues provided by the ECM on disease progression, and how improving our understanding of ECM remodeling in these pathological conditions is crucial for uncovering novel therapeutic targets and treatment strategies. This can only be achieved through the use of appropriate in vitro and in vivo models to mimic disease, and with technologies that enable accurate monitoring, imaging and quantification of the ECM.

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

Background

Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective.

Methods

In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples.

Results

Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs). In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer.

Conclusions

A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

Combination of improved (18)O incorporation and multiple reaction monitoring: a universal strategy for absolute quantitative verification of serum candidate biomarkers of liver cancer

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS), which is an alternative to immunoassay methods such as ELISA and Western blotting, has been used to alleviate the bottlenecks of high-throughput verification of biomarker candidates recently. However, the inconvenience and high isotope consumption required to obtain stably labeled peptide impedes the broad application of this method. In our study, the (18)O-labeling method was introduced to generate stable isotope-labeled peptides instead of the Fmoc chemical synthesis and Qconcat recombinant protein synthesis methods. To make (18)O-labeling suitable for absolute quantification, we have added the following procedures: (1) RapiGest SF and microwave heating were added to increase the labeling efficiency; (2) trypsin was deactivated completely by chemical modification using tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAA) to prevent back-exchange of (18)O to (16)O, and (3) MRM parameters were optimized to maximize specificity and better distinguish between (18)O-labeled and unlabeled peptides. As a result, the (18)O-labeled peptides can be prepared in less than 1 h with satisfactory efficiency (>97%) and remained stable for 1 week, compared to traditional protocols that require 5 h for labeling with poor stability. Excellent separation of (18)O-labeled and unlabeled peptides was achieved by the MRM-MS spectrum. Finally, through the combined improvement in (18)O-labeling with multiple reaction monitoring, an absolute quantification strategy was developed to quantitatively verify hepatocellular carcinoma-related biomarker candidates, namely, vitronectin and clusterin, in undepleted serum samples. Sample preparation and capillary-HPLC analysis were optimized for high-throughput applications. The reliability of this strategy was further evaluated by method validation, with accuracy (%RE) and precision (%RSD) of less than 20% and good linearity (r(2) > 0.99), and clinical validation, which were consistent with previously reported results. In summary, our strategy can promote broader application of SID-MRM-MS for biomarkers from discovery to verification regarding the significant advantages of the convenient and flexible generation of internal standards, the reduction in the sample labeling steps, and the simple transition.

The pursuit of oncotargets through understanding defective cell regulation

More effective anticancer agents are essential, as has too often been demonstrated by the paucity of therapeutics which preserve life. Their discovery is very difficult. Many approaches are being applied, from testing folk medicines to automated high throughput screening of large chemical libraries. Mutations in cancer cells create dysfunctional regulatory systems. This Perspective summarizes an approach to applying defective molecular control mechanisms as oncotargets on which drug discoveries against cancer can be based.

Physico-mechanical aspects of extracellular matrix influences on tumorigenic behaviors

Tumor progression in vitro has traditionally been studied in the context of two-dimensional (2D) environments. However, it is now well accepted that 2D substrates are unnaturally rigid compared to the physiological substrate known as extracellular matrix (ECM) that is in direct contact with both normal and tumorigenic cells in vivo. Hence, the patterns of interactions, as well as the strategies used by cells in order to penetrate the ECM, and migrate through a three-dimensional (3D) environment are notoriously different than those observed in 2D. Several substrates, such as collagen I, laminin, or complex mixtures of ECM components have been used as surrogates of native 3D ECM to more accurately study cancer cell behaviors. In addition, 3D matrices developed from normal or tumor-associated fibroblasts have been produced to recapitulate the mesenchymal 3D environment that assorted cells encounter in vivo. Some of these substrates are being used to evaluate physico-mechanical effects on tumor cell behavior. Physiological 3D ECMs exhibit a wide range of rigidities amongst different tissues while the degree of stromal stiffness is known to change during tumorigenesis. In this review we describe some of the physico-mechanical characteristics of tumor-associated ECMs believed to play important roles in regulating epithelial tumorigenic behaviors.

Simplistic pathways or complex networks?

Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer.