PATHOME: an algorithm for accurately detecting differentially expressed subpathways

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.

Expression of hepatocyte nuclear factor 4 alpha, wingless-related integration site, and β-catenin in clinical gastric cancer

BACKGROUND

Gastric cancer (GC) is the second most common cause of cancer-related deaths worldwide. Hepatocyte nuclear factor 4 alpha (HNF4α) that belongs to the nuclear hormone receptor superfamily, is overexpressed in GC tissues, and might be involved in the development of GC by regulating its downstream wingless-related integration site (WNT)/β-catenin signaling.

AIM

To clarify the expression of HNF4α/WNT5a/β-catenin signaling proteins in clinical GC tissues.

METHODS

We immunohistochemically stained pathological blocks of GC and matched para-cancerous tissues. The intensity of HNF4α, WNT5a and β-catenin staining in the tumor cells was determined according to cell rates and staining intensity. The correlations between GC and HNF4α, WNT5a, and β-catenin expression using chi-square and paired chi-square tests. Relationships between double-positive HNF4α and WNT5a expression and types of gastric tumor tissues were assessed using regression analysis. Correlations between HNF4α and WNT5a expression at the RNA level in GC tissues found in the TCGA database were analyzed using Pearson correlation coefficients.

RESULTS

We found more abundant HNF4α and WNT5a proteins in GC, especially in mucinous adenocarcinoma and mixed GC than in adjacent tissues (P < 0.001). Low and high levels of cytoplasmic β-catenin respectively expressed in GC and adjacent tissues (P < 0.001) were not significantly associated with pathological parameters.

CONCLUSION

The expressions of HNF4α and WNT5a could serve as early diagnostic biomarkers for GC.

MLDEG: A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation

MOTIVATION

Identifying differentially expressed genes (DEGs) in transcriptome data is a very important task. However, performances of existing DEG methods vary significantly for data sets measured in different conditions and no single statistical or machine learning model for DEG detection perform consistently well for data sets of different traits. In addition, setting a cutoff value for the significance of differential expressions is one of confounding factors to determine DEGs.

RESULTS

We address these problems by developing an ensemble model that refines the heterogeneous and inconsistent results of the existing methods by taking accounts into network information such as network propagation and network property. DEG candidates that are predicted with weak evidence by the existing tools are re-classified by our proposed ensemble model for the transcriptome data. Tested on 10 RNA-seq datasets downloaded from gene expression omnibus (GEO), our method showed excellent performance of winning the first place in detecting ground truth (GT) genes in eight datasets and find almost all GT genes in six datasets. On the other hand, performances of all existing methods varied significantly for the 10 data sets. Because of the design principle, our method can accommodate any new DEG methods naturally.

AVAILABILITY

The source code of our method is available at https://github.com/jihmoon/MLDEG.

Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer

Gastric cancer (GC) is one of the most lethal cancers worldwide; it has a high mortality rate, particularly in East Asia. Recently, genetic events (e.g., mutations and copy number alterations) and molecular signaling associated with histologically different GC subtypes (diffuse and intestinal) have been elucidated. However, metabolic differences among the histological GC subtypes have not been studied systematically. In this study, we utilized transcriptome-based genome-scale metabolic models (GEMs) to identify differential metabolic pathways between Lauren diffuse and intestinal subtypes. We found that diverse metabolic pathways, including cholesterol homeostasis, xenobiotic metabolism, fatty acid metabolism, the MTORC1 pathway, and glycolysis, were dysregulated between the diffuse and intestinal subtypes. Our study provides an overview of the metabolic differences between the two subtypes, possibly leading to an understanding of metabolism in GC heterogeneity.

Berberine retarded the growth of gastric cancer xenograft tumors by targeting hepatocyte nuclear factor 4α

BACKGROUND

Gastric cancer is the third deadliest cancer in the world and ranks second in incidence and mortality of cancers in China. Despite advances in prevention, diagnosis, and therapy, the absolute number of cases is increasing every year due to aging and the growth of high-risk populations, and gastric cancer is still a leading cause of cancer-related death. Gastric cancer is a consequence of the complex interaction of microbial agents, with environmental and host factors, resulting in the dysregulation of multiple oncogenic and tumor-suppressing signaling pathways. Global efforts have been made to investigate in detail the genomic and epigenomic heterogeneity of this disease, resulting in the identification of new specific and sensitive predictive and prognostic biomarkers. Trastuzumab, a monoclonal antibody against the HER2 receptor, is approved in the first-line treatment of patients with HER2+ tumors, which accounts for 13%-23% of the gastric cancer population. Ramucirumab, a monoclonal antibody against VEGFR2, is currently recommended in patients progressing after first-line treatment. Several clinical trials have also tested novel agents for advanced gastric cancer but mostly with disappointing results, such as anti-EGFR and anti-MET monoclonal antibodies. Therefore, it is still of great significance to screen specific molecular targets for gastric cancer and drugs directed against the molecular targets.

