A network model for angiogenesis in ovarian cancer

Deciphering the Molecular Crosstalk of Endoplasmic Reticulum Stress and Immune Infiltration in Endometriosis

BACKGROUND

Endometriosis (EMs), characterized by ectopic endometrial growth causing infertility. Endoplasmic reticulum stress (ERS) is an important cellular defense mechanism. However, the correlation between ERS and EMs remains unclear. We aimed to investigate the relationship between them, identify biomarkers, and offer new insights into the treatment of EMs.

METHODS

Two RNA expression datasets (GSE120103 and GSE25628) related to EMs in Homo sapiens were used to identify ERS-differentially expressed genes (ERS-DEGs). Protein-protein interaction (PPI) networks and CytoHubba (Cytoscape) identified ERS-associated HUB genes, with receiver operating characteristic curves (ROC) evaluating diagnostic value. Constructed mRNA-microRNA (miRNA)/RNA-transcription factor (TF) interaction networks and performed ssGSEA to compare immune infiltration between EMs patients and controls. Real-time quantitative polymerase chain reaction (RT-qPCR), western blotting (WB), and immunohistochemistry (IHC) were performed to assess potential biomarker levels.

RESULTS

Thirty-three ERS-DEGs were identified, with nine HUB genes (HSPA5, XBP1, HSP90B1, DNAJC3, PDIA3, PDIA6, PDIA4, HERPUD1, and MANF) demonstrating diagnostic efficacy (AUC > 0.7). Furthermore, immune infiltration revealed a significant relationship between immune cell abundance and HUB gene expression. Experimental validation confirmed the consistency of four biomarkers (HSPA5, HSP90B1, PDIA6, and HERPUD1). Regulatory network analysis identified 62 miRNAs and 44 TFs interacting with HUB genes, suggesting a multifactorial immunometabolic axis.

CONCLUSIONS

We identified four biomarkers (HSPA5, HSP90B1, PDIA6, and HERPUD1) associated with ERS that offer new insights into the detection and treatment of EMs. Our findings indicate an abnormal response to ERS and immune system infiltration contribute to the progression of EMs.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Network-based drug repurposing for schizophrenia

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Circulating Blood Prognostic Biomarker Signatures for Hemorrhagic Cerebral Cavernous Malformations (CCMs)

Cerebral cavernous malformations (CCMs) are a neurological disorder characterized by enlarged intracranial capillaries in the brain, increasing the susceptibility to hemorrhagic strokes, a major cause of death and disability worldwide. The limited treatment options for CCMs underscore the importance of prognostic biomarkers to predict the likelihood of hemorrhagic events, aiding in treatment decisions and identifying potential pharmacological targets. This study aimed to identify blood biomarkers capable of diagnosing and predicting the risk of hemorrhage in CCM1 patients, establishing an initial set of circulating biomarker signatures. By analyzing proteomic profiles from both human and mouse CCM models and conducting pathway enrichment analyses, we compared groups to identify potential blood biomarkers with statistical significance. Specific candidate biomarkers primarily associated with metabolism and blood clotting pathways were identified. These biomarkers show promise as prognostic indicators for CCM1 deficiency and the risk of hemorrhagic stroke, strongly correlating with the likelihood of hemorrhagic cerebral cavernous malformations (CCMs). This lays the groundwork for further investigation into blood biomarkers to assess the risk of hemorrhagic CCMs.

Integration of Single-Cell RNA-Sequencing and Network Analysis to Investigate Mechanisms of Drug Resistance

Innate resistance and therapeutic-driven development of resistance to anticancer drugs is a common complication of cancer therapy. Understanding mechanisms of drug resistance can lead to development of alternative therapies. One strategy is to subject drug-sensitive and drug-resistant variants to single-cell RNA-seq (scRNA-seq) and to subject the scRNA-seq data to network analysis to identify pathways associated with drug resistance. This protocol describes a computational analysis pipeline to study drug resistance by subjecting scRNA-seq expression data to Passing Attributes between Networks for Data Assimilation (PANDA), an integrative network analysis tool that incorporates protein-protein interactions (PPI) and transcription factor (TF)-binding motifs.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials

Epidemiological evidence shows that diabetic patients have an increased cancer risk and a higher mortality rate. Glucose could play a central role in metabolism and growth of many tumor types, and this possible mechanism is supported by the high rate of glucose demand and uptake in cancer. Thus, growing evidence suggests that hyperglycemia contributes to cancer progression but also to its onset. Many mechanisms underlying this association have been hypothesized, such as insulin resistance, hyperinsulinemia, and increased inflammatory processes. Inflammation is a common pathophysiological feature in both diabetic and oncological patients, and inflammation linked to high glucose levels sensitizes microenvironment to tumorigenesis, promoting the development of malignant lesions by altering and sustaining a pathological condition in tissues. Glycemic control is the first goal of antidiabetic therapy, and glucose level reduction has also been associated with favorable outcomes in cancer. Here, we describe key events in carcinogenesis focusing on hyperglycemia as supporter in tumor progression and in particular, related to the role of a specific hypoglycemic drug class, sodium-glucose linked transporters (SGLTs). We also discuss the use of SGLT2 inhibitors as a novel potential cancer therapy. Our meta-analysis showed that SGLT-2 inhibitors were significantly associated with an overall reduced risk of cancer as compared to placebo (RR = 0.35, CI 0.33-0.37, P = 0. 00) with a particular effectiveness for dapaglifozin and ertuglifozin (RR = 0. 06, CI 0. 06-0. 07 and RR = 0. 22, CI 0. 18-0. 26, respectively). Network Medicine approaches may advance the possible repurposing of these drugs in patients with concomitant diabetes and cancer.

Constructing gene regulatory networks using epigenetic data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer

Background

Overcoming drug resistance is critical for increasing the survival rate of prostate cancer (PCa). Docetaxel is the first cytotoxic chemotherapeutical approved for treatment of PCa. However, 99% of PCa patients will develop resistance to docetaxel within 3 years. Understanding how resistance arises is important to increasing PCa survival.

Methods

In this study, we modeled docetaxel resistance using two PCa cell lines: DU145 and PC3. Using the Passing Attributes between Networks for Data Assimilation (PANDA) method to model transcription factor (TF) activity networks in both sensitive and resistant variants of the two cell lines. We identified edges and nodes shared by both PCa cell lines that composed a shared TF network that modeled changes which occur during acquisition of docetaxel resistance in PCa. We subjected the shared TF network to connectivity map analysis (CMAP) to identify potential drugs that could disrupt the resistant networks. We validated the candidate drug in combination with docetaxel to treat docetaxel-resistant PCa in both in vitro and in vivo models.

Results

In the final shared TF network, 10 TF nodes were identified as the main nodes for the development of docetaxel resistance. CMAP analysis of the shared TF network identified trichostatin A (TSA) as a candidate adjuvant to reverse docetaxel resistance. In cell lines, the addition of TSA to docetaxel enhanced cytotoxicity of docetaxel resistant PCa cells with an associated reduction of the IC50 of docetaxel on the resistant cells. In the PCa mouse model, combination of TSA and docetaxel reduced tumor growth and final weight greater than either drug alone or vehicle.

Conclusions

We identified a shared TF activity network that drives docetaxel resistance in PCa. We also demonstrated a novel combination therapy to overcome this resistance. This study highlights the usage of novel application of single cell RNA-sequencing and subsequent network analyses that can reveal novel insights which have the potential to improve clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09048-0.

Placental Gene Co-expression Network for Maternal Plasma Lipids Revealed Enrichment of Inflammatory Response Pathways

Maternal dyslipidemia during pregnancy has been associated with suboptimal fetal growth and increased cardiometabolic diseasse risk in offspring. Altered placental function driven by placental gene expression is a hypothesized mechanism underlying these associations. We tested the relationship between maternal plasma lipid concentrations and placental gene expression. Among 64 pregnant women from the NICHD Fetal Growth Studies–Singleton cohort with maternal first trimester plasma lipids we extracted RNA-Seq on placental samples obtained at birth. Placental gene co-expression networks were validated by regulatory network analysis that integrated transcription factors and gene expression, and genome-wide transcriptome analysis. Network analysis detected 24 gene co-expression modules in placenta, of which one module was correlated with total cholesterol (r = 0.27, P-value = 0.03) and LDL-C (r = 0.31, P-value = 0.01). Genes in the module (n = 39 genes) were enriched in inflammatory response pathways. Out of the 39 genes in the module, three known lipid-related genes (MPO, PGLYRP1 and LTF) and MAGEC2 were validated by the regulatory network analysis, and one known lipid-related gene (ALX4) and two germ-cell development-related genes (MAGEC2 and LUZP4) were validated by genome-wide transcriptome analysis. Placental gene expression signatures associated with unfavorable maternal lipid concentrations may be potential pathways underlying later life offspring cardiometabolic traits.

