DeSigN: connecting gene expression with therapeutics for drug repurposing and development

Transcription

1 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 DOI /s RESEARCH Open Access DeSgN: connectng gene expresson wth therapeutcs for drug repurposng and development Bernard Kok Bang Lee 1,2, Ka Hung Tong 1,2, Jt Kang Chang 3,4, Chee Sun Lew 3,4,5, Zanal Arff Abdul Rahman 1, Ak Choon Tan 6, Tsung Fe Khang 5,7 and Sok Chng Cheong 1,2* From The 27th Internatonal Conference on Genome Informatcs Shangha, Chna. 3-5 October 2016 Abstract Background: The drug dscovery and development ppelne s a long and arduous process that nevtably hampers rapd drug development. Therefore, strateges to mprove the effcency of drug development are urgently needed to enable effectve drugs to enter the clnc. Precson medcne has demonstrated that genetc features of cancer cells can be used for predctng drug response, and emergng evdence suggest that gene-drug connectons could be predcted more accurately by explorng the cumulatve effects of many genes smultaneously. Results: We developed DeSgN, a web-based tool for predctng drug effcacy aganst cancer cell lnes usng gene expresson patterns. The algorthm correlates phenotype-specfc gene sgnatures derved from dfferentally expressed genes wth pre-defned gene expresson profles assocated wth drug response data (IC 50 ) from 140 drugs. DeSgN successfully predcted the rght drug senstvty outcome n four publshed GEO studes. Addtonally, t predcted bosutnb, a Src/Abl knase nhbtor, as a senstve nhbtor for oral squamous cell carcnoma (OSCC) cell lnes. In vtro valdaton of bosutnb n OSCC cell lnes demonstrated that ndeed, these cell lnes were senstve to bosutnb wth IC 50 of μm. As further confrmaton, we demonstrated expermentally that bosutnb has ant-prolferatve actvty n OSCC cell lnes, demonstratng that DeSgN was able to robustly predct drug that could be benefcal for tumour control. Conclusons: DeSgN s a robust method that s useful for the dentfcaton of canddate drugs usng an nput gene sgnature obtaned from gene expresson analyss. Ths user-frendly platform could be used to dentfy drugs wth unantcpated effcacy aganst cancer cell lnes of nterest, and therefore could be used for the repurposng of drugs, thus mprovng the effcency of drug development. Keywords: Cell lne, Gene expresson, DeSgN, Cancer, Drug repurposng * Correspondence: sokchng.cheong@cancerresearch.my 1 Department of Oral & Maxllofacal Clncal Scences, Faculty of Dentstry, Unversty of Malaya, Kuala Lumpur, Malaysa 2 Oral Cancer Research Group, Cancer Research Malaysa, No. 1, Jalan SS12/1A, Subang Jaya, Selangor, Malaysa Full lst of author nformaton s avalable at the end of the artcle The Author(s) Open Access Ths artcle s dstrbuted under the terms of the Creatve Commons Attrbuton 4.0 Internatonal Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded you gve approprate credt to the orgnal author(s) and the source, provde a lnk to the Creatve Commons lcense, and ndcate f changes were made. The Creatve Commons Publc Doman Dedcaton waver ( apples to the data made avalable n ths artcle, unless otherwse stated.

2 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 2 of 11 Background The drug dscovery and development ppelne s a long and arduous process, one that s resource-ntensve and tme-consumng, makng these the man barrers for rapd drug development. Furthermore, the attrton rate s hgh, underscorng the need to mprove strateges n drug development and n expandng the usage of already approved drugs [1]. Fortunately, the avalablty of a large pool of drugs provdes convenent canddates for drug repurposng, whch can contrbute to reducng the tme for fndng new, effectve chemotherapeutc strateges [2]. The current challenge s to develop dscovery ppelnes to prortze testng of already approved drugs, partcularly n cancers wth lmted chemotherapy optons, such as oral cancer [3]. Lessons from laboratory and clncal studes have demonstrated that genetc features of tumours ether n the form of mutatonal data or gene expresson sgnatures could be used to predct response to targeted therapes, and ths has formed the bass of precson medcne that s currently practsed n the clnc [4 6]. To extend on the advancements n our ablty to characterze the cancer genome to unprecedented depth, these nformaton can be used to lnk genetc features to drug response, whch affords an opportunty to systematze the testng of drug canddates for expandng the spectrum of avalable cancer drugs for treatment. Snce the late 1980s, the NCI-60 panel of cancer cell lnes has been used to systematcally dentfy ant-cancer compounds and more recently, to dentfy bomarkers of response [7, 8]. In 2012, the repertore of cancer cell lnes used was expanded substantally wth the ncluson of new data from the Genomcs of Drug Senstvty n Cancer (GDSC) and Cancer Cell Lne Encyclopeda (CCLE) projects where 707 and 860 cancer cell lnes respectvely were assembled for ant-cancer drug testng. Unquely, more than 13 cancer types are represented n these panels, and more mportantly, these cell lnes are well-charactersed wth respect to ther gene expresson and mutatonal nformaton [9, 10]. Addtonally, more than 50% of these cell lnes were subjected to hgh-throughput drug screenng and ther response to a large panel of drugs have been documented systematcally [9, 10]. The development of computatonal tools that could take advantage of the avalablty of hgh throughput gene expresson data to mne patterns of assocaton between drug senstvty and gene expresson sgnatures began wth the semnal work by Lamb et al. who