From NPC Therapeutic Target Identification to Potential Treatment Strategy

Published OnlineFirst August 17, 2010; DOI: 10.1158/1535-7163.MCT-09-0966

Therapeutic Discovery Molecular Cancer Therapeutics From NPC Therapeutic Target Identification to Potential Treatment Strategy

Ming-Ying Lan1,3,10, Chi-Long Chen6,7, Kuan-Ting Lin4,8, Sheng-An Lee11, Wu-Lung R. Yang9, Chun-Nan Hsu8,12, Jaw-Ching Wu3, Ching-Yin Ho5,10, Jin-Ching Lin2,3, and Chi-Ying F. Huang3,4

Abstract Nasopharyngeal carcinoma (NPC) is relatively rare in Western countries but is a common cancer in southern Asia. Many differentially expressed genes have been linked to NPC; however, how to prioritize therapeutic targets and potential drugs from unsorted gene lists remains largely unknown. We first collected 558 upregulated and 993 downregulated NPC genes from published microarray data and the primary literatures. We then postulated that conversion of gene signatures into the protein-protein interaction network and analyzing the network topologically could provide insight into key regulators involved in tumorigenesis of NPC. Of particular interest was the presence of cliques, called fully connected subgraphs, in the inferred NPC networks. These clique-based hubs, connecting with more than three queries and ranked higher than other nodes in the NPC protein-protein interaction network, were further narrowed down by pathway analysis to retrieve 24 upregulated and 6 downregulated bottleneck genes for predicting NPC carcinogenesis. Moreover, additional oncogenes, tumor suppressor genes, genes involved in protein complexes, and genes obtained after functional profiling were merged with the bottleneck genes to form the final gene signature of 38 upregulated and 10 downregulated genes. We used the initial and final NPC gene signatures to query the Connectivity Map, respectively, and found that target reduction through our pipeline could efficiently uncover potential drugs with cytotoxicity to NPC cancer cells. An integrative Web site (http://140.109.23.188:8080/NPC) was established to facilitate future NPC research. This in silico approach, from target prioritization to potential drugs identification, might be an effective method for various cancer researches. Mol Cancer Ther; 9(9); 2511–23. ©2010 AACR.

Introduction with a complex interaction of genetic, EBV exposure, environmental, and dietary factors. Although some on- Nasopharyngeal carcinoma (NPC) is a rare malignan- cogenes, tumor suppressor genes, and microarray cy in most parts of the world but is one of the most expression data have been previously reported in common cancers among those of Chinese or Asian an- NPC, a complete understanding of the pathogenesis of cestry. The etiology of NPC is thought to be associated NPC in the context of global gene expression remains to be elucidated (1–9). Protein-protein interactions (PPI) are important for vir- Authors' Affiliations: Departments of 1Otolaryngology and 2Radiation tually every biological process. In a PPI network, nodes Oncology, Taichung Veterans General Hospital, Taichung, Taiwan; having more than one connection with another node are Institutes of 3Clinical Medicine and 4Biomedical Informatics, and 5Department of Medicine, School of Medicine, National Yang-Ming defined as hubs and are more likely to be essential (10, University; 6Department of Pathology, Taipei Medical University; 11).ThekeychallengefacingadiseasePPInetworkis 7Department of Pathology, WanFang Hospital; 8Institute of Information Science, Academia Sinica; 9Department of Computer Science and the identification of a node or combination of nodes in Information Engineering, National Taiwan University; and 10Department the network whose perturbation might result in a desired of Otolaryngology, Taipei Veterans General Hospital, Taipei, Taiwan; therapeutic outcome. We have previously constructed an 11Department of Computer Science and Information Engineering, Kainan University, Taoyuan, Taiwan; and 12Information Sciences integrated PPI web service, POINeT (12), as a bioinfor- Institute, University of Southern California, Marina del Rey, California matics tool to construct and to analyze the NPC network Note: Supplementary material for this article is available at Molecular in this study. Cancer Therapeutics Online (http://mct.aacrjournals.org/). In addition to elucidating the pathogenesis of NPC, the Corresponding Authors: Chi-Ying F. Huang, Institute of Clinical Medi- refinement of current treatment modalities is also impor- cine, National Yang-Ming University, Taipei 112, Taiwan. Phone: 886- tant. Although NPC is highly radiosensitive and chemo- 228267904; Fax: 886-228224045. E-mail: [email protected] and Jin-Ching Lin, Department of Radiation Oncology, Taichung Veterans sensitive, the treatment of patients with locoregionally General Hospital, Taichung 114, Taiwan. Phone: 886-423592525 advanced disease remains problematic. Many specific ext. 5613; Fax: 886-423741316. E-mail: [email protected] molecular-targeted therapies, epigenetic therapies, and doi: 10.1158/1535-7163.MCT-09-0966 EBV-based immunotherapy have been developed and ©2010 American Association for Cancer Research. are in clinical trials (Supplementary Table S1; ref. 1).

