Volume 18 Supplement 1
DeSigN: connecting gene expression with therapeutics for drug repurposing and development
© The Author(s). 2017
Published: 25 January 2017
The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously.
We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8–1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.
DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.
KeywordsCell line Gene expression DeSigN Cancer Drug repurposing
The drug discovery and development pipeline is a long and arduous process, one that is resource-intensive and time-consuming, making these the main barriers for rapid drug development. Furthermore, the attrition rate is high, underscoring the need to improve strategies in drug development and in expanding the usage of already approved drugs . Fortunately, the availability of a large pool of drugs provides convenient candidates for drug repurposing, which can contribute to reducing the time for finding new, effective chemotherapeutic strategies . The current challenge is to develop discovery pipelines to prioritize testing of already approved drugs, particularly in cancers with limited chemotherapy options, such as oral cancer . Lessons from laboratory and clinical studies have demonstrated that genetic features of tumours either in the form of mutational data or gene expression signatures could be used to predict response to targeted therapies, and this has formed the basis of precision medicine that is currently practised in the clinic [4–6]. To extend on the advancements in our ability to characterize the cancer genome to unprecedented depth, these information can be used to link genetic features to drug response, which affords an opportunity to systematize the testing of drug candidates for expanding the spectrum of available cancer drugs for treatment.
Since the late 1980s, the NCI-60 panel of cancer cell lines has been used to systematically identify anti-cancer compounds and more recently, to identify biomarkers of response [7, 8]. In 2012, the repertoire of cancer cell lines used was expanded substantially with the inclusion of new data from the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) projects where 707 and 860 cancer cell lines respectively were assembled for anti-cancer drug testing. Uniquely, more than 13 cancer types are represented in these panels, and more importantly, these cell lines are well-characterised with respect to their gene expression and mutational information [9, 10]. Additionally, more than 50% of these cell lines were subjected to high-throughput drug screening and their response to a large panel of drugs have been documented systematically [9, 10].
The development of computational tools that could take advantage of the availability of high throughput gene expression data to mine patterns of association between drug sensitivity and gene expression signatures began with the seminal work by Lamb et al. who developed the Connectivity Map (CMap) algorithm . Subsequently, other bioinformatics tools were developed. For example, NFFinder searches for relationships between drugs, diseases and a phenotype of interest using transcriptomic data as input . Using the same concept, the drug-to-protein associations were evaluated by the DMAP tool that resulted in the formation of 438,004 drug-to-protein effect relationships . The Functional Module Connectivity Map (FMCM), which extends CMap by constructing a functional network of a set of differentially expressed genes, showed validation results for four drugs that could affect cell viability in colorectal cancer cell lines .
While GDSC provides large amounts of drug response data from arrays of cell lines, additional analyses are needed to extrapolate drug efficacy to new datasets. For example, GDSC shows that the head and neck cancer cell lines FADU and HSC-3 are reported to respond to the heat shock protein 90 (Hsp90) inhibitor 17-AAG . However, predicting which inhibitors are likely to be efficacious in new cell lines derived from cancer patients remains a challenge.
To exploit the GDSC data for predicting drug sensitivity, we developed DeSigN (Differentially Expressed Gene Signatures - Inhibitors), a CMap-inspired  bioinformatics pipeline that enables gene expression patterns from experimental data to be linked to gene expression patterns associated with drug response in a cancer cell line database. To demonstrate proof-of-concept of the practical usefulness of DeSigN, we conducted two validation experiments. The first involves the examination of reported efficacy of drug candidates against four different cancer cell lines that are prioritized by DeSigN. The second is an experimental validation of the sensitivity of a set of oral squamous cell carcinoma (OSCC) cell lines to bosutinib, a Src/Abl kinase inhibitor that is currently used for treating leukemia but predicted by DeSigN to be effective against OSCC cell lines.
Differentially Expressed Gene Signatures - Inhibitors (DeSigN) platform
Differential expression of microarray gene expression data for the sensitive and the resistant phenotype was done using the Linear Models for Microarray data (limma) algorithm . The result from limma for each inhibitor was sorted and converted into ranked lists according to the gene’s moderated t-statistic (rank 1 for largest value). This reference database was used to connect the queries and return rank-ordered list of inhibitors for a particular query (Fig. 1a).
