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Martin J. Aryee, PhD
Assistant Professor of Pathology, Harvard Medical SchoolAssistant Molecular Pathologist, Massachusetts General Hospital
Molecular Pathology UnitMassachusetts General Hospital149 13th Street, 6th FloorCharlestown, MA 02129Phone: 617-726-5690
My research involves computational methods that enable us to elucidate the genetic and epigenetic basis of cancer and other diseases from large genomic datasets.
We develop statistical methods to improve our understanding of tumor cell-to-cell variability and its relationship to cancer progression. Much of this work relates to the computational and statistical challenges posed by single-cell transcriptome and epigenome data.
Different tumors, even of the same type, can harbor extremely heterogeneous genetic and epigenetic alterations. To investigate the role of epigenetic stochasticity in cancer, we recently applied a statistical model to study patterns of inter- and intra-individual tumor heterogeneity during metastasis. We established that metastatic prostate cancer patients develop distinctly unique DNA methylation signatures that are subsequently maintained across metastatic dissemination. The stability of these individualized DNA methylation profiles has implications for the promise of epigenetic alterations as diagnostic and therapeutic targets in cancer.
Unlike genome sequencing which has well established experimental and analytical protocols, epigenome mapping strategies are still in their infancy and, like other high-throughput techniques, are plagued by technical artifacts. A central theme of our research involves the development of methods for extracting signal from noisy high-throughput genomic assays. The goal of such preprocessing methods is transform raw data from high-throughput assays into reliable measures of the underlying biological process.
Until recently, studies of DNA methylation in cancer had focused almost exclusively on CpG dense regions in gene promoters. We helped develop the statistical tools used to analyze the first genome-scale DNA methylation assays designed without bias towards CpG islands. These tools enabled the discovery that the majority of both tissue-specific and cancer-associated variation occurs in regions outside of CpG islands. We showed that there is a strong overlap between genomic regions involved in normal tissue differentiation, reprogramming during induced pluripotency, and cancer.
Epigenomic studies of complex disease
Despite the discovery of numerous disease-associated genetic variants, the majority of phenotypic variance remains unexplained for most diseases, suggesting that non-genetic factors play a significant role. Part of the explanation will lie in a better understanding of epigenetic mechanisms. These mechanisms are influenced by both genetic and environmental effects and, as downstream effectors of these factors, may be more directly related to phenotype. However, the broad extent of epigenetic dysregulation in cancer and many other diseases complicates the search for the small subset of alterations with a causal role in pathogenesis. We are developing computational methods to integrate genome-wide genetic and epigenetic data with the goal of identifying the subset of functionally important epigenetic alterations.
See our lab website for more information: http://aryee.mgh.harvard.edu
Divy Kangeyan, PhD Student, HSPHCaleb Lareau, PhD Student, HSPHJosé Malagón López, PhD Fellow, MGHZack McCaw, PhD Student, HSPHKelly Mosesso, PhD Student, HSPHAkpéli Nordor, PhD Student Institut Curie & Université Paris DescartesVishal Thapar, PhD Research Scientist, MGH
Coverage recommendations for methylation analysis by whole genome bisulfite sequencing
Michael J. Ziller, Kasper D. Hansen, Alexander Meissner, Martin J. Aryee
Nat Methods. Author manuscript; available in PMC 2015 Sep 1.
Published in final edited form as: Nat Methods. 2015 Mar; 12(3): 230–232. Published online 2014 Nov 2.doi: 10.1038/nmeth.3152
Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays
Martin J. Aryee, Andrew E. Jaffe, Hector Corrada-Bravo, Christine Ladd-Acosta, Andrew P. Feinberg, Kasper D. Hansen, Rafael A. Irizarry
Bioinformatics. 2014 May 15; 30(10): 1363–1369. Published online 2014 Jan 28. doi: 10.1093/bioinformatics/btu049
DNA methylation alterations exhibit intra-individual stability and inter-individual heterogeneity in prostate cancer metastases
Martin J Aryee, Wennuan Liu, Julia C Engelmann, Philipp Nuhn, Meltem Gurel, Michael C Haffner, David Esopi, Rafael A Irizarry, Robert H Getzenberg, William G Nelson, Jun Luo, Jianfeng Xu, William B Isaacs, G Steven Bova, Srinivasan Yegnasubramanian
Sci Transl Med. Author manuscript; available in PMC 2013 Jul 23.
Published in final edited form as: Sci Transl Med. 2013 Jan 23; 5(169): 169ra10. doi: 10.1126/scitranslmed.3005211
Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis
Yun Liu, Martin J. Aryee, Leonid Padyukov, M. Daniele Fallin, Espen Hesselberg, Arni Runarsson, Lovisa Reinius, Nathalie Acevedo, Margaret Taub, Marcus Ronninger, Klementy Shchetynsky, Annika Scheynius, Juha Kere, Lars Alfredsson, Lars Klareskog, Tomas J. Ekström, Andrew P. Feinberg
Nat Biotechnol. Author manuscript; available in PMC 2013 Aug 1.
Published in final edited form as: Nat Biotechnol. 2013 Feb; 31(2): 142–147. Published online 2013 Jan 20.doi: 10.1038/nbt.2487
Accurate genome-scale percentage DNA methylation estimates from microarray data
Martin J. Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P. Feinberg, Srinivasan Yegnasubramanian, Rafael A. Irizarry
Biostatistics. 2011 Apr; 12(2): 197–210. Published online 2010 Sep 21. doi: 10.1093/biostatistics/kxq055
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