Clinical Trial Registration:ClinicalTrials.gov, identifier NCT00912132.

Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma 'omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. SIGNIFICANCE: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Gene Targeting in Disease Networks

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

PUMA: PANDA Using MicroRNA Associations

Motivation

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.

Availability and implementation

PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

The use of biological networks such as protein-protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new "communities" (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes.

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering

Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these "undruggable" targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Endothelial cell mineralocorticoid receptors oppose VEGF-induced gene expression and angiogenesis

Aldosterone is a key factor in adverse cardiovascular remodeling by acting on the mineralocorticoid receptor (MR) in different cell types. Endothelial MR activation mediates hypertrophy, inflammation and fibrosis. Cardiovascular remodeling is often accompanied by impaired angiogenesis, which is a risk factor for the development of heart failure. In this study, we evaluated the impact of MR in endothelial cells on angiogenesis. Deoxycorticosterone acetate (DOCA)-induced hypertension was associated with capillary rarefaction in the heart of WT mice but not of mice with cell type-specific MR deletion in endothelial cells. Consistently, endothelial MR deletion prevented the inhibitory effect of aldosterone on the capillarization of subcutaneously implanted silicon tubes and on capillary sprouting from aortic ring segments. We examined MR-dependent gene expression in cultured endothelial cells by RNA-seq and identified a cluster of differentially regulated genes related to angiogenesis. We found opposing effects on gene expression when comparing activation of the mineralocorticoid receptor in ECs to treatment with vascular endothelial growth factor (VEGF), a potent activator of angiogenesis. In conclusion, we demonstrate here that activation of endothelial cell MR impaired angiogenic capacity and lead to capillary rarefaction in a mouse model of MR-driven hypertension. MR activation opposed VEGF-induced gene expression leading to the dysregulation of angiogenesis-related gene networks in endothelial cells. Our findings underscore the pivotal role of endothelial cell MR in the pathophysiology of hypertension and related heart disease.

Gene regulatory network construction identified NFYA as a diffuse subtype-specific prognostic factor in gastric cancer

Lauren classification is a pathology-based gastric cancer (GC) subtyping system, which is widely used in the clinical treatment of patients with GC. However, genome-scale molecular characteristics to distinguish between diffuse (DF) and intestinal (IT) GC remain incompletely characterized, particularly at the transcriptional regulatory level. In the present study, gene regulatory networks were constructed using the Passing Attributes between Networks for Data Assimilation (PANDA) algorithm for DF, IT and mixed GC. The results indicated that >85% of transcription factor (TF)-target edges were shared among all three GC subtypes. In TF enrichment analysis, 13 TFs, including nuclear transcription factor Y subunit α (NFYA) and forkhead box L1, were activated in DF GC, whereas 8 TFs, including RELA proto-oncogene and T-cell leukemia homeobox 1 (TLX1), were activated in IT GC. Out of these identified TFs, NFYA [Hazard ratio (HR) (95% confidence interval, CI)=0.560 (0.349, 0.900), P=0.017] and sex determining region Y [HR (95% CI)=0.603 (0.375, 0.969), P=0.037] were identified as independent prognostic factors in DF GC, but not in IT GC, whereas TLX1 [HR (95% CI)=0.547 (0.321, 0.9325), P=0.027] was identified as an independent prognostic factor in IT GC, but not in DF GC. Verification at the cellular level was also performed; interference of NFYA expression using small interfering RNA in MGC803 cells (DF GC-derived cells) markedly inhibited cell growth and colony formation. Similar effects were also detected in SGC-7901 cells (IT GC-derived cells), but to a lesser extent. In conclusion, identified gene regulatory networks differed between distinct GC subtypes, in which the same TFs had different biological effects. Specifically, NFYA was identified as a DF subtype-specific independent prognostic factor in GC.

