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Penalized differential pathway analysis of integrative oncogenomics studies

Author

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  • van Wieringen Wessel N.

    (Department of Epidemiology and Biostatistics, VU University Medical Center, P.O. Box 7075, 1007 MB Amsterdam, The Netherlands Department of Mathematics, VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands)

  • van de Wiel Mark A.

    (Department of Epidemiology and Biostatistics, VU University Medical Center, P.O. Box 7075, 1007 MB Amsterdam, The Netherlands)

Abstract

Through integration of genomic data from multiple sources, we may obtain a more accurate and complete picture of the molecular mechanisms underlying tumorigenesis. We discuss the integration of DNA copy number and mRNA gene expression data from an observational integrative genomics study involving cancer patients. The two molecular levels involved are linked through the central dogma of molecular biology. DNA copy number aberrations abound in the cancer cell. Here we investigate how these aberrations affect gene expression levels within a pathway using observational integrative genomics data of cancer patients. In particular, we aim to identify differential edges between regulatory networks of two groups involving these molecular levels. Motivated by the rate equations, the regulatory mechanism between DNA copy number aberrations and gene expression levels within a pathway is modeled by a simultaneous-equations model, for the one- and two-group case. The latter facilitates the identification of differential interactions between the two groups. Model parameters are estimated by penalized least squares using the lasso (L1) penalty to obtain a sparse pathway topology. Simulations show that the inclusion of DNA copy number data benefits the discovery of gene-gene interactions. In addition, the simulations reveal that cis-effects tend to be over-estimated in a univariate (single gene) analysis. In the application to real data from integrative oncogenomic studies we show that inclusion of prior information on the regulatory network architecture benefits the reproducibility of all edges. Furthermore, analyses of the TP53 and TGFb signaling pathways between ER+ and ER- samples from an integrative genomics breast cancer study identify reproducible differential regulatory patterns that corroborate with existing literature.

Suggested Citation

  • van Wieringen Wessel N. & van de Wiel Mark A., 2014. "Penalized differential pathway analysis of integrative oncogenomics studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 141-158, April.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:2:p:141-158:n:2
    DOI: 10.1515/sagmb-2013-0020
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    References listed on IDEAS

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    1. Christoph Lengauer & Kenneth W. Kinzler & Bert Vogelstein, 1998. "Genetic instabilities in human cancers," Nature, Nature, vol. 396(6712), pages 643-649, December.
    2. Wessel N. van Wieringen & Mark A. van de Wiel, 2009. "Nonparametric Testing for DNA Copy Number Induced Differential mRNA Gene Expression," Biometrics, The International Biometric Society, vol. 65(1), pages 19-29, March.
    3. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    4. Michael R. Stratton & Peter J. Campbell & P. Andrew Futreal, 2009. "The cancer genome," Nature, Nature, vol. 458(7239), pages 719-724, April.
    5. van Wieringen Wessel N & Berkhof Johannes & van de Wiel Mark A, 2010. "A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-30, June.
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