AIM

To investigate the effect and mechanism of berberine against tumor growth in gastric cancer xenograft models and to explore the role of hepatocyte nuclear factor 4α (HNF4α)-WNT5a/β-catenin pathways played in the antitumor effects of berberine.

METHODS

MGC803 and SGC7901 subcutaneous xenograft models were established. The control group was intragastrically administrated with normal saline, and the berberine group was administrated intragastrically with 100 mg/kg/d berberine. The body weight of nude mice during the experiment was measured to assess whether berberine has any adverse reaction. The volume of subcutaneous tumors during this experiment was recorded to evaluate the inhibitory effect of berberine on the growth of MGC803 and SGC7901 subcutaneous transplantation tumors. Polymerase chain reaction assays were conducted to evaluate the alteration of transcriptional expression of HNF4α, WNT5a and β-catenin in tumor tissues and liver tissues from the MGC803 and SGC7901 xenograft models. Western blotting and IHC were performed to assess the protein expression of HNF4α, WNT5a and β-catenin in tumor tissues and liver tissues from the MGC803 and SGC7901 xenograft models.

RESULTS

In the both MGC803 and SGC7901 xenograft tumor models, berberine significantly reduced tumor volume and weight and thus retarded the growth rate of tumors. In the SGC7901 and MGC803 subcutaneously transplanted tumor models, berberine down-regulated the expression of HNF4α, WNT5a and β-catenin in tumor tissues from both transcription and protein levels. Besides, berberine also suppressed the protein expression of HNF4α, WNT5a and β-catenin in liver tissues.

CONCLUSION

Berberine retarded the growth of MGC803 and SGC7901 xenograft model tumors, and the mechanism behind these anti-growth effects might be the downregulation of the expression of HNF4α-WNT5a/β-catenin signaling pathways both in tumor tissues and liver tissues of the xenograft models.

Second-Generation JK-206 Targets the Oncogenic Signal Mediator RHOA in Gastric Cancer

Ras homologous A (RHOA), a signal mediator and a GTPase, is known to be associated with the progression of gastric cancer (GC), which is the fourth most common cause of death in the world. Previously, we designed pharmacologically optimized inhibitors against RHOA, including JK-136 and JK-139. Based on this previous work, we performed lead optimization and designed novel RHOA inhibitors for greater anti-GC potency. Two of these compounds, JK-206 and JK-312, could successfully inhibit the viability and migration of GC cell lines. Furthermore, using transcriptomic analysis of GC cells treated with JK-206, we revealed that the inhibition of RHOA might be associated with the inhibition of the mitogenic pathway. Therefore, JK-206 treatment for RHOA inhibition may be a new therapeutic strategy for regulating GC proliferation and migration.

rPAC: Route based pathway analysis for cohorts of gene expression data sets

Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, "Proportion of Significance" (PS) and "Average Route Score" (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC's scoring competency, we first used a large amount of simulated data and compared the method's performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method

MOTIVATION

Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts.

RESULTS

Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents.

AVAILABILITY AND IMPLEMENTATION

The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Targeting the crosstalk between canonical Wnt/β-catenin and inflammatory signaling cascades: A novel strategy for cancer prevention and therapy

Emerging scientific evidence indicates that inflammation is a critical component of tumor promotion and progression. Most cancers originate from sites of chronic irritation, infections and inflammation, underscoring that the tumor microenvironment is largely orchestrated by inflammatory cells and pro-inflammatory molecules. These inflammatory components are intimately involved in neoplastic processes which foster proliferation, survival, invasion, and migration, making inflammation the primary target for cancer prevention and treatment. The influence of inflammation and the immune system on the progression and development of cancer has recently gained immense interest. The Wnt/β-catenin signaling pathway, an evolutionarily conserved signaling strategy, has a critical role in regulating tissue development. It has been implicated as a major player in cancer development and progression with its regulatory role on inflammatory cascades. Many naturally-occurring and small synthetic molecules endowed with inherent anti-inflammatory properties inhibit this aberrant signaling pathway, making them a promising class of compounds in the fight against inflammatory cancers. This article analyzes available scientific evidence and suggests a crosslink between Wnt/β-catenin signaling and inflammatory pathways in inflammatory cancers, especially breast, gastrointestinal, endometrial, and ovarian cancer. We also highlight emerging experimental findings that numerous anti-inflammatory synthetic and natural compounds target the crosslink between Wnt/β-catenin pathway and inflammatory cascades to achieve cancer prevention and intervention. Current challenges, limitations, and future directions of research are also discussed.