developed the Connectvty Map (CMap) algorthm [11]. Subsequently, other bonformatcs tools were developed. For example, NFFnder searches for relatonshps between drugs, dseases and a phenotype of nterest usng transcrptomc data as nput [12]. Usng the same concept, the drug-to-proten assocatons were evaluated by the DMAP tool that resulted n the formaton of 438,004 drug-to-proten effect relatonshps [13]. The Functonal Module Connectvty Map (FMCM), whch extends CMap by constructng a functonal network of a set of dfferentally expressed genes, showed valdaton results for four drugs that could affect cell vablty n colorectal cancer cell lnes [14]. Whle GDSC provdes large amounts of drug response data from arrays of cell lnes, addtonal analyses are needed to extrapolate drug effcacy to new datasets. For example, GDSC shows that the head and neck cancer cell lnes FADU and HSC-3 are reported to respond to the heat shock proten 90 (Hsp90) nhbtor 17-AAG [9]. However, predctng whch nhbtors are lkely to be effcacous n new cell lnes derved from cancer patents remans a challenge. To explot the GDSC data for predctng drug senstvty, we developed DeSgN (Dfferentally Expressed Gene Sgnatures - Inhbtors), a CMap-nspred [11] bonformatcs ppelne that enables gene expresson patterns from expermental data to be lnked to gene expresson patterns assocated wth drug response n a cancer cell lne database. To demonstrate proof-of-concept of the practcal usefulness of DeSgN, we conducted two valdaton experments. The frst nvolves the examnaton of reported effcacy of drug canddates aganst four dfferent cancer cell lnes that are prortzed by DeSgN. The second s an expermental valdaton of the senstvty of a set of oral squamous cell carcnoma (OSCC) cell lnes to bosutnb, a Src/Abl knase nhbtor that s currently used for treatng leukema but predcted by DeSgN to be effectve aganst OSCC cell lnes. Methods Dfferentally Expressed Gene Sgnatures - Inhbtors (DeSgN) platform DeSgN s a web-based bonformatcs tool for assocatng gene sgnatures wth drug response phenotype based on IC 50 data, wth the am of dentfyng novel drugs that have good potental to be repurposed for cancer therapy. The DeSgN algorthm (Fg. 1) conssts of three key components: () a reference database that contans a set of pre-defned gene expresson profles assocated wth drug response data to 140 drugs; () a set of dfferentally expressed gene (DEG) sgnatures as query nput and () a pattern-matchng algorthm for evaluatng smlarty between the query gene sgnature and drug-assocated gene expresson profles n the reference database. Reference database We bult the reference database usng baselne mcroarray data and drug senstvty data obtaned from the Genomcs of Drug Senstvty n Cancer (GDSC) project. We frst downloaded the raw CEL mcroarray data fles of sold tumour cell lnes from GDSC [9] (normalzed usng the MAS5 algorthm). The probe sets were collapsed to gene symbols usng Gene Set Enrchment Analyss [15] wth

3 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 3 of 11 Fg. 1 Workflow of DeSgN. a A reference database of cell lnes that are senstve and resstant to drugs avalable n the GDSC database was created. Verson 1.0 contans 140 drugs wth ther unque ranked-based gene sgnatures. b Dfferental expressed gene sgnatures are generated from dfferental expresson analyss of cell lnes from two dstnct expermental condtons, e.g. cell lne gene expresson data from tumour samples versus normal samples. The up and down-regulated genes (log 2 fold change > 1 and p-value < 0.01) thus selected wll be used to query the DeSgN database. c A ranked-based lst of nhbtors s generated, wth Connectvty Score between 1 (maxmal effcacy) and 1 (mnmal effcacy). Ths allows users to prortze the testng of these canddates HT HG-U133A chp as reference, ths process resulted n 12,772 unque genes. For each drug, we classfed the cancer cell lnes drug response phenotype (resstant or senstve) n the followng way. We frst ranked the cell lnes by ther IC 50 values (lowest to hghest). Cell lnes wth IC 50 that were U standard devatons larger than the medan IC 50 of all cell lnes were consdered to be resstant; those that were L standard devatons smaller were consdered to be senstve. We chose the parameters U and L carefully on a case-by-case bass. These two cut-offs were generally values where sharp transtons n IC 50 were observed n the scatter plot of log 10 (IC 50 ) aganst rank. About 20 cell lnes each from the senstve and resstant phenotype were thus defned. The lst of senstve and resstant cell lnes defned for the 140 nhbtors n DeSgN s provded n Addtonal fle 1: Table S1. An example for the drug Mtomycn-C s shown n Fg. 2. Dfferental expresson of mcroarray gene expresson data for the senstve and the resstant phenotype was done usng the Lnear Models for Mcroarray data (lmma) algorthm [16]. The result from lmma for each nhbtor was sorted and converted nto ranked lsts accordng to the gene smoderated t-statstc (rank 1 for largest value). Ths reference database was used to connect the queres and return rankordered lst of nhbtors for a partcular query (Fg. 1a). Query sgnature Dfferentally expressed genes (DEG) obtaned from mcroarray or RNA-Seq gene expresson data of cell lnes of two dfferent phenotype classes were used to query DeSgN. DEGs were selected usng jont flterng of p-value and fold change [17], wth threshold value set at log 2 fold change > 1 and p-value < 0.01 (Fg. 1b). Pattern-matchng algorthm A pattern-matchng algorthm based on the nonparametrc Kolmogorov-Smrnov (KS) statstc [11] was used to assocate query sgnatures to the drug-specfc, rankordered gene expresson profle database. The KS test s a rank-based pattern matchng approach mplemented n the Connectvty Map [11], and ts goal s to correlate nhbtors n GDSC that enrch for smlar DEG based on the IC 50 drug senstvty profles. The Connectvty Score s computed accordng to [11] as follows. Let N be the total number of genes n the reference database, and T the number of genes n the query sgnature for up- or down-regulated genes. For every drug n the reference database, we compute the rank-ordered (usng moderated t-statstc) lst R for all N genes. Let j ndex the query genes n such a way that R(j), the rank of the j-th gene n the N total number of genes, s monotone

