Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Combining multiomics and drug perturbation profiles to identify muscle-specific treatments for spinal muscular atrophy

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by loss of survival motor neuron (SMN) protein. While SMN restoration therapies are beneficial, they are not a cure. We aimed to identify potentially novel treatments to alleviate muscle pathology combining transcriptomics, proteomics, and perturbational data sets. This revealed potential drug candidates for repurposing in SMA. One of the candidates, harmine, was further investigated in cell and animal models, improving multiple disease phenotypes, including lifespan, weight, and key molecular networks in skeletal muscle. Our work highlights the potential of multiple and parallel data-driven approaches for the development of potentially novel treatments for use in combination with SMN restoration therapies.

Transcriptome Signature Reversion as a Method to Reposition Drugs Against Cancer for Precision Oncology

Transcriptome signature reversion (TSR) has been hypothesized as a promising method for discovery and use of existing noncancer drugs as potential drugs in the treatment of cancer (i.e., drug repositioning, drug repurposing). The TSR assumes that drugs with the ability to revert the gene expression associated with a diseased state back to its healthy state are potentially therapeutic candidates for that disease. This article reviews methodology of TSR and critically discusses key TSR studies. In addition, potential conceptual and computational improvements of this novel methodology are discussed as well as its current and possible future application in precision oncology trials.

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Systems Biology Approaches to Investigate Genetic and Epigenetic Molecular Progression Mechanisms for Identifying Gene Expression Signatures in Papillary Thyroid Cancer

Thyroid cancer is the most common endocrine cancer. Particularly, papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. Up to now, there are few researches discussing the pathogenesis and progression mechanisms of PTC from the viewpoint of systems biology approaches. In this study, first we constructed the candidate genetic and epigenetic network (GEN) consisting of candidate protein–protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big database mining. Secondly, system identification and system order detection methods were applied to prune candidate GEN via next-generation sequencing (NGS) and DNA methylation profiles to obtain the real GEN. After that, we extracted core GENs from real GENs by the principal network projection (PNP) method. To investigate the pathogenic and progression mechanisms in each stage of PTC, core GEN was denoted in respect of KEGG pathways. Finally, by comparing two successive core signaling pathways of PTC, we not only shed light on the causes of PTC progression, but also identified essential biomarkers with specific gene expression signature. Moreover, based on the identified gene expression signature, we suggested potential candidate drugs to prevent the progression of PTC with querying Connectivity Map (CMap).

Gene expression profile-based drug screen identifies SAHA as a novel treatment for NAFLD

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide.

Knockdown of B7H6 inhibits tumor progression in triple-negative breast cancer

The B7 family, the most common family of secondary signaling molecules, consists of eight cell-surface proteins, which regulate the T-cell mediated immune response by delivering co-inhibitory or co-stimulatory signals through their corresponding ligands. Among them, natural killer cell cytotoxicity receptor 3 ligand 1 (NCR3LG1, also known as B7H6) has been reported as a new member, and is involved in tumor progression of various types of human cancer. However, the role of B7H6 in triple-negative breast cancer (TNBC) remains unknown. In the present study, western blotting was performed to determine the protein expression levels of B7H6 in a normal mammary epithelial cell line (MCF-10A), non-TNBC breast cancer cell lines (MCF-7 and AU565) and TNBC cell lines (MDA-MB-231 and MDA-MB-468). B7H6 was knocked down using small interfering RNA, and an MTT assay was performed to determine proliferation ability, flow cytometry was used to analyze apoptosis, and Transwell and wound-healing assays were performed to measure migration ability. Expression of proliferation-associated proteins (SMAD family member 4 and β-catenin) and apoptosis-associated proteins (BCL2 associated X, BCL2 apoptosis regulator and caspase-3) were analyzed by western blotting. The results demonstrated that B7H6 was highly expressed in TNBC cells, and that knockdown of B7H6 inhibited cell proliferation and migration, and promoted apoptosis. Furthermore, the results revealed that proliferation and apoptosis-associated proteins were altered in the B7H6-knockdown MDA-MB-231 cells. In conclusion, the present study demonstrated that B7H6 may have significant roles in the regulation of cell proliferation, apoptosis and migration of TNBC cells.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Using the Connectivity Map to discover compounds influencing human osteoblast differentiation