A Non-canonical Wnt Signature Correlates With Lower Survival in Gastric Cancer

Genetic evidence suggests a role for the Wnt/β-catenin pathway in gastric cancer. However, Wnt5a, regarded as a prototypical non-canonical Wnt ligand, has also been extensively associated with this disease. Therefore, the roles of the Wnt signaling pathway in gastric cancer initiation and progression, and particularly the precise mechanisms by which the non-canonical Wnt pathway might promote the development and progression of gastric cancer, are not entirely well understood. This article analyzes publicly available gene and protein expression data and reveals the existence of a WNT5A/FZD2/FZD7/ROR2 signature, which correlates with tumor-infiltrating and mesenchymal cell marker expression. High expression of FZD7 and ROR2 correlates with a shared gene and protein expression profile, which in turn correlates with poor prognosis. In summary, the findings presented in this article provide an updated view of the relative contributions of the Wnt/β-catenin and non-canonical Wnt pathways in gastric cancer.

The Potential Anticancer Activity of Phytoconstituents against Gastric Cancer-A Review on In Vitro, In Vivo, and Clinical Studies

Gastric cancer belongs to the heterogeneous malignancies and, according to the World Health Organization, it is the fifth most commonly diagnosed cancer in men. The aim of this review is to provide an overview on the role of natural products of plant origin in the therapy of gastric cancer and to present the potentially active metabolites which can be used in the natural therapeutical strategies as the support to the conventional treatment. Many of the naturally spread secondary metabolites have been proved to exhibit chemopreventive properties when tested on the cell lines or in vivo. This manuscript aims to discuss the pharmacological significance of both the total extracts and the single isolated metabolites in the stomach cancer prevention and to focus on their mechanisms of action. A wide variety of plant-derived anticancer metabolites from different groups presented in the manuscript that include polyphenols, terpenes, alkaloids, or sulphur-containing compounds, underlines the multidirectional nature of natural products.

Identification of differentially expressed subpathways via a bilevel consensus scoring of network topology and gene expression

Identifying differentially expressed subpathways connected to the emergence of a disease that can be considered as candidates for pharmacological intervention, with minimal off-target effects, is a daunting task. In this direction, we present a bilevel subpathway analysis method to identify differentially expressed subpathways that are connected with an experimental condition, while taking into account potential crosstalks between subpathways which arise due to their connectivity in a combined multi-pathway network. The efficacy of the method is demonstrated on a hematopoietic stem cell aging dataset, with findings corroborated using recent literature.

Preventative and Therapeutic Effects of Metformin in Gastric Cancer: A New Contribution of an Old Friend

Gastric cancer (GC) is a cancer with high prevalence, and is one of the leading causes of cancer death worldwide. Metformin is a widely used hypoglycemic agent for type-2 diabetes mellitus (T2DM). Recently, metformin has drawn increasing attention in the field of cancer research for its emerging anti-cancer roles. However, the efficacy and underlying molecular mechanisms of metformin in the prevention and treatment for GC remain controversial. This review summarized the present clinical and mechanistic studies that investigated the efficacy of metformin in GC. It was found that the majority of clinical studies affirmed protective roles of metformin in both gastric cancer risk and survival rate. In addition, metformin’s effects in the prevention and treatment for GC involve multiple pathways mainly via AMPK and IGF-1R. It was concluded that metformin presents a unique opportunity for application against GC, but further clinical and mechanistic investigations are required to solidify the roles of metformin in GC.