Identification of the Transcription Factor Relationships Associated with Androgen Deprivation Therapy Response and Metastatic Progression in Prostate Cancer

Background: Patients with locally advanced or recurrent prostate cancer typically undergo androgen deprivation therapy (ADT), but the benefits are often short-lived and the responses variable. ADT failure results in castration-resistant prostate cancer (CRPC), which inevitably leads to metastasis. We hypothesized that differences in tumor transcriptional programs may reflect differential responses to ADT and subsequent metastasis. Results: We performed whole transcriptome analysis of 20 patient-matched Pre-ADT biopsies and 20 Post-ADT prostatectomy specimens, and identified two subgroups of patients (high impact and low impact groups) that exhibited distinct transcriptional changes in response to ADT. We found that all patients lost the AR-dependent subtype (PCS2) transcriptional signatures. The high impact group maintained the more aggressive subtype (PCS1) signal, while the low impact group more resembled an AR-suppressed (PCS3) subtype. Computational analyses identified transcription factor coordinated groups (TFCGs) enriched in the high impact group network. Leveraging a large public dataset of over 800 metastatic and primary samples, we identified 33 TFCGs in common between the high impact group and metastatic lesions, including SOX4/FOXA2/GATA4, and a TFCG containing JUN, JUNB, JUND, FOS, FOSB, and FOSL1. The majority of metastatic TFCGs were subsets of larger TFCGs in the high impact group network, suggesting a refinement of critical TFCGs in prostate cancer progression. Conclusions: We have identified TFCGs associated with pronounced initial transcriptional response to ADT, aggressive signatures, and metastasis. Our findings suggest multiple new hypotheses that could lead to novel combination therapies to prevent the development of CRPC following ADT.

Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78-100] survival compared with 61% (95% CI, 45-82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538-47. ©2018 AACR.

Detecting phenotype-driven transitions in regulatory network structure

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense “communities” of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Cells are controlled by complex regulatory networks, and disruptions in the structure of these networks can lead to disease. Understanding disease requires that we accurately identify changes in gene regulatory network structure. However, cellular networks have tens of thousands of components with complex connections between them. Megha Padi from the University of Arizona and John Quackenbush from Dana-Farber Cancer Institute developed a new algorithm that is far more effective than previous methods at finding disease-associated modules in regulatory networks. Applying this to ovarian cancer, they found new regulatory processes that may lead to more targeted treatments. In human breast tissue, they found that sex-specific differences were driven by hormone signaling and differentiation pathways. Decoding how network modules promote new functions may help to better model the relationship between genotype and phenotype.

Estimating drivers of cell state transitions using gene regulatory network models

Background

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks.

Results

Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state.

Conclusions

We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.

Understanding Tissue-Specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Gene regulatory pattern analysis reveals essential role of core transcriptional factors' activation in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. Genome-scale molecular characteristics and regulatory mechanisms that distinguish TNBC from other subtypes remain incompletely characterized.

Results

By combining gene expression analysis and PANDA network, we defined three different TF regulatory patterns. A core TNBC-Specific TF Activation Driven Pattern (TNBCac) was specifically identified in TNBC by computational analysis. The essentialness of core TFs (ZEB1, MZF1, SOX10) in TNBC was highlighted and validated by cell proliferation analysis. Furthermore, 13 out of 35 co-targeted genes were also validated to be targeted by ZEB1, MZF1 and SOX10 in TNBC cell lines by real-time quantitative PCR. In three breast cancer cohorts, non-TNBC patients could be stratified into two subgroups by the 35 co-targeted genes along with 5 TFs, and the subgroup that more resembled TNBC had a worse prognosis.

Methods

We constructed gene regulatory networks in breast cancer by Passing Attributes between Networks for Data Assimilation (PANDA). Co-regulatory modules were specifically identified in TNBC by computational analysis, while the essentialness of core translational factors (TF) in TNBC was highlighted and validated by in vitro experiments. Prognostic effects of different factors were measured by Log-rank test and displayed by Kaplan-Meier plots.

Conclusions

We identified a core co-regulatory module specifically existing in TNBC, which enabled subtype re-classification and provided a biologically feasible view of breast cancer.

Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer

Ovarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomolecules-genes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolites-by integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.

Regulatory network changes between cell lines and their tissues of origin

BACKGROUND

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.

RESULTS

We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.

CONCLUSIONS

Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Differential connectivity of gene regulatory networks distinguishes corticosteroid response in asthma

BACKGROUND

Variations in drug response between individuals have prevented us from achieving high drug efficacy in treating many complex diseases, including asthma. Genetics plays an important role in accounting for such interindividual variations in drug response. However, systematic approaches for addressing how genetic factors and their regulators determine variations in drug response in asthma treatment are lacking.

OBJECTIVE

We sought to identify key transcriptional regulators of corticosteroid response in asthma using a novel systems biology approach.

