Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

BMC Cancer

Volumn 15, Issue 1, 2015, Pages

  Dong, Zuoli ^a   Zhang, Naiqian ^a   Li, Chun ^b   Wang, Haiyun ^c   Fang, Yun ^a   Wang, Jun ^a   Zheng, Xiaoqi ^a  

^a SHANGHAI NORMAL UNIVERSITY   (China)

^b BOHAI UNIVERSITY   (China)

^c TONGJI UNIVERSITY   (China)

Author keywords

Drug sensitivity prediction; Feature selection; Recursive feature elimination

Indexed keywords

5 (2,6 DICHLOROBENZYLSULFONYL) 3 [3,5 DIMETHYL 4 [2 (1 PYRROLIDINYLMETHYL) 1 PYRROLIDINYLCARBONYL] 1H PYRROL 2 YLMETHYLENE] 1,3 DIHYDRO 2H INDOL 2 ONE; 5 CHLORO 4 [[2 (ISOPROPYLSULFONYL)PHENYL]AMINO] 2 [[2 METHOXY 4 [4 (4 METHYL 1 PIPERAZINYL) 1 PIPERIDINYL]PHENYL]AMINO]PYRIMIDINE; AEW 541; ANTINEOPLASTIC AGENT; CHIR 265; CRIZOTINIB; DOVITINIB; ERLOTINIB; IRINOTECAN; LAPATINIB; LBW 242; N (2,3 DIHYDROXYPROPOXY) 3,4 DIFLUORO 2 (2 FLUORO 4 IODOANILINO)BENZAMIDE; N [3 (5 CHLORO 1H PYRROLO[2,3 B]PYRIDINE 3 CARBONYL) 2,4 DIFLUOROPHENYL]PROPANESULFONAMIDE; NUTLIN 3; PACLITAXEL; PALBOCICLIB; PANOBINOSTAT; SARACATINIB; SELUMETINIB; SORAFENIB; TANESPIMYCIN; TOPOTECAN; UNCLASSIFIED DRUG; VANDETANIB; TUMOR PROTEIN;

ARTICLE; CANCER CELL LINE; CANCER CELL LINE ENCYCLOPEDIA; CARCINOGENESIS; CLINICAL EFFECTIVENESS; CONCEPTUAL FRAMEWORK; CORRELATIONAL STUDY; DATA ANALYSIS; DATA BASE; DRUG SENSITIVITY; GENE EXPRESSION; GENOMICS; MACHINE LEARNING; MEASUREMENT ACCURACY; PERSONALIZED MEDICINE; PREDICTION; RECURSIVE FEATURE SELECTION TOOL; SUPPORT VECTOR MACHINE; TREATMENT RESPONSE; VALIDATION STUDY; BIOSYNTHESIS; DRUG EFFECTS; GENE EXPRESSION REGULATION; GENETICS; HUMAN; NEOPLASMS; PATHOLOGY; TUMOR CELL LINE;

ANTINEOPLASTIC AGENTS; CELL LINE, TUMOR; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; NEOPLASM PROTEINS; NEOPLASMS; PRECISION MEDICINE; SUPPORT VECTOR MACHINE;

EID: 84934276279     PISSN: None     EISSN: 14712407     Source Type: Journal    
DOI: 10.1186/s12885-015-1492-6     Document Type: Article

References (29)

