2003-2007 B.S., Applied Mathematics, Rochester Institute of Technology, Rochester, NY
2007-2011 Ph.D., Biostatistics, Brown University, Providence, RI
2011-2013 Post-Doc, Quantitative Biosciences in Cancer, Dartmouth College, Hanover, NH
2014- Graduate Certificate, Bioinformatics, Stanford University Center for Professional Development, Palo Alto, CA
2011 Eastern North American Region (ENAR) Distinguished Student Paper Award
2011 Ruth L. Kirschstein National Research Service Awardee (NRSA)
2013 University of Kansas Cancer Center (KUCC) Pilot Grant Awardee
2013 Invited to serve as an Academic Editor for PLoS One.
2014 Frontiers Junior Faculty Career Development (KL2) Award Recipient
2014 Feature story in the University of Kansas Cancer Center (KUCC) newsletter Link to Full Story
2015 Department of Biostatistics Outstanding Teacher Awardee
2015 Prostate Cancer Dream Challenge Round 1 All Stars. Link to Full Story
Personal Mission Statement:
Throughout my career, my number one priority has been to utilize my training in statistics and background in the quantitative sciences to understand the biology of living systems. As a doctoral student in biostatistics at Brown University, I was fortunate to have the opportunity to work in a multidisciplinary environment that involved the application of my quantitative training as a means toward understanding the role of epigenetics in states of human health and disease. This multidisciplinary research experience provided many important lessons, not the least of which was that the size and complexity of the typical ‘omic data set is a major bottleneck in the translation of raw ‘omic data into clinically and biologically important information. This realization has been the driving force behind my research program, which aims to develop and apply novel bioinformatics/statistical methodologies for identifying biologically and clinically relevant patterns in high-throughput ‘omic’ data.
Shortly after joining University of Kansas Medical Center in the fall of 2013, I was awarded a Frontiers Junior Faculty Career Development Award (project 1KL2TR000119) to develop and apply novel statistical methods for integrating multiple different ‘omic data types to better understand ovarian cancer risk and prognosis, as well as University of Kansas Cancer Center (KUCC) pilot award to develop prognostic models for predicting bladder cancer recurrence-risk using clinical, epidemiologic, and molecular biomarkers. In addition to this work, examples of my past and ongoing statistical methodological work include: model-based clustering, finite mixture models, classification, prediction and forecasting models.
High-dimensional genomic data, statistical genomics, mixture models, clustering and classification, molecular epidemiology, epigenetics, and DNA methylation.
1. Marsit CJ, Koestler DC, Christensen BC, Karagas MR, Houseman EA, Kelsey KT. DNA Methylation Array Analysis Identifies Profiles of Blood-Derived DNA Methylation Associated With Bladder Cancer. Journal of Clinical Oncology. 2011;29(9):1133-9. doi: 10.1200/jco.2010.31.3577. PubMed PMID: WOS:000288532500025.
2. Langevin SM, Houseman EA, Accomando WP, Koestler DC, Christensen BC, Nelson HH, et al. Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics. 2014;9(6):884-95. doi: 10.4161/epi.28575. PubMed PMID: WOS:000337179600012.
3. Koestler DC, Ombao H, Bender J. Ensemble-based methods for forecasting census in hospital units. Bmc Medical Research Methodology. 2013;13. doi: 10.1186/1471-2288-13-67. PubMed PMID: WOS:000320233000001.
4. Koestler DC, Marsit CJ, Christensen BC, Kelsey KT, Houseman EA. A recursively partitioned mixture model for clustering time-course gene expression data. Translational cancer research. 2014;3(3):217-32.
5. Koestler DC, Marsit CJ, Christensen BC, Karagas MR, Bueno R, Sugarbaker DJ, et al. Semi-supervised recursively partitioned mixture models for identifying cancer subtypes. Bioinformatics. 2010;26(20):2578-85. doi: 10.1093/bioinformatics/btq470. PubMed PMID: WOS:000282749700011.