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It is not clear how to elucidate key regulators and iden- we have developed CliquePOINT, which was embedded tify potential drugs for NPC treatment. To address these into POINeT, to calculate these cliques in the NPC PPI questions, we first collected NPC-associated genes network. Expanding the definition of the 3-clique, we also (http://140.109.23.188:8080/NPC) and hypothesized counted the number of 4-cliques and 5-cliques in the NPC that the PPI network, derived from the gene signature, PPI network, and there is no clique larger than 5-cliques in could be analyzed topologically to prioritize potential the NPC PPI network. targets. We further performed pathway analysis and ap- We further collected and integrated the complex infor- plied gene signatures to drug-gene interaction databases mation to obtain an abundant data set from public do- and Connectivity Map (cMap; refs. 13, 14) to find poten- main databases, including the Human Protein Reference tial drugs for the treatment of NPC. It is supposed that a Database (20), the Protein Interacting in the Nucleus da- small molecule may potentially reverse the disease signa- tabase (21), and the Comprehensive Resource of Mamma- ture if the molecule-induced signature is significantly lian protein complexes (22), and asked whether the negatively correlated with the disease-induced signature cliques identified from the PPI network were involved in cMap (15–17). In short, identifying potential drugs to in protein complexes. The cliques having more than three treat NPC by using an in silico screening approach fol- proteins involved in complexes were reported. lowed by empirical validation might be easier and faster Ranking the hubs in the PPI network. To elucidate the than those traditional drug discovery pipelines that re- relative roles of each node, we analyzed node centrality quire tremendous effort and time. through POINeT, including degree centrality (DC), closeness centrality (CC), and eccentricity centrality (EC). DC Materials and Methods is the number of link incident upon a node. CC repre- sents the closeness between nodes in the biological net- Computational methods work. EC is the longest distance required for a given Acquiring NPC-related gene sets and constructing node to reach the entire network. By conducting central- NPC PPI network. Two major components constituted ity calculation, nodes in global networks can be ranked the NPC-related gene expression signatures in this and filtered using various network analysis formulas. study. One component included the collection of the The enriched pathways from the ConsensusPathDB microarray profiles from three studies (Supplementary overrepresentation analysis. We used ConsensusPathDB Table S2; refs. 4, 5, 7). All microarray data were the re- (23) to perform overrepresentation analysis on the four sult of nontreated NPC tissues compared with normal sets of gene lists: (a) upregulated genes in NPC, (b) down- nasopharyngeal tissues. regulated genes in NPC, (c), upregulated genes after The second part of the gene collections consisted of the clique analysis, and (d) downregulated genes after text mining of NPC-related PubMed abstracts. By March clique analysis. The significant pathway results were of 2008, we extracted 4,939 abstracts from PubMed con- ranked by using an F score instead of the P value given taining the keyword “Nasopharyngeal carcinoma” but by ConsensusPathDB. The F score was used to normalize not having the keywords “SNP” or “polymorphism.” To two parameters: (a) the percentage of overlapping genes further extract the genes mentioned in the abstracts, we in the pathway and (b) the percentage of overlapping first entered all these abstracts into the Adaptive Internet genes in the input list. To normalize these, we used the Intelligent Agents laboratory's Gene Mention Tagger (18). following formula: The Gene Name Service (19) was used to translate these gene names into corresponding gene identifiers, such as 2ðA BÞ theofficialgenesymbolandtheEntrezgeneID.Then, Fscore ¼ ðA þ BÞ we manually read the top 10 abstracts with most genes mentioned from the above method and another 150 abstracts published from 2007 to 2008 to further annotate the genes as upregulated or downregulated genes. These We compared the P values to evaluate whether the P genes and annotations can be accessed through our Web values degrade after clique analysis and thereby give site (http://140.109.23.188:8080/NPC), and the Web site each pathway a score of degradation (0 for No and 1 tools used in this study are summarized in Supplementa- for Yes). ry Table S3. We inputted the above-collected NPC-related The final NPC gene signature. The 98 up-clique and 51 genes as query terms into the POINeT (12) to detect the down-clique genes were used as queries to perform func- PPI in NPC. tional annotation clustering on the Database for Annota- Evaluation of cliques and complexes from the PPI tion, Visualization, and Integrated Discovery (DAVID; network. The cliques of the PPI network were calculated ref. 24), respectively. The clustering was done on seven from the following definition of cliques, a term borrowed pathway resources: BBID, BIOCARTA, EC_NUMBER, from Graph Theory. A clique is a part of a graph in which KEGG_COMPOUND, KEGG_PATHWAY, KEGG_REAC- all its nodes are completely connected to each other. In TION, and PANTHER_PATHWAY. The classification other words, a 3-clique is a completely connected graph stringency was set to “Medium.” For each cluster, we fur- of three nodes, which is a triangle. From this definition, ther intersected the genes of the pathways to obtain the