Osteoporosis is a common skeletal disorder characterized by low bone mass leading to increased bone fragility and fracture susceptibility. Identification of factors influencing osteoblast differentiation and bone formation is very important. Previously, we identified parbendazole to be a novel compound that stimulates osteogenic differentiation of human mesenchymal stromal cells (hMSCs), using gene expression profiling and bioinformatic analyzes, including the Connectivity Map (CMap), as an in-silico approach. The aim for this paper is to identify additional compounds affecting osteoblast differentiation using the CMap. Gene expression profiling was performed on hMSCs differentiated to osteoblasts using Illumina microarrays. Our osteoblast gene signature, the top regulated genes 6 hr after induction by dexamethasone, was uploaded into CMap (www.broadinstitute.org/cmap/). Through this approach we identified compounds with gene signatures positively correlating (withaferin-A, calcium folinate, amylocaine) or negatively correlating (salbutamol, metaraminol, diprophylline) to our osteoblast gene signature. All positively correlating compounds stimulated osteogenic differentiation, as indicated by increased mineralization compared to control treated cells. One of three negatively correlating compounds, salbutamol, inhibited dexamethasone-induced osteoblastic differentiation, while the other two had no effect. Based on gene expression data of withaferin-A and salbutamol, we identified HMOX1 and STC1 as being strongly differentially expressed . shRNA knockdown of HMOX1 or STC1 in hMSCs inhibited osteoblast differentiation. These results confirm that the CMap is a powerful approach to identify positively compounds that stimulate osteogenesis of hMSCs, and through this approach we can identify genes that play an important role in osteoblast differentiation and could be targets for novel bone anabolic therapies.

Long Non-Coding RNAs Serve as Diagnostic Biomarkers of Preeclampsia and Modulate Migration and Invasiveness of Trophoblast Cells

Background

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of tumor progression. However, the effects of lncRNAs in preeclampsia are not entirely clear. The aim of this study was to demonstrate the potential of lncRNAs to serve as biomarkers in preeclampsia.

Material/Methods

The RNA expression levels of lncRNAs (NR_027457, BC030099, AF037219, NR_024178, AF085938, G43016, G36948, NR_029420, NR_024015, AK002210, NR_026643, and AL049277) in the serum of patients with preeclampsia and in the serum of normal controls were measured by qRT-PCR. The area under the curve (AUC), the optimal cut-off values, the specificity, and the sensitivity of NR_027457, AF085938, G36948, and AK002210 were analyzed by receiver operating characteristic (ROC) curve analysis. We designed RNA interference species to suppress NR_027457 and G36948 and identified the roles of NR_027457 and G36948 in the functions of a trophoblast cell line (HTR-8/SVneo).

Results

The qRT-PCR results indicated that NR_027457 and AF085938 were significantly up-regulated, whereas G36948 and AK002210 were significantly down-regulated in preeclampsia. We found that NR_027457, AF085938, G36948, and AK002210 had potential diagnostic value for the detection of preeclampsia. Furthermore, the levels of NR_027457, AF085938, G36948, and AK002210 in the serum of patients were significantly different before vs. after surgery. The silencing of NR_027457 inhibited the proliferation, migration, and invasion abilities of HTR-8/SVneo cells, while the silencing of G36948 promoted the proliferation, migration, and invasion abilities of HTR-8/SVneo cells.

Conclusions

NR_027457, AF085938, G36948, and AK002210 can serve as potential diagnostic biomarkers in preeclampsia, and NR_027457 and G36948 might be involved in the pathogenesis of preeclampsia.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer

Background

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients.

Methods

We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines.

Results

Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability.

Conclusions

Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.

Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Connectivity Map-based discovery of parbendazole reveals targetable human osteogenic pathway

Osteoporosis is a common skeletal disorder characterized by low bone mass leading to increased bone fragility and fracture susceptibility. In this study, we have identified pathways that stimulate differentiation of bone forming osteoblasts from human mesenchymal stromal cells (hMSCs). Gene expression profiling was performed in hMSCs differentiated toward osteoblasts (at 6 h). Significantly regulated genes were analyzed in silico, and the Connectivity Map (CMap) was used to identify candidate bone stimulatory compounds. The signature of parbendazole matches the expression changes observed for osteogenic hMSCs. Parbendazole stimulates osteoblast differentiation as indicated by increased alkaline phosphatase activity, mineralization, and up-regulation of bone marker genes (alkaline phosphatase/ALPL, osteopontin/SPP1, and bone sialoprotein II/IBSP) in a subset of the hMSC population resistant to the apoptotic effects of parbendazole. These osteogenic effects are independent of glucocorticoids because parbendazole does not up-regulate glucocorticoid receptor (GR) target genes and is not inhibited by the GR antagonist mifepristone. Parbendazole causes profound cytoskeletal changes including degradation of microtubules and increased focal adhesions. Stabilization of microtubules by pretreatment with Taxol inhibits osteoblast differentiation. Parbendazole up-regulates bone morphogenetic protein 2 (BMP-2) gene expression and activity. Cotreatment with the BMP-2 antagonist DMH1 limits, but does not block, parbendazole-induced mineralization. Using the CMap we have identified a previously unidentified lineage-specific, bone anabolic compound, parbendazole, which induces osteogenic differentiation through a combination of cytoskeletal changes and increased BMP-2 activity.

