Convergent random forest predictor: Methodology for predicting drug response from genome-scale data applied to anti-TNF response

Genomics

Volumn 94, Issue 6, 2009, Pages 423-432

  Bienkowska, Jadwiga R ^a   Dalgin, Gul S ^a   Batliwalla, Franak ^b   Allaire, Normand ^a   Roubenoff, Ronenn ^a   Gregersen, Peter K ^b   Carulli, John P ^a  

^a BIOGEN   (United States)

^b FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH   (United States)

Author keywords

Classifiers; Drug response prediction; TNF block therapy

Indexed keywords

ADALIMUMAB; BIOLOGICAL MARKER; CARBONATE DEHYDRATASE IX; ETANERCEPT; FRUCTOSE 1,6 BISPHOSPHATE; GELATINASE B; GENE PRODUCT; HISTONE DEACETYLASE 4; HISTONE DEACETYLASE 5; INFLIXIMAB; PHOSPHOGLYCERATE KINASE; PHOSPHOGLYCERATE KINASE 1; PROTEIN IFIMT1; PROTEIN MXRA7; PROTEIN SLC37A1; PROTEIN TSPYL5; SYNAPTOJANIN; TUMOR NECROSIS FACTOR INHIBITOR; UNCLASSIFIED DRUG;

ACCURACY; ARTICLE; BLOOD SAMPLING; CLASSIFICATION ALGORITHM; CLINICAL ARTICLE; CONTROLLED STUDY; DISEASE CLASSIFICATION; GENE EXPRESSION PROFILING; GENETIC ANALYSIS; GENETIC IDENTIFICATION; GENETIC SELECTION; GENETIC TRANSCRIPTION; HUMAN; INTERMETHOD COMPARISON; PHARMACOGENOMICS; PRIORITY JOURNAL; RANDOM FOREST; RHEUMATOID ARTHRITIS; SUPPORT VECTOR MACHINE; TREATMENT RESPONSE; VALIDATION PROCESS;

ADENOCARCINOMA; ALGORITHMS; ANTIRHEUMATIC AGENTS; ARTHRITIS, RHEUMATOID; BIOLOGICAL MARKERS; BREAST NEOPLASMS; CLUSTER ANALYSIS; DECISION TREES; DISEASE PROGRESSION; DRUG MONITORING; FEMALE; GENE EXPRESSION PROFILING; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LEUKEMIA, MYELOID, ACUTE; MALE; NEOPLASM METASTASIS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRECURSOR CELL LYMPHOBLASTIC LEUKEMIA-LYMPHOMA; PROGNOSIS; PROSTATIC NEOPLASMS; TRANSCRIPTION, GENETIC; TREATMENT OUTCOME; TUMOR NECROSIS FACTOR-ALPHA;

EID: 70449088927     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2009.08.008     Document Type: Article

References (37)

1. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
2. Krishnapuram B., Carin L., and Hartemink A.J. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. J. Comput. Biol. 11 (2004) 227-242
3. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
4. Breiman L. Random forests. Mach. Learn. 45 (2001) 5-32
5. Enot D.P., Beckmann M., Overy D., and Draper J. Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals. Proc. Natl. Acad. Sci. U. S. A. 103 (2006) 14865-14870
6. Gunther E.C., Stone D.J., Gerwien R.W., Bento P., and Heyes M.P. Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proc. Natl. Acad. Sci. U. S. A. 100 (2003) 9608-9613
7. Mehrian-Shai R., Chen C.D., Shi T., Horvath S., Nelson S.F., Reichardt J.K.V., and Sawyers C.L. Insulin growth factor-binding protein 2 is a candidate biomarker for PTEN status and PI3K/Akt pathway activation in glioblastoma and prostate cancer. Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 5563-5568
8. Seligson D.B., Horvath S., Shi T., Yu H., Tze S., Grunstein M., and Kurdistani S.K. Global histone modification patterns predict risk of prostate cancer recurrence. Nature 435 (2005) 1262-1266
9. Shi T., Seligson D., Belldegrun A.S., Palotie A., and Horvath S. Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma. Mod. Path. 18 (2005) 547-557
10. Liaw A., and Wiener M. Classification and regression by randomForest. Rnews 2 (2002) 18-22
11. van 't Veer L.J., Dai H., van de Vijver M.J., He Y.D., Hart A.A., Mao M., Peterse H.L., van der Kooy K., Marton M.J., Witteveen A.T., Schreiber G.J., Kerkhoven R.M., Roberts C., Linsley P.S., Bernards R., and Friend S.H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415 (2002) 530-536
12. Díaz-Uriarte R., and Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics (2006) 7
13. Jiang H., Deng Y., Chen H.S., Tao L., Sha Q., Chen J., Tsai C.J., and Zhang S. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics (2004) 5
14. Zhang X., Lu X., Shi Q., Xu X.Q., Leung H.C., Harris L.N., Iglehart J.D., Miron A., Liu J.S., and Wong W.H. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 197 (2006) 7
15. Ein-Dor L., Kela I., Getz G., Givol D., and Domany E. Outcome signature genes in breast cancer: is there a unique set. Bioinformatics 21 (2005) 171-178
16. Fan C., Oh D.S., Wessels L., Weigelt B., Nuyten D.S., Nobel A.B., van't Veer L.J., and Perou C.M. Concordance among gene-expression-based predictors for breast cancer. N. Engl. J. Med. 355 (2006) 560-569
17. Irizarry R.A., Bolstad B.M., Collin F., Cope L.M., Hobbs B., and Speed T.P. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31 4 (2003) e15
18. Allaire N.E., Rieder L.E., Bienkowska J., and Carulli J.P. Experimental comparison and cross-validation of Affymetrix HT plate and cartridge array gene expression platforms. Genomics 92 5 (2008) 359-365
19. Canales R.D., Luo Y., Willey J.C., Austermiller B., Barbacioru C.C., Boysen C., Hunkapiller K., Jensen R.V., Knight C.R., Lee K.Y., Ma Y., Maqsodi B., Papallo A., Peters E.H., Poulter K., Ruppel P.L., Samaha R.R., Shi L., Yang W., Zhang L., and Goodsaid F.M. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24 (2006) 1115-1122
20. Shi L., Reid L.H., Jones W.D., Shippy R., Warrington J.A., Baker S.C., Collins P.J., de Longueville F., Kawasaki E.S., Lee K.Y., Luo Y., Sun Y.A., Willey J.C., Setterquist R.A., Fischer G.M., Tong W., Dragan Y.P., Dix D.J., Frueh F.W., Goodsaid F.M., Herman D., Jensen R.V., Johnson C.D., Lobenhofer E.K., Puri R.K., Schrf U., Thierry-Mieg J., Wang C., Wilson M., Wolber P.K., Zhang L., Amur S., Bao W., Barbacioru C.C., Lucas A.B., Bertholet V., Boysen C., Bromley B., Brown D., Brunner A., Canales R., Cao X.M., Cebula T.A., Chen J.J., Cheng J., Chu T.M., Chudin E., Corson J., Corton J.C., Croner L.J., Davies C., Davison T.S., Delenstarr G., Deng X., Dorris D., Eklund A.C., Fan X.H., Fang H., Fulmer-Smentek S., Fuscoe J.C., Gallagher K., Ge W., Guo L., Guo X., Hager J., Haje P.K., Han J., Han T., Harbottle H.C., Harris S.C., Hatchwell E., Hauser C.A., Hester S., Hong H., Hurban P., Jackson S.A., Ji H., Knight C.R., Kuo W.P., LeClerc J.E., Levy S., Li Q.Z., Liu C., Liu Y., Lombardi M.J., Ma Y., Magnuson S.R., Maqsodi B., McDaniel T., Mei N., Myklebost O., Ning B., Novoradovskaya N., Orr M.S., Osborn T.W., Papallo A., Patterson T.A., Perkins R.G., Peters E.H., Peterson R., et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24 (2006) 1151-1161
21. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
22. Singh D., Febbo P.G., Ross K., Jackson D.G., Manola J., Ladd C., Tamayo P., Renshaw A.A., D'Amico A.V., Richie J.P., Lander E.S., Loda M., Kantoff P.W., Golub T.R., and Sellers W.R. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203-209
23. Sing T., Sander O., Beerenwinkel N., and Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics 21 (2005) 3940-3941
24. Brogdon J.L., Xu Y., Szabo S.J., An S., Buxton F., Cohen D., and Huang Q. Histone deacetylase activities are required for innate immune cell control of Th1 but not Th2 effector cell function. Blood 109 (2007) 1123-1130
25. Edens R.E., Dagtas S., and Gilbert K.M. Histone deacetylase inhibitors induce antigen specific anergy in lymphocytes: a comparative study. Int. Immunopharmacol. 6 (2006) 1673-1681
26. Gialitakis M., Kretsovali A., Spilianakis C., Kravariti L., Mages J., Hoffmann R., Hatzopoulos A.K., and Papamatheakis J. Coordinated changes of histone modifications and HDAC mobilization regulate the induction of MHC class II genes by Trichostatin A. Nucleic Acids Res. 34 (2006) 765-772
27. Raya A., Revert F., Navarro S., and Saus J. Characterization of a novel type of serine/threonine kinase that specifically phosphorylates the human good pasture antigen. J. Biol. Chem. 274 (1999) 12642-12649
28. Revert F., Merino R., Monteagudo C., Macias J., Peydro A., Alcacer J., Muniesa P., Marquina R., Blanco M., Iglesias M., Revert-Ros F., Merino J., and Saus J. Increased Goodpasture antigen-binding protein expression induces type IV collagen disorganization and deposit of immunoglobulin A in glomerular basement membrane. Am. J. Pathol. 171 (2007) 1419-1430
29. Karlsson J.A., Kristensen L.E., Kapetanovic M.C., Gulfe A., Saxne T., and Geborek P. Treatment response to a second or third TNF-inhibitor in RA: results from the South Swedish Arthritis Treatment Group Register. Rheumatology (Oxford) 47 (2008) 507-513
30. Thomassen M., Tan Q., Eiriksdottir F., Bak M., Cold S., and Kruse T.A. Comparison of gene sets for expression profiling: prediction of metastasis from low-malignant breast cancer. Clin. Cancer Res. 13 (2007) 5355-5360
31. Rizki A., Weaver V., Lee S., Rozenberg G., Chin K., Myers C., Bascom J., Mott J., Semeiks J., Grate L., Mian I., Borowsky A., Jensen R., Idowu M., Chen F., Chen D., Petersen O., Gray J., and Bissell M. A human breast cell model of preinvasive to invasive transition. Cancer Res. 68 (2008) 1378-1387
32. Trastour C., Benizri E., Ettore F., Ramaioli A., Chamorey E., Pouyssegur J., and Berra E. HIF-1alpha and CA IX staining in invasive breast carcinomas: prognosis and treatment outcome. Int. J. Cancer 120 (2007) 1451-1458
33. Fransen J., and van Riel P.L. The Disease Activity Score and the EULAR response criteria. Clin. Exp. Rheumatol. 23 (2005) S93-S99
34. van Gestel A.M., Haagsma C.J., and van Riel P.L. Validation of rheumatoid arthritis improvement criteria that include simplified joint counts. Arthritis Rheum. 41 (1998) 1845-1850
35. Genetelman R., Carey J., Huber W., Irizarry R., and Dudoit S. Bioinformatics and Computational Biology Solutions using R and Bioconductor (2005), Springer
36. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag
37. Gentleman R., Carey J., Huber W., Irizarry R., and Dudoit S. Bioinformatics and Computational Biology Solutions using R and Bioconductor (2005), Springer, New York