The Annals of Applied Statistics

Detection of epigenomic network community oncomarkers

Thomas E. Bartlett and Alexey Zaikin

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term “network community oncomarkers”. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset.

Article information

Source
Ann. Appl. Stat., Volume 10, Number 3 (2016), 1373-1396.

Dates
Received: July 2015
Revised: March 2016
First available in Project Euclid: 28 September 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1475069611

Digital Object Identifier
doi:10.1214/16-AOAS939

Mathematical Reviews number (MathSciNet)
MR3553228

Zentralblatt MATH identifier
06775270

Keywords
Computational biology stochastic networks community detection epigenomics

Citation

Bartlett, Thomas E.; Zaikin, Alexey. Detection of epigenomic network community oncomarkers. Ann. Appl. Stat. 10 (2016), no. 3, 1373--1396. doi:10.1214/16-AOAS939. https://projecteuclid.org/euclid.aoas/1475069611


Export citation

References

  • Airoldi, E. M., Blei, D. M., Fienberg, S. E. and Xing, E. P. (2008). Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9 1981–2014.
  • Barabási, A.-L. and Oltvai, Z. N. (2004). Network biology: Understanding the cell’s functional organization. Nat. Rev. Genet. 5 101–113.
  • Bartlett, T. E. (2015). Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions. Preprint. Available at arXiv:1506.04928.
  • Bartlett, T. E., Olhede, S. C. and Zaikin, A. (2014). A DNA methylation network interaction measure, and detection of network oncomarkers. PLoS ONE 9 e84573.
  • Bartlett, T. E. and Zaikin, A. (2016). Supplement to “Detection of epigenomic network community oncomarkers.” DOI:10.1214/16-AOAS939SUPP.
  • Bartlett, T. E., Zaikin, A., Olhede, S. C., West, J., Teschendorff, A. E. and Widschwendter, M. (2013). Corruption of the intra-gene DNA methylation architecture is a hallmark of cancer. PLoS ONE 8 e68285.
  • Beguerisse-Díaz, M., Garduño-Hernández, G., Vangelov, B., Yaliraki, S. N. and Barahona, M. (2014). Interest communities and flow roles in directed networks: The Twitter network of the UK riots. J. R. Soc. Interface 11 20140940.
  • Bhagat, R., Chadaga, S., Premalata, C. S., Ramesh, G., Ramesh, C., Pallavi, V. R. and Krishnamoorthy, L. (2012). Aberrant promoter methylation of the RASSF1A and APC genes in epithelial ovarian carcinoma development. Cellular Oncology 35 473–479.
  • Bickel, P. J. and Chen, A. (2009). A nonparametric view of network models and Newman–Girvan and other modularities. Proc. Natl. Acad. Sci. USA 106 21068–21073.
  • Bonetta, L. (2006). Genome sequencing in the fast Lane. Nature Methods 3 141.
  • Brocks, D., Assenov, Y., Minner, S., Bogatyrova, O., Simon, R., Koop, C., Oakes, C., Zucknick, M., Lipka, D. B., Weischenfeldt, J. et al. (2014). Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Reports 8 798–806.
  • Christensen, B. C., Houseman, E. A., Marsit, C. J., Zheng, S., Wrensch, M. R., Wiemels, J. L., Nelson, H. H., Karagas, M. R., Padbury, J. F., Bueno, R. et al. (2009). Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genetics 5 e1000602.
  • Clune, J., Mouret, J.-B. and Lipson, H. (2013). The evolutionary origins of modularity. Proc. R. Soc. Lond., B Biol. Sci. 280 20122863.
  • Collins, F. and Barker, A. (2007). Mapping the cancer genome. Scientific American Magazine 296 50–57.
  • Cooney, C. A. (2007). Epigenetics–DNA-based mirror of our environment? Dis. Markers 23 121–137.
  • Cox, D. R. (1972). Regression models and life-tables. J. R. Stat. Soc. Ser. B. Stat. Methodol. 34 187–220.
  • Feinberg, A. P., Ohlsson, R. and Henikoff, S. (2006). The epigenetic progenitor origin of human cancer. Nat. Rev. Genet. 7 21–33.