METHODS

We used Passing Attributes between Networks for Data Assimilations (PANDA) to construct the gene regulatory networks associated with good responders and poor responders to inhaled corticosteroids based on a subset of 145 white children with asthma who participated in the Childhood Asthma Management Cohort. PANDA uses gene expression profiles and published relationships among genes, transcription factors (TFs), and proteins to construct the directed networks of TFs and genes. We assessed the differential connectivity between the gene regulatory network of good responders versus that of poor responders.

RESULTS

When compared with poor responders, the network of good responders has differential connectivity and distinct ontologies (eg, proapoptosis enriched in network of good responders and antiapoptosis enriched in network of poor responders). Many of the key hubs identified in conjunction with clinical response are also cellular response hubs. Functional validation demonstrated abrogation of differences in corticosteroid-treated cell viability following siRNA knockdown of 2 TFs and differential downstream expression between good responders and poor responders.

CONCLUSIONS

We have identified and validated multiple TFs influencing asthma treatment response. Our results show that differential connectivity analysis can provide new insights into the heterogeneity of drug treatment effects.

Estimating gene regulatory networks with pandaR

CONTACT

johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu.

AVAILABILITY AND IMPLEMENTATION

PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

Prediction of oral cancer recurrence using dynamic Bayesian networks

We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices. Moreover, the proposed methodology aims to (i) assess the prognosis of patients regarding oral cancer recurrence and at the same time, (ii) provide important information about the underlying biological processes of the disease.

RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective

Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer.

ABBREVIATIONS

CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-β: transforming growth factor-β.

Construction of disease-specific transcriptional regulatory networks identifies co-activation of four gene in esophageal squamous cell carcinoma

Even though various molecules may serve as biomarkers, little is known concerning the mechanisms underlying the carcinogenesis of ESCC, particularly the transcriptional regulatory network. Thus, in the present study, paired ESCC and non-cancerous (NC) tissues were assayed by Affymetrix microarray assays. Passing Attributes between Networks for Data Assimilation (PANDA) was used to construct networks between transcription factors (TFs) and their targets. AnaPANDA program was applied to compare the regulatory networks. A hypergeometric distribution model-based target profile similarity analysis was utilized to find co-activation effects using both TF-target networks and differential expression data. There were 1,116 genes upregulated and 1,301 genes downregulated in ESCC compared with NC tissues. In TF-target networks, 16,970 ESCC-specific edges and 9,307 NC-specific edges were identified. Edge enrichment analysis by AnaPANDA indicated 17 transcription factors (NFE2L2, ELK4, PAX6, TLX1, ESR1, ZNF143, TP53, REL, ELF5, STAT1, TBP, NHLH1, FOXL1, SOX9, STAT3, ELK1, and HOXA5) suppressed in ESCC and 5 (SPIB, BRCA1, MZF1, MAFG and NFE2L1) activated in ESCC. For SPIB, MZF1, MAFG and NFE2L1, a strong and significant co-activation effect among them was detected in ESCC. In conclusion, the construction of transcriptional regulatory networks found SPIB, MZF1, MAFG and NFE2L1 co-activated in ESCC, which provides distinctive insight into the carcinogenesis mechanism of ESCC.

An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets

Background

A microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status.

Methods

We integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts (n = 65, n = 27, and n = 25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n = 80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness.

Results

In the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor.

Conclusions

Loss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-017-0465-4) contains supplementary material, which is available to authorized users.

Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

BACKGROUND

De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.

METHODS

In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships. The algorithm, CorEx, efficiently optimizes over multivariate mutual information and can be iteratively applied to generate a hierarchy of relatively independent latent factors. The learned latent factors are used to stratify patients for survival analysis with respect to both single factors and combinations. These analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies.

RESULTS

Analysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a microRNA that modulates epigenetics. Further, combinations of factors lead to a synergistic survival advantage in some cases.

CONCLUSIONS

In contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are found to both have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366 possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.