1. Griffith LG, Swartz MA. Capturing complex 3D tissue physiology in vitro. Nat Rev Mol Cell Bio. 2006;7(3):211-24.
2. Richmond A, Su YJ. Mouse xenograft models vs GEM models for human cancer therapeutics. Dis Model Mech. 2008;1(2-3):78-82.
3. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity (vol 483, pg 603, 2012). Nature. 2012;492(7428):290-290.
4. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570-U587.
5. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer. 2006;6(10):813-23.
6. Heiser LM, Jwang N, Talcott CL, Laderoute KR, Knapp M, Guan YH, Hu Z, Ziyad S, Weber BL, Laquerre S, et al. Integrated analysis of breast cancer cell lines reveals unique signaling pathways. Genome Biol. 2009;10(3).
7. Kutalik Z, Beckmann JS, Bergmann S. A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nat Biotechnol. 2008;26(5):531-9.
8. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ. Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data (vol 3, 96ra77, 2011). Sci Transl Med. 2011;3(102).
9. Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, et al. Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci U S A. 2001;98(19):10787-92.
10. Riddick G, Song H, Ahn S, Walling J, Borges-Rivera D, Zhang W, Fine HA. Predicting in vitro drug sensitivity using Random Forests. Bioinformatics. 2011;27(2):220-4.
11. Lee JK, Havaleshko DM, Cho H, Weinstein JN, Kaldjian EP, Karpovich J, Grimshaw A, Theodorescu D. A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proc Natl Acad Sci U S A. 2007;104(32):13086-91.
12. Holleman A, Cheok MH, den Boer ML, Yang W, Veerman AJ, Kazemier KM, Pei D, Cheng C, Pui CH, Relling MV, et al. Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment. N Engl J Med. 2004;351(6):533-42.
13. Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJWL, Quackenbush J. Inconsistency in large pharmacogenomic studies. Nature. 2013;504(7480):389.
14. MacConaill LE, Garraway LA. Clinical Implications of the Cancer Genome. J Clin Oncol. 2010;28(35):5219-27.
15. Cree IA. Designing personalized cancer treatments. J Control Release. 2013;172(2):405-9.
16. Glaysher S, Yiannakis D, Gabriel FG, Johnson P, Polak ME, Knight LA, Goldthorpe Z, Peregrin K, Gyi M, Modi P, Rahamim J, Smith ME, Amer K, Addis B, Poole M, Narayanan A, Gulliford TJ , Andreotti PE, Cree IA. Resistance gene expression determines the in vitro chemosensitivity of non-small cell lung cancer (NSCLC). BMC Cancer. 2009;9(300):V9.
17. Geeleher P, Cox NJ, Huang SR, et al. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014;15:R47.
18. Polat K, GüneŞ S. A new feature method on classification of medical datasets: Kernel F-score feature selection. ESWA. 2009;36(7):10367-73.
19. Ding S. Feature Selection based F-score and ACO Algorithm in Support Vector Machine. Knowledge Acquisition and Modeling. 2009;1:19-23.
20. Anaissi A, Kennedy PJ, Goyal M, Catchpoole DR. A balanced iterative random forest for gene selection from microarray data. BMC Bioinformatics. 2013;14:261.
21. Jang IS, Neto EC, Guinney J, Friend SH, Margolin AA. Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. PSB. 2014;19:63-74.
22. Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJ. QuackenbushJ, Haibe-Kains B: Comparison and validation of genomic predictors for anticancer drug sensitivity. J Am Med InformAssoc. 2013;20(4):597-602.
23. Fong CW, Chua MS, McKie AB, Ling SHM, Mason L, Li R, Lo TL, Leung HY, So SKS, et al. Sprouty 2, an inhibitor of mitogen-activated protein kinase signaling, is down-regulated in hepatocellular carcinoma. Cancer Res. 2006;66(4):2048-58.
24. Costa VL, Henrique R, Danielsen SA, Duarte-Pereira S, Eknaes M, Skotheim RI, Rodrigues A, Magalhaes JS, Oliveira J, Lothe RA, et al. Three Epigenetic Biomarkers, GDF15, TMEFF2, and VIM, Accurately Predict Bladder Cancer from DNA-Based Analyses of Urine Samples. Clin Cancer Res. 2010;16(23):5842-51.
25. Lian ZQ, Wang Q, Li WP, Zhang AQ, Wu L. Screening of significantly hypermethylated genes in breast cancer using microarray-based methylated-CpG island recovery assay and identification of their expression levels. Int J Oncol. 2012;41(2):629-38.
26. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.
27. Genuer R, Poggi J-M, Tuleau-Malot C. Variable selection using Random Forests. Pattern Recognition Letters, Elsevier. 2010;31(14):2225-36.
28. Han L, Embrechts MJ, Szymanski B, Sternickel K, Ross A. Random Forests Feature Selection with Kernel Partial Least Squares: Detecting Ischemia from MagnetoCardiograms. ESANN 2006,V1:221-226.
29. Costello JC, Heiser LM, Georgii E, Gonen M, Menden MP, Wang NJ, Bansal M, Ammad-Ud-Din M, Hintsanen P, Khan SA, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32(12):1202-12.