  • Fleischer, T., Frigessi, A., Johnson, K. C., Edvardsen, H., Touleimat, N., Klajic, J., Riis, M. L., Haakensen, V., Wärnberg, F., Naume, B. et al. (2014). Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biol 15 435.
  • Gao, F., Shi, L., Russin, J., Zeng, L., Chang, X., He, S., Chen, T. C., Giannotta, S. L., Weisenberger, D. J., Zada, G. et al. (2013). DNA methylation in the malignant transformation of meningiomas. PloS One 8 e54114.
  • Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99 7821–7826 (electronic).
  • Hampton, T. (2006). Cancer genome atlas. JAMA: The Journal of the American Medical Association 296 1958–1958.
  • Harrell, F. E. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, Berlin.
  • Holland, P. W., Laskey, K. B. and Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks 5 109–137.
  • Hotelling, H. (1936). Relations between two sets of variates. Biometrika 28 321–377.
  • Jacob, L., Neuvial, P. and Dudoit, S. (2012). More power via graph-structured tests for differential expression of gene networks. Ann. Appl. Stat. 6 561–600.
  • Johnstone, I. M. and Silverman, B. W. (2004). Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences. Ann. Statist. 32 1594–1649.
  • Jones, P. A. (2012). Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13 484–492.
  • Kang, G. H., Shim, Y.-H., Jung, H.-Y., Kim, W. H., Ro, J. Y. and Rhyu, M.-G. (2001). CpG island methylation in premalignant stages of gastric carcinoma. Cancer Research 61 2847–2851.
  • Kang, G. H., Lee, S., Kim, J.-S. and Jung, H.-Y. (2003). Profile of aberrant CpG island methylation along multistep gastric carcinogenesis. Laboratory Investigation 83 519–526.
  • Katenka, N. and Kolaczyk, E. D. (2012). Inference and characterization of multi-attribute networks with application to computational biology. Ann. Appl. Stat. 6 1068–1094.
  • Kishida, Y., Natsume, A., Kondo, Y., Takeuchi, I., An, B., Okamoto, Y., Shinjo, K., Saito, K., Ando, H., Ohka, F. et al. (2012). Epigenetic subclassification of meningiomas based on genome-wide DNA methylation analyses. Carcinogenesis 33 436–441.
  • Lai, F. and Shiekhattar, R. (2014). Where long noncoding RNAs meet DNA methylation. Cell Res. 24 263–264.
  • Latouche, P., Birmelé, E. and Ambroise, C. (2011). Overlapping stochastic block models with application to the French political blogosphere. Ann. Appl. Stat. 5 309–336.
  • Li, C. and Li, H. (2010). Variable selection and regression analysis for graph-structured covariates with an application to genomics. Ann. Appl. Stat. 4 1498–1516.
  • Li, C. and Wang, J. (2014). Quantifying the underlying landscape and paths of cancer. J. R. Soc. Interface 11 20140774.
  • Luo, Y., Wong, C.-J., Kaz, A. M., Dzieciatkowski, S., Carter, K. T., Morris, S. M., Wang, J., Willis, J. E., Makar, K. W., Ulrich, C. M. et al. (2014). Differences in DNA methylation signatures reveal multiple pathways of progression from adenoma to colorectal cancer. Gastroenterology 147 418–429.
  • Maekawa, R., Sato, S., Yamagata, Y., Asada, H., Tamura, I., Lee, L., Okada, M., Tamura, H., Takaki, E., Nakai, A. et al. (2013). Genome-wide DNA methylation analysis reveals a potential mechanism for the pathogenesis and development of uterine leiomyomas. PloS One 8 e66632.
  • Mardia, K. V. (2013). Statistical approaches to three key challenges in protein structural bioinformatics. J. R. Stat. Soc. Ser. C. Appl. Stat. 62 487–514.
  • Nandi, A. K., Sumana, A. and Bhattacharya, K. (2014). Social insect colony as a biological regulatory system: Modelling information flow in dominance networks. J. R. Soc. Interface 11 20140951.