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“bottleneck” genes. Twenty-four upregulated and six immunohistochemistry (IHC) with the institutional re- downregulated bottleneck genes were obtained. view board approval. Briefly, 5 to 6 μm of paraffin sec- Among cliques, those, including oncogenes, tumor tions were deparaffinized and placed into citrate buffer suppressor genes, genes involved in complex, and genes for antigen retrieval once placed inside a microwave found by group functional profiling, were added into the oven. After the sections were cooled down and rinsed bottleneck genes list to obtain the final gene signature of with PBS, the sections were incubated with 5% normal NPC. It includes 38 upregulated and 10 downregulated goat serum followed by reaction with primary antibody genes (Supplementary Table S4). for 30 minutes at room temperature, then washed with Hierarchical clustering the final gene signature in PBS thrice at 3 minutes each. The sections were reacted KEGG pathways. We used the final gene signature as with biotinylated second antibody followed by streptavi- queries to conduct the functional annotation clustering din- biotin complex in the LsAB detection kit (DAKO) at of DAVID against the KEGG pathway database. A perl room temperature for 10 minutes and washed with PBS script was written to convert the pathway records (P < again. The sections were colorized using freshly prepared 0.05) into a gct file, which can be uploaded onto GenePat- diaminobenzidine solution containing H2O2 for 2 to 5 tern to perform hierarchical clustering and visualization. minutes. After washing with running water and counter- For upregulated and downregulated genes, the values staining with hematoxylin, the sections were dehydrated are 1 (red) and −1 (green), respectively. The distance mea- and mounted. Positive staining showed brownish granu- sure for both genes (row) and pathways (column) was set lar deposits in the nuclei of cells. Adenocarcinoma and to “Pearson correlation, absolute value.” normal mucosa gland of the colon were used as positive Food and Drug Administration–approved drug targets. and negative controls, respectively, for the expression of To collect target genes of Food and Drug Administra- p53 and MYC, whereas follicular lymphoma was used tion (FDA)–approved drugs, the chemical-protein links for the positive and negative control of the expression of from STITCH (25) was downloaded. Then, Gene Name BCL2 and BAX (Supplementary Fig. S1). Service (19) was used to translate the protein ID to its Cell culture and cell viability test. NPC cell lines, corresponding HUGO-approved gene symbol and En- TW01, TW03, and TW04 provided by Dr. C.T. Lin trez gene ID. The DrugCard file from Drug Bank (26) (National Taiwan University, Taoyuan, Taiwan), were was downloaded. We selected FDA-approved drugs, derived from primary nasopharyngeal tumors of Chinese mapped the drugs' corresponding genes with the NPC patients with de novo NPC and had been tested and upregulated genes, and finally identified known drug authenticated (27). NPC cell line BM1, provided by targets in the NPC upregulated PPI network. Dr. S.K. Liao (Chang Gung University, Taoyuan, Taiwan), Applying NPC gene signatures to cMap. Functional was derived from bone metastatic lesions of an NPC pa- connections between various NPC gene signatures and tient (28). NPC cell lines were maintained in DMEM with gene signatures induced by small molecules were ex- 10% fetal bovine serum containing penicillin (100 U/mL) μ plored using the cMap database (13, 14). The upregulated and streptomycin (100 g/mL) in 5% CO2 at 37°C. Cell genes were grouped and their probe sets formed the up viability was determined using the 2,3-bis[2-methoxy-4- tag file and so did the downregulated genes. These two nitro-5-sulfophenyl]-2H-tetrazolium-5-carboxanilide files were used to query the cMap database, and the re- inner salt (XTT) cell viability assay kit (Sigma-Aldrich), sults showed the most significant similarities and dissim- according to the manufacturer's instructions. Twenty- ilarities to the database profiles. The 558 up and 993 down four hours after seeding cells at a concentration of 2 × genes would convert to >1,000 probe sets. Because the 103 cells per well in 100 μL culture medium in a 96-well cMap could only take up to 1,000 probe sets per input, microplate, cells were then treated with Trichostatin A three groups of NPC genes were used. The first group con- (Sigma-Aldrich) and Trifluoperazine (Sigma-Aldrich), sists of 100 randomly chosen sets of 100 upregulated/ the selected small molecules from cMap. Cells were downregulated probe sets from whole 558 up and 993 exposed with or without small molecules for 72 hours down NPC gene signature. The second group consists of at different concentrations. Then, the cells were incubated 399 upregulated and 443 downregulated probe sets, with medium containing XTT in an amount equal to 20% which represent first 70% ranked queries served as hubs. of the culture medium volume for 2 hours. Absorbance The third group, the final gene signature, consists of 38 up was measured using a microplate reader (Spectral genes and 10 down genes. Only drugs with negative Max250) at 450 nm. scores and a P value of <0.05 were retained. Results Biological methods Immunohistochemical analysis in NPC. Formalin- NPC gene collections fixed, paraffin-embedded biopsy specimens of 143 NPC To systematically analyze the gene expression signa- cases were collected and analyzed for detection of the ex- tures of NPC and identify potential drugs for NPC, we pression of p53 (mouse anti-human p53, 1:50) and BCL2 have set up in silico approaches (Fig. 1). We collected (mouse anti-BCL2, 1:80; DAKO), BAX (mouse anti-BAX, the NPC gene sets from two sources: one gene set from 1:400), and MYC (mouse anti-MYC, 1:50; Santa Cruz) by PubMed with 70 upregulated and 78 downregulated

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Inferred NPC PPI network To uncover the potential interaction networks of these seemingly unrelated NPC upregulated and downregulated genes, the Web site tool, POINeT, was used to detect the PPI in NPC, and results were summarized in Supplementary Table S5. Despite many queries without interacting proteins, based on our PPI collections in POI- NeT, the queries of NPC-related proteins formed a highly connected interactome. A total of 8,231 and 7,728 PPIs were identified in the upregulated and downregulated NPC PPI networks, respectively. The fundamental struc- tural details revealed that 257 of 558 NPC upregulated genes interact with each other and form 492 query-query PPIs, constituting the interaction networks. On the other hand, 324 of 993 NPC downregulated queries form 395 query-query PPIs.