Differentially expressed genes (DEG) obtained from microarray or RNA-Seq gene expression data of cell lines of two different phenotype classes were used to query DeSigN. DEGs were selected using joint filtering of p-value and fold change , with threshold value set at log2 fold change > 1 and p-value < 0.01 (Fig. 1b).
A pattern-matching algorithm based on the nonparametric Kolmogorov-Smirnov (KS) statistic  was used to associate query signatures to the drug-specific, rank-ordered gene expression profile database. The KS test is a rank-based pattern matching approach implemented in the Connectivity Map , and its goal is to correlate inhibitors in GDSC that enrich for similar DEG based on the IC50 drug sensitivity profiles.
DeSigN returns a ranked list of inhibitors that have the highest Connectivity Score between the DEG and the ranked-order gene expression profiles in the reference database, with S ranging between 1 (maximal efficacy) and −1 (minimal efficacy) (Fig. 1c).
The DeSigN web interface
The DeSigN website is freely available at http://design.cancerresearch.my/. Its web interface is implemented in PHP (v7.0) with the support of jQuery (v1.4.2), and hosted using the Apache Server. The reference database is generated and managed using MySQL database (v5.5.49). DeSigN makes use of the AJAX feature to quickly load content without reloading the pages. All queries are sent to the Java-based computing cluster to perform parallel computation. A help document providing a guide for users to query and navigate DeSigN is available in the website, with examples given. Except the pattern-matching algorithm, which was programmed in Java and the Graphical User Interface (GUI), which was built using PHP, the other methods were implemented in R version 3.3.0.
NCBI Gene Expression Omnibus (GEO) datasets
GEO studies used to validate DeSigN prediction
Number of sensitive samples
Number of resistant samples
Coldren et al. 
Liu et al. 
Wang et al. 
Saiki et al. 
Five oral squamous cell carcinoma (OSCC; ORL-48, ORL-150, ORL-156, ORL-196 and ORL-204) and three normal oral keratinocyte (NOK) cultures previously developed in our laboratory  were used to validate bosutinib, a drug candidate predicted by DeSigN to be effective. The RNA-Seq data of these cells were subjected to differential analysis (OSCC versus NOK) using DESeq2 [19, 20]. DEG generated from DESeq2 was used as the query signature in DeSigN to shortlist candidate drugs for experimental validation.
All ORL cell lines and HSC-4 (sensitive control for response to bosutinib) were cultured in Dulbecco’s Modified Eagle Medium (DMEM)/F12 (1:1) supplemented with 10% (v/v) heat-inactivated fetal calf serum (FBS), 100 IU Penicillin/Streptomycin and 0.5 μg/ml hydrocortisone as described previously . NOK were cultured in keratinocyte serum-free media (KSFM; GIBCO, Carlsbad, CA, USA) supplemented with 25 μg/ml bovine pituitary extract, 0.2 ng/ml epidermal growth factor, 0.031 mM calcium chloride and 100 IU Penicillin/Streptomycin (GIBCO, Carlsbad, CA, USA) as described previously . The breast cancer cell line MCF7 (resistant control for response to bosutinib) was cultured in RPMI 1640 medium (GIBCO, Carlsbad, CA, USA) supplemented with 10% (v/v) heat-inactivated FBS and 100 IU Penicillin/Streptomycin. All cultures were incubated in a humidified atmosphere of 5% CO2 at 37 °C.
Viability assay using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT)
The effect of bosutinib on the selected OSCC cell lines was determined using MTT assay with 1.5–8 × 103 cells per well as described previously . Cells were treated with 0.04–5 μM of bosutinib, and cell viability was measured after 72 h of treatment. DMSO (0.5%) served as vehicle control. The two-sample t-test was used to assess whether the difference in the sample mean of IC50 between the tested cell lines was statistically significant (p-value < 0.05). Experiments were repeated at least three times.