  • Navarro, A., Yin, P., Monsivais, D., Lin, S. M., Du, P., Wei, J.-J. and Bulun, S. E. (2012). Genome-wide DNA methylation indicates silencing of tumor suppressor genes in uterine leiomyoma. PLoS ONE 7 e33284.
  • Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38 321–330.
  • Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E (3) 69 026113.
  • Olhede, S. C. and Wolfe, P. J. (2014). Network histograms and universality of blockmodel approximation. Proc. Natl. Acad. Sci. USA 111 14722–14727.
  • Palla, G., Lovász, L. and Vicsek, T. (2010). Multifractal network generator. Proc. Natl. Acad. Sci. USA 107 7640–7645.
  • Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D.-Y., Pollack, J. R. and Wang, P. (2010). Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Ann. Appl. Stat. 4 53–77.
  • Qin, T. and Rohe, K. (2013). Regularized spectral clustering under the degree-corrected stochastic blockmodel. In Advances in Neural Information Processing Systems 3120–3128. Lake Tahoe, Nevada.
  • Reznik, E., Watson, A. and Chaudhary, O. (2013). The stubborn roots of metabolic cycles. J. R. Soc. Interface 10 20130087.
  • Riolo, M. A. and Newman, M. E. J. (2012). First-principles multiway spectral partitioning of graphs. Preprint. Available at arXiv:1209.5969.
  • Saavedra, S., Rohr, R. P., Gilarranz, L. J. and Bascompte, J. (2014). How structurally stable are global socioeconomic systems? J. R. Soc. Interface 11 20140693.
  • Shen-Orr, S. S., Milo, R., Mangan, S. and Alon, U. (2002). Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31 64–68.
  • Taylor, I. W., Linding, R., Warde-Farley, D., Liu, Y., Pesquita, C., Faria, D., Bull, S., Pawson, T., Morris, Q. and Wrana, J. L. (2009). Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27 199–204.
  • Tran, T.-D. and Kwon, Y.-K. (2013). The relationship between modularity and robustness in signalling networks. J. R. Soc. Interface 10 20130771.
  • Van Hoesel, A. Q., Sato, Y., Elashoff, D. A., Turner, R. R., Giuliano, A. E., Shamonki, J. M., Kuppen, P. J. K., van de Velde, C. J. H. and Hoon, D. S. B. (2013). Assessment of DNA methylation status in early stages of breast cancer development. British Journal of Cancer 108 2033–2038.
  • Venters, B. J. and Pugh, B. F. (2013). Genomic organization of human transcription initiation complexes. Nature 502 53–58.
  • Verschuur-Maes, A. H., de Bruin, P. C. and van Diest, P. J. (2012). Epigenetic progression of columnar cell lesions of the breast to invasive breast cancer. Breast Cancer Res. Treat. 136 705–715.
  • Vu, D. Q., Hunter, D. R. and Schweinberger, M. (2013). Model-based clustering of large networks. Ann. Appl. Stat. 7 1010–1039.
  • Wagner, A. (2002). Estimating coarse gene network structure from large-scale gene perturbation data. Genome Res. 12 309–315.
  • Wei, P. and Pan, W. (2010). Network-based genomic discovery: Application and comparison of Markov random-field models. J. R. Stat. Soc. Ser. C. Appl. Stat. 59 105–125.
  • Xie, W., Schultz, M. D., Lister, R., Hou, Z., Rajagopal, N., Ray, P., Whitaker, J. W., Tian, S., Hawkins, R. D., Leung, D. et al. (2013). Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153 1134–1148.
  • Yamamoto, E., Suzuki, H., Yamano, H., Maruyama, R., Nojima, M., Kamimae, S., Sawada, T., Ashida, M., Yoshikawa, K., Kimura, T. et al. (2012). Molecular dissection of premalignant colorectal lesions reveals early onset of the CpG island methylator phenotype. Am. J. Pathol. 181 1847–1861.

Supplemental materials

  • Supplementary tables and figures. Supplementary Tables S1–S5 and Supplementary Figures S1–S2.