The inferred NPC network consists of highly interactive cliques and complexes Of particular interests in the inferred NPC PPI network is the presence of cliques (29), which refer to completely connected subgraphs. Nodes within a clique have interactions with all the others. In our analysis, the NPC query-query network contains 198 and 21 subgraphs of cliques in upregulated and downregulated genes, respectively. In the upregulated PPI network, there are 170 3-cliques,264-cliques, and 2 5-cliques (Supplementary Table S6; Fig. 2A). The count of cliques in NPC PPI network is much higher than the count of cliques in a random PPI network (Supplementary Fig. S2). The top 30 proteins involved in cliques are listed and ranked by the number of associated cliques (Supplementary Table S7). BRCA1, MYC, EGFR, TP53, and CDC2 are the top five proteins participating in a large number of cliques. The analysis of node centrality characteristics may provide insights into the relative roles and features of each node. To address whether clique proteins are relatively Figure 1. Schematic illustration of in silico approaches to narrow down more important hubs in the PPI network, we prioritized NPC genes for target identification and potential drug discovery. We first the nodes of the major subnetwork, which consists of 247 extracted and reorganized 558 upregulated and 933 downregulated gene query proteins (or nodes), in the NPC upregulated PPI signatures from PubMed and various microarray studies. Then, we network (Supplementary Table S8). The 3,725 nodes of derived the 98 upregulated clique and 51 downregulated clique genes from the PPI network by clique analysis. These clique genes were used level one major subnetwork, which consists of query pro- to query DAVID for pathway analysis to obtain 24 upregulated and teins with neighbor nodes, were also ranked. Different 6 downregulated bottleneck genes that curb multiple pathways. The ranking methods, including DC, EC, and CC, were used. cancer-related pathways were used to search for the drugs currently Those nodes, which are also clique proteins, are ranked under clinical trials. These bottleneck genes, combined with oncogenes and genes found by group functional profiling, were used to query the higher than those that are not clique proteins (Fig. 3). DrugBank and STITCH. To increase the number of the query genes used Because cliques have more interactions than the rest in cMap, additional genes that appeared in complexes were added. of the graph, and these protein interactions may be re- A total of 38 upregulated and 10 downregulated genes were used as sponsible for the formation of protein complexes or func- final gene signature for querying cMap to identify potential dugs. tional modules (30), we further integrated and searched for protein complexes from the Human Protein Reference Database (20), the Comprehensive Resource of Mamma- genes, and the other from three major microarray studies lian protein complexes (22), and the Protein Interacting in (Supplementary Table S2; refs. 4, 5, 7) with 512 upregu- the Nucleus database (21). Of upregulated cliques, there lated genes and 936 downregulated genes. By merging are five 3-cliques and four 4-cliques involved in five these two data sets, the gene expression signature of protein complexes (Table 1; Fig. 2B). The DNA syn- NPC contained 558 upregulated genes and 993 down- thesome, also known as the DNA replication complex, regulated genes (http://140.109.23.188:8080/NPC). consists of 15 subunits, including DNA polymerase,

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DNA topoisomerase, and the replication factor C com- and the TNF-α/NF-κB pathway. To the best of our knowl- plex (31). The replication factor C complex is a hetero- edge, this finding will be the first report to provide the pentameric protein that is essential for DNA replication relationship between these complexes and NPC carcino- and repair, and is also a clamp loader required for the genesis. Few proteins in the above five complexes that loading of PCNA onto dsDNA (32–34). The BASC are related to NPC include RFC1, PCNA, TOP1, ATM, complex, BRCA1-associated genome surveillance that MLH1, RPL21, and RPL31. consists of ATM, BLM, MSH2, MSH6, MLH1, and RF- C, is involved in the recognition and repair of aberrant Oncogenes and tumor suppressor genes in NPC DNA structure (35). Another complex, the hNop56p- clique genes associated preribosomal ribonucleoprotein complex, is Six oncogenes, including EGFR, ERBB2, MYC, RELB, associated with ribosome biogenesis (36). Interestingly, NFKB2,andCCND1,werefoundinthe4-cliques and many proteins are shared in these complexes. Finally, 5-cliques from the inferred upregulated NPC network. there is one complex involved in the tumor necrosis Overexpression of these oncogenes in NPC, except factor-α (TNF-α)f/NF-κB pathway (37). The above find- ERBB2, was suggested to be related to NPC carcinogen- ing raises the possibility that NPC pathogenesis might esis (Supplementary Table S7; refs. 38–42). Three tumor be related to aberrant DNA replication, DNA repair, suppressor genes were found in the 51 downregulated

Figure 2. Highly interactive cliques and complexes are associated with NPC gene signature. A, 4-cliques and 5-cliques of NPC PPI network. The query-query interaction network of the NPC upregulated genes is a highly connected network that contains 26 4-cliques and two 5-cliques. The two 5-cliques are grouped in red circles. Yellow, oncogenes; green, tumor suppressor genes. BRCA1, TP53, MYC, EGFR, and CDC2 are the top five proteins involved in the largest number of cliques. B, five major complexes associated with NPC upregulated gene signatures. Red, upregulated genes; green, downregulated genes. Dark red, clique genes (upregulated cliques); dark green, clique genes (downregulated cliques).

