Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling

Influence of batch effect correction methods on drug induced differential gene expression profiles

Background

Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.

Results

Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.

Conclusion

Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3028-6) contains supplementary material, which is available to authorized users.

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm

MOTIVATION

Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.

RESULTS

Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles

Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.

Differential Expression of Prostaglandin I2 Synthase Associated with Arachidonic Acid Pathway in the Oral Squamous Cell Carcinoma

Introduction

Differential expression of genes encoding cytochrome P450 (CYP) and other oxygenases enzymes involved in biotransformation mechanisms of endogenous and exogenous compounds can lead to oral tumor development.

Objective

We aimed to identify the expression profile of these genes, searching for susceptibility biomarkers in oral squamous cell carcinoma.

Patients and Methods

Sixteen oral squamous cell carcinoma samples were included in this study (eight tumor and eight adjacent non-tumor tissues). Gene expression quantification was performed using TaqMan Array Human CYP450 and other Oxygenases 96-well plate (Applied Biosystems) by real time qPCR. Protein quantification was performed by ELISA and IHC methods. Bioinformatics tools were used to find metabolic pathways related to the enzymes encoded by differentially expressed genes. Results. CYP27B1, CYP27A1, CYP2E1, CYP2R1, CYP2J2, CYP2U1, CYP4F12, CYP4X1, CYP4B1, PTGIS, ALOX12, and MAOB genes presented differential expression in the oral tumors. After correction by multiple tests, only the PTGIS (Prostaglandin I2 Synthase) gene presented significant differential expression (P < 0.05). The PTGIS gene and protein were reduced in oral tumors.

Conclusion

PTGIS presents downexpression in oral tumors. PTGIS play an important role in the arachidonic acid metabolism. Arachidonic acid and/or metabolites are derived from this pathway, which can influence the regulation of important physiological mechanisms in tumorigenesis process.

Effects of selective serotonin-reuptake inhibitors (SSRIs) in JEG-3 and HIPEC cell models of the extravillous trophoblast

INTRODUCTION

Between 2 and 10% of pregnant women are treated with selective serotonin-reuptake inhibitors (SSRIs) for depression. The extravillous trophoblasts (evTBs), which migrate and invade maternal tissues, are crucial for embryo implantation and remodeling of maternal spiral arteries. Poor migration/invasion of evTBs can cause serious pregnancy complications, yet the effects of SSRIs on these processes has never been studied. To determine the effects of five SSRIs (fluoxetine, norfluoxetine, citalopram, sertraline and venlafaxine) on migration/invasion, we used JEG-3 and HIPEC cells as evTB models.

METHODS

Cells were treated with increasing concentrations (0.03-10 μM) of SSRIs. Cell proliferation was monitored using an impedance-based system and cell cycle by flow cytometry. Migration was determined using a scratch test, and metalloproteinase (MMP) activities, by zymography. Invasion markers were determined by RT-qPCR.

RESULTS

Fluoxetine and sertraline (10 μM) significantly decreased cell proliferation by 94% and by 100%, respectively, in JEG-3 cells, and by 58.6% and 100%, respectively, in HIPEC cells. Norfluoxetine increased MMP-9 activity in JEG-3 cells by 2.0% at 0.03 μM and by 43.9% at 3 μM, but decreased MMP-9 activity in HIPEC cells by 63.7% at 3 μM. Sertraline at 0.03 μM increased mRNA level of TIMP-1 in JEG-3 cells by 36% and that of ADAM-10 by 85% and 115% at 0.3 and 3 μM, respectively. In HIPEC cells, venlafaxine at 0.03 and 0.3 μM, increased ADAM-10 mRNA levels by 156% and 167%, respectively.

DISCUSSION

This study shows that SSRIs may affect evTBs homeostasis at therapeutic levels and provides guidance for future research.

Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation

Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.

