In our lab we are developing and applying novel advanced statistical methods, drawn from diverse areas such as graph theory, network physics, machine learning and signal processing, to help tackle some of the outstanding challenges in cancer genomics and epigenomics.
We are particularly interested in using the language of networks and statistical mechanics to help elucidate the systems biology principles underlying normal cellular differentiation and how this process is altered in complex diseases such as cancer. We are developing a framework based on statistical mechanics and network theory, in order to help identify the key signaling pathways and nodes in differentiation, cancer and drug resistance.
Another application of networks and graph theory is to the integrative analysis of large-scale multi-dimensional omic data sets. For instance, we have recently used interactome and regulatory networks to integrate epigenetic and gene expression data together, to identify functional epigenetic drivers in cancer and ageing. We are continuing to explore and develop such "Systems Epigenomics" approaches.
We are also involved in numerous more data-driven projects, in close collaboration with clinicians and biologists. One key area of interest is in cancer risk prediction and early detection: we are involved in the analysis of DNA methylation data from large prospective studies in order to assess if DNA methylation marks measured years in advance of disease diagnosis can predict the risk of disease. Another area of interest is in refining the molecular taxonomy of cancers, using perturbation molecular signature approaches to identify novel prognostic and predictive cancer subtypes.
See Further Information link above for more detailed information.
2009 VIP Wellcome Trust Award.
2008-present Heller Research Fellowship.
2005-2006 Isaac Newton Trust Fellowship.
2003-2005 CMI (Cambridge-MIT Institute) Research Fellow.
2001-2003 EPSRC Research Fellowship.
1997-1999 PPARC Research Studentship.
1997,1998,1999 Cambridge European Trust Awards.
1995/96 Wolfson College Jennings Prize.
1994 Award of the 1994/95 Tait Medal and Robert Schlapp Prize in Physics.
Science Faculty Vacation Scholarship
1. Teschendorff AE, Gao Y, Jones A, Ruebner M, Beckmann MW, Wachter DL, Fasching PA, Widschwendter M. DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer. Nat Commun. 2016 Jan 29;7:10478. doi: 10.1038/ncomms10478. PubMed PMID: 26823093; PubMed Central PMCID: PMC4740178.
2. Gao Y, Jones A, Fasching PA, Ruebner M, Beckmann MW, Widschwendter M,Teschendorff AE. The integrative epigenomic-transcriptomic landscape of ER positive breast cancer. Clin Epigenetics. 2015 Dec 9;7:126. doi: 10.1186/s13148-015-0159-0. eCollection 2015. PubMed PMID: 26664652; PubMed Central PMCID: PMC4673726.
3. Teschendorff AE, Yang Z, Wong A, Pipinikas CP, Jiao Y, Jones A, Anjum S, Hardy R, Salvesen HB, Thirlwell C, Janes SM, Kuh D, Widschwendter M. Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer. JAMA Oncol. 2015 Jul;1(4):476-85. doi: 10.1001/jamaoncol.2015.1053. Erratum in: JAMA Oncol. 2015 Jul;1(4):544. PubMedPMID: 26181258.
4. Yang Z, Jones A, Widschwendter M, Teschendorff AE. An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer. Genome Biol. 2015 Jul 14;16:140. doi: 10.1186/s13059-015-0699-9. PubMed PMID: 26169266; PubMed Central PMCID: PMC4501092.
5. Yuan T, Jiao Y, de Jong S, Ophoff RA, Beck S, Teschendorff AE. An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging. PLoS Genet. 2015 Feb 18;11(2):e1004996. doi: 10.1371/journal.pgen.1004996. eCollection2015 Feb. PubMed PMID: 25692570; PubMed Central PMCID: PMC4334892.
6. Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics. 2014 Aug 15;30(16):2360-6. doi: 10.1093/bioinformatics/btu316. Epub 2014 May 2. PubMed PMID: 24794928.
7. Teschendorff AE, Sollich P, Kuehn R. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods. 2014 Jun 1;67(3):282-93. doi: 10.1016/j.ymeth.2014.03.013. Epub 2014 Mar 24. PubMed PMID: 24675401.
8. West J, Widschwendter M, Teschendorff AE. Distinctive topology of age-associated epigenetic drift in the human interactome. Proc Natl Acad Sci U S A. 2013 Aug 27;110(35):14138-43. doi: 10.1073/pnas.1307242110. Epub 2013 Aug 12. PubMed PMID: 23940324; PubMed Central PMCID: PMC3761591.
9. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013 Jan 15;29(2):189-96. doi: 10.1093/bioinformatics/bts680. Epub 2012 Nov 21. PubMed PMID: 23175756; PubMed Central PMCID: PMC3546795.
10. Teschendorff AE, Liu X, Caren H, Pollard SM, Beck S, Widschwendter M, Chen L. The dynamics of DNA methylation covariation patterns in carcinogenesis. PLoS Comput Biol. 2014 Jul10;10(7):e1003709.doi:10.1371/journal.pcbi.1003709. eCollection 2014 Jul. PubMed PMID: 25010556; PubMed Central PMCID: PMC4091688.
Education & Academic Background
1) PhD Theoretical Particle Physics, University of Cambridge, May 2000.
2) Certificate of Advanced Study in Mathematics, University of Cambridge, Awarded Distinction, July 1996.
3) BSc (Hon) Mathematical Physics, University of Edinburgh, Awarded 1st Class, July 1995.
Currently, I am a Principal Investigator at the CAS-MPG Partner Institute for Computational Biology in Shanghai, leading a group in Computational Systems Genomics. I am also a PrincipalResearch Associate at the Department of Women's Cancer, University College London and a Royal Society Newton Advanced Fellow at the UCL Cancer Institute. My broad research interest is in Statistical Bioinformatics with an emphasis on Statistical Cancer Epigenomics and Cancer System-omics. Specifically, I am interested in the development and application of statistical methods, notably network physics and signal processing algorithms, to enable a more meaningful interpretation of multidimensional cancer genomic and epigenomic data. The goal is to use computational approaches for hypothesis generation and to help pave the way for improved diagnostic and early detection tools as well as clinically more relevant molecular classifications of common cancers.