4 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 4 of 11 Fg. 2 Example of log 10 (IC 50 ) rank plot to defne drug response phenotype. The sold lne represents the medan IC 50 values of nhbtor Mtomycn-C whereas the lower and upper dashed lnes represent the cut-off for classfyng cell lnes nto senstve or resstant phenotypes, respectvely ncreasng. For j =1,2,, T, we compute the followng two values for each up- and down-regulated gene sgnatures: a ¼ max 1 j T b ¼ max 1 j T j T Rj ðþ ; N Rj ðþ N ðj 1Þ : T Subsequently, for each nhbtor, theks-lkestatstcs forup-anddown-regulatedquerygenesgnature,ks up and ks down, are computed as (subscrpt omtted) 8 < a; f a > b; ks ¼ 0; f a ¼ b; : b; f a < b: The Enrchment Score (ES 1 ) for drug n the reference database s set to zero f both ks up and ks down have the same sgn; otherwse, ES = ks up ks down. The Connectvty Score (S ) for non-zero nstances s a normalzed Enrchment Score computed as: 8 ES >< S ¼ P ; f ES > 0; >: ES ; f ES < 0; Q where P =max ES and Q = mn ES are the normalzng constants. DeSgN returns a ranked lst of nhbtors that have the hghest Connectvty Score between the DEG and the ranked-order gene expresson profles n the reference database, wth S rangng between 1 (maxmal effcacy) and 1 (mnmal effcacy) (Fg. 1c). To evaluate the statstcal sgnfcance of S,weuseda permutaton approach to smulate the null dstrbuton of S.Thus,m random gene sets, each havng the same sze as the sze of the nput gene sgnature, were smulated. Each gene set then yelds S random (k), where k ndexes the random gene set. The p-value was computed as p value ¼ 8 1 m >< 1 m ( 1 X m max I m S ð k¼1 >: f S ¼ 0; X m I S randomð k Þ> S k¼1 ð Þ ; f S > 0; X m I S randomð k Þ< S k¼1 ð Þ ; f S < 0; randomð k Þ> S Þ ; 1 m X m I S ð randomð k Þ< S k¼1 ) Þ ; where I A s the ndcator functon that takes the value 1 f event A occurs, and 0 otherwse. Here, we set m = The DeSgN web nterface The DeSgN webste s freely avalable at Its web nterface s mplemented n PHP (v7.0) wth the support of jquery (v1.4.2), and hosted usng the Apache Server. The reference database s generated and managed usng MySQL database (v5.5.49). DeSgN makes use of the AJAX feature to quckly load content wthout reloadng the pages. All queres are sent to the Java-based computng cluster to perform parallel computaton. A help document provdng a gude for users to query and navgate DeSgN s avalable n the webste, wth examples gven. Except the pattern-matchng algorthm, whch was programmednjavaandthegraphcaluserinterface(gui), whch was bult usng PHP, the other methods were mplemented n R verson

5 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 5 of 11 NCBI Gene Expresson Omnbus (GEO) datasets To demonstrate how DeSgN could be used to predct canddate drugs, we used dfferentally expressed genes generated from ER-postve breast cancer versus normal tssue reported by Clarke et al. [18] that can also be accessed from the NCBI Gene Expresson Omnbus (GEO) database under the accesson number GSE In addton, four drug senstvty studes publshed n the NCBI GEO database were used to valdate DeSgN (Table 1). The mcroarray gene expresson data from these fve GEO studes were subjected to dfferental analyss usng the GEO2R functon provded by NCBI (verson nfo: R , Bobase , GEOquery , lmma ). For the four valdaton sets, we defned senstve cell lnes as havng IC 50 <1 μm and resstant cell lnes as havng IC 50 >1 μm. The choce of these four studes was guded by several ncluson and excluson crtera. We ncluded studes where: () The medan of the dstrbuton of gene expresson values of each sample were more or less equal; () The subject of the drug senstvty study was Homo sapens; () Drug treatment was gven for at least 48 h; (v) Only one nhbtor was used. We excluded blood cancer-related studes. For each study, a lst of DEG was dentfed and used to query DeSgN. Cell culture Fve oral squamous cell carcnoma (OSCC; ORL-48, ORL- 150, ORL-156, ORL-196 and ORL-204) and three normal oral keratnocyte (NOK) cultures prevously developed n our laboratory [19] were used to valdate bosutnb, a drug canddate predcted by DeSgN to be effectve. The RNA- Seq data of these cells were subjected to dfferental analyss (OSCC versus NOK) usng DESeq2 [19, 20]. DEG generated fromdeseq2wasusedasthequerysgnaturendesgnto shortlst canddate drugs for expermental valdaton. All ORL cell lnes and HSC-4 (senstve control for response to bosutnb) were cultured n Dulbecco s Modfed Eagle Medum (DMEM)/F12 (1:1) supplemented wth 10% (v/v) heat-nactvated fetal calf serum (FBS), 100 IU Penclln/Streptomycn and 0.5 μg/ml hydrocortsone as descrbed prevously [19]. NOK were cultured n keratnocyte serum-free meda (KSFM; GIBCO, Carlsbad, CA, USA) supplemented wth 25 μg/ml bovne ptutary extract, 0.2 ng/ml epdermal growth factor, mm calcum chlorde and 100 IU Penclln/Streptomycn (GIBCO, Carlsbad, CA, USA) as descrbed prevously [19]. The breast cancer cell lne MCF7 (resstant control for response to bosutnb) was cultured n RPMI 1640 medum (GIBCO, Carlsbad, CA, USA) supplemented wth 10% (v/v) heatnactvated FBS and 100 IU Penclln/Streptomycn. All cultures were ncubated n a humdfed atmosphere of 5% CO 2 at 37 C. Vablty assay usng 3-(4,5-dmethylthazol-2-yl)-2,5- dphenyltetrazolum bromde (MTT) The effect of bosutnb on the selected OSCC cell lnes was determned usng MTT assay wth cells per well as descrbed prevously [19]. Cells were treated wth μm of bosutnb, and cell vablty was measured after 72 h of treatment. DMSO (0.5%) served as vehcle control. The two-sample t-test was used to assess whether the dfference n the sample mean of IC 50 between the tested cell lnes was statstcally sgnfcant (p-value < 0.05). Experments were repeated at least three tmes. Apoptoss assay Apoptoss was quantfed usng a FITC Annexn V Apoptoss Detecton Kt (BD Boscences, San Jose, CA, USA) accordng to the manufacturer s nstructons. Brefly, floatng and attached cells were collected at 24, 48 and 72 h after bosutnb treatment at 1 μm, and then staned usng FITC Annexn V/Propdum odde (PI). Apoptoss detecton was performed usng BD FACSCANTO II flow cytometer and data was analyzed usng the BD FACSDva software (BD Boscences, San Jose, CA, USA). For each of the three tme ponts, the two-sample t-test was used to test whether the mean of total number of apoptotc events dffered sgnfcantly (p-value < 0.05) between bosutnb-treated cells and the vehcle control (0.01% DMSO) cells. Experments were repeated at least two tmes. Prolferaton assay The ant-prolferatve effect of bosutnb on the OSCC cell lnes were examned usng Clck-T EdU Cell Prolferaton Assay Kt (Invtrogen, Carlsbad, CA, USA) as prevously descrbed [19]. The cell lnes ORL-48, ORL-204 and ORL- 196 were treated wth μm bosutnb, for 24 h and cell prolferatonevaluatonwasbasedon5-ethynyl-2 -deoxyurdne (EdU) ncorporaton accordng to the manufacturer s protocol. Images were captured from 4 to 11 dfferent felds Table 1 GEO studes used to valdate DeSgN predcton GEO reference Drug Response Number of senstve samples Number of resstant samples Platform Reference GSE4342 Geftnb Senstve GPL96 Coldren et al. [24] GSE16179 Lapatnb Senstve 3 3 GPL570 Lu et al. [35] GSE9633 Dasatnb Senstve 11 5 GPL571 Wang et al. [36] GSE35141 Gemctabne Resstant 6 6 GPL4133 Sak et al. [37]

6 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 6 of 11 of each treatment concentraton and further analyzed usng EBImage [21]. The percentage of EdU-labelled cells was expressed as the percentage of red fluorescent nucle over the total number cells reflected by DAPI-staned nucle and the data s presented as relatve percentage compared to control cells (0 μm). The two-sample t-test was used to test whether the dfference n the relatve percentage of EdU + cells dffered sgnfcantly (p-value < 0.05) between treatment and vehcle control for the three cell lnes. Experments were repeated at least two tmes. Results Runnng DeSgN To demonstrate how DeSgN can be used to generate a lst of prortzed canddate drugs, we tested dfferentally expressed genes (DEG) generated from ER-postve breast cancer cell lne compared to normal tssues (GSE42568; Fg. 3a) [18]. From the database (Fg. 3b), DeSgN returned a lst of 11 ranked nhbtors together wth ther target protens (Fg. 3c). Of note, the two top-scorng drugs, AICAR and BIBW2992 are drugs that are actvely beng studed as therapeutcs aganst ER-postve breast cancer. The drug AICAR, whch targets AMPK, have shown to have ant-prolferatve effects n ER-postve breast cancer cell lnes [22]. Further, a Phase II clncal tral demonstrated that BIBW2992 was able to nduce stable dsease n more than 50% of ER-postve metastatc breast cancer that has progressed on letrozole monotherapy when used n combnaton wth letrozole [23]. DeSgN also predcted resstance of ER-postve breast cancer cells aganst drugs wth strong negatve Connectvty Score such as dasatnb and mdostaurn. The lst of DEG from GSE42568 used to query DeSgN s provded n Addtonal fle 2: Table S2. Valdaton results GSE4342 s a study that demonstrated the senstve response of 17 non-small cell lung cancer (NSCLC) cell lnes to geftnb (EGFR-nhbtor) treatment [24]. By queryng DeSgN usng 205 up- and 137 down-regulated genes, two drugs - geftnb and BIBW2992, were returned wth postve Connectvty Score (p-value < 0.05). As expected, geftnb was returned as the top-ranked nhbtor wth Connectvty Score of 1.00 and p-value < (Fg. 4). Interestngly, BIBW2992, also known as afatnb, a second generaton EGFR nhbtor, s ranked second wth a sgnfcant Connectvty Score of 0.93 (p-value = 0.021). For each of the four studes, DeSgN returned Connectvty Scores that correctly correlated drug response outcome that was consstent wth the respectve publshed GEO studes. In all these studes, DeSgN successfully assocated nput gene sgnatures wth the rght drugs, all wth statstcally sgnfcant p-values (Table 2). The lst of DEG of each study used to query DeSgN s provded n Addtonal fle 3: Table S3; Addtonal fle 4: Table S4; Addtonal fle 5: Table S5 and Addtonal fle 6: Table S6. Usng DeSgN to shortlst potentally effcacous nhbtors for OSCC cell lnes As we demonstrated that DeSgN could correctly predct drug response from publshed data, we next used DeSgN to dentfy nhbtors that could control the growth of OSCC cell lnes. The gene sgnature for dfferental gene Fg. 3 Example of a result page from DeSgN. Users can supply the dfferentally expressed genes for ther study n the boxes n the Panel (a). Addtonal nformaton such as lst of genes and drugs currently avalable n DeSgN can be found n Panel (b). Panel (c) shows the Connectvty Score results. Error messages (e.g. nvald gene symbols or redundant gene symbol) are produced n Panel (d) to alert users of potental problems wth nput data