Assessment of citalopram and escitalopram on neuroblastoma cell lines. Cell toxicity and gene modulation

Selective serotonin reuptake inhibitors (SSRI) are common antidepressants which cytotoxicity has been assessed in cancers notably colorectal carcinomas and glioma cell lines. We assessed and compared the cytotoxicity of 2 SSRI, citalopram and escitalopram, on neuroblastoma cell lines. The study was performed on 2 non-MYCN amplified cell lines (rat B104 and human SH-SY5Y) and 2 human MYCN amplified cell lines (IMR32 and Kelly). Citalopram and escitalopram showed concentration-dependent cytotoxicity on all cell lines. Citalopram was more cytotoxic than escitalopram. IMR32 was the most sensitive cell line. The absence of toxicity on human primary Schwann cells demonstrated the safety of both molecules for myelin. The mechanisms of cytotoxicity were explored using gene-expression profiles and quantitative real-time PCR (qPCR). Citalopram modulated 1 502 genes and escitalopram 1 164 genes with a fold change ≥ 2. 1 021 genes were modulated by both citalopram and escitalopram; 481 genes were regulated only by citalopram while 143 genes were regulated only by escitalopram. Citalopram modulated 69 pathways (KEGG) and escitalopram 42. Ten pathways were differently modulated by citalopram and escitalopram. Citalopram drastically decreased the expression of MYBL2, BIRC5 and BARD1 poor prognosis factors of neuroblastoma with fold-changes of -107 (p<2.26 10⁻⁷), -24.1 (p<5.6 10⁻⁹) and -17.7 (p<1.2 10⁻⁷). CCNE1, AURKA, IGF2, MYCN and ERBB2 were more moderately down-regulated by both molecules. Glioma markers E2F1, DAPK1 and CCND1 were down-regulated. Citalopram displayed more powerful action with broader and distinct spectrum of action than escitalopram.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy

Motivation

Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods.

Results

We first designed an expression weighted cosine (EWCos) method to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed ensemble of multiple drug repositioning approaches (EMUDRA) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs.

Availability and implementation

The EMUDRA R package is available at doi: 10.7303/syn11510888.

Supplementary information

Supplementary data are available at Bioinformatics online.

Circulating and disseminated tumor cells: diagnostic tools and therapeutic targets in motion

Enumeration of circulating tumor cells (CTCs) in peripheral blood with the gold standard CellSearchTM has proven prognostic value for tumor recurrence and progression of metastatic disease. Therefore, the further molecular characterization of isolated CTCs might have clinical relevance as liquid biopsy for therapeutic decision-making and to monitor disease progression. The direct analysis of systemic cancer appears particularly important in view of the known disparity in expression of therapeutic targets as well as epithelial-to-mesenchymal transition (EMT)-based heterogeneity between primary and systemic tumor cells, which all substantially complicate monitoring and therapeutic targeting at present. Since CTCs are the potential precursor cells of metastasis, their in-depth molecular profiling should also provide a useful resource for target discovery. The present review will discuss the use of systemically spread cancer cells as liquid biopsy and focus on potential target antigens.

Circulating Tumor Cells

In most solid tumors, it is distant metastases rather than the primary tumor which limit the prognosis. Distant metastases are caused by circulating tumor cells (CTCs) which actively invade the blood stream, attach to the endothelium in the target organ, invade the surrounding parenchyma, and form new tumors. Among many other capabilities such as migration or immune escape, CTCs require tumor-forming capacities and can therefore be considered stem cell-like cells. This chapter describes the enrichment and isolation of live CTCs from clinical blood samples for molecular characterization and other downstream applications.

Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference

Background: A range of computational methods that rely on the analysis of genome-wide expression datasets have been developed and successfully used for drug repositioning. The success of these methods is based on the hypothesis that introducing a factor (in this case, a drug molecule) that could reverse the disease gene expression signature will lead to a therapeutic effect. However, it has also been shown that globally reversing the disease expression signature is not a prerequisite for drug activity. On the other hand, the basic idea of significant anti-correlation in expression profiles could have great value for establishing diet-disease associations and could provide new insights into the role of dietary interventions in disease. Methods: We performed an integrated analysis of publicly available gene expression profiles for foods, diseases and drugs, by calculating pairwise similarity scores for diet and disease gene expression signatures and characterizing their topological features in protein-protein interaction networks. Results: We identified 485 diet-disease pairs where diet could positively influence disease development and 472 pairs where specific diets should be avoided in a disease state. Multiple evidence suggests that orange, whey and coconut fat could be beneficial for psoriasis, lung adenocarcinoma and macular degeneration, respectively. On the other hand, fructose-rich diet should be restricted in patients with chronic intermittent hypoxia and ovarian cancer. Since humans normally do not consume foods in isolation, we also applied different algorithms to predict synergism; as a result, 58 food pairs were predicted. Interestingly, the diets identified as anti-correlated with diseases showed a topological proximity to the disease proteins similar to that of the corresponding drugs. Conclusions: In conclusion, we provide a computational framework for establishing diet-disease associations and additional information on the role of diet in disease development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations.

Isolation of Circulating Tumor Cells in an Orthotopic Mouse Model of Colorectal Cancer

Despite the advantages of easy applicability and cost-effectiveness, subcutaneous mouse models have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Orthotopic mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in hollow organs such as the large bowel. In order to produce uniform tumors which reliably grow and metastasize, standardized techniques of tumor cell preparation and injection are critical. We have developed an orthotopic mouse model of colorectal cancer (CRC) which develops highly uniform tumors and can be used for tumor biology studies as well as therapeutic trials. Tumor cells from either primary tumors, 2-dimensional (2D) cell lines or 3-dimensional (3D) organoids are injected into the cecum and, depending on the metastatic potential of the injected tumor cells, form highly metastatic tumors. In addition, CTCs can be found regularly. We here describe the technique of tumor cell preparation from both 2D cell lines and 3D organoids as well as primary tumor tissue, the surgical and injection techniques as well as the isolation of CTCs from the tumor-bearing mice, and present tips for troubleshooting.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer

Despite the advantages of easy applicability and cost-effectiveness, colorectal cancer mouse models based on tumor cell injection have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Genetically engineered mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in large organs such as the colon in which only a single tumor is desired. As a result, an immunocompetent, genetically engineered mouse model of colorectal cancer was developed which develops highly uniform tumors and can be used for tumor biology studies as well as therapeutic trials. Tumor development is initiated by surgical, segmental infection of the distal colon with adeno-cre virus in compound conditionally mutant mice. The tumors can be easily detected and monitored via colonoscopy. We here describe the surgical technique of segmental adeno-cre infection of the colon, the surveillance of the tumor via high-resolution colonoscopy and present the resulting colorectal tumors.

Computational Discovery of Niclosamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37 Signaling

BACKGROUND & AIMS

Drug repositioning offers a shorter approval process than new drug development. We therefore searched large public datasets of drug-induced gene expression signatures to identify agents that might be effective against hepatocellular carcinoma (HCC).

METHODS

We searched public databases of messenger RNA expression patterns reported from HCC specimens from patients, HCC cell lines, and cells exposed to various drugs. We identified drugs that might specifically increase expression of genes that are down-regulated in HCCs and reduce expression of genes up-regulated in HCCs using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. We evaluated the anti-tumor activity of niclosamide and its ethanolamine salt (NEN) in HCC cell lines (HepG2, Huh7, Hep3B, Hep40, and PLC/PRF/5), primary human hepatocytes, and 2 mouse models of HCC. In one model of HCC, liver tumor development was induced by hydrodynamic delivery of a sleeping beauty transposon expressing an activated form of Ras (v12) and truncated β-catenin (N90). In another mouse model, patient-derived xenografts were established by implanting HCC cells from patients into livers of immunocompromised mice. Tumor growth was monitored by bioluminescence imaging. Tumor-bearing mice were fed a regular chow diet or a chow diet containing niclosamide or NEN. In a separate experiment using patient-derived xenografts, tumor-bearing mice were given sorafenib (the standard of care for patients with advanced HCC), NEN, or niclosamide alone; a combination of sorafenib and NEN; or a combination sorafenib and niclosamide in their drinking water, or regular water (control), and tumor growth was monitored.