Expression Profiling Identifies Bezafibrate as Potential Therapeutic Drug for Lung Adenocarcinoma

Drug-induced gene expression patterns that invert disease profiles have recently been illustrated to be a new strategy for drug-repositioning. In the present study, we validated this approach and focused on prediction of novel drugs for lung adenocarcinoma (AC), for which there is a pressing need to find novel therapeutic compounds. Firstly, connectivity map (CMap) analysis computationally predicted bezafibrate as a putative compound against lung AC. Then this hypothesis was verified by in vitro assays of anti-proliferation and cell cycle arrest. In silico docking evidence indicated that bezafibrate could target cyclin dependent kinase 2(CDK2), which regulates progression through the cell cycle. Furthermore, we found that bezafibrate can significantly down-regulate the expression of CDK2 mRNA and p-CDK2. Using a nude mice xenograft model, we also found that bezafibrate could inhibit tumor growth of lung AC in vivo. In conclusion, this study proposed bezafibrate as a potential therapeutic option for lung AC patients, illustrating the potential of in silico drug screening.

Identification of alsterpaullone as a novel small molecule inhibitor to target group 3 medulloblastoma

Advances in the molecular biology of medulloblastoma revealed four genetically and clinically distinct subgroups. Group 3 medulloblastomas are characterized by frequent amplifications of the oncogene MYC, a high incidence of metastasis, and poor prognosis despite aggressive therapy. We investigated several potential small molecule inhibitors to target Group 3 medulloblastomas based on gene expression data using an in silico drug screen. The Connectivity Map (C-MAP) analysis identified piperlongumine as the top candidate drug for non-WNT medulloblastomas and the cyclin-dependent kinase (CDK) inhibitor alsterpaullone as the compound predicted to have specific antitumor activity against Group 3 medulloblastomas. To validate our findings we used these inhibitors against established Group 3 medulloblastoma cell lines. The C-MAP predicted drugs reduced cell proliferation in vitro and increased survival in Group 3 medulloblastoma xenografts. Alsterpaullone had the highest efficacy in Group 3 medulloblastoma cells. Genomic profiling of Group 3 medulloblastoma cells treated with alsterpaullone confirmed inhibition of cell cycle-related genes, and down-regulation of MYC. Our results demonstrate the preclinical efficacy of using a targeted therapy approach for Group 3 medulloblastomas. Specifically, we provide rationale for advancing alsterpaullone as a targeted therapy in Group 3 medulloblastoma.

Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data

Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.

Computer algorithms that find new uses for known drugs can accelerate the development of new therapies for many diseases, including cancer. One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease. A major difficulty with this strategy is variability: different gene expression signatures of the same disease or drug treatment can show poor overlap across studies. Since existing algorithms analyze one signature at a time, this means that the drug candidates they identify may reverse some signatures of a disease but not others. For many diseases, dozens of signatures from different labs are now available in online databases. Combining knowledge across all signatures should lead to better drug predictions. Here, we design a meta-analysis pipeline that takes in a large set of disease signatures and then identifies drugs that consistently reverse deleterious gene changes. We apply our method to find new drug candidates for lung cancer, using 21 signatures. We show that our meta-analysis pipeline increases the reproducibility of top drug hits, and then extensively characterize new lung cancer drug candidates in silico.