Wnt5a Signaling in Gastric Cancer

Gastric cancer remains an important health challenge, accounting for a significant number of cancer-related deaths worldwide. Therefore, a deeper understanding of the molecular mechanisms involved in gastric cancer establishment and progression is highly desirable. The Wnt pathway plays a fundamental role in development, homeostasis, and disease, and abnormal Wnt signaling is commonly observed in several cancer types. Wnt5a, a ligand that activates the non-canonical branch of the Wnt pathway, can play a role as a tumor suppressor or by promoting cancer cell invasion and migration, although the molecular mechanisms explaining these roles have not been fully elucidated. Wnt5a is increased in gastric cancer samples; however, most gastric cancer cell lines seem to exhibit little expression of this ligand, thus raising the question about the source of this ligand in vivo. This review summarizes available research about Wnt5a expression and signaling in gastric cancer. In gastric cancer, Wnt5a promotes invasion and migration by modulating integrin adhesion turnover. Disheveled, a scaffolding protein with crucial roles in Wnt signaling, mediates the adhesion-related effects of Wnt5a in gastric cancer cells, and several studies provide growing support for a model whereby Disheveled-interacting proteins mediates Wnt5a signaling to modulate cytoskeleton dynamics. However, Wnt5a might induce other effects in gastric cancer cells, such as cell survival and induction of gene expression. On the other hand, the available evidence suggests that Wnt5a might be expressed by cells residing in the tumor microenvironment, where feedback mechanisms sustaining Wnt5a secretion and signaling might be established. This review analyzes the possible functions of Wnt5a in this pathological context and discusses potential links to mechanosensing and YAP/TAZ signaling.

Rational design of small molecule RHOA inhibitors for gastric cancer

Previously, we identified Ras homologous A (RHOA) as a major signaling hub in gastric cancer (GC), the third most common cause of cancer death in the world, prompting us to rationally design an efficacious inhibitor of this oncogenic GTPase. Here, based on that previous work, we extend those computational analyses to further pharmacologically optimize anti-RHOA hydrazide derivatives for greater anti-GC potency. Two of these, JK-136 and JK-139, potently inhibited cell viability and migration/invasion of GC cell lines, and mouse xenografts, diversely expressing RHOA. Moreover, JK-136's binding affinity for RHOA was >140-fold greater than Rhosin, a nonclinical RHOA inhibitor. Network analysis of JK-136/-139 vs. Rhosin treatments indicated downregulation of the sphingosine-1-phosphate, as an emerging cancer metabolic pathway in cell migration and motility. We assert that identifying and targeting oncogenic signaling hubs, such as RHOA, represents an emerging strategy for the design, characterization, and translation of new antineoplastics, against gastric and other cancers.

Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer

The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Systematic Inspection of the Clinical Relevance of TP53 Missense Mutations in Gastric Cancer

The "guardian of the genome," TP53, is one of the most frequently mutated genes of all cancers. Despite the important biological roles of TP53, the clinical relevance of TP53 mutations, in gastric cancer (GC), remains largely unknown. Here, we systematically assessed clinical relevance, in terms of TP53 mutation positions, finding substantial variability. Thus, we hypothesized that the position of the TP53 mutation might affect clinical outcomes in GC. We systematically inspected missense mutations in TP53, from a TCGA (The Cancer Genome Atlas) GC dataset in UCSC Xena repository. Specifically, we examined five aspects of each mutational position: (1) the whole gene body; (2) known hot-spots; (3) the DNA-binding domain; (4) the secondary structure of the domain; and (5) individual mutation positions. We then analyzed the clinical outcomes for each aspect. These results showed that, in terms of secondary structure, patients with mutations in turn regions showed poor prognosis, compared to those with mutations in beta strand regions (log rank ${\text{p}}= {{0.043}}$p=0.043). Also, in terms of individual mutation positions, patients having mutations at R248 showed poorer survival than other patients having mutations at different TP53 positions (log rank ${\text{p}}= {{0.035}}$p=0.035).

RHOA in Gastric Cancer: Functional Roles and Therapeutic Potential

The well-known signal mediator and small GTPase family member, RHOA, has now been associated with the progression of specific malignancies. In this review, we appraise the biomedical literature regarding the role of this enzyme in gastric cancer (GC) signaling, suggesting potential clinical significance. To that end, we examined RHOA activity, with regard to second-generation hallmarks of cancer, finding particular association with the hallmark "activation of invasion and metastasis." Moreover, an abundance of studies show RHOA association with Lauren classification diffuse subtype, in addition to poorly differentiated GC. With regard to therapeutic value, we found RHOA signaling to influence the activity of specific widely used chemotherapeutics, and its possible antagonism by various dietary constituents. We also review currently available targeted therapies for GC. The latter, however, showed a paucity of such agents, underscoring the urgent need for further investigation into treatments for this highly lethal malignancy.