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It is interesting to note that there are three tumor suppressor genes found in the 98 upregulated cliques, in- cludingBRCA1,TP53,andFAS.Briefly,BRCA1,a nuclear phosphoprotein, plays a role in maintaining ge- nomic stability. Mutations in BRCA1 are responsible for ∼40% of inherited breast cancers and >80% of inherited breast and ovarian cancers; however, its expression in NPC is still unknown. TP53 encodes the tumor protein p53, which responds to diverse cellular stresses to regu- late target genes that induce cell cycle arrest, apoptosis, senescence, and DNA repair. In normal cells, p53 is rap- idly turned over by a negative feedback loop mediated by MDM2. Mutant p53, noted in 30% to 50% of cancer, is unable to induce MDM2 transcription and escapes degradation, thereby leading to its accumulation at a very high level in cancer (44). Although p53 levels are high in NPC, the mutation of TP53 gene is relatively rare. Accumulated p53 in NPC is believed to be mediated by EBV LMP1 (9, 40, 45). Two reasons have been proposed to explain why wild-type p53 fails to induce apoptosis in NPC: low ARF levels due to promoter hypermethylation and excess mutated p63. Wild-type p53 function may be eliminated by the inactivation of the ARF gene, which encodes proteins that sequester MDM2 from antagonizing p53 (44). Mutated p63, which lacks the NH2-terminal transactivation domain required to activate apoptosis, binds to normal p63 (and p53; ref. 9). FAS protein is a member of the TNF receptor superfamily and contains Figure 3. In the inferred NPC PPI network, queries with characteristics of a death domain. It plays a central role in the physio- cliques belong to the top-ranked targets as determined by centrality logic regulation of programmed cell death and has calculation. The nodes of the major subnetwork (query-query PPI) and been implicated in the pathogenesis of various malig- level one major subnetwork of the NPC upregulated PPI network are ranked by DC, CC, and EC. Gray, nodes of the major subnetwork (A) nancies and diseases of the immune system. Fas ligand and level one major subnetwork (B). Red, nodes, also clique proteins. overexpression is an unfavorable prognostic marker in Ninety-eight queries that participated in the inferred cliques are ranked NPC (46, 47). relatively higher than the other nodes in the NPC PPI major subnetwork and level one major subnetwork. Findings by gene group functional profiling To address how our NPC signature might turn biological process term groups (by Gene Ontology) on or off, 98 clique genes, including CDKN1A, MLH1, and ATM. Both upregulated and 51 downregulated clique genes were CDKN1A and ATM are downregulated in NPC (7, 43). subjected to g:Profiler, respectively (48). A large biologi- The description of these genes can be accessed through cal process term group is shared by both upregulated and our Web site (http://140.109.23.188:8080/NPC). downregulated clique genes. The group is mainly related

Table 1. Five major complexes associated with NPC after analysis using three public domain databases

Complex (total proteins numbers) Proteins involved in complex and Clique numbers NPC gene signature

DNA synthesome (15) TOP1, TOP2A, RFC1, RFC3, RFC2, RFC5 2 Replication factor C complex (5) RFC1, RFC3, RFC2, RFC5 2 BASC complex (9) BRCA1, RFC1, RFC2, MSH6, MLH1, ATM 1 hNop56p-associated preribosomal NCL, NPM1, TOP1 1 ribonucleoprotein complexes (104) TNF-α/NF-kB pathway (12) NFKB2, NFKBIA, RELB 1

NOTE: The proteins involved in complexes and proteins that are in NPC upregulated cliques are listed.