Response of microRNAs to in vitro treatment with graphene oxide

Graphene oxide (GO) can be potentially used in biomedical and nonbiomedical products. The in vivo studies have demonstrated that GO is predominantly deposited in the lung. In the present study, we employed SOLiD sequencing technique to investigate the molecular control of in vitro GO toxicity in GLC-82 pulmonary adenocarcinoma cells by microRNAs (miRNAs), a large class of short noncoding RNAs acting to post-transcriptionally inhibit gene expression. In GLC-82 cells, GO exposure at concentrations more than 50 mg/L resulted in severe reduction in cell viability, induction of lactate dehydrogenase leakage, reactive oxygen species production and apoptosis, and dysregulation of cell cycle. GO was localized in cytosol, mitochondria, endoplasmic reticulum, and nucleus of cells. Based on SOLiD sequencing, we identified 628 up-regulated and 25 down-regulated miRNAs in GO-exposed GLC-82 cells. Expression of some selected dysregulated miRNAs was concentration-dependent in GO-exposed GLC-82 cells. The dysregulated miRNAs and their predicted targeted genes were involved in many biological processes. By combining both information on targeted genes for dysregulated miRNAs and known signaling pathways for apoptosis control, we hypothesize that the dysregulated miRNAs could activate both a death receptor pathway by influencing functions of tumor necrosis factor α receptor and caspase-3 and a mitochondrial pathway by affecting functions of p53 and Bcl-2 in GO-exposed GLC-82 cells. Our results provide an important molecular basis at the miRNA level for explaining in vitro GO toxicity. Our data will be also useful for developing new strategies to reduce GO toxicity such as surface chemical modification.

Functional Module Connectivity Map (FMCM): a framework for searching repurposed drug compounds for systems treatment of cancer and an application to colorectal adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.

HIF-1α silencing suppresses growth of lung adenocarcinoma A549 cells through induction of apoptosis

Lung adenocarcinoma (AC) is one of the most deadly malignancies. The disease has a low five-year survival rate; therefore, the identification of novel therapeutic agents is required. This study aimed to investigate the effect of small interfering RNA (siRNA) targeting hypoxia‑inducible factor 1α (HIF‑1α) on the growth of AC A549 cells. A549 cells were transfected with various concentrations of HIF‑1α or control siRNA, and the effect on HIF‑1α expression was analyzed using quantitative polymerase chain reaction and western blot analysis. The effects of HIF-1α siRNA on growth inhibition and apoptosis were then assessed using standard methods. HIF‑1α siRNA treatment significantly reduced HIF‑1α mRNA and protein expression in A549 cells. Furthermore, the downregulation of HIF-1α expression inhibited the growth of A549 cells and induced apoptosis of A549 cells by upregulating caspase-3 expression. The present in vitro study demonstrates that the downregulation of HIF‑1α is capable of suppressing AC A549 cell growth, through the induction of apoptosis. This suggests that HIF‑1α inhibition may represent a promising strategy for the treatment of AC.

Systems biology in drug discovery and development

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

ER homeostasis and motility of NSCLC cell lines can be therapeutically targeted with combined Hsp90 and HDAC inhibitors

BACKGROUND AND OBJECTIVE

Lung cancer remains the most common cause of cancer-related death in the world for which novel systemic treatments are urgently needed. Protein homeostasis that regulates protein levels and their fold is critical for cancer cell proliferation and survival. A complex network of cellular organelles and signaling cascades is involved in control of protein homeostasis including endoplasmic reticulum (ER). Thus, proteins in control of ER homeostasis are increasingly recognized as potential therapeutic targets. Molecular chaperone heat shock protein 90 (Hsp90) and histone deacetylase (HDAC) play an important role in ER homeostasis. Previous studies demonstrate that Hsp90 and HDAC inhibitors are individually functional against lung cancer. In this work we suggested that combined Hsp90 and HDAC inhibitors may elevate ER stress thereby enhancing the anti non small lung cancer (NSCLC) activity.

METHODS AND RESULTS

Using an in vitro cell line model we demonstrated that 17-DMAG (HSP90 inhibitor) co-administration with PTACH (HDAC inhibitor) caused elevated ER stress (immunoblotting) (more than 110%↑, p < 0.05) accompanied by apoptotic cell death (Annexin V) (7-21%↑, p < 0.05). Moreover, 17-DMAG/PTACH treated cells lost the ability to migrate (scratch test) (57-85%↓ of scratch closure, p < 0.05).

CONCLUSIONS

Our findings provide proof-of-concept that targeting ER homeostasis is therapeutically beneficial in lung cancer cell lines. Indeed, the elevated ER stress caused by 17-DMAG/PTACH combined treatment leads to increased cell death of NSCLC cell lines compared to the application of the drugs separately.