RESULTS

Based on gene expression signatures, we identified 3 anthelmintics that significantly altered the expression of genes that are up- or down-regulated in HCCs. Niclosamide and NEN specifically reduced the viability of HCC cells: the agents were at least 7-fold more cytotoxic to HCCs than primary hepatocytes. Oral administration of NEN to mice significantly slowed growth of genetically induced liver tumors and patient-derived xenografts, whereas niclosamide did not, coinciding with the observed greater bioavailability of NEN compared with niclosamide. The combination of NEN and sorafenib was more effective at slowing growth of patient-derived xenografts than either agent alone. In HepG2 cells and in patient-derived xenografts, administration of niclosamide or NEN increased expression of 20 genes down-regulated in HCC and reduced expression of 29 genes up-regulated in the 274-gene HCC signature. Administration of NEN to mice with patient-derived xenografts reduced expression of proteins in the Wnt-β-catenin, signal transducer and activator of transcription 3, AKT-mechanistic target of rapamycin, epidermal growth factor receptor-Ras-Raf signaling pathways. Using immunoprecipitation assays, we found NEN to bind cell division cycle 37 protein and disrupt its interaction with heat shock protein 90.

CONCLUSIONS

In a bioinformatics search for agents that alter the HCC-specific gene expression pattern, we identified the anthelmintic niclosamide as a potential anti-tumor agent. Its ethanolamine salt, with greater bioavailability, was more effective than niclosamide at slowing the growth of genetically induced liver tumors and patient-derived xenografts in mice. Both agents disrupted interaction between cell division cycle 37 and heat shock protein 90 in HCC cells, with concomitant inhibition of their downstream signaling pathways. NEN might be effective for treatment of patients with HCC.

Chronic therapy with selective serotonin reuptake inhibitors and survival in newly diagnosed cancer patients

Depression might be associated with shorter disease specific survival. Selective serotonin reuptake inhibitors (SSRIs) were previously reported to increase the risk of certain malignancies. We aimed to evaluate the impact of SSRIs on cancer mortality. Five retrospective cohort studies were conducted in a UK population-representative database that included all individuals with an incident diagnosis of melanoma, breast, prostate lung and colorectal cancer. The primary exposure of interest was continuous use of SSRIs with past use as the comparison reference. Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CI). The study included 5,591 newly diagnosed cancer patients. Continuous SSRI use was associated with lower survival compared to past users for melanoma, breast, prostate, lung and colorectal cancers with HRs for the risk of death of 2.02 (95% CI 1.24-3.28), 1.91 (95% CI 1.53-2.38), 1.79 (95% CI 1.38-2.33), 1.44 (95% CI 1.19-1.75) and 1.51 (95% CI 1.21-1.72) respectively. The incidence of death during the first 2 years following cancer diagnosis associated with continuous SSRI use were elevated for breast (1.72, 95% CI 1.30-2.27), prostate (1.64, 95% CI 1.20-2.24) and lung cancers (1.45, 95% CI 1.26-1.66). In conclusion, continuous use of SSRIs might be associated with lower survival in cancer patients.

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.