6. Koestler DC, Marsit CJ, Christensen BC, Accomando W, Langevin SM, Houseman EA, et al. Peripheral Blood Immune Cell Methylation Profiles Are Associated with Nonhematopoietic Cancers. Cancer Epidemiology Biomarkers & Prevention. 2012;21(8):1293-302. doi: 10.1158/1055-9965.epi-12-0361. PubMed PMID: WOS:000307433800007.
7. Koestler DC, Li J, Baron JA, Tsongalis GJ, Butterly LF, Goodrich M, et al. Distinct patterns of DNA methylation in conventional adenomas involving the right and left colon. Modern Pathology. 2014;27(1):145-55. doi: 10.1038/modpathol.2013.104. PubMed PMID: WOS:000329206200015.
8. Koestler DC, Jones M, Kobor M. The era of integrative genomics: more data or better methods? Epigenomics. 2014;6(5):463-7. doi: 10.2217/epi.14.44. PubMed PMID: WOS:000345619900005.
9. Koestler DC, Christensen BC, Marsit CJ, Kelsey KT, Houseman EA. Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures. Statistical Applications in Genetics and Molecular Biology. 2013;12(2):225-40. doi: 10.1515/sagmb-2012-0068. PubMed PMID: WOS:000319394900006.
10. Koestler DC, Christensen BC, Karagas MR, Marsit CJ, Langevin SM, Kelsey KT, et al. Blood-based profiles of DNA methylation predict the underlying distribution of cell types A validation analysis. Epigenetics. 2013;8(8):816-26. doi: 10.4161/epi.25430. PubMed PMID: WOS:000327626300007.
11. Koestler DC, Chalise P, Cicek MS, Cunningham JM, Armasu S, Larson MC, et al. Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer. Bmc Medical Genomics. 2014;7. doi: 10.1186/1755-8794-7-8. PubMed PMID: WOS:000331819100001.
12. Koestler DC, Avissar-Whiting M, Houseman EA, Karagas MR, Marsit CJ. Differential DNA Methylation in Umbilical Cord Blood of Infants Exposed to Low Levels of Arsenic in Utero. Environmental Health Perspectives. 2013;121(8):971-7. doi: 10.1289/ehp.1205925. PubMed PMID: WOS:000323711700027.
13. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. Bmc Bioinformatics. 2012;13. doi: 10.1186/1471-2105-13-86. PubMed PMID: WOS:000312891600001.
14. Christensen B, Smith A, Zheng S, Koestler D, Houseman EA, Marsit CJ, et al. IDH MUTATION DEFINES METHYLATION CLASS AND SURVIVAL IN HUMAN GLIOMA. Neuro-Oncology. 2010;12:68. PubMed PMID: WOS:000285082400294.
15. Wilhelm-Benartzi CS, Koestler DC, Karagas MR, Flanagan JM, Christensen BC, Kelsey KT, et al. Review of processing and analysis methods for DNA methylation array data. British Journal of Cancer. 2013;109(6):1394-402. doi: 10.1038/bjc.2013.496. PubMed PMID: WOS:000324812600002.
1. Koestler DC. Semi-supervised methods for analyzing high-dimensional genomic data. Statistical Diagnostics of Cancer: Genetics and Genomics Data. Wiley-Blackwell (2013).
2. Koestler DC and Houseman EA. Model based clustering analysis of DNA methylation array data. Computational and Statistical Epigenomics. Ed 1, Springer (2015).
Other Scholarly Works
1. Koestler DC. Contributed to the September 2013 issue of AMSTAT news, “Post-doctoral Fellowships, Programs, and Opportunities (http://magazine.amstat.org)
2. Raghavan R and Koestler DC. Accelerating ovarian cancer drug discovery using bioinformatics. AAPS blog post. (http://aapsblog.aaps.org/tag/devin-c-koestler/)
3. Wika E. Statistics bootcamp: estimating pi with R and Buffon’s needle. Significance Magazine. Mentored graduate student Eric Wika in the preparation of this article. (http://www.statslife.org.uk)