Assistant Professor, Department of Biostatistics
B.S., Applied Mathematics, Rochester Institute of Technology, Rochester, NY
Ph.D., Biostatistics, Brown University, Providence, RI
Post-Doc, Quantitative Biosciences in Cancer, Dartmouth College, Hanover, NH
2011 - Eastern North American Region (ENAR) Distinguished Student Paper Award
2011 - Ruth L. Kirschstein National Research Service Awardee (NRSA)
High-dimentional genomic data, statistical genomics, mixture models, clustering and classification, molecular epidemiology, epigenetics, and DNA methylation.
The broad mission of my research involves the development and application of bioinformatics/statistical methodologies for analyzing high-throughput 'omic' data. I also have deeply rooted interest in epigenetics and molecular epidemiology, specifically DNA methylation and its implications for human health and disease. Depending on the disease or condition being studied, its possible that only a small fraction of the molecular markers measured (i.e., CpG sites, genes, ect.) vary across subjects in any biologically meaningful way. My job is therefore to develop new and/or apply existing statistical methodologies for identifying these markers and to understand their functional role in the context of the disease under study. An appropriate analogy for this task is the age-old adage, "finding a needle in a haystack" - actually it is often like trying to find a needle in a barn full of haystacks! In addition to my methodological interests, which include, multivariate statistics, mixture models, and mixed-effects models, I am also passionate about my collaborations, which span from environmental health, the human microbiome, to a wide variety of different epigenetics studies. The shared theme across my collaborative research projects is the use of high-dimensional genomic data to gain further insight into some biological process.
1. Koestler D.C., Marsit C.J., Christensen B.C., Karagas M.R., Bueno R., Sugarbaker D.J., Kelsey K.T. & Houseman E.A. (2010). Semi-supervised recursively partitioned mixture models for identifying cancer subtypes. Bioinformatics, 26(20), 2578-85. PMID: 20834038.
2. Marsit C.J., Koestler D.C., Christensen B.C., Karagas M.R., Houseman E.A. & Kelsey K.T. (2011). DNA methylation array analysis identifies profiles of blood-derived DNA methylation associated with bladder cancer. Journal of Clinical Oncology, 29(9), 1133-9. PMID: 21343564.
3. Christensen B.C., Smith A., Zheng S., Koestler D.C., Houseman E.A., Marsit C.J., Wiemels J.L., Nelson H.N., Karagas M.R., Wrensch M.R., Kelsey K.T. & Wiencke J.K. (2010). DNA methylation, isocitrate dehydrogenase mutation, and survival in glioma. Journal of the National Cancer Institute, 103(2), 143-53. PMID: 21163902.
4. Avissar-Whiting M., Koestler D.C., Houseman E.A., Christensen B.C., Kelsey K.T. & Marsit C.J. (2011). Polycomb group genes are targets of aberrant DNA methylation in renal cell carcinoma. Epigenetics, 6(6), 703 -9. PMID: 21610323.
5. Langevin S.M., Koestler D.C., Christensen B.C., Wiencke J.K., Nelson H., Houseman E.A., Marsit C.J. & Kelsey K.T. (2012). DNA methylation profiles in peripheral blood are predictive of head and neck squamous cell carcinoma. Epigenetics, 7(3), 291-9. PMID: 22430805.
6. Houseman E.A., Accomando W., Koestler D.C., Christensen B.C., Kelsey K.T. & Wiencke J.K. (2012). DNA methylation arrays as a surrogate measure of cell mixtures. BMC Bioinformatics, doi: 10.1186/1471-2105-13-86. PMID: 22568884.
7. Madan J., Koestler D.C., Stanton B., Davidson L., Moore J.H., Sogin M., Saxena D., Hampton T., Palumbo P., Guill M., Karagas M.R., O'Toole G.A. & Hibberd P.H. (2012). The developing respiratory and intestinal microbiome in cystic fribrosis infancy. MBio, doi:pii: e00251-12. 10.1128/mBio.00251-12. PMID: 22911969.
8. Koestler D.C., Marsit C.J., Christensen B.C., Langevin S.M., Accomando W., Houseman E.A., Karagas M.R., Wiencke J.K. & Kelsey K.T. (2012). Peripheral blood immune cell methylation profiles are associated with non-hematopoietic cancers. Cancer Epidemiology Biomarkers & Prevention. 21(8), 1293-302. PMID: 22714737.
9. Koestler D.C., Christensen B.C., Marsit C.J., Kelsey K.T. & Houseman E.A. (2013). Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures. Statistical Applications in Genetics and Molecular Biology, [Epub ahead of print]. PMID: 23468465.
10. Cicek M.S., Koestler D.C., Fridley B.R., Armasu, S.M., Kalli K.R., Winterhoff B.R., Chien J., Fan J., Bibikova M., Block M.A., Olson J.E., Charbonneau B., Shridhar V., Cunningham J.M. & Goode E.L. (2013). Ovarian cancer methylation profiles vary by histological subtype. Human Molecular Genetics, [Epub ahead of print]. PMID: 23571109.
11. Koestler D.C.#, Li J, Baron J, Dietrich A, Tsongalis G, Butterly L, Goodrich M, Lesseur C, Karagas M.R., Marsit CJ, Moore JH, Andrew A, Srivastava A. (2013). Differential methylation of colon adenomas of the right side. Modern Pathology. [Epub ahead of print]. PMID: 23868178.
12. Koestler DC#, Christensen BC#, Karagas MR, Marsit CJ, Langevin SM, Kelsey KT, Wiencke JK, Houseman EA. (2013). Blood based patterns of DNA methylation predict the underlying distribution of cell types: a validation analysis. Epigenetics, 8(8), 816-26. PMID: 23903776.
13. Koestler DC, Ombao H , and Bender J. (2013). Ensemble-based methods for forecasting census in hospital units. BMC Medical Research Methodology, doi: 10.1186/1471-2288-13-67. PMID: 23721123.
14. Koestler DC, Avissar-Whiting, Karagas MR, and Marsit CJ. (2013). Differential DNA methylation in umbilical cord blood of infants exposed to low levels of arsenic in-utero. Environmental Health Perspectives, 121(8), 971-7. PMID: 23757598.
15. Wilhelm-Benartzi C#, Koestler DC#, Karagas MR, Flanagan JM, Christensen BC, Kelsey KT, Marsit CJ, Houseman EA, and Brown R. Good practice in DNA methylation array processing and analysis. British Journal of Cancer. [In press].
# - Authors contributed equally to this work.
Devin C. Koestler, Ph.D.
Assistant Professor, Department of Biostatistics