A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer

Journal of Clinical Bioinformatics

Von-Wun Soo²  Hsiang-Yuan Yeh¹  Shih-Heng Yeh² 

[1] Department of Computer Science, National Tsing Hua University, HsinChu 300, Taiwan;Institute of Information Systems and Applications, National Tsing Hua University, HsinChu 300, Taiwan

关键词: prostate cancer;    interactome network;    gene expression;    network flow;    Drug target prediction;   

Others  :  806129
DOI  :  10.1186/2043-9113-2-1

 received in 2011-09-22, accepted in 2012-01-13,  发布年份 2012

【 摘 要 】

Background

Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.

Methods

We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.

Results

We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.

Conclusions

Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

【 授权许可】

   
2012 Yeh et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Vogelstein B, Kinzler KW: Cancer genes and the pathways they control. Nat Med 2004, 10(8):789-99.
• [2]Smith C: Hitting the target. Nature 2003, 422:341-347.
• [3]Drews J: Drug Discovery: A historical perspective. Science 2000, 287:1960-1964.
• [4]Lamb J, Crawford E, Peck D, Modell J, Blat I, Wrobel M, Lerner J, Brunet J, Subramanian A, Ross K, Reich M, Hieronymus H, Wei G, Armstrong S, Haggarty S, Clemons P, Wei R, Carr S, Lander E, Golub T: The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313:1929-1935.
• [5]Campillos M, Kuhn M, Gavin A, Jensen L, Bork P: Drug target identification using side-effect similarity. Science 2008, 321:263-266.
• [6]Cheng AC, Coleman RG, Smyth KTL: Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 2007, 25:71-75.
• [7]Bleakley K, Yamanishi Y: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 2009, 25:2397-2403.
• [8]Keiser MJ, Setola V, Irwin JJ: Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
• [9]Yamanishi Y, Araki M, Gutteridge A: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008, 24:i232-i240.
• [10]Kuhn M, Campillos M, Gonzalez P: Large-scale prediction of drug-target relationships. FEBS Lett 2008, 582:1283-1290.
• [11]Bakheet TM, Doig AJ: Properties and identification of antibiotic drug targets. BMC Bioinformatics 2010, 20:195.
• [12]Dolan ME, Ni L, Camon E, Blake JA: A procedure for assessing go annotation consistency. Bioinformatics 2005, 21(suppl 1):i136-i143.
• [13]Barabasi AL, Oltvai ZN: Network biology: Understanding the cell's functional organization. Nature Reviews Genetics 2004, 5(2):101-13.
• [14]Holme P, Kim BJ, Yoon CN, Han SK: Attack vulnerability of complex networks. Phys Rev E 2002, 65(5):1-14.
• [15]Bossi A, Lehner B: Tissue specificity and the human protein interaction network. Mol Syst Biol 2009, 5:260.
• [16]Estrada E: Protein bipartivity and essentiality in the yeast protein-protein interaction network. Journal of Proteome Research 2006, 5(9):2177-2184.
• [17]Newman MEJ: A measure of betweenness centrality based on random walks. Social Networks 2005, 27:39-54.
• [18]Hormozdiari F, Salari R, Bafna V, Sahinalp SC: Protein-protein interaction network evaluation for identifying potential drug targets. J Comput Biol 2010, 17(5):669-684.
• [19]Walhout AJM: Unraveling transcription regulatory networks by protein-DNA and protein-protein interaction mapping. Genome Research 2006, 16:1445-1454.
• [20]Wang CY, Chen BS: Integrated cellular network of transcription regulations and protein-protein interactions. BMC Systems Biology 2010, 4:20.
• [21]Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525.
• [22]Kelley B, Sharan R, Karp R, Sittler T, Root D, Ideker T: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci 2003, 100:11394-11399.
• [23]Prieto C, De Las Rivas J: APID: Agile Protein Interaction Data Analyzer. Nucleic Acids Research 2006, 1(34):W298-302.
• [24]Liu HC, Arias CR, Soo VW: BioIR:An approach to public domain resource integration of human protein-protein interaction. The proceeding of the Asia Pacific Bioinformatics Conference (APBC) 2009.
• [25]Yeh HY, Cheng SW, Lin YC, Yeh CY, Lin SF, Soo VW: Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency. BMC Medical Genomics 2009, 2:70. BioMed Central Full Text
• [26]Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, Bumgarner RE, Schadt EE: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics 2008, 40:854-861.
• [27]Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, Gifford DK: Computational discovery of gene modules and regulatory networks. Nat Biotechnol 2003, 21(11):1337-1342.
• [28]Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic acids research 2008, 36:D901-906.
• [29]McKusuck VA: Mendelian inheritance in man and its online version. The American Journal of Human Genetics 2007, 80(4):588-604.