7 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 7 of 11 Fg. 4 DeSgN predcton result for GSE4342. Geftnb s predcted to be senstve, wth sgnfcant Connectvty Score of 1.00 and p-value < expresson between OSCC cell lnes and NOK contaned 69 and 86 up- and down-regulated genes (Addtonal fle 7: Table S7). Nne potentally effcacous drugs were returned by DeSgN, wth another fve drugs were predcted to be resstant (Fg. 5), wth p-values < The rankng results corroborated well wth recent fndngs. Two of the canddates, BIBW2992 (ranked fourth) and bosutnb (ranked eghth), have been recently reported to be effectve aganst head and neck squamous cell carcnoma (HNSCC) cell lnes [25]. We set out to further evaluate the effcacy of bosutnb, whch targets Src and Abl, as t s a recently FDA-approved drug for treatng BCR-ABL leukemc patents and have no known effects aganst HNSCC or OSCC, therefore the effcacy of bosutnb s unantcpated when used aganst OSCC cell lnes. For expermental valdaton of bosutnb s effcacy aganst OSCC, we tested t n three OSCC cell lnes (ORL- 196, ORL-204 and ORL-48). All three OSCC cell lnes (Table 3, Addtonal fle 8: Fgure S8) were found to have sgnfcantly lower mean IC 50 value compared to ther senstve head and neck squamous cell carcnoma control (HSC-4, IC 50 : 1.82 μm). Aganst the resstant control, MCF-7, all three OSCC cell lnes also had sgnfcant lower mean IC 50 (Table 3, Addtonal fle 8: Fgure S8). Ths fndng s supported by fluorescence-actvated cell sortng (FACS) analyss of the cells where bosutnb nduced cell death n OSCC cell lnes n a tme-dependent manner (Fg. 6a, Addtonal fle 9: Table S9). In partcular, ORL-196 cells were found to be more responsve to bosutnb, as close to 35% of apoptotc cells were detected as early as 24 h of treatment, whle ORL-48 and ORL-204 remaned unaffected. By 72 h, a sgnfcant number of apoptotc cells (35 90%) were detected n all the OSCC cell lnes (p-values < 0.01), ndcatng the cytotoxc effect of bosutnb n these OSCC cells. Further confrmaton from the Clck-T EdU cell prolferaton assay showed clearly that bosutnb nhbted the prolferaton of ORL-48, ORL-196 and ORL-204 cells as demonstrated by the sgnfcant reducton n the number of prolferatng cells (red-staned cells) compared to the non-treated cells (Fg. 6b). ORL-196 and ORL-204 demonstrated growth nhbton of ~70 80% (p-value = 0.03, n = 3; p-value = 0.049, n = 2 respectvely) whlst ORL-48 showed growth nhbton of ~40% followng bosutnb treatment at 1 μm for72h(p-value = 0.04, n = 2) (Fg. 6c, Addtonal fle 10: Table S10 and Addtonal fle 11: Fgure S11). The level of nhbton n the OSCC cell lnes corroborated well wth ther mean IC 50 value for bosutnb. Taken together, these bologcal observatons demonstrated that bosutnb confers ant-prolferatve and cytotoxc effects n the tested OSCC cell lnes. Dscusson We have developed DeSgN, a web-based bonformatcs tool that allows users to query large publc database of cancer cell lne gene expresson and drug response data such as GDSC. We showed explctly that queryng DeSgN usng dfferentally expressed gene sgnatures Table 2 NCBI GEO datasets valdaton summary GEO reference Reported drug Expected drug senstvty DeSgN rank DeSgN drug Target Connectvty Score p-value GSE4342 Geftnb Senstve 1 Geftnb EGFR GSE16179 Lapatnb Senstve 6 Lapatnb EGFR, ERBB GSE9633 Dasatnb Senstve 6 Dasatnb ABL, SRC, KIT, PDGFR GSE35141 Gemctabne Resstant 129 Gemctabne DNA replcaton

8 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 8 of 11 Fg. 5 DeSgN predcton results for OSCC cell lnes. Nne drugs were predcted to be effcacous (blue box) whereas fve were predcted to have mnmal effcacy on the OSCC cell lnes (red box) could reveal potentally effcacous canddate drugs, as shown n the GSE4342 analyses. BIBW2992 (a newer generaton of EGFR nhbtor currently approved for treatng NSCLC patents who are refractory to geftnb and erlotnb), for example, could potentally replace geftnb, a frst-generaton EGFR tyrosne knase nhbtors (TKI) that s ncreasngly becomng a non-vable soluton as cancer cells of NSCLC patents treated wth geftnb nevtably develop resstance and relapse, wth 8 10 months of medan tme to progresson [26 28] To date, many cases of successful drug repurposng studes have been reported, an exemplary study beng that of methotrexate, a drug frst developed for treatng leukema, and subsequently repurposed to treat a wde spectrum of cancers rangng from breast, ovaran, bladder to head and neck cancers [29, 30]. Here, we demonstrated the success of DeSgN n gudng the selecton of bosutnb as a canddatedrugaganstoscc(asubsetofhnscc)celllnes. Emergng evdence supports the possble use of bosutnb for the treatment of HNSCC. Frst, the molecular target of bosutnb, Src has been reported to be a frequently altered gene n HNSCC and has been dentfed as a promsng drug target [31]. Second, an analyss of gene expresson Table 3 Mean IC 50 relatve to HSC-4 and MCF7 (μm) OSCC Cell lnes Mean IC 50 ± SE -log 10 (p-value) relatve to HSC-4 ORL-196 (n = 4) 0.75 ± ORL-204 (n = 3) 0.90 ± ORL-48 (n = 5) 1.19 ± HSC-4 (n = 3) 1.82 ± MCF7 (n = 3) ± log 10 (p-value) relatve to MCF7 data from 42 HNSCC cell lnes also predcted that bosutnb has ant-tumour effect on HNSCC [25]. To the best of our knowledge ths s the frst tme bosutnb was shown expermentally to have potency n OSCC cell lnes. Whle tools such as