Next-generation personalized drug discovery: the tripeptide GHK hits center stage in chronic obstructive pulmonary disease

Chronic lung diseases (CLDs), including chronic obstructive pulmonary disease (COPD), are the second leading cause of death worldwide. The first report of database-driven drug discovery in carefully phenotyped COPD specimens has now been published in Genome Medicine, combining gene expression data in defined emphysematous areas with connectivity-map-based compound discovery. This joint effort may lead the way to novel and potentially more efficient concepts of personalized drug discovery for COPD in particular, and CLD in general.

See research article http://genomemedicine.com/content/4/8/67/abstract

A chemical genetic approach identifies piperazine antipsychotics as promoters of CNS neurite growth on inhibitory substrates

Injury to the central nervous system (CNS) can result in lifelong loss of function due in part to the regenerative failure of CNS neurons. Inhibitory proteins derived from myelin and the astroglial scar are major barriers for the successful regeneration of injured CNS neurons. Previously, we described the identification of a novel compound, F05, which promotes neurite growth from neurons challenged with inhibitory substrates in vitro, and promotes axonal regeneration in vivo (Usher et al., 2010). To identify additional regeneration-promoting compounds, we used F05-induced gene expression profiles to query the Broad Institute Connectivity Map, a gene expression database of cells treated with >1300 compounds. Despite no shared chemical similarity, F05-induced changes in gene expression were remarkably similar to those seen with a group of piperazine phenothiazine antipsychotics (PhAPs). In contrast to antipsychotics of other structural classes, PhAPs promoted neurite growth of CNS neurons challenged with two different glial derived inhibitory substrates. Our pharmacological studies suggest a mechanism whereby PhAPs promote growth through antagonism of calmodulin signaling, independent of dopamine receptor antagonism. These findings shed light on mechanisms underlying neurite-inhibitory signaling, and suggest that clinically approved antipsychotic compounds may be repurposed for use in CNS injured patients.

Hsp90 inhibitors and drug resistance in cancer: the potential benefits of combination therapies of Hsp90 inhibitors and other anti-cancer drugs

Hsp90 is a chaperone protein that interacts with client proteins that are known to be in the cell cycle, signaling and chromatin-remodeling pathways. Hsp90 inhibitors act additively or synergistically with many other drugs in the treatment of both solid tumors and leukemias in murine tumor models and humans. Hsp90 inhibitors potentiate the actions of anti-cancer drugs that target Hsp90 client proteins, including trastuzumab (Herceptin™) which targets Her2/Erb2B, as Hsp90 inhibition elicits the drug effects in cancer cell lines that are otherwise resistant to the drug. A phase II study of the Hsp90 inhibitor 17-AAG and trastuzumab showed that this combination therapy has anticancer activity in patients with HER2-positive metastatic breast cancer progressing on trastuzumab. In this review, we discuss the results of Hsp90 inhibitors in combination with trastuzumab and other cancer drugs. We also discuss recent results from yeast focused on the genetics of drug resistance when Hsp90 is inhibited and the implications that this might have in understanding the effects of genetic variation in treating cancer in humans.

Discovering biological connections between experimental conditions based on common patterns of differential gene expression

Background

Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expression alterations. The growing volume of gene expression data in open data repositories such as the NCBI Gene Expression Omnibus (GEO) presents an opportunity to identify these gene expression similarities on a large scale across a diverse collection of datasets. We have developed a fast, pattern-based computational approach, named openSESAME (Search of Expression Signatures Across Many Experiments), that identifies datasets enriched in samples that display coordinate differential expression of a query signature. Importantly, openSESAME performs this search without prior knowledge of the phenotypic or experimental groups in the datasets being searched. This allows openSESAME to identify perturbations of gene expression that are due to phenotypic attributes that may not have been described in the sample annotation included in the repository.

To demonstrate the utility of openSESAME, we used gene expression signatures of two biological perturbations to query a set of 75,164 human expression profiles that were generated using Affymetrix microarrays and deposited in GEO. The first query, using a signature of estradiol treatment, identified experiments in which estrogen signaling was perturbed and also identified differences in estrogen signaling between estrogen receptor-positive and -negative breast cancers. The second query, which used a signature of silencing of the transcription factor p63 (a key regulator of epidermal differentiation), identified datasets related to stratified squamous epithelia or epidermal diseases such as melanoma.

Conclusions

openSESAME is a tool for leveraging the growing body of publicly available microarray data to discover relationships between different biological states based on common patterns of differential gene expression. These relationships may serve to generate hypotheses about the causes and consequences of specific patterns of observed differential gene expression. To encourage others to explore the utility of this approach, we have made a website for performing openSESAME queries freely available at http://opensesame.bu.edu.