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.
Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7, 2007-2013), Research Infrastructures action, under the grant agreement No. FP7-269553 (EpiRadBio project). The authors thank Johannes Berkhof and an anonymous referee for constructive criticism in the early and final stages of this work, respectively.
References
Aaltonen, K. (2008): Cyclins in breast cancer, Helsinki, Finland: University of Helsinki.Suche in Google Scholar
Asimit, J. L., I. L. Andrulis and S. B. Bull (2011): “Regression models, scan statistics and reappearance probabilities to detect regions of association between gene expression and copy number,” Stat. Med., 30, 1157–1178.Suche in Google Scholar
Band, A. M. and M. Laiho (2011): “Crosstalk of tgf- β and estrogen receptor signaling in breast cancer,” J. Mammary Gland Biol., 16, 109–115.Suche in Google Scholar
Burdette, J. E. and T. K. Woodruff (2007): “Activin and estrogen crosstalk regulates transcription in human breast cancer cells,” Endocr-Relat. Cancer, 14, 679–689.Suche in Google Scholar
Chin, K., S. DeVries, J. Fridlyand, P. T. Spellman, R. Roydasgupta, W. L. Kuo, A. Lapuk, R. M. Neve, Z. Qian, T. Ryder, F. Chen, H. Feiler, T. Tokuyasu, C. Kingsley, S. Dairkee, Z. Meng, K. Chew, D. Pinkel, A. Jain, B. M. Ljung, L. Esserman, D. G. Albertson, F. M. Waldman and J. W. Gray (2006): “Genomic and transcriptional aberrations linked to breast cancer pathophysiologies,” Cancer Cell, 10, 529–541.10.1016/j.ccr.2006.10.009Suche in Google Scholar PubMed
Chitale, D., Y. Gong, B. S. Taylor, S. Broderick, C. Brennan, R. Somwar, B. Golas, L. Wang, N. Motoi, J. Szoke, J. M. Reinersman, J. Major, C. Sander, V. E. Seshan, M. F. Zakowski, V. Rusch, W. Pao, W. Gerald and M. Ladanyi (2009): “An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFRmutant tumors,” Oncogene, 28, 2773172783.10.1038/onc.2009.135Suche in Google Scholar PubMed PubMed Central
Choi, H., Z. S. Qin, and D. Ghosh (2010): “A double-layered mixture model for the joint analysis of DNA copy number and gene expression data,” J. Comput. Biol., 17, 121–137.Suche in Google Scholar
Davidson, R. and J. G. MacKinnon (2004): Econometric theory and methods, 4th Edition, New York: Oxford University Press.Suche in Google Scholar
De Jong, H. (2002): “Modeling and simulation of genetic regulatory systems: a literature review,” J. Comput. Biol., 9, 67–103.Suche in Google Scholar
Derynck, R., R. J. Akhurst and A. Balmain (2001): “Tgf- β signaling in tumor suppression and cancer progression,” Nat. Genet., 29, 117–129.Suche in Google Scholar
Eeckhoute, J., J. S. Carroll, T. R. Geistlinger, M. I. Torres-Arzayus and M. Brown (2006): “A cell-type-specific transcriptional network required for estrogen regulation of cyclin D1 and cell cycle progression in breast cancer,” Gene. Dev., 20, 2513–2526.10.1101/gad.1446006Suche in Google Scholar PubMed PubMed Central
Fernández-Cuesta, L., S. Anaganti, P. Hainaut and M. Olivier (2011): “Estrogen levels act as a rheostat on p53 levels and modulate p53-dependent responses in breast cancer cell lines,” Breast Cancer Res. Tr., 125, 35–42.Suche in Google Scholar
Gardner, T. S., D. Di Bernardo, D. Lorenz and J. J. Collins (2003): “Inferring genetic networks and identifying compound mode of action via expression pro-filing,” Science, 301, 102–105.10.1126/science.1081900Suche in Google Scholar PubMed
Goeman, J. J. (2010): “L1 penalized estimation in the Cox proportional hazards model,” Biometrical J., 52, 70–84.Suche in Google Scholar
Ideker, T. and N. J. Krogan (2012): “Differential network biology,” Molecular Syst. Biol., 8, doi:10.1038/msb.2011.99.10.1038/msb.2011.99Suche in Google Scholar PubMed PubMed Central