NFFnder, DMAP and FMCM that adopted the CMap concept make use of large publc databases such as GEO, DrugMatrx, STITCH and HAPPI as ther reference, DeSgN has ts unqueness whereby t explctly captalzes on the large panel of 707 human cancer cell lnes n GDSC that have well-characterzed gene expresson and drug response data (Table 4). Specfcally, DeSgN constructs drug-assocated gene expresson profle of resstant and senstve cell lnes from these 707 cell lnes, whereas CMap assocates response to a drug by constructng gene expresson profles of pre- and post-treatment condtons usng only four cell lnes. DeSgN utlzes the cumulatve gene expresson effect of many genes rather than one or a handful of genes, n ths case global baselne DEGs. We beleve through pan-cancer approach as suggested by The Cancer Genome Atlas (TCGA) Research Network, nherent genetc smlartes between human cancer cell lnes could result n the dentfcaton of relevant canddate drugs that have htherto not been tested [32]. The new leads derved from DeSgN are mportant for acceleratng the dscovery of new drugs for HNSCC treatment, whch s currently lmted to cetuxmab, where ths drug remans the only FDA-approved targeted therapy for advanced HNSCC [3]. Importantly, we would lke to emphasze that all canddates wth postve and sgnfcant Connectvty Score should be equally consdered for valdaton nstead of consderng just the few top-ranked canddates, snce factors such as cost of drug, ease of avalablty, method of admnsterng, sde

9 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 9 of 11 Fg. 6 Dfferental senstvty of OSCC cell lnes, ORL-48, ORL-196 and ORL-204 to bosutnb. a Bosutnb nduced apoptoss n OSCC cell lnes. ORL-48, ORL-196 and ORL-204 cells were treated wth 1 μm of bosutnb for 24, 48 and 72 h followed by Annexn V/PI stanng coupled wth flow cytometry analyss. The bars represent mean percentage of apoptotc cells ± SE of each cell lne of at least two experments. * denotes p-value < 0.05 relatve to control cells. b Bosutnb nhbted the prolferaton of OSCC cells as demonstrated by the reduced number of prolferatng cells (red staned cells) followng 72 h treatment at 1 μm. The blue-staned nucle represent the total number of cells n a feld whle the red-staned nucle represent prolferatng cells that have ncorporated the EdU label. c OSCC cell prolferaton was sgnfcantly nhbted by bosutnb wth ORL-196 showng the greatest senstvty (~80% nhbton) followed by ORL-204 (~70% nhbton) and ORL-48 (~50% nhbton) after bosutnb treatment at 1 μm for 72 h. * denotes sgnfcance of p-value < 0.05 effects and other factors, are mportant practcal consderatons n the clncal settng. The current mplementaton of DeSgN uses dfferentally expressed genes as startng ponts to assocate gene sgnatures wth drug response phenotype. Ths nput s not necessarly optmal, as genes that are nvolved n dysregulated pathways n the pathogeness of cancer may not always have ther expresson substantally altered [33]. Snce hgher-order nformaton such as network context and post-translatonal modfcaton ncludng reversble Table 4 Comparsons of tools that utlzed Connectvty Map concept Tools Relatonshp feature Reference database DeSgN Global baselne DEGs to drug response GDSC NFFnder Transcrptomc data to drugs, dseases and experts GEO, CMap and DrugMatrx DMAP Proten/gene to drug response STITCH and HAPPI FMCM Pre- and post-treatment gene CMap expresson to drug response phosphorylaton or acylaton are not explctly ntegrated n the current verson, future mprovements to DeSgN wll focus on ntegratng these types of data. For future work, we also ntend to expand drug coverage n Verson 1.0 of DeSgN by ncorporatng the gene expresson and drug response data from Cancer Therapeutcs Response Portal (CTRP) [34] and other largescale pharmacogenomcs studes. We antcpate that De- SgN wll evolve as more cell lne gene expresson and drug response data become avalable. Conclusons DeSgN provdes proof-of-concept for the feasblty of usng a computatonal approach to shortlst the most promsng drug canddates for effectve drug repurposng n cancer treatment. We expect that DeSgN wll contnue to evolve based on usage feedback from the communty of cancer researchers, as well as mprovements n methods for mnng gene sgnatures that have strong network context.

10 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 10 of 11 Addtonal fles Addtonal fle 1: Table S1. Lst of senstve and resstant cell lnes for each of the 140 drugs n DeSgN. (XLS 88 kb) Addtonal fle 2: Table S2. Dfferentally expressed genes from ERpostve breast cancer versus normal (GSE42568). (XLS 204 kb) Addtonal fle 3: Table S3. Dfferentally expressed genes from GSE4342. (XLS 56 kb) Addtonal fle 4: Table S4. Dfferentally expressed genes from GSE (XLS 266 kb) Addtonal fle 5: Table S5. Dfferentally expressed genes from GSE9633. (XLS 110 kb) Addtonal fle 6: Table S6. Dfferentally expressed genes from GSE (XLS 45 kb) Addtonal fle 7: Table S7. Dfferentally expressed genes from OSCC cell lnes. (XLS 38 kb) Addtonal fle 8: Fgure S8. Mean IC 50 of each cell lne from MTT assay. The bars represent mean IC 50 ± SE of at least three experments. (TIF 705 kb) Addtonal fle 9: Table S9. Mean apoptotc cells relatve to control (%). (XLS 40 kb) Addtonal fle 10: Table S10. Mean EdU + cells relatve to control (%). (XLS 47 kb) Addtonal fle 11: Fgure S11. Bosutnb sgnfcantly nhbts the prolferaton of OSCC cells n dose-dependent manner. OSCC cell lnes were treated wth bosutnb at μm for 72 h and the effect of bosutnb on cell prolferaton was determned by Clck-T cell prolferaton assay. * denotes p-value < 0.05 relatve