Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

BMC Bioinformatics

Patrick Warnat²  Roland Eils¹  Benedikt Brors² 

[1] Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biology, University of Heidelberg, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany

[2] Department of Theoretical Bioinformatics, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany

关键词: cancer;    classification;    cross-platform analysis;    DNA microarray;    gene expression profiling;   

Others  :  1170241
DOI  :  10.1186/1471-2105-6-265

 received in 2005-03-29, accepted in 2005-11-04,  发布年份 2005

【 摘 要 】

Background

The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.

Results

In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis.

Conclusion

Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

【 授权许可】

   
2005 Warnat et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
• [2]Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000, 406:536-540.
• [3]van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536.
• [4]Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18:405-412.
• [5]Mitchell SA, Brown KM, Henry MM, Mintz M, Catchpoole D, LaFleur B, Stephan DA: Inter-platform comparability of microarrays in acute lymphoblastic leukemia. BMC Genomics 2004, 5:71. BioMed Central Full Text
• [6]Parmigiani G, Garrett-Mayer ES, Anbazhagan R, Gabrielson E: A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin Cancer Res 2004, 10:2922-2927.
• [7]Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y, Eickhoff H, Kloppel G, Lehrach H, Mellgard B, Costello CM, Schreiber S: A comparison of oligonucleotide and cDNA-based microarray systems. Physiol Genomics 2004, 16:361-370.
• [8]Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM: Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res 2002, 62:4427-4433.
• [9]Choi JK, Yu U, Kim S, Yoo OJ: Combining multiple microarray studies and modeling interstudy variation. Bioinformatics 2003, 19(Suppl 1):i84-90.
• [10]Ghosh D, Barette TR, Rhodes D, Chinnaiyan AM: Statistical issues and methods for meta-analysis of microarray data: A case study in prostate cancer. Funct Integr Genomics 2003, 3:180-188.
• [11]Choi JK, Choi JY, Kim DG, Choi DW, Kim BY, Lee KH, Yeom YI, Yoo HS, Yoo OJ, Kim S: Integrative analysis of multiple gene expression profiles applied to liver cancer study. FEBS Lett 2004, 565:93-100.
• [12]Jiang H, Deng Y, Chen HS, Tao L, Sha Q, Chen J, Tsai CJ, Zhang S: Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics 2004, 5:81. BioMed Central Full Text
• [13]Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 2004, 101:9309-9314.
• [14]Wang J, Coombes KR, Highsmith WE, Keating J, Abruzzo LV: Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: A meta-analysis of three microarray studies. Bioinformatics 2004, 20:3166-3178.
• [15]Ntzani EE, Ioannidis JP: Predictive ability of DNA microarrays for cancer outcomes and correlates: an empirical assessment. Lancet 2003, 362:1439-1444.
• [16]Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005, 365:488-492.
• [17]Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM: A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A 2003, 100:9991-9996.
• [18]Bloom G, Yang IV, Boulware D, Kwong KY, Coppola D, Eschrich S, Quackenbush J, Yeatman TJ: Multi-platform, multi-site, microarray-based human tumour classification. Am J Pathol 2004, 164:9-16.
• [19]Toedling J, Spang R: Assessment of Five Microarray Experiments on Gene Expression Profiling of Breast Cancer. [http://citeseer.ist.psu.edu/611350.html] webcitePoster Presentation RECOMB 2003.
• [20]Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci U S A 2001, 98:15149-15154.
• [21]Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, Schultz PG, Powell SM, Moskaluk CA, Frierson HF Jr, Hampton GM: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 2001, 61:7388-7393.
• [22]Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature 2001, 412:822-826.
• [23]Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J, Moskaluk CA, Frierson HF Jr, Hampton GM: Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 2001, 61:5974-5978.
• [24]Gruvberger S, Ringner M, Chen Y, Panavally S, Saal LH, Borg A, Ferno M, Peterson C, Meltzer PS: Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 2001, 61:5979-5984.
• [25]West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A 2001, 98:11462-11467.
• [26]Bullinger L, Dohner K, Bair E, Frohling S, Schlenk RF, Tibshirani R, Dohner H, Pollack JR: Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 2004, 350:1605-1616.
• [27]Valk PJ, Verhaak RG, Beijen MA, Erpelinck CA, Barjesteh van Waalwijk van Doorn-Khosrovani S, Boer JM, Beverloo HB, Moorhouse MJ, van der Spek PJ, Lowenberg B, Delwel R: Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 2004, 350:1617-1628.