Apoptosis was quantified using a FITC Annexin V Apoptosis Detection Kit (BD Biosciences, San Jose, CA, USA) according to the manufacturer’s instructions. Briefly, floating and attached cells were collected at 24, 48 and 72 h after bosutinib treatment at 1 μM, and then stained using FITC Annexin V/Propidium iodide (PI). Apoptosis detection was performed using BD FACSCANTO™ II flow cytometer and data was analyzed using the BD FACSDiva™ software (BD Biosciences, San Jose, CA, USA). For each of the three time points, the two-sample t-test was used to test whether the mean of total number of apoptotic events differed significantly (p-value < 0.05) between bosutinib-treated cells and the vehicle control (0.01% DMSO) cells. Experiments were repeated at least two times.
The anti-proliferative effect of bosutinib on the OSCC cell lines were examined using Click-iT EdU Cell Proliferation Assay Kit (Invitrogen, Carlsbad, CA, USA) as previously described . The cell lines ORL-48, ORL-204 and ORL-196 were treated with 0.3–3 μM bosutinib, for 24 h and cell proliferation evaluation was based on 5-ethynyl-2′-deoxyuridine (EdU) incorporation according to the manufacturer’s protocol. Images were captured from 4 to 11 different fields of each treatment concentration and further analyzed using EBImage . The percentage of EdU-labelled cells was expressed as the percentage of red fluorescent nuclei over the total number cells reflected by DAPI-stained nuclei and the data is presented as relative percentage compared to control cells (0 μM). The two-sample t-test was used to test whether the difference in the relative percentage of EdU+ cells differed significantly (p-value < 0.05) between treatment and vehicle control for the three cell lines. Experiments were repeated at least two times.
NCBI GEO datasets validation summary
Expected drug sensitivity
ABL, SRC, KIT, PDGFR
Using DeSigN to shortlist potentially efficacious inhibitors for OSCC cell lines
Mean IC50 relative to HSC-4 and MCF7 (μM)
OSCC Cell lines
Mean IC50 ± SE
-log10(p-value) relative to HSC-4
-log10(p-value) relative to MCF7
ORL-196 (n = 4)
0.75 ± 0.03
ORL-204 (n = 3)
0.90 ± 0.04
ORL-48 (n = 5)
1.19 ± 0.05
HSC-4 (n = 3)
1.82 ± 0.03
MCF7 (n = 3)
12.22 ± 1.32
Further confirmation from the Click-iT EdU cell proliferation assay showed clearly that bosutinib inhibited the proliferation of ORL-48, ORL-196 and ORL-204 cells as demonstrated by the significant reduction in the number of proliferating cells (red-stained cells) compared to the non-treated cells (Fig. 6b). ORL-196 and ORL-204 demonstrated growth inhibition of ~70–80% (p-value = 0.03, n = 3; p-value = 0.049, n = 2 respectively) whilst ORL-48 showed growth inhibition of ~40% following bosutinib treatment at 1 μM for 72 h (p-value = 0.04, n = 2) (Fig. 6c, Additional file 10: Table S10 and Additional file 11: Figure S11). The level of inhibition in the OSCC cell lines corroborated well with their mean IC50 value for bosutinib. Taken together, these biological observations demonstrated that bosutinib confers anti-proliferative and cytotoxic effects in the tested OSCC cell lines.