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to the regulation of biological processes, cell cycle, cell pathway, and cell cycle. It implies that the ATM could death, and cell development. These important biological be an important missing piece in NPC. functions are altered, thereby leading to the activation of To investigate how the final NPC gene signature con- p53 to deal with the disturbed physiologic circumstances. nect with each other in pathways, we manually referred Among the downregulated clique genes, three genes, in- the KEGG pathways to draw a possible molecular mech- cluding CDKN1A, HDAC3, and PRKCZ, are shown to be anism of NPC carcinogenesis (Fig. 5A). To confirm the ex- related to the “regulation of programmed cell death” and pression of selected final upregulated genes, IHC studies the “regulation of apoptosis” by using Traceable author. of TP53, BCL2, BAX, and MYC were done. All of them The genes with Traceable author references in the upre- are overexpressed in tumor cells (Fig. 5B). gulated clique genes in the phosphorylation group are ERBB2, STAT1, and TYK2 (Supplementary Fig. S3). Over- FDA-approved drug targets all, we used gene group profiling to further identify three To annotate the NPC upregulated genes with FDA- downregulated genes and three upregulated genes that approved drug targets, we integrated databases from relate to the growth of tumors. STITCH (25) and Drug Bank (26). We thereby derived 566 and 827 drug target upregulated and downregu- Pathway analysis of NPC gene signature lated genes, respectively. Two hundred eighty-nine To find the enriched pathways of our NPC gene signa- and 203 FDA-approved drugs target up-clique and ture, we performed an overrepresentation pathway anal- down-clique genes, respectively (Supplementary Table ysis on ConsensusPathDB (23). Under the threshold of a S12). The 191 drugs target up-bottleneck genes and on- P value of <0.01, there were 484 enriched pathways for cogenes (Supplementary Table S13), whereas 100 drugs upregulated genes and 222 enriched pathways for down- target down-bottleneck genes and tumor suppressor regulated genes in the original NPC signature; 409 and genes (Supplementary Fig. S4). Some well-known chemo- 294 enriched pathways were found for upregulated and therapeutic agents already used in several cancers are downregulated genes, respectively, by using the clique among the top 100 drug target up-clique genes. These analysis. To avoid the complication that small pathways drugs include paclitaxel, doxorubicin, etoposide, and cis- are relatively easier to rank higher according to their platin. Many of these drugs are being studied in NPC clin- P value, we used the F score to normalize the ranking. ical trials (Supplementary Table S1), suggesting that our From the results of the intersection of the top 100 en- target prioritizations, particularly those not currently riched pathways of upregulated gene signature, many being used in clinical trials, might reveal potential thera- pathways are directly related to cancer, such as the p53 peutic agents for the treatment of NPC, alone or in com- signaling pathway, cell cycle–related pathways, bladder bination with older chemotherapeutic agents. cancer pathways, lung cancer pathways, prostate cancer pathways, and pancreatic cancer pathways (Supplemen- Finding candidate drugs for NPC from drugs being tary Table S9 and S10). Moreover, most of the enriched used or being studied in clinical trials in cancers pathways and their P values did not degrade after clique whose pathways are related to NPC analysis, suggesting that the clique analysis tends to re- From the results of the pathway analysis, NPC may move genes not involved in the enriched pathways of our be related to several cancer pathways, including pros- NPC gene signature. tate cancer, bladder cancer, pancreatic cancer, chronic Furthermore, we performed another pathway analysis myeloid leukemia, colorectal cancer, and small cell lung for NPC final gene signature (Supplementary Table S4) cancer. We derived 1,692 chemical names with 3,603 by using DAVID. The clustering result shows that the clinical trial records of the six types of cancers with re- final gene signature can be divided into three groups fined search limited on drug from the ClinicalTrials da- (Supplementary Table S11). All groups are closely relat- tabase (Supplementary Table S3). By intersecting the ed to cancers, signaling, and cell communications. This chemical names with 289 up-clique drugs, we obtained analysis provides a convenient way to biologically inter- 106 up-clique drugs under clinical trials. We then man- pret at the “biological module” level (24). To provide a ually selected 83 drugs that are used as antitumor more insightful view of the relationships between the drugs in those clinical trials. Of the 83 drugs, 11 drugs final gene signature and KEGG pathways, we down- are under NPC clinical trials (Supplementary Table S1). loaded the pathway records (P < 0.01) to perform hier- Moreover, 66 of the 83 drugs are targeting up-bottleneck archical clustering (Fig. 4) using GenePattern. Most of genes and oncogenes. After excluding the drugs already the pathways are shown to have downregulated genes in clinical trial for NPC, 57 drugs remain (Supplementary that might cause disruption, whereas there are five Table S14). These candidate drugs might be important pathways having no downregulated blocks. They are potential drugs for future NPC treatment. In addition, 26 amyotrophic lateral sclerosis, Jak-STAT signaling, adipo- chemotherapeutic agents suggested to treat these cancers cytokine signaling, neurodegenerative disease, and cell at different stages were retrieved from the National Com- communication pathways. In addition, the tumor sup- prehensive Cancer Network clinical practice guidelines pressor, ATM, is shown to be downregulated in only (Supplementary Table S15). Individual or combined usage antitumor pathways such as apoptosis, p53 signaling of the above FDA-approved drugs may improve current

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Figure 4. The heat map showing KEGG pathways with corresponding NPC final gene signature. In a given pathway, the upregulated genes and the downregulated genes are denoted as the red blocks and the green blocks, respectively. Amyotrophic lateral sclerosis, Jak-STAT signaling, adipocytokine signaling, neurodegenerative disease, and cell communication are the pathways without downregulated genes in the figure.

NPC treatment with enhanced therapeutic effects and signatures to query the cMap database. The first group minimized side effects. are genes randomly selected from whole NPC 559 upregulated and 993 downregulated gene signature; the Identifying potential small molecules for NPC second group consists of first 70% ranked queries treatment by applying NPC gene signatures to cMap served as hubs; the third group are the final gene signa- Bioactive small molecules in cMap that reverse the ture, consisting of 38 upregulated and 10 downregu- gene signature of NPC may be the potential drugs to lated genes. By querying cMap with the first, the kill NPC cells. We used three groups of NPC gene second, and the third group of genes, there are 6, 8,

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and 8 drugs respectively among the 10 top-ranked small Discussion molecules with antitumor effect (either from cell viability tests or PubMed literatures; Table 4). Here, we Here, we constructed NPC gene signature from the show cell viability tests of two drugs, trichostatin A microarray data of NPC tissues due to poor correlation and trifluoperazine, whose gene signatures in cMap sig- between the microarray data of the NPC cell lines and nificantly negatively correlated with gene signatures of tissues (4). Although we can also find potential drugs, NPC (Fig. 6A and B). Trichostatin A, a member of his- e.g., trichostatin A and trifluoperazine, for NPC by tone deacetylase inhibitors, has been used with other NPC cell line signature (Supplementary Table S16), antineoplastic agents in several clinical trials. Trifluoper- we believe the gene signature collected from many pa- azine, a typical antipsychotic drug of the phenothiazine tient profiles, but not cell line derived from one patient, group, can induce apoptosis of B16 melanoma cells (49) is more representative of the NPC disease. In fact, and leukemic cells (50). Both of them may have poten- several studies have identified promising drugs by tial for treating NPC in the future. querying cMap with the gene signatures of tumor