• [28]Grimwade D, Walker H, Oliver F, Wheatley K, Harrison C, Harrison G, Rees J, Hann I, Stevens R, Burnett A, Goldstone A: The importance of diagnostic cytogenetics on outcome in AML: Analysis of 1,612 patients entered into the MRC AML 10 trial. The Medical Research Council Adult and Children's Leukaemia Working Parties. Blood 1998, 92:2322-2333.
• [29]Bloomfield CD, Lawrence D, Byrd JC, Carroll A, Pettenati MJ, Tantravahi R, Patil SR, Davey FR, Berg DT, Schiffer CA, Arthur DC, Mayer RJ: Frequency of prolonged remission duration after high-dose cytarabine intensification in acute myeloid leukemia varies by cytogenetic subtype. Cancer Res 1998, 58:4173-4179.
• [30]Frohling S, Schlenk RF, Breitruck J, Benner A, Kreitmeier S, Tobis K, Dohner H, Dohner K: Prognostic significance of activating FLT3 mutations in younger adults (16 to 60 years) with acute myeloid leukemia and normal cytogenetics: A study of the AML Study Group Ulm. Blood 2002, 100:4372-4380.
• [31]Schnittger S, Schoch C, Dugas M, Kern W, Staib P, Wuchter C, Loffler H, Sauerland CM, Serve H, Buchner T, Haferlach T, Hiddemann W: Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: Correlation to cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood 2002, 100:59-66.
• [32]Thiede C, Steudel C, Mohr B, Schaich M, Schakel U, Platzbecker U, Wermke M, Bornhauser M, Ritter M, Neubauer A, Ehninger G, Illmer T: Analysis of FLT3-activating mutations in 979 patients with acute myelogenous leukemia: Association with FAB subtypes and identification of subgroups with poor prognosis. Blood 2002, 99:4326-4335.
• [33]Schoch C, Kohlmann A, Schnittger S, Brors B, Dugas M, Mergenthaler S, Kern W, Hiddemann W, Eils R, Haferlach T: Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles. Proc Natl Acad Sci U S A 2002, 99:10008-10013.
• [34]Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 2002, 99:6567-6572.
• [35]Zeisig BB, Milne T, Garcia-Cuellar MP, Schreiner S, Martin ME, Fuchs U, Borkhardt A, Chanda SK, Walker J, Soden R, Hess JL, Slany RK: Hoxa9 and Meis1 are key targets for MLL-ENL-mediated cellular immortalization. Mol Cell Biol 2004, 24:617-628.
• [36]Kamashev D, Vitoux D, The HD: PML-RARA-RXR oligomers mediate retinoid and rexinoid/cAMP cross-talk in acute promyelocytic leukemia cell differentiation. J Exp Med 2004, 199:1163-1174.
• [37]Cazzaniga G, Tosi S, Aloisi A, Giudici G, Daniotti M, Pioltelli P, Kearney L, Biondi A: The tyrosine kinase abl-related gene ARG is fused to ETV6 in an AML-M4Eo patient with a t(1;12)(q25;p13): Molecular cloning of both reciprocal transcripts. Blood 1999, 94:4370-4373.
• [38]Staber PB, Linkesch W, Zauner D, Beham-Schmid C, Guelly C, Schauer S, Sill H, Hoefler G: Common alterations in gene expression and increased proliferation in recurrent acute myeloid leukemia. Oncogene 2004, 23:894-904.
• [39]Aisenberg AC, Wilkes BM, Jacobson JO: The bcl-2 gene is rearranged in many diffuse B-cell lymphomas. Blood 1988, 71:969-972.
• [40]Li Q, Ahmed S, Loeb JA: Development of an autocrine neuregulin signaling loop with malignant transformation of human breast epithelial cells. Cancer Res 2004, 64:7078-7085.
• [41]Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA: Creation of human tumour cells with defined genetic elements. Nature 1999, 400:464-468.
• [42]Li X, Rao S, Wang Y, Gong B: Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucl Acids Res 2004, 32:2685-2694.
• [43]Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001, 29:365-371.
• [44]Guo Z, Zhang T, Li X, Wang Q, Xu J, Yu H, Zhu J, Wang H, Wang C, Topol EJ, Rao S: Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58. BioMed Central Full Text
• [45]The R project for statistical computing [http://www.r-project.org] webcite
• [46]Open source software for the analysis of genomic data [http://www.bioconductor.org] webcite
• [47]Huber W, Heydebreck A, Sültmann H, Poustka A, Vingron M: Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002, 18(Suppl 1):96-104.
• [48]Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19:185-193.
• [49]The UniGene database by NCBI [http://www.ncbi.nlm.nih.gov/UniGene] webcite
• [50]Liu H, Hussain F, Tan CL, Dash M: Discretization: An enabling technique. Data Mining and Knowledge Discovery 2002, 6:393-423.
• [51]Salzberg SL: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1997, 1:317-327.
• [52]Support Vector Machine Implementation [http://www.csie.ntu.edu.tw/~cjlin/libsvm] webcite
• [53]Guyon I, Weston J, Barnhill S: Gene selection for cancer classification using support vector machines. Machine Learning 2002, 46:389-422.
• [54]Implementation of the Recursive Feature Elimination Method [http://www.hds.utc.fr/~ambroise/doku.php?id=softwares:softwares] webcite
• [55]Murtagh F: Multidimensional Clustering Algorithms. Wuerzburg: Physica-Verlag; 1985.