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).

Differential effects, on oncogenic pathway signalling, by derivatives of the HNF4 α inhibitor BI6015

Background

Gastric cancer (GC) is a highly heterogeneous disease with few “targeted” therapeutic options. Previously, we demonstrated involvement of the transcription factor HNF4α in human GC tumours, and the developmental signal mediator, WNT5A, as a prognostic GC biomarker. One previously developed HNF4α antagonist, BI6015, while not advancing beyond preclinical stages, remains useful for studying GC.

Methods

Here, we characterised the antineoplastic signalling activity of derivatives of BI6015, including transfer of the nitro group from the para position, relative to a methyl group on its benzene ring, to the ortho- and meta positions. We assessed binding efficacy, through surface plasmon resonance and docking studies, while biologic activity was assessed by antimitogenic efficacy against a panel of GC cell lines, and dysregulated transcriptomes, followed by pathway and subpathway analysis.

Results

The para derivative of BI6105 was found substantially more growth inhibitory, and effective, in downregulating numerous oncogenic signal pathways, including the embryonic cascade WNT. The ortho and meta derivatives, however, failed to downregulate WNT or other embryonic signalling pathways, unable to suppress GC growth.

Conclusion

Straightforward strategies, employing bioinformatics analyses, to facilitate the effective design and development of “druggable” transcription factor inhibitors, are useful for targeting specific oncogenic signalling pathways, in GC and other cancers.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

Berberine Attenuated Proliferation, Invasion and Migration by Targeting the AMPK/HNF4α/WNT5A Pathway in Gastric Carcinoma

Background: Recent epidemiologic studies have found that patients with diabetes have a higher risk of gastric cancer (GC), and the long-term use of metformin is associated with a lower risk of gastric cancer. It is believed that blocking tumor energy metabolic alterations is now emerging as a new therapeutic approach of cancer. Berberine, a natural isoquinoline alkaloid, could modulate lipid metabolism and glucose homeostasis by regulating the expression of HNF4α in many metabolic diseases. Here, we investigated the effect of Berberine on GC and its possible molecular mechanism through targeting HNF4α.

Methods and Results: (1) AGS and SGC7901 gastric cancer cells were treated with Berberine (BBR). We found that in AGS and SGC7901 cell, BBR inhibited cell proliferation in a time- and dose-dependent manner through downregulating C-myc. BBR also induced G₀-G₁ phase arrest with the decreased expression of cyclin D1. Moreover, BBR attenuated the migration and invasion by downregulating MMP-3. (2) The lentivirus infection was used to silence the expression of HNF4α in SGC7901 cell. The results demonstrated that the knockdown of HNF4α in SGC7901 slowed cells proliferation, induced S phase arrest and dramatically attenuated gastric cancer cells’ metastasis and invasion. (3) We performed GC cells perturbation experiments through BI6015 (an HNF4α antagonist), AICAR (an AMPK activator), Compound C (AMPK-kinase inhibitor), metformin and BBR. Our findings indicated that BBR downregulated HNF4α while upregulating p-AMPK. Moreover, the inhibition of HNF4α by BBR was AMPK dependent. (4) Then the LV-HNF4α-RNAi SGC7901 cell model was used to detect the downstream of HNF4α in vitro. The results showed that the knockdown of HNF4α significantly decreased WNT5A and cytoplasmic β-catenin, but increased E-cadherin in vitro. Berberine also downregulated WNT5A and cytoplasmic β-catenin, the same as LV-HNF4α-RNAi and BI6015 in GC cells. (5) Finally, the SGC7901 and LV-HNF4α-RNAi SGC7901 mouse-xenograft model to evaluate the effect of BBR and HNF4α gene on GC tumor growth. The result illustrated that BBR and knockdown of HNF4α suppressed tumor growth in vivo, and BBR decreased HNF4α, WNT5A and cytoplasmic β-catenin levels, the same effect as HNF4α knockout in vivo.

Conclusion: BBR not only had proliferation inhibition effect, attenuated the invasion and migration on GC cell lines, but also suppressed the GC tumor growth in vivo. The anti-gastric cancer mechanism of BBR might be involved in AMPK-HNF4α-WNT5A signaling pathway. HNF4α antagonists, such as BBR, could be a promising anti-gastric cancer treatment supplement.