Integration of Pathway Knowledge and Dynamic Bayesian Networks for the Prediction of Oral Cancer Recurrence

Oral squamous cell carcinoma has been characterized as a complex disease which involves dynamic genomic changes at the molecular level. These changes indicate the worth to explore the interactions of the molecules and especially of differentially expressed genes that contribute to cancer progression. Moreover, based on this knowledge the identification of differentially expressed genes and related molecular pathways is of great importance. In the present study, we exploit differentially expressed genes in order to further perform pathway enrichment analysis. According to our results we found significant pathways in which the disease associated genes have been identified as strongly enriched. Furthermore, based on the results of the pathway enrichment analysis we propose a methodology for predicting oral cancer recurrence using dynamic Bayesian networks. The methodology takes into consideration time series gene expression data in order to predict a disease recurrence. Subsequently, we are able to conjecture about the causal interactions between genes in consecutive time intervals. Concerning the performance of the predictive models, the overall accuracy of the algorithm is 81.8% and the area under the ROC curve 89.2% regarding the knowledge from the overrepresented pre-NOTCH Expression and processing pathway.

Proceedings of the third international molecular pathological epidemiology (MPE) meeting

Molecular pathological epidemiology (MPE) is a transdisciplinary and relatively new scientific discipline that integrates theory, methods, and resources from epidemiology, pathology, biostatistics, bioinformatics, and computational biology. The underlying objective of MPE research is to better understand the etiology and progression of complex and heterogeneous human diseases with the goal of informing prevention and treatment efforts in population health and clinical medicine. Although MPE research has been commonly applied to investigating breast, lung, and colorectal cancers, its methodology can be used to study most diseases. Recent successes in MPE studies include: (1) the development of new statistical methods to address etiologic heterogeneity; (2) the enhancement of causal inference; (3) the identification of previously unknown exposure-subtype disease associations; and (4) better understanding of the role of lifestyle/behavioral factors on modifying prognosis according to disease subtype. Central challenges to MPE include the relative lack of transdisciplinary experts, educational programs, and forums to discuss issues related to the advancement of the field. To address these challenges, highlight recent successes in the field, and identify new opportunities, a series of MPE meetings have been held at the Dana-Farber Cancer Institute in Boston, MA. Herein, we share the proceedings of the Third International MPE Meeting, held in May 2016 and attended by 150 scientists from 17 countries. Special topics included integration of MPE with immunology and health disparity research. This meeting series will continue to provide an impetus to foster further transdisciplinary integration of divergent scientific fields.

PyPanda: a Python package for gene regulatory network reconstruction

PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of 'omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis.

AVAILABILITY AND IMPLEMENTATION

The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda CONTACT: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl.

Diet-induced weight loss leads to a switch in gene regulatory network control in the rectal mucosa

BACKGROUND

Weight loss may decrease risk of colorectal cancer in obese individuals, yet its effect in the colorectum is not well understood. We used integrative network modeling, Passing Attributes between Networks for Data Assimilation, to estimate transcriptional regulatory network models from mRNA expression levels from rectal mucosa biopsies measured pre- and post-weight loss in 10 obese, pre-menopausal women.

RESULTS

We identified significantly greater regulatory targeting of glucose transport pathways in the post-weight loss regulatory network, including "regulation of glucose transport" (FDR=0.02), "hexose transport" (FDR=0.06), "glucose transport" (FDR=0.06) and "monosaccharide transport" (FDR=0.08). These findings were not evident by gene expression analysis alone. Network analysis also suggested a regulatory switch from NFΚB1 to MAX control of MYC post-weight loss.

CONCLUSIONS

These network-based results expand upon standard gene expression analysis by providing evidence for a potential mechanistic alteration caused by weight loss.

LPRP: A Gene-Gene Interaction Network Construction Algorithm and Its Application in Breast Cancer Data Analysis

The importance of the construction of gene-gene interaction (GGI) network to better understand breast cancer has previously been highlighted. In this study, we propose a novel GGI network construction method called linear and probabilistic relations prediction (LPRP) and used it for gaining system level insight into breast cancer mechanisms. We construct separate genome-wide GGI networks for tumor and normal breast samples, respectively, by applying LPRP on their gene expression datasets profiled by The Cancer Genome Atlas. According to our analysis, a large loss of gene interactions in the tumor GGI network was observed (7436; 88.7 % reduction), which also contained fewer functional genes (4757; 32 % reduction) than the normal network. Tumor GGI network was characterized by a bigger network diameter and a longer characteristic path length but a smaller clustering coefficient and much sparse network connections. In addition, many known cancer pathways, especially immune response pathways, are enriched by genes in the tumor GGI network. Furthermore, potential cancer genes are filtered in this study, which may act as drugs targeting genes. These findings will allow for a better understanding of breast cancer mechanisms.

Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

BACKGROUND

Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.

RESULTS

Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses.

CONCLUSIONS

There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets.