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Andrew Teschendorff

Dr Andrew Teschendorff

Dr Andrew Teschendorff

Research Fellow

Interests and expertise (Subject groups)

Grants awarded

Dissection of Intra-Sample Epigenetic Heterogeneity using Blind Source Separation Algorithms

Scheme: Newton Advanced Fellowship

Organisation: University College London

Dates: Mar 2015-Mar 2018

Value: £84,000

Summary: Research in my lab focuses on the development and application of novel statistical methods to help analyze and interpret complex genomic data, with a special focus on Cancer Epigenomics and Cancer Systems Biology. Through the use of innovative biostatistical tools, our ultimate goals are (i) to identify epigenetic biomarkers for the early detection and risk prediction of common cancers, (ii) to identify the regulatory epigenetic networks disrupted in ageing and cancer, (iii) to define clinically sensible cancer taxonomies for personalized medicine, and (iv) to improve our understanding of the systems-biological principles underlying cancer. In more detail, we have contributed methods for the integrative analysis of gene expression and DNA methylation data leading to the discovery of a gene which is silenced epigenetically and which contributes to the causal development of endometrial cancer. We are continuing to explore and improve upon models for integrative analysis of DNA methylation and gene expression data, including data generated from the International Human Epigenome Consortium as well as the NIH Epigenome Roadmap. Having developed highly popular statistical methods for normalizing DNA methylation data, we continue to be very active in this field, developing improved statistical algorithms to deconvolve the effects of confounding factors and cellular heterogeneity in Epigenome-Wide Association and Cancer Epigenome studies. We are also actively exploring innovative statistical algorithms for developing epigenetic based novel risk prediction and early detection tools for common cancers, in close collaboration with clinicians. Finally, we are also actively working towards a novel theoretical framework for understanding stem cell and cancer biology at a systems-level. This framework is based on statistical mechanical principles and using these principles as a novel paradigm aimed at providing a unified picture for understanding a variety of different phenomena, including cellular differentiation and cancer genesis.

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