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A Network Systems Approach to Identify Functional Epigenetic Drivers in Cancer

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Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases

Part of the book series: Translational Bioinformatics ((TRBIO,volume 4))

Abstract

Aberrant epigenetic regulation is a key cancer hallmark. Epigenetic changes observed in pre-neoplastic lesions and cancer also provides a promising avenue for the discovery of novel biomarkers for early detection, diagnosis and prognosis, as well as offering novel therapeutic opportunities. However, the biological interpretation and functional significance of the epigenetic changes in cancer is still unclear. This chapter describes an emerging computational systems framework for elucidating the observed epigenetic deregulation in cancer and other complex diseases. As we shall see, the novel graph-theoretical approach presented here provides a powerful framework for the identification of epigenetic biomarkers associated with common phenotypes. Moreover, it provides a convenient platform in which to perform integrative multi-dimensional analysis, allowing functional epigenetic modules driving disease to be identified. We illustrate the computational method with applications to ageing and the early detection of endometrial cancer. The methods and data presented here provide a concrete example of systems-medicine: the application of a systems-approach to identify a biomarker with great potential for clinical application.

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Acknowledgments

AET was supported by a Heller Research Fellowship. The authors wish to thank everyone involved in the endometrial cancer project, specially Allison Jones and Helga Salvesen for collaborative work on this project.

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Authors and Affiliations

  1. Statistical Cancer Genomics, UCL Cancer Institute, 72 Huntley Street, London WC1E 6BT, UK

    Andrew E. Teschendorff

  2. CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, 200031, Shanghai, China

    Andrew E. Teschendorff

  3. Department of Women’s Cancer, University College London, London WC1E 6BT, UK

    Martin Widschwendter

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Correspondence to Andrew E. Teschendorff .

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  1. Center for Systems Biology, Soochow University, Suzhou, People's Republic of China

    Bairong Shen

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Teschendorff, A.E., Widschwendter, M. (2013). A Network Systems Approach to Identify Functional Epigenetic Drivers in Cancer. In: Shen, B. (eds) Bioinformatics for Diagnosis, Prognosis and Treatment of Complex Diseases. Translational Bioinformatics, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7975-4_7

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