Figure 5. Possible molecular mechanism of NPC carcinogenesis by NPC bottleneck genes and IHC of selected proteins. A, possible molecular mechanism of NPC carcinogenesis. Red blocks, genes upregulated in NPC; blue blocks, downregulated genes. Gene names marked in red are oncogenes, and gene names marked in green are tumor suppressor genes. Arrow, activation; gray line, inhibition. If two blocks are close to each other, they form complexes. Bigger red arrow, the pathway reinforced because of the lack of inhibitor and existing of enhancer. B, IHC of selected proteins in NPC tumor. The tumor cells of NPC samples were positive for p53 (A, a), BCL2 (B, b), BAX (C, c), and MYC (D, d) by IHC. The sections were developed by diaminobenzidine and counterstained with hematoxylin. The expression of p53 was mainly in the nuclei of tumor cells; the BCL2 and BAX were mainly in the cytoplasm; and the MYC was presented in nuclei and cytoplasm of the target cells. Origin magnification, ×200 (A, B, C, and D); Origin magnification, ×400 (a, b, c, and d).

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Table 2. The top 15 proteins ranked by different centrality in the major subnetwork

Rank DC EC CC

1 GRB2 EGFR EGFR 2 MYC EPB41 GRB2 3 TP53 ERBB2 MYC 4 EGFR ADA TP53 5 BRCA1 CDH3 BRCA1 6 NFKB2 PDCD6IP NPM1 7 CDC2 PDCD6 CDC2 8 EPB41 SH2B3 PRKDC 9 SMAD1 SH2D3A MAPK1 10 ERBB2 SCAMP3 KPNB1 11 STAT3 LRPPRC ESR1 12 NFKBIA TOB1 RPS27A 13 HDAC2 CDK2 ABL1 14 STAT1 TNK2 NCL 15 BCL2 CDK4 STAT3

NOTE: The clique proteins are in bold.

method. From both the clique and complex analysis, we hypothesize that perturbation of these cliques and/or complexes, which do not act independently but act Figure 6. cMap analysis results. Dose-dependent cytotoxicity of Trichostatin A (A) and Trifluoperazine (B). NPC cell lines were incubated intimately in an intertwined network, might result in a with various concentrations of Trichostatin A and Trifluoperazine for 72 h. defective NPC PPI network. It is likely that targeting them Cell viability was evaluated by XTT cell viability assay. Columns, mean with new therapies may be another way to treat NPC. from three independent experiments; bars, SD. Furthermore, the clique proteins were used for pathway analysis to obtain the bottleneck genes, which

samples (15–17). Nevertheless, continuous effort to collect additional NPC signature from various NPC cell lines is required. Table 3. The top 15 proteins ranked by To ask whether genes participate individually or inter- different centrality in the level one major act with each other to form a network, we hypothesize subnetwork that converting gene signature into PPI network might help reveal the potential “hubs” in the inferred network. Rank DC EC CC Hence, we use POINeT to construct the NPC PPI net- 1 MYC GRB2 MYC work. From the inferred NPC PPI network, although 2 TP53 PTMA EGFR the downregulated network contained more query genes 3 GRB2 STAT3 BRCA1 than the upregulated network, the number of PPIs of the 4 CDC2 MYC TP53 downregulated network is less than that of the upregu- 5 EGFR ADA CDC2 lated network (Supplementary Table S5). The data sug- 6 BRCA1 CDC2 KPNA2 gest that the upregulated PPI network in NPC is more 7 EPB41 NUP155 GRB2 compact than the downregulated PPI network. 8 STAT3 CD44 KPNB1 It is difficult to prioritize targets involved in the carcino- 9 STAT1 GTF3C4 NPM1 genesis of NPC, especially for those differentially ex- 10 HDAC2 CCT6A PRKDC pressed signatures from multiple sources. Cliques 11 NFKB2 NPM1 CSE1L having more interactions within themselves than with 12 PRKDC NFYA STAT1 the rest of the graph may be an essential modularity in 13 EZR NFKBIA STAT3 the network (30). As clique proteins are ranked higher than 14 CDK4 NFKB2 HDAC2 those that are not clique proteins (Tables 2 and 3; Fig. 3), 15 KPNB1 NCL EPB41 this implies that clique proteins might play important roles in the NPC PPI network. Further validating these ranking NOTE: The clique proteins are in bold. results is needed to find the most appropriate hub-ranking

