Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs

BMC Bioinformatics

Volumn 13, Issue 1, 2012, Pages

  Khan, Suleiman A ^a   Faisal, Ali ^a   Mpindi, John P ^b   Parkkinen, Juuso A ^a   Kalliokoski, Tuomo ^c   Poso, Antti ^d   Kallioniemi, Olli P ^b   Wennerberg, Krister ^b   Kaski, Samuel ^a,b  

^a AALTO UNIVERSITY   (Finland)

^b UNIVERSITY OF HELSINKI   (Finland)

^c NOVARTIS PHARMA AG   (Switzerland)

^d UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL EFFECTS; BIOLOGICAL FEATURES; BIOLOGICAL RESPONSE; CANCER CELL LINES; CANCER CELLS; CELL LINES; CELLULAR RESPONSE; CHEMICAL AND BIOLOGICALS; CHEMICAL FEATURES; CHEMICAL SPACE; CHEMICAL SYSTEMS; DATA-DRIVEN ANALYSIS; DESCRIPTORS; DNA DAMAGE RESPONSE; DRUG EFFECTS; HYDROPHOBIC PROPERTIES; INTEGRATED ANALYSIS; LEUKEMIA CELLS; LIVING CELL; METABOLIC STRESS; MICROARRAY GENE EXPRESSION; MOLECULAR PROPERTIES; MULTIPLE TARGETS; PROTEIN TARGETS; SPECIFIC CHEMICAL STRUCTURES; STRUCTURE-FUNCTION RELATIONSHIP; VOLSURF;

BIOELECTRIC POTENTIALS; CELL CULTURE; CELLS; CHEMICAL COMPOUNDS; DIGITAL STORAGE; GENE EXPRESSION; HYDROGEN BONDS; STRUCTURAL PROPERTIES;

CHEMICAL ANALYSIS;

ANTINEOPLASTIC AGENT; PHARMACOLOGICAL BIOMARKER; TRANSCRIPTOME;

ARTICLE; CHEMISTRY; DNA MICROARRAY; DRUG EFFECT; GENE EXPRESSION PROFILING; GENETICS; HUMAN; HUMAN GENOME; METHODOLOGY; NEOPLASM; STATISTICS; STRUCTURE ACTIVITY RELATION; SYSTEMS BIOLOGY;

ANTINEOPLASTIC AGENTS; BIOMARKERS, PHARMACOLOGICAL; GENE EXPRESSION PROFILING; GENOME, HUMAN; HUMANS; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIP; SYSTEMS BIOLOGY; TRANSCRIPTOME;

EID: 84861545549     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-13-112     Document Type: Article

References (30)

1. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, Whaley R, Glennon RA, Hert J, Thomas KL, Edwards DD, Shoichet BK, Roth BL. Predicting new molecular targets for known drugs. Nature 2009, 462:175-191. 10.1038/nature08506, 2784146, 19881490.
2. Iskar M, Campillos M, Kuhn M, Jensen LJ, Noort VV, Bork P. Drug-Induced Regulation of Target Expression. PLoS Computational Biology 2010, 6(9).
3. Cramer MRD III, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988, 110(18).
4. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 2006, 313(5795):1929-1935. 10.1126/science.1132939, 17008526.
5. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology 2008, 4(11):682-690. 10.1038/nchembio.118, 18936753.
6. Tatonetti NP. Predicting drug side-effects by chemical systems biology. Genome Biology 2009, 10(238).
7. Spicker JS, Brunak S, Frederiksen KS, Toft H. Integration of Clinical Chemistry, Expression, and Metabolite Data Leads to Better Toxicological Class Separation. Toxicological Sciences 2008, 102(2):444-454.
8. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, di Bernardo D. Discovery of drug mode of action and drug repositioning from transcriptional responses. PNAS 2010, 107(33):14621-14626. 10.1073/pnas.1000138107, 2930479, 20679242.
9. Hotelling H. Relations between two sets of variants. Biometrika 1936, 28:321-327.
10. Atias N, Sharan R. An Algorithmic Framework for Predicting Side-Effects of Drugs. Journal of Computational Biology 2011, 18(3):207-218. 10.1089/cmb.2010.0255, 21385029.
11. Cruciani G, Pastora M, Gubab W. VolSurf, a new tool for the pharmacokinetic optimization of lead compounds. Eur. J. Pharm. Sci 2000, 11(2):29-39.
12. Naylor E, Arredouani A, Vasudevan SR, Lewis AM, Parkesh R, Mizote A, Rosen D, Thomas JM, Izumi M, Ganesan A, Galione A, Churchill GC. Identification of a chemical probe for NAADP by virtual screening. Nature Chemical Biology 2009, 5(4):220-226. 10.1038/nchembio.150, 2659327, 19234453.
13. Blower PE, Yang C, Fligner MA, Verducci JS, Yu L, Richman S, Weinstein J. Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. The Pharmacogenomics Journal 2002, 2(4):259-271. 10.1038/sj.tpj.6500116, 12196914.
14. Cheng T, Wang Y, Bryant SH. Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules. Bioinformatics 2010, 26(22):2881-2888. 10.1093/bioinformatics/btq550, 2971579, 20947527.
15. Wang J, Wang J, Dai J, Jung Y, Wei CL, Wang Y, Havens AM, Hogg PJ, Keller ET, Pienta KJ, Nor JE, Wang CY, Taichman RS. A glycolytic mechanism regulating an angiogenic switch in prostate cancer. Cancer Res. 2007, 67(1):149-59. 10.1158/0008-5472.CAN-06-2971, 17210694.
16. Gessner T, Vaughan LA, Beehler BC, Bartels CJ, Baker RM. Elevated pentose cycle and glucuronyltransferase in daunorubicin-resistant P388 cells. Cancer Res 1999, 50(13):3921-7.
17. Scoumanne A, Chen X. Protein methylation: a new mechanism of p53 tumor suppressor regulation. Histol Histopathol 2008, 23(9):1143-9. 2762123, 18581285.
18. Venna J, Peltonen J, Nybo K, Aidos H, Kaski S. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal Machine Learning Research 2010, 11:451-490.
19. Venna J, Kaski S. Nonlinear dimensionality reduction as information retrieval. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on Artificial Intelligence and Statistics. Omnipress 2007. JMLR Workshop and Conference Proceedings, Vol. 2: AISTATS 2007.
20. Prassas I, Diamandis EP. Novel therapeutic applications of cardiac glycosides. Nat Rev Drug Discov 2008, 7(11):926-35. 10.1038/nrd2682, 18948999.
21. Vasilyeva A, Clodfelter JE, Gorczynski MJ, Gerardi AR, King SB, Salsbury F, Scarpinato KD. Parameters of Reserpine Analogs That Induce MSH2/MSH6-Dependent Cytotoxic Response. J Nucleic Acids 2010, 2010(2010):162018. 2946608, 20936178.
22. Sanz M, Burnett A, Lo-Coco F, Löwenberg B. FLT3 inhibition as a targeted therapy for acute myeloid leukemia. Curr Opin Oncol 2009, 21(6):594-600. 10.1097/CCO.0b013e32833118fd, 19684517.
23. Irizarry RA. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 2003, 31(4).
24. Wold H. Estimation of principal components and related models by iterative least squares 1966, 391-420. Multivariate, Analysis.
25. Leurgans SE, Moyeed RA, Silverman BW. Canonical Correlation Analysis when the Data are Curves. Journal of the Royal Statistical Society B 1993, 55(3):725-740.
26. Gonzalez I, Dejean S, . CCA An R Package to extend Canonical Correlation Analysis. Journal of Statistical Software 2008, 23(12). CCA.
27. Bickel P, Li B. Regularization in Statistics. TEST 2006, 15(2):271-344.
28. Caldas J, Gehlenborg N, Faisal A, Brazma A, Kaski S. Probabilistic retrieval and visualization of biologically relevant microarray experiments. Bioinformatics 2009, 25(12):145-153. ISMB/ECCB.
29. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics 2001, 17:509-519. 10.1093/bioinformatics/17.6.509, 11395427.
30. Kuemmerle NB, Rysman E, Lombardo PS, Flanagan AJ, Lipe BC, Wells WA, Pettus JR, Froehlich HM, Memoli VA, Morganelli PM, Swinnen JV, Timmerman LA, Chaychi L, Fricano CJ, Eisenberg BL, Coleman WB, Kinlaw WB. Lipoprotein lipase links dietary fat to solid tumor cell proliferation. Mol Cancer Ther 2011, 10:427-436. 10.1158/1535-7163.MCT-10-0802, 3074101, 21282354.