PerPAS: Topology-Based Single Sample Pathway Analysis Method

Identification of intracellular pathways that play key roles in cancer progression and drug resistance is a prerequisite for developing targeted cancer treatments. The era of personalized medicine calls for computational methods that can function with one sample or a very small set of samples. Developing such methods is challenging because standard statistical approaches pose several limiting assumptions, such as number of samples, that prevent their application when approaches to one. We have developed a novel pathway analysis method called PerPAS to estimate pathway activity at a single sample level by integrating pathway topology and transcriptomics data. In addition, PerPAS is able to identify altered pathways between cancer and control samples as well as to identify key nodes that contribute to the pathway activity. In our case study using breast cancer data, we show that PerPAS can identify highly altered pathways that are associated with patient survival. PerPAS identified four pathways that were associated with patient survival and were successfully validated in three independent breast cancer cohorts. In comparison to two other pathway analysis methods that function at a single sample level, PerPAS had superior performance in both synthetic and breast cancer expression datasets. PerPAS is a free R package (http://csbi.ltdk.helsinki.fi/pub/czliu/perpas/).

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.

Systematic approach identifies RHOA as a potential biomarker therapeutic target for Asian gastric cancer

Gastric cancer (GC) is a highly heterogeneous disease, in dire need of specific, biomarker-driven cancer therapies. While the accumulation of cancer “Big Data” has propelled the search for novel molecular targets for GC, its specific subpathway and cellular functions vary from patient to patient. In particular, mutations in the small GTPase gene RHOA have been identified in recent genome-wide sequencing of GC tumors. Moreover, protein overexpression of RHOA was reported in Chinese populations, while RHOA mutations were found in Caucasian GC tumors. To develop evidence-based precision medicine for heterogeneous cancers, we established a systematic approach to integrate transcriptomic and genomic data. Predicted signaling subpathways were then laboratory-validated both in vitro and in vivo, resulting in the identification of new candidate therapeutic targets. Here, we show: i) differences in RHOA expression patterns, and its pathway activity, between Asian and Caucasian GC tumors; ii) in vitro and in vivo perturbed RHOA expression inhibits GC cell growth in high RHOA-expressing cell lines; iii) inverse correlation between RHOA and RHOB expression; and iv) an innovative small molecule design strategy for RHOA inhibitors. In summary, RHOA, and its oncogenic signaling pathway, represent a strong biomarker-driven therapeutic target for Asian GC. This comprehensive strategy represents a promising approach for the development of “hit” compounds.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network

Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability.

In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder.

The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .

CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery

The recent creation of enormous, cancer-related “Big Data” public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types). For user-provided single/multi-biomarkers, CANES evaluates the performance of single/multi-biomarker candidates, based on four classification methods, support vector machine, random forest, neural networks, and classification and regression trees. In addition, CANES offers several advantages over earlier analysis tools, including: 1) survival analysis; 2) evaluation of mature miRNAs as markers for user-defined diagnostic or prognostic purposes; and 3) provision of a “pan-cancer” summary view, based on each single marker. We believe that such “landscape” evaluation of single/multi-biomarkers, for diagnostic therapeutic/prognostic decision-making, will be highly valuable for the discovery and “repurposing” of existing biomarkers (and their specific targeted therapies), leading to improved patient therapeutic stratification, a key component of targeted therapy success for the avoidance of therapy resistance.

MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data

Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information.

AVAILABILITY

http://biohealth.snu.ac.kr/software/MIDAS/.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

Gastric cancer associated signaling pathways and interventions

Gastric cancer is one of the most common malignant tumors in China, and main traditional treatments are surgery and chemotherapy. However, since the majority of cases of gastric cancer are diagnosed in the late stage, the best chance of operation has been missed. What's more, some cases are not sensitive to chemotherapy. Therefore, the management of metastasis and spread of gastric cancer is a big challenge. With the development of medical molecular biology, more and more signaling pathways have been elucidated. Blocking these signaling pathways may reverse cancer occurrence and development, improve the sensitivity of gastric cancer cells to chemotherapy, and prevent cancer cell metastasis. This article reviews the signaling pathways closely related to gastric cancer, such as the mitogen-activated protein kinase pathway, PI3K-Akt-mTOR pathway, AMPK pathway, NF-kappa B-COX-2 pathway and HNF4a-Wnt pathway, with an aim to provide new clues to the clinical treatment of this malignancy.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Clinical Relevance and Molecular Phenotypes in Gastric Cancer, of TP53 Mutations and Gene Expressions, in Combination With Other Gene Mutations