• [30]Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Research 2004, 1:277-280.
• [31]Li LC, Zhao H, Shiina H, Kane CJ, Dahiya R: PGDB: a curated and integrated database of genes related to the prostate. Nucleic Acids Research 2003, 31(1):291-293.
• [32]Goldberg A, Tarjan R: A new approach to the maximum-flow problem. Journal of the ACM (JACM) 1988, 35(4):921-940.
• [33]Cherkassky BV, Golderg AV: On implementing push relabel method for the maximum flow problem. Algorithmica 1994, 19:390-410.
• [34]Floyd RW: Algorithm 97: Shortest Path. Communications of the ACM 1992, 5:345.
• [35]Sridhar P, Kahveci T, Ranka S: An iterative algorithm for metabolic network-based drug target identification. Pacific Symposium on Biocomputing 2007, 12:88-99.
• [36]Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR: Gene expression profiling identifies clinically relevant subtypes of prostate cancer. In Proceedings of the National Academy of Sciences USA 2004, 101(3):811-816.
• [37]Sherlock G, Hernandez-Boussard T, Kasarskis A, Binkley G, Matese JC, Dwight SS, Kaloper M, Weng S, Jin H, Ball CA, Eisen MB, Spellman PT, Brown PO, Botstein D, Cherry JM: The stanford microarray database. Nucleic Acids Research 2001, 29:152-155.
• [38]Savli H, Szendroi A, Romics I, Nagy B: Gene network and canonical pathway analysis in prostate cancer: a microarray study. Experimental and Molecular Medicine 2008, 40(2):176-185.
• [39]Wang H, Yu D, Agrawal S, Zhang R: Experimental therapy of human prostate cancer y inhibiting mdm2 expression with novel mixed-backbone antisense oligonucletides: in vitro and in vivo activities and mechanisms. Prostate 2003, 54:194-205.
• [40]Kim JH, Xu C, Keum YS, Reddy B, Conney A, Tony Kong AN: Inhibition of EGFR signaling in human prostate cancer PC-3 cells by combination treatment with s-phenylethyl isothiocyanate and curcumin. Carcinogenesis 2006, 27:475-482.
• [41]Festuccia C, Gravina GL, Biordi L, D'Ascenzo S, Dolo V, Ficorella C, Ricevuto E, Tombolini V: Effects of EGFR tyrosine kinase inhibitor erlotinib in prostate cancer cells in vitro. The Prostate 2009, 69:1529-1537.
• [42]Chen A, Tsau YW, Lin CH: Novel methods to identify biologically relevant genes for leukemia and prostate cancer from gene expression profiles. BMC Genomics 2010, 11:274. BioMed Central Full Text
• [43]Jones E, Pu H, Kyprianou N: Targeting TGF-beta in prostate cancer: therapeutic possibilities during tumor progression. Expert Opin Ther Targets 2009, 13(2):227-34.
• [44]Rochester MA, Riedemann J, Hellawell GO, Brewster SF, Macaulay VM: Silencing of the IGF1R gene enhances sensitivity to DNA-damaging agents in both PTEN wild-type and mutant human prostate cancer. Cancer Gene Therapy 2005, 12:90-100.
• [45]Junya F, Christopher JW, Brett PM, Martin EG, Michael EC: ATL1101 Prostate Cancer Drug Tumour Suppression Data to be Presented at US Cancer Meeting. Antisense Therapeutics 2010.
• [46]Hardy S, Tremblay ML: Protein tyrosine phosphatases: new markers and targets in oncology? Current Oncology 2008, 15(1):5-8.
• [47]Trudel D, Fradet Y, Meyer F, Harel F, Tetu B: Significance of MMP-2 expression in prostate cancer: an immunohistochemical study. Cancer Research 2003, 63(23):8511-8515.
• [48]Gautschi O, Heighway J, Mack PC, Purnell PR, Lara PN Jr, Gandara DR: Aurora kinases as anticancer drug targets. Clinical Cancer Research 2008, 14(6):1639-1648.
• [49]Flamand V, Zhao H, Peehl DM: Targeting monoamine oxidase A in advanced prostate cancer. Journal of Cancer Research and Clinical Oncology 2010, 136(11):1761-1771.
• [50]Terracciano D, Mazzarella C, Di Carlo A, Mariano A, Ferro M, Di Lorenzo G, Giordano A, Altieri V, De Placido S, Macchia V: Effects of the ErbB1/ErbB2 kinase inhibitor GW2974 on androgen-independent prostate cancer PC-3 cell line growth and NSE, chromogranin A and osteopontin content. Oncol Rep 2010, 24(1):213-217.
• [51]Delsite R, Djakiew D: Anti-proliferative effect of the kinase inhibitor K252a on human prostatic carcinoma cell lines. Journal of Andrology 1996, 17:481-490.
• [52]Demetrius L, Thomas M: Robustness and network evolution--an entropic principle. Physica A: Statistical Mechanics and its Applications 2005, 3(4):682-696.
• [53]Sole RV, Valverde S: Information Theory of Complex Networks: on Evolution and Architectural Constraints. Springer 2004, 207(4):1-15.
• [54]JUNG - Java Universal Network/Graph Framework [http://jung.sourceforge.net/] webcite
• [55]Pearson K: The problem of the Random Walk. Nature 1905, 72:294.
• [56]Davis J, Goadrich M: The Relationship Between Precision-Recall and ROC Curves. In Proceedings of the Twenty-Third International Conference on Machine Learning. Pittsburgh, PA; 2006.