Jörnsten, R., T. Abenius, T. Kling, L. Schmidt, E. Johansson, T. E. M. Nordling, B. Nordlander, C. Sander, P. Gennemark, K. Funa, B. Nilsson, L. Lindahl and S. Nelander (2011): “Network modeling of the transcriptional effects of copy number aberrations in glioblastoma,” Molecular Syst. Biol., 7.10.1038/msb.2011.17Suche in Google Scholar PubMed PubMed Central
Kallioniemi, O. P., A. Kallioniemi, W. Kurisu, A. Thor, L. C. Chen, H. S. Smith, F. M. Waldman, D. Pinkel and J. W. Gray (1992): “ERBB2 amplification in breast cancer analyzed by fluorescence in situ hybridization,” PNAS, 89, 5321–5325.10.1073/pnas.89.12.5321Suche in Google Scholar PubMed PubMed Central
Kauraniemi, P. and A. Kallioniemi (2006): “Activation of multiple cancer associated genes at the ERBB2 amplicon in breast cancer,” Endocr-Relat. Cancer, 13, 39–49.Suche in Google Scholar
Lengauer, C., K. Kinzler and B. Vogelstein (1998): “Genetic instabilities in human cancers,” Nature, 396, 623–627.10.1038/386623a0Suche in Google Scholar PubMed
Lenz, G., G. W. Wright, N. C. Emre, H. Kohlhammer, S. S. Dave, R. E. Davis, S. Carty, L. T. Lam, A. L. Shaffer, W. Xiao, J. Powell, A. Rosenwald, G. Ott, H. K. Muller-Hermelink, R. D. Gascoyne, J. M. Connors, E. Campo, E. S. Jaffe, J. Delabie, E. B. Smeland, L. M. Rimsza, R. I. Fisher, D. D. Weisenburger, W. C. Chan and L. M. Staudt (2008): “Molecular subtypes of diffuse large B-cell lymphoma arise by distinct genetic pathways,” PNAS, 105, 13520–13525.10.1073/pnas.0804295105Suche in Google Scholar PubMed PubMed Central
Logsdon, B. A. and J. Mezey (2010): “Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations,” PLoS Comput. Biol., 6, e1001014.Suche in Google Scholar
Malumbres, M. and M. Barbacid (2009): “Cell cycle, CDKs and cancer: a changing paradigm,” Nat. Rev. Cancer, 9, 153–166.Suche in Google Scholar
Margolin, A. A. and A. Califano (2007): “Theory and limitations of genetic network inference from microarray data,” Ann. NY Acad. Sci., 1115, 51–72.Suche in Google Scholar
Margolin, A. A., I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. D. Favera and A. Califano (2006): “ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,” BMC Bioinformatics, 7(Suppl 1), S7.10.1186/1471-2105-7-S1-S7Suche in Google Scholar PubMed PubMed Central
Meinshausen, N. and P. Bühlmann (2010): “Stability selection,” J. Roy. Stat. Society B, 74, 417–473.Suche in Google Scholar
Ogata, H., S. Goto, K. Sato, W. Fujibuchi, H. Bono and M. Kanehisa (1999): “KEGG: Kyoto encyclopedia of genes and genomes,” Nucleic Acid Res., 27, 29–34.Suche in Google Scholar
Paruthiyil, S., H. Parmar, V. Kerekatte, G. R. Cunha, G. L. Firestone and D. C. Leitman (2004): “Estrogen receptor β inhibits human breast cancer cell proliferation and tumor formation by causing a g2 cell cycle arrest,” Cancer Res., 64, 423–428.10.1158/0008-5472.CAN-03-2446Suche in Google Scholar
Peng, J., J. Zhu, A. Bergamaschi, W. Han, D.-Y. Noh, J. R. Pollack and P. Wang (2010): “Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer,” Ann. Appl. Stat., 4, 53–77.Suche in Google Scholar
Pollack, J. R., T. Sorlie, C. M. Perou, C. A. Rees, S. S. Jeffrey, P. E. Lonning, R. Tibshirani, D. Botstein, A. L. Borresen-Dale and P. O. Brown (2002): “Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors,” PNAS, 99, 12963–12968.10.1073/pnas.162471999Suche in Google Scholar PubMed PubMed Central
Rinaldo, A. (2009): “Properties and refinements of the fused lasso,” Ann. Stat., 37, 2922–2952.Suche in Google Scholar
Rosenbaum, P. R. (2005): Observational study. In: Everitt, B. S., Howell, D. C. (Eds.), Encyclopedia of Statistics in Behavioral Science, Volume 3, New York: John Wiley, 1451–1462.Suche in Google Scholar
Shipley, B. (2000): Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inference, Cambridge, UK: Cambridge University Press.10.1017/CBO9780511605949Suche in Google Scholar