to control untreated cells (0 μm). (TIF 785 kb) Abbrevatons CMap: Connectvty Map; DEG: Dfferentally expressed gene; DeSgN: Dfferentally Expressed Gene Sgnatures Inhbtors; GDSC: Genomcs of Drug Senstvty n Cancer; NOK: Normal oral keratnocyte; OSCC: Oral squamous cell carcnoma Acknowledgements The authors would lke to thank Me Fong Ng for her assstance n desgnng the Fg. 1 n ths study and Nur Syafnaz Zanal for techncal assstance. Cancer Research Malaysa s a non-proft research organzaton. We are commtted to an understandng of cancer preventon, dagnoss and treatment through a fundamental research program. Declaratons Ths artcle has been publshed as part of BMC Genomcs Volume 18 Supplement 1, 2016: Proceedngs of the 27th Internatonal Conference on Genome Informatcs: genomcs. The full contents of the supplement are avalable onlne at bmcgenomcs.bomedcentral.com/artcles/supplements/volume-18-supplement-1. Fundng Ths study and the subsequent costs of publcaton was funded by Hgh Impact Research, Mnstry of Hgher Educaton (HIR-MOHE) from Unversty of Malaya (UM.C/625/1/HIR/MOHE/DENT-03) and other sponsors of the Cancer Research Malaysa. Avalablty of data and materal The authors declare that [the/all other] data supportng the fndngs of ths study are avalable wthn the artcle [and ts supplementary nformaton fles]. Authors contrbutons BKBL carred out data analyss and prepared the manuscrpt. BKBL and KHT carred out the expermental valdaton. BKBL, JKC and CSL desgned, developed and mplemented the DeSgN web nterface. ZAAR, ACT, TFK and SCC conceved and supervsed the overall study, desgn the analyses, and partcpated n draftng and edtng of the manuscrpt. All authors have read, edted and approved the current verson of the manuscrpt. Competng nterests The authors declare that they have no competng nterests. Consent for publcaton Not applcable. Ethcs approval and consent to partcpate Not applcable, human or anmal subjects were not used n the study. Author detals 1 Department of Oral & Maxllofacal Clncal Scences, Faculty of Dentstry, Unversty of Malaya, Kuala Lumpur, Malaysa. 2 Oral Cancer Research Group, Cancer Research Malaysa, No. 1, Jalan SS12/1A, Subang Jaya, Selangor, Malaysa. 3 Data Intensve Computng Centre, Research Management & Innovaton Complex, Unversty of Malaya, Kuala Lumpur, Malaysa. 4 Department of Computer System & Technology, Faculty of Computer Scence & Informaton Technology, Unversty of Malaya, Kuala Lumpur, Malaysa. 5 Centre for Data Scence, Unversty of Malaya, Kuala Lumpur, Malaysa. 6 Dvson of Medcal Oncology, School of Medcne, Unversty of Colorado Anschutz Medcal Campus, Aurora, CO 80045, USA. 7 Insttute of Mathematcal Scences, Unversty of Malaya, Kuala Lumpur, Malaysa. Publshed: 25 January 2017 References 1. Hutchnson L, Krk R. Hgh drug attrton rates where are we gong wrong? Nat Rev Cln Oncol. 2011;8(4): Huang R, Southall N, Wang Y, Yasgar A, Shnn P, Jadhav A, Nguyen DT, Austn CP. The NCGC pharmaceutcal collecton: a comprehensve resource of clncally approved drugs enablng repurposng and chemcal genomcs. Sc Transl Med. 2011;3(80):80ps Vermorken JB, Mesa R, Rvera F, Remenar E, Kaweck A, Rottey S, Erfan J, Zabolotnyy D, Kenzer HR, Cupssol D, et al. Platnum-based chemotherapy plus cetuxmab n head and neck cancer. N Engl J Med. 2008;359(11): Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst RS, Ince WL, Janne PA, Januaro T, Johnson DH, et al. Mutatons n the epdermal growth factor receptor and n KRAS are predctve and prognostc ndcators n patents wth non-small-cell lung cancer treated wth chemotherapy alone and n combnaton wth erlotnb. J Cln Oncol. 2005;23(25): Pao W, Wang TY, Rely GJ, Mller VA, Pan Q, Ladany M, Zakowsk MF, Heelan RT, Krs MG, Varmus HE. KRAS mutatons and prmary resstance of lung adenocarcnomas to geftnb or erlotnb. PLoS Med. 2005;2(1):e Chapman PB, Hauschld A, Robert C, Haanen JB, Ascerto P, Larkn J, Dummer R, Garbe C, Testor A, Mao M, et al. Improved survval wth vemurafenb n melanoma wth BRAF V600E mutaton. N Engl J Med. 2011; 364(26): Shoemaker RH. The NCI60 human tumour cell lne antcancer drug screen. Nat Rev Cancer. 2006;6(10): Chen JJ, Knudsen S, Mazn W, Dahlgaard J, Zhang B. A 71-gene sgnature of TRAIL senstvty n cancer cells. Mol Cancer Ther. 2012;11(1): Garnett MJ, Edelman EJ, Hedorn SJ, Greenman CD, Dastur A, Lau KW, Grennger P, Thompson IR, Luo X, Soares J, et al. Systematc dentfcaton of genomc markers of drug senstvty n cancer cells. Nature. 2012;483(7391): Barretna J, Capongro G, Stransky N, Venkatesan K, Margoln AA, Km S, Wlson CJ, Lehár J, Kryukov GV, Sonkn D, et al. The Cancer Cell Lne Encyclopeda enables predctve modellng of antcancer drug senstvty. Nature. 2012;483(7391): Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanan A, Ross KN, et al. The Connectvty Map: usng gene-expresson sgnatures to connect small molecules, genes, and dsease. Scence. 2006;313(5795): Setoan J, Franch M, Martnez M, Tabas-Madrd D, Sorzano CO, Bakker A, Gonzalez-Couto E, Elvra J, Pascual-Montano A. NFFnder: an onlne bonformatcs tool for searchng smlar transcrptomcs experments n the context of drug repostonng. Nuclec Acds Res. 2015;43(W1):W Huang H, Nguyen T, Ibrahm S, Shantharam S, Yue Z, Chen JY. DMAP: a connectvty map database to enable dentfcaton of novel drug repostonng canddates. BMC Bonformatcs. 2015;16 Suppl 13:S Chung FH, Chang YR, Tseng AL, Sung YC, Lu J, Huang MC, Ma N, Lee HC. Functonal Module Connectvty Map (FMCM): a framework for searchng repurposed drug compounds for systems treatment of cancer and an applcaton to colorectal adenocarcnoma. PLoS One. 2014;9(1):e86299.