An integrated network platform for contextual prioritization of drugs and pathways

Repurposing of drugs to novel disease indications has promise for faster clinical translation. However, identifying the best drugs for a given pathological context is not trivial. We developed an integrated random walk-based network framework that combines functional biomolecular relationships and known drug-target interactions as a platform for contextual prioritization of drugs, genes and pathways. We show that the use of gene-centric or drug-centric data, such as gene expression data or a phenotypic drug screen, respectively, within this network platform can effectively prioritize drugs and pathways, respectively, to the studied biological context. We demonstrate that various genomic data can be used as contextual cues to effectively prioritize drugs to the studied context, while similarly, phenotypic drug screen data can be used to effectively prioritize genes and pathways to the studied phenotypic context. As a proof-of-principle, we showcase the use of our platform to identify known and novel drug indications against different subsets of breast cancers through contextual prioritization based on genome-wide gene expression, shRNA and drug screen and clinical survival data. The integrated network and associated methods are incorporated into the NetWalker suite for functional genomics analysis ().

Leveraging big data to transform target selection and drug discovery

The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine.

Anti-depressant therapy and cancer risk: a nested case-control study

Previous studies demonstrated a possible association between anti-depressant therapy with selective serotonin reuptake inhibitors (SSRI) and tricyclic anti-depressants (TCA), several genetic and hormonal pathways and cancer risk, with inconsistent results. Exposure to serotonin-norepinephrine reuptake inhibitors (SNRI) was not studied extensively. We sought to evaluate the association between exposure to SSRIs, TCAs and SNRIs and the five most common solid tumors. We conducted nested case-control studies using a large UK population-representative database. Cases were those with any medical code for the specific malignancy. For every case, four controls matched on age, sex, practice site, and duration of follow-up before index date were selected using incidence-density sampling. Exposure of interest was SSRI, SNRI or TCA therapy before index date. Odds ratios (ORs) and 95% CIs were estimated for each anti-depressant class using conditional logistic-regression analysis, adjusted for potential confounders, such as obesity, smoking history and alcohol consumption.

RESULTS

109,096 cancer patients and 426,402 matched controls were included. Current SSRI users with treatment initiation>one year before index date had modestly higher risk for lung and breast cancers with ORs of 1.27 (95% CI 1.16-1.38) and 1.12 (95% CI 1.06-1.18), respectively. Among current TCA users, there was a higher risk only for lung cancers with OR of 1.45 (95% CI 1.31-1.6). There was no statistically significant association between current SNRI therapy and cancer risk.

DISCUSSION

Treatment with SSRI and TCA might be associated with increased lung cancer risk. SSRI therapy might be associated with modest increase in breast cancer risk.

Big data for health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates

Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time-to-event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.

A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles

Background

Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis.

Results

The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity.

Conclusions

The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.

Discovery and validation of the antimetastatic activity of citalopram in colorectal cancer

Inverse gene expression profiling was recently shown to help drug repositioning. We showed that this approach works best for cancer and predicted novel drug candidates that may reduce metastasis in colorectal cancer. Antimetastatic activity of our predicted candidate, citalopram, was validated in an orthotopic mouse model of metastatic colorectal cancer.

Optimal drug prediction from personal genomics profiles

Cancer patients often show heterogeneous drug responses such that only a small subset of patients is sensitive to a given anticancer drug. With the availability of large-scale genomic profiling via next-generation sequencing, it is now economically feasible to profile the whole transcriptome and genome of individual patients in order to identify their unique genetic mutations and differentially expressed genes, which are believed to be responsible for heterogeneous drug responses. Although subtyping analysis has identified patient subgroups sharing common biomarkers, there is no effective method to predict the drug response of individual patients precisely and reliably. Herein, we propose a novel computational algorithm to predict the drug response of individual patients based on personal genomic profiles, as well as pharmacogenomic and drug sensitivity data. Specifically, more than 600 cancer cell lines (viewed as individual patients) across over 50 types of cancers and their responses to 75 drugs were obtained from the genomics of drug sensitivity in cancer database. The drug-specific sensitivity signatures were determined from the changes in genomic profiles of individual cell lines in response to a specific drug. The optimal drugs for individual cell lines were predicted by integrating the votes from other cell lines. The experimental results show that the proposed drug prediction algorithm can be used to improve greatly the reliability of finding optimal drugs for individual patients and will, thus, form a key component in the precision medicine infrastructure for oncology care.