Sorlie, T., C. M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M. B. Eisen, M. Van de Rijn, S. S. Jeffrey, T. Thorsen, H. Quist, J. C. Matese, P. O. Brown, D. Botstein, P. E. Lonningg and A.-L. Borresen-Dale (2001): “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” PNAS, 98, 10869–10874.10.1073/pnas.191367098Suche in Google Scholar PubMed PubMed Central
Stratton, M. R., P. J. Campbell and P. A. Futreal (2009): “The cancer genome,” Nature, 458, 719–724.10.1038/nature07943Suche in Google Scholar PubMed PubMed Central
Tibshirani, R. J. and J. Taylor (2011): “The solution path of the generalized lasso,” Ann. Stat., 39, 1335–1371.Suche in Google Scholar
Van de Wiel, M. A., F. Picard, W. N. Van Wieringen and B. Ylstra (2011): “Preprocessing and downstream analysis of microarray dna copy number profiles,” Briefings in Bioinformatics, 12, 10–21.10.1093/bib/bbq004Suche in Google Scholar PubMed
Van Wieringen, W. N. and M. A. Van de Wiel (2009): “Nonparametric testing for DNA copy number induced differential mRNA gene expression,” Biometrics, 65, 19–29.10.1111/j.1541-0420.2008.01052.xSuche in Google Scholar PubMed
Van Wieringen, W. N. and A. W. Van der Vaart (2011): “Statistical analysis of the cancer cell’s molecular entropy using high-throughput data,” Bioinformatics, 27, 556–563.10.1093/bioinformatics/btq704Suche in Google Scholar PubMed
Van Wieringen, W. N., J. Berkhof and M. A. Van de Wiel (2010): “A random coefficients model for regional co-expression associated with DNA copy number,” Stat. Appl. Genet. Mol. Biol., 9, Article 25: 1–28.Suche in Google Scholar
Vogelstein, B. and K. W. Kinzler (2004): “Cancer genes and the pathways they control,” Nat. Med., 10, 789–799.Suche in Google Scholar
Voit, E. O. (2000): Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists, Cambridge, UK: Cambridge University Press.Suche in Google Scholar
Xiong, M., J. Li and X. Fang (2004): “Identification of genetic networks,” Genetics, 166, 1037–1052.10.1093/genetics/166.2.1037Suche in Google Scholar
Yager, J. D. and N. E. Davidson (2006): “Estrogen carcinogenesis in breast cancer,” New Engl. J. Med., 354, 270–282.Suche in Google Scholar
Yuan, Y., R. M. Rueda, C. Curtis and F. Markowetz (2011): “Penalized regression elucidates hotspots mediating subtype-specific transcriptional responses in breast cancer,” Bioinformatics, 27, 2679–2685.10.1093/bioinformatics/btr450Suche in Google Scholar PubMed
Zhang, Y., J. W. M. Martens, J. X. Yu, J. Jiang, A. M. Sieuwerts, M. Smid, J. G. M. Klijn, Y. Wang and J. A. Foekens (2009): “Copy number alterations that predict metastatic capability of human breast cancer,” Cancer Res., 69, 3795–3801.Suche in Google Scholar
©2014 by Walter de Gruyter Berlin/Boston
Artikel in diesem Heft
- frontmatter
- Research Articles
- Combining dependent F-tests for robust association of quantitative traits under genetic model uncertainty
- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
- Scan statistics analysis for detection of introns in time-course tiling array data
- Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure
- Improved variational Bayes inference for transcript expression estimation
- Efficient identification of context dependent subgroups of risk from genome-wide association studies
Artikel in diesem Heft
- frontmatter
- Research Articles
- Combining dependent F-tests for robust association of quantitative traits under genetic model uncertainty
- Penalized differential pathway analysis of integrative oncogenomics studies
- A data-smoothing approach to explore and test gene-environment interaction in case-parent trios
- Scan statistics analysis for detection of introns in time-course tiling array data
- Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure
- Improved variational Bayes inference for transcript expression estimation
- Efficient identification of context dependent subgroups of risk from genome-wide association studies