11 The Author(s) BMC Genomcs 2017, 18(Suppl 1):934 Page 11 of Subramanan A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gllette MA, Paulovch A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrchment analyss: a knowledge-based approach for nterpretng genome-wde expresson profles. Proc Natl Acad Sc U S A. 2005;102(43): Rtche ME, Phpson B, Wu D, Hu Y, Law CW, Sh W, Smyth GK. lmma powers dfferental expresson analyses for RNA-sequencng and mcroarray studes. Nuclec Acds Res. 2015;43(7):e Xao Y, Hsao TH, Suresh U, Chen HI, Wu X, Wolf SE, Chen Y. A novel sgnfcance score for gene selecton and rankng. Bonformatcs. 2014;30(6): Clarke C, Madden SF, Doolan P, Aherne ST, Joyce H, O Drscoll L, Gallagher WM, Hennessy BT, Morarty M, Crown J, et al. Correlatng transcrptonal networks to breast cancer survval: a large-scale coexpresson analyss. Carcnogeness. 2013;34(10): Fadlullah MZ, Chang IK, Donne KR, Yee PS, Gan CP, Sam KK, Tong KH, Ng AK, Martn D, Lm KP, et al. Genetcally-defned novel oral squamous cell carcnoma cell lnes for the development of molecular therapes. Oncotarget. 2016;7(19): Love MI, Huber W, Anders S. Moderated estmaton of fold change and dsperson for RNA-seq data wth DESeq2. Genome Bol. 2014;15(12): Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. EBImage an R package for mage processng wth applcatons to cellular phenotypes. Bonformatcs. 2010;26(7): El-Masry OS, Brown BL, Dobson PR. Effects of actvaton of AMPK on human breast cancer cell lnes wth dfferent genetc backgrounds. Oncol Lett. 2012;3(1): Gunzer K, Joly F, Ferrero JM, Glgorov J, de Mont-Serrat H, Uttenreuther- Fscher M, Pellng K, Wnd S, Bousquet G, Msset JL. A phase II study of afatnb, an rreversble ErbB famly blocker, added to letrozole n patents wth estrogen receptor-postve hormone-refractory metastatc breast cancer progressng on letrozole. Sprngerplus. 2016;5: Coldren CD, Helfrch BA, Wtta SE, Sugta M, Lapadat R, Zeng C, Baron A, Frankln WA, Hrsch FR, Gerac MW, et al. Baselne gene expresson predcts senstvty to geftnb n non-small cell lung cancer cell lnes. Mol Cancer Res. 2006;4(8): Nchols AC, Black M, Yoo J, Pnto N, Fernandes A, Habe-Kans B, Boutros PC, Barrett JW. Explotng hgh-throughput cell lne drug screenng studes to dentfy canddate therapeutc agents n head and neck cancer. BMC Pharmacol Toxcol. 2014;15: Maemondo M, Inoue A, Kobayash K, Sugawara S, Ozum S, Isobe H, Gemma A, Harada M, Yoshzawa H, Knoshta I, et al. Geftnb or chemotherapy for non-small-cell lung cancer wth mutated EGFR. N Engl J Med. 2010;362(25): Sequst LV, Waltman BA, Das-Santagata D, Dgumarthy S, Turke AB, Fdas P, Bergethon K, Shaw AT, Gettnger S, Cosper AK, et al. Genotypc and hstologcal evoluton of lung cancers acqurng resstance to EGFR nhbtors. Sc Transl Med. 2011;3(75):75ra Stnchcombe TE. Recent advances n the treatment of non-small cell and small cell lung cancer. F1000Prme Rep. 2014;6: Vortherms AR, Dang HN, Doyle RP. Antcancer conjugates and cocktals based on methotrexate and nucleosde synergsm. Cln Med Oncol. 2009;3: Gupta SC, Sung B, Prasad S, Webb LJ, Aggarwal BB. Cancer drug dscovery by repurposng: teachng new trcks to old dogs. Trends Pharmacol Sc. 2013;34(9): Pckerng CR, Zhang J, Yoo SY, Bengtsson L, Moorthy S, Neskey DM, Zhao M, Ortega Alves MV, Chang K, Drummond J, et al. Integratve genomc characterzaton of oral squamous cell carcnoma dentfes frequent somatc drvers. Cancer Dscov. 2013;3(7): Cancer Genome Atlas Research Network, Wensten JN, Collsson EA, Mlls GB, ShawKR,OzenbergerBA,EllrottK,ShmulevchI,SanderC,StuartJM,etal.The Cancer Genome Atlas Pan-Cancer analyss project. Nat Genet. 2013;45(10): de la Fuente A. From dfferental expresson to dfferental networkng - dentfcaton of dysfunctonal regulatory networks n dseases. Trends Genet. 2010;26(7): Basu A, Bodycombe NE, Cheah JH, Prce EV, Lu K, Schaefer GI, Ebrght RY, Stewart ML, Ito D, Wang S, et al. An nteractve resource to dentfy cancer genetc and lneage dependences targeted by small molecules. Cell. 2013;154(5): Lu L, Greger J, Sh H, Lu Y, Greshock J, Annan R, Halsey W, Sathe GM, Martn AM, Glmer TM. Novel mechansm of lapatnb resstance n HER2-postve breast tumor cells: actvaton of AXL. Cancer Res. 2009;69(17): Wang XD, Reeves K, Luo FR, Xu LA, Lee F, Clark E, Huang F. Identfcaton of canddate predctve and surrogate molecular markers for dasatnb n prostate cancer: ratonale for patent selecton and effcacy montorng. Genome Bol. 2007;8(11):R Sak Y, Yoshno Y, Fujmura H, Manabe T, Kudo Y, Shmada M, Mano N, Nakano T, Lee Y, Shmzu S, et al. DCK s frequently nactvated n acqured gemctabne-resstant human cancer cells. Bochem Bophys Res Commun. 2012;421(1): Submt your next manuscrpt to BoMed Central and we wll help you at every step: We accept pre-submsson nqures Our selector tool helps you to fnd the most relevant journal We provde round the clock customer support Convenent onlne submsson Thorough peer revew Incluson n PubMed and all major ndexng servces Maxmum vsblty for your research Submt your manuscrpt at