We have developed DeSigN, a web-based bioinformatics tool that allows users to query large public database of cancer cell line gene expression and drug response data such as GDSC. We showed explicitly that querying DeSigN using differentially expressed gene signatures could reveal potentially efficacious candidate drugs, as shown in the GSE4342 analyses. BIBW2992 (a newer generation of EGFR inhibitor currently approved for treating NSCLC patients who are refractory to gefitinib and erlotinib), for example, could potentially replace gefitinib, a first-generation EGFR tyrosine kinase inhibitors (TKI) that is increasingly becoming a non-viable solution as cancer cells of NSCLC patients treated with gefitinib inevitably develop resistance and relapse, with 8–10 months of median time to progression [26–28]
To date, many cases of successful drug repurposing studies have been reported, an exemplary study being that of methotrexate, a drug first developed for treating leukemia, and subsequently repurposed to treat a wide spectrum of cancers ranging from breast, ovarian, bladder to head and neck cancers [29, 30]. Here, we demonstrated the success of DeSigN in guiding the selection of bosutinib as a candidate drug against OSCC (a subset of HNSCC) cell lines. Emerging evidence supports the possible use of bosutinib for the treatment of HNSCC. First, the molecular target of bosutinib, Src has been reported to be a frequently altered gene in HNSCC and has been identified as a promising drug target . Second, an analysis of gene expression data from 42 HNSCC cell lines also predicted that bosutinib has anti-tumour effect on HNSCC . To the best of our knowledge this is the first time bosutinib was shown experimentally to have potency in OSCC cell lines.
Comparisons of tools that utilized Connectivity Map concept
Global baseline DEGs to drug response
Transcriptomic data to drugs, diseases and experts
GEO, CMap and DrugMatrix
Protein/gene to drug response
STITCH and HAPPI
Pre- and post-treatment gene expression to drug response
The new leads derived from DeSigN are important for accelerating the discovery of new drugs for HNSCC treatment, which is currently limited to cetuximab, where this drug remains the only FDA-approved targeted therapy for advanced HNSCC . Importantly, we would like to emphasize that all candidates with positive and significant Connectivity Score should be equally considered for validation instead of considering just the few top-ranked candidates, since factors such as cost of drug, ease of availability, method of administering, side effects and other factors, are important practical considerations in the clinical setting.
The current implementation of DeSigN uses differentially expressed genes as starting points to associate gene signatures with drug response phenotype. This input is not necessarily optimal, as genes that are involved in dysregulated pathways in the pathogenesis of cancer may not always have their expression substantially altered . Since higher-order information such as network context and post-translational modification including reversible phosphorylation or acylation are not explicitly integrated in the current version, future improvements to DeSigN will focus on integrating these types of data.
For future work, we also intend to expand drug coverage in Version 1.0 of DeSigN by incorporating the gene expression and drug response data from Cancer Therapeutics Response Portal (CTRP)  and other large-scale pharmacogenomics studies. We anticipate that DeSigN will evolve as more cell line gene expression and drug response data become available.
DeSigN provides proof-of-concept for the feasibility of using a computational approach to shortlist the most promising drug candidates for effective drug repurposing in cancer treatment. We expect that DeSigN will continue to evolve based on usage feedback from the community of cancer researchers, as well as improvements in methods for mining gene signatures that have strong network context.
Differentially expressed gene
Differentially Expressed Gene Signatures – Inhibitors
Genomics of Drug Sensitivity in Cancer
Normal oral keratinocyte
Oral squamous cell carcinoma
The authors would like to thank Mei Fong Ng for her assistance in designing the Fig. 1 in this study and Nur Syafinaz Zainal for technical assistance. Cancer Research Malaysia is a non-profit research organization. We are committed to an understanding of cancer prevention, diagnosis and treatment through a fundamental research program.
This article has been published as part of BMC Genomics Volume 18 Supplement 1, 2016: Proceedings of the 27th International Conference on Genome Informatics: genomics. The full contents of the supplement are available online at http://bmcgenomics.biomedcentral.com/articles/supplements/volume-18-supplement-1.
This study and the subsequent costs of publication was funded by High Impact Research, Ministry of Higher Education (HIR-MOHE) from University of Malaya (UM.C/625/1/HIR/MOHE/DENT-03) and other sponsors of the Cancer Research Malaysia.
Availability of data and material
The authors declare that [the/all other] data supporting the findings of this study are available within the article [and its supplementary information files].
BKBL carried out data analysis and prepared the manuscript. BKBL and KHT carried out the experimental validation. BKBL, JKC and CSL designed, developed and implemented the DeSigN web interface. ZAAR, ACT, TFK and SCC conceived and supervised the overall study, design the analyses, and participated in drafting and editing of the manuscript. All authors have read, edited and approved the current version of the manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Not applicable, human or animal subjects were not used in the study.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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