While altered TP53 is the most frequent mutation in gastric cancer (GC), its association with molecular or clinical phenotypes (e.g., overall survival, disease-free survival) remains little known. To that end, we can use genome-wide approaches to identify altered genes significantly related to mutated TP53. Here, we identified significant differences in clinical outcomes, as well as in molecular phenotypes, across specific GC tumor subpopulations, when combining TP53 with other signaling networks, including WNT and its related genes NRXN1, CTNNB1, SLITRK5, NCOR2, RYR1, GPR112, MLL3, MTUS2, and MYH6. Moreover, specific GC subpopulations indicated by dual mutation of NRXN1 and TP53 suggest different drug responses, according to the Connectivity Map, a pharmacological drug-gene association tool. Overall, TP53 mutation status in GC is significantly relevant to clinical or molecular categories. Thus, our approach can potentially provide a patient stratification strategy by dissecting previously unknown multiple TP53-mutated patient groups.

DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases.

AVAILABILITY AND IMPLEMENTATION

DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: tassos.bezerianos@nus.edu.sgSupplementary information: Supplementary data are available at Bioinformatics online.

Signal Transduction Pathways of EMT Induced by TGF-β, SHH, and WNT and Their Crosstalks

Epithelial-to-mesenchymal transition (EMT) is a key step in development, wound healing, and cancer development. It involves cooperation of signaling pathways, such as transformation growth factor-β (TGF-β), Sonic Hedgehog (SHH), and WNT pathways. These signaling pathways crosstalk to each other and converge to key transcription factors (e.g., SNAIL1) to initialize and maintain the process of EMT. The functional roles of multi-signaling pathway crosstalks in EMT are sophisticated and, thus, remain to be explored. In this review, we focused on three major signal transduction pathways that promote or regulate EMT in carcinoma. We discussed the network structures, and provided a brief overview of the current therapy strategies and drug development targeted to these three signal transduction pathways. Finally, we highlighted systems biology approaches that can accelerate the process of deconstructing complex networks and drug discovery.

Improving gastric cancer preclinical studies using diverse in vitro and in vivo model systems

BACKGROUND

"Biomarker-driven targeted therapy," the practice of tailoring patients' treatment to the expression/activity levels of disease-specific genes/proteins, remains challenging. For example, while the anti-ERBB2 monoclonal antibody, trastuzumab, was first developed using well-characterized, diverse in vitro breast cancer models (and is now a standard adjuvant therapy for ERBB2-positive breast cancer patients), trastuzumab approval for ERBB2-positive gastric cancer was largely based on preclinical studies of a single cell line, NCI-N87. Ensuing clinical trials revealed only modest patient efficacy, and many ERBB2-positive gastric cancer (GC) patients failed to respond at all (i.e., were inherently recalcitrant), or succumbed to acquired resistance.

METHOD

To assess mechanisms underlying GC insensitivity to ERBB2 therapies, we established a diverse panel of GC cells, differing in ERBB2 expression levels, for comprehensive in vitro and in vivo characterization. For higher throughput assays of ERBB2 DNA and protein levels, we compared the concordance of various laboratory quantification methods, including those of in vitro and in vivo genetic anomalies (FISH and SISH) and xenograft protein expression (Western blot vs. IHC), of both cell and xenograft (tissue-sectioned) microarrays.

RESULTS

The biomarker assessment methods strongly agreed, as did correlation between RNA and protein expression. However, although ERBB2 genomic anomalies showed good in vitro vs. in vivo correlation, we observed striking differences in protein expression between cultured cells and mouse xenografts (even within the same GC cell type). Via our unique pathway analysis, we delineated a signaling network, in addition to specific pathways/biological processes, emanating from the ERBB2 signaling cascade, as a potential useful target of clinical treatment. Integrated analysis of public data from gastric tumors revealed frequent (10 - 20 %) amplification of the genes NFKBIE, PTK2, and PIK3CA, each of which resides in an ERBB2-derived subpathway network.

CONCLUSION

Our comprehensive bioinformatics analyses of highly heterogeneous cancer cells, combined with tumor "omics" profiles, can optimally characterize the expression patterns and activity of specific tumor biomarkers. Subsequent in vitro and in vivo validation, of specific disease biomarkers (using multiple methodologies), can improve prediction of patient stratification according to drug response or nonresponse.

SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment

Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

HNF4α is a therapeutic target that links AMPK to WNT signalling in early-stage gastric cancer

BACKGROUND

Worldwide, gastric cancer (GC) is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects.

OBJECTIVE

To determine oncogenic mechanisms and novel therapeutic targets specific for GC by identifying commonly dysregulated genes from the tumours of both Asian-Pacific and Caucasian patients.

METHODS

We generated transcriptomic profiles of 22 Caucasian GC tumours and their matched non-cancerous samples and performed an integrative analysis across different GC gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signalling pathways by RNAi and/or pharmacological inhibition.

RESULTS

Hepatocyte nuclear factor-4α (HNF4α) upregulation was a key signalling event in gastric tumours from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumour cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signalling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest and tumour growth inhibition. HNF4α also regulated WNT signalling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumours.

CONCLUSIONS

Our results indicate that HNF4α is a targetable oncoprotein in GC, is regulated by AMPK signalling through AMPKα and resides upstream of WNT signalling. HNF4α may regulate 'metabolic switch' characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signalling cascade represents a potentially targetable pathway for drug development.

CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis

MOTIVATION

In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time.

RESULTS

To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways.

AVAILABILITY AND IMPLEMENTATION

CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/

CONTACT

tassos.bezerianos@nus.edu.sg

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network Comparison of Inflammation in Colorectal Cancer and Alzheimer's Disease

Recently, a large clinical study revealed an inverse correlation of individual risk of cancer versus Alzheimer's disease (AD). However, no explanation exists for this anticorrelation at the molecular level; however, inflammation is crucial to the pathogenesis of both diseases, necessitating a need to understand differing signaling usage during inflammatory responses distinct to both diseases. Using a subpathway analysis approach, we identified numerous well-known and previously unknown pathways enriched in datasets from both diseases. Here, we present the quantitative importance of the inflammatory response in the two disease pathologies and summarize signal transduction pathways common to both diseases that are affected by inflammation.

Integrating Microarray Data and GRNs

With the completion of the Human Genome Project and the emergence of high-throughput technologies, a vast amount of molecular and biological data are being produced. Two of the most important and significant data sources come from microarray gene-expression experiments and respective databanks (e,g., Gene Expression Omnibus-GEO (http://www.ncbi.nlm.nih.gov/geo)), and from molecular pathways and Gene Regulatory Networks (GRNs) stored and curated in public (e.g., Kyoto Encyclopedia of Genes and Genomes-KEGG (http://www.genome.jp/kegg/pathway.html), Reactome (http://www.reactome.org/ReactomeGWT/entrypoint.html)) as well as in commercial repositories (e.g., Ingenuity IPA (http://www.ingenuity.com/products/ipa)). The association of these two sources aims to give new insight in disease understanding and reveal new molecular targets in the treatment of specific phenotypes.Three major research lines and respective efforts that try to utilize and combine data from both of these sources could be identified, namely: (1) de novo reconstruction of GRNs, (2) identification of Gene-signatures, and (3) identification of differentially expressed GRN functional paths (i.e., sub-GRN paths that distinguish between different phenotypes). In this chapter, we give an overview of the existing methods that support the different types of gene-expression and GRN integration with a focus on methodologies that aim to identify phenotype-discriminant GRNs or subnetworks, and we also present our methodology.

A pathway-based approach for identifying biomarkers of tumor progression to trastuzumab-resistant breast cancer

Although trastuzumab is a successful targeted therapy for breast cancer patients with tumors expressing HER2 (ERBB2), many patients eventually progress to drug resistance. Here, we identified subpathways differentially expressed between trastuzumab-resistant vs. -sensitive breast cancer cells, in conjunction with additional transcriptomic preclinical and clinical gene datasets, to rigorously identify overexpressed, resistance-associated genes. From this approach, we identified 32 genes reproducibly upregulated in trastuzumab resistance. 25 genes were upregulated in drug-resistant JIMT-1 cells, which also downregulated HER2 protein by >80% in the presence of trastuzumab. 24 genes were downregulated in trastuzumab-sensitive SKBR3 cells. Trastuzumab sensitivity was restored by siRNA knockdown of these genes in the resistant cells, and overexpression of 5 of the 25 genes was found in at least one of five refractory HER2 + breast cancer. In summary, our rigorous computational approach, followed by experimental validation, significantly implicate ATF4, CHEK2, ENAH, ICOSLG, and RAD51 as potential biomarkers of trastuzumab resistance. These results provide further proof-of-concept of our methodology for successfully identifying potential biomarkers and druggable signal pathways involved in tumor progression to drug resistance.