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were combined with those being also oncogenes, tumor to lessen the effect of BCL2. High level of MYC, an on- suppressor genes, and genes involved in complex and coprotein, activates the CCND1 and DNK4/6 complex, genes found by group functional profiling to form the which results in cell proliferation. Validation the ex- final gene signature for NPC (Supplementary Table S4). pression of additional prioritized targets by IHC is The final gene signature is effective to depict the pos- needed to further elucidate NPC carcinogenesis. sible molecular mechanism of NPC carcinogenesis (Fig. The final gene signature is also used to query cMap for 5A). We further performed IHC studies of selected four finding potential drugs (Table 4). In cMap analysis, there upregulated genes from final gene signatures, including are fewer genes in the third group (the final gene signa- TP53, BCL2, BAX, and MYC. Consistent with our anal- ture) compared with the first group (randomization from ysis, all of them were upregulated (Fig. 5B). High level the original gene set), but more promising drugs on the of TP53 has been suggested to be related to EBV as top-ranked drug list are obtained (Table 4). This indicated previously discussed (9, 40, 45). In regard to the apo- that the narrowed down method is effective to filter ptosis pathway, although BCL2 acts as an antiapoptosis somehow noisy genome-wide data. Although there are protein by inhibiting BAX, upregulation of BAX seems only 38 upregulated and 10 downregulated genes in

Table 4. Top 10 small molecules in cMap analysis queried by NPC gene signatures

Rank Group 1 Description

1 Trichostatin A HDAC inhibitors 2 LY-294002 PI3-kinase inhibitor 3 Vorinostat HDAC inhibitors 4 Medrysone Corticosteroid 5 Sirolimus Mammalian target of rapamycin inhibitor 6 Resveratrol Antibacterial and antifungal phytoalexin 7 Meticrane diuretic 8 Phthalylsulfathiazole A synthetic opioid, potent analgesic 9 Sulconazole Antifungal 10 Tanespimycin HSP 90 inhibitor

Rank Group 2 Description

1 Trichostatin A HDAC inhibitors 2 LY-294002 PI3-Kinase inhibitor 3 Sirolimus Mammalian target of rapamycin inhibitor 4 Tanespimycin HSP 90 inhibitor 5 Trifluoperazine Antipsychotic, phenothiazine, calmodulin inhibitor 6 Prochlorperazine Dopamine D2-receptor antagonist 7 Phthalylsulfathiazole A potent analgesic, analogue of pethidine 8 Thiostrepton Antibiotic, also targeting forkhead box M1 9 Resveratrol Antibacterial and antifungal phytoalexin 10 Bufexamac Drug used for joint and muscular pain

Rank Group 3 Description

1 Trichostatin A HDAC inhibitors 2 Ifenprodil A selective inhibitor of the NMDA receptor 3 Etacrynic acid Loop diuretic, inhibit cysteine proteases 4 Miconazole Antifungal 5 Nortriptyline Antidepressant, TCA 6 Fulvestrant ER downregulator 7 Trifluoperazine Antipsychotic, phenothiazine, calmodulin inhibitor 8 Gossypol Inhibitor of antiapoptotic Bcl-2 family proteins 9 Astemizole Antihistamine, histamine H1-receptor antagonist 10 Perphenazine Antipsychotic, phenothiazines

NOTE: Drugs in boldface are those with antitumor effects shown either by cell viability tests or literatures (PubMed).

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the final gene signature, these genes are highly represen- genes. This inventory provides a niche for NPC PPI net- tative of NPC gene signature. However, which gene se- work construction, target prioritization, and potential lection method (including the randomization method) is drug identification. The interaction between prioritized most appropriate for querying cMap needs further exper- NPC targets (e.g., cliques and bottleneck genes) and drugs imental investigation. highlights a promising approach to address disease-related By finding FDA-approved drug targets in the NPC networks and to uncover potential new therapeutic upregulated PPI network, we obtain possible candidate agents. Validating potential drugs found by in silico ap- drugs for treating NPC (Supplementary Table S12 and proaches both in vitro and in vivo are required in the future. S13). It is supposed that when the drug disturbs more crucial nodes in the NPC PPI, the NPC network may Disclosure of Potential Conflicts of Interest be destroyed. We further surveyed the ClinicalTrials database. Those drugs being used in clinical trials for No potential conflicts of interest were disclosed. treating several cancers whose pathway related to Grant Support NPC pathogenesis may have a better chance to treat NPC, especially those 66 drugs targeting up-bottleneck Grants from National Service Center grant NSC98-3112-B-010-021- genes and oncogenes. There are 9 drugs (13.6%) of the CC1 (C.Y. Huang) and grant NSC92-2320-B-038-057 (C.L. Chen), grants 66 drugs already under NPC clinical trials. One can from Ministry of Education, Aim for the Top University Plan (National Yang Ming University), Taipei Veterans General Hospital (V99ER2-011) choose the remaining 57 potential drugs (Supplement- and Center of Excellence for Cancer Research at Taipei Veterans General ary Table S14), which fit the above criteria, for future Hospital (DOH99-TD-C-111-007; C.Y. Huang), and a grant from Taipei Medical University-Center of Excellence for Cancer Research (C.L. Chen NPC clinical trials. and C.Y. Huang). The costs of publication of this article were defrayed in part by the payment Conclusions of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. To the best of our knowledge, this is the first integrative Web site (http://140.109.23.188:8080/NPC) to depict Received 10/25/2009; revised 06/02/2010; accepted 06/28/2010; the expression patterns of differentially expressed NPC published OnlineFirst 08/17/2010.

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From NPC Therapeutic Target Identification to Potential Treatment Strategy

Ming-Ying Lan, Chi-Long Chen, Kuan-Ting Lin, et al.

Mol Cancer Ther 2010;9:2511-2523. Published OnlineFirst August 17, 2010.

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