A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities

Journal of Clinical Bioinformatics

Philippe Broët¹  Wilson Toussile²  Hervé Perdry²  Cyprien Mbogning³ 

[1] Assistance Publique – Hôpitaux de Paris, Hôpital Paul Brousse, Villejuif, France;Faculty of Medicine Paris-Sud, 63 rue Gabriel Peri, 94276 Le Kremlin-Bicêtre, France;Inserm U669, 14-16 Avenue Paul-Vaillant-Couturier, 94807 Villejuif, France

关键词: Genomic;    Disease taxonomy;    Lung cancer;    Tree-based regression;    Recursive partitioning;   

Others  :  800939
DOI  :  10.1186/2043-9113-4-6

 received in 2013-12-23, accepted in 2014-04-08,  发布年份 2014

【 摘 要 】

Background

Dissecting the genomic spectrum of clinical disease entities is a challenging task. Recursive partitioning (or classification trees) methods provide powerful tools for exploring complex interplay among genomic factors, with respect to a main factor, that can reveal hidden genomic patterns. To take confounding variables into account, the partially linear tree-based regression (PLTR) model has been recently published. It combines regression models and tree-based methodology. It is however computationally burdensome and not well suited for situations for which a large number of exploratory variables is expected.

Methods

We developed a novel procedure that represents an alternative to the original PLTR procedure, and considered different selection criteria. A simulation study with different scenarios has been performed to compare the performances of the proposed procedure to the original PLTR strategy.

Results

The proposed procedure with a Bayesian Information Criterion (BIC) achieved good performances to detect the hidden structure as compared to the original procedure. The novel procedure was used for analyzing patterns of copy-number alterations in lung adenocarcinomas, with respect to Kirsten Rat Sarcoma Viral Oncogene Homolog gene (KRAS) mutation status, while controlling for a cohort effect. Results highlight two subgroups of pure or nearly pure wild-type KRAS tumors with particular copy-number alteration patterns.

Conclusions

The proposed procedure with a BIC criterion represents a powerful and practical alternative to the original procedure. Our procedure performs well in a general framework and is simple to implement.

【 授权许可】

   
2014 Mbogning et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 3.

【 参考文献 】

• [1]Roberts P, Stinchcombe T: Kras mutation: should we test for it, and does it matter? J Clin Oncol 2013, 31(8):1112-21.
• [2]Rajagopalan H, Lengauer C: Aneuploidy and cancer. Nature 2004, 432:338-341.
• [3]Breiman L, Olshen JH, Stone CJ: Classification and Regression Trees. Belmont, California: Wadsworth International Group; 1984.
• [4]Breiman L: Random forest. Technical Report, Department of Statistics, University of California at Berkeley. 2002
• [5]Diaz-Uriarte R, Alvarez de Andrés S: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006, 7(1):1-13. BioMed Central Full Text
• [6]Guan X, Chance MR, Barnholtz-Sloan JS: Splitting random forest (srf) for determining compact sets of genes that distinguish between cancer subtypes. J Clin Bioinform 2012, 2(1):1-12.
• [7]Liaw A, Wiener M: Classification and regression by randomforest. R News 2002, 2(3):18-22.
• [8]Chen J, Yu K, Hsing A, Therneau TM: A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genet Epidemiol 2007, 31:238-251.
• [9]Yu K, Wheeler W, Li Q, Bergen AW, Caporaso N, Chatterjee N, Chen J: A partially linear tree-based regression model for multivariate outcomes. Biometrics 2010, 66(1):89-96.
• [10]Akaike H: A new look at the statistical model identification. IEEE Trans Automat Control 1974, AC-19:716-723.
• [11]Schwarz G: Estimating the dimension of a model. Ann Stat 1978, 6:461-464.
• [12]Fan J, Zhang C, Zhang J: Generalized likelihood ratio statistics and wilks phenomenon. Ann Stat 2001, 29(1):153-193.
• [13]Broët P, Dalmasso C, Tan E, Alifano M, Zhang S, Wu J, Lee M, Régnard J, Lim D, Koong H, Agasthian T, Miller L, Lim E, Camilleri-Broët S, Tan P: Genomic profiles specific to patient ethnicity in lung adenocarcinoma. Clin Cancer Res 2011, 17(11):3542-50.
• [14]Dalmasso C, Broët P: Detection of chromosomal abnormalities using high resolution arrays in clinical cancer research. J Biomed Inform 2011, 44(6):936-942.