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Devin C. Koestler, Ph.D.

Associate Professor, Department of Biostatistics & Data Science
Associate Director, Biostatistics and Informatics Shared Resource, The University of Kansas Cancer Center
Satellite Director, Bioinformatics, Kansas-INBRE (K-INBRE)
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
Graduate Certificate, Bioinformatics, Stanford University Center for Professional Development (in progress)

2011 - Eastern North American Region (ENAR) Distinguished Student Paper Award
2011 - Ruth L. Kirschstein National Research Service Awardee (NRSA)
2015 - Department of Biostatistics & Data Science Outstanding Graduate Teaching Award Recipient
2015 - Top performers (team Jayhawks), Prostate Cancer DREAM Challenge
2013 - 2017 - Associate Member, University of Kansas Cancer Center
2014 - 2017 - Academic Editor, PLoS One
2018 - Member, University of Kansas Cancer Center

Research Focus

High-dimensional genomic data, statistical 'omics, mixture models, clustering and classification, molecular epidemiology, epigenetics, and DNA methylation.


Personal Statement

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, it is possible that only a small fraction of the molecular markers measured (i.e., CpG sites, genes, etc.) 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. 


Scholarly Activity and Academic Credentials

Google Scholar Profile Page

Software and R-code

Workshops and Tutorials


In the News and Web Coverage of Research

2018 - Joint first author of a paper that was highlighted as one of the top 3 research contributions in cancer informatics in 2017 (

2017 - “A mathematical crystal ball gazes into future of prostate cancer treatment”, press release, EurekAlert (

2015 - Feature story in the KU Medical Center News regarding team JayHawks’ performance in the Prostate Cancer Dream Challenge (

2014 - Feature story in the University of Kansas Cancer Center (KUCC) newsletter (

2014 - Statistics bootcamp: estimating pi with R and Buffon's needle.  Significance Magazine.  Mentored graduate student Eric Wika in the preparation of this article. (

2014 - Accelerating ovarian cancer drug discovery using bioinformatics.  AAPS blog post.  (

2013 - Featured in 2013 issue of AMSTAT news, "Post-doctoral Fellowships, Programs, and Opportunities" (

2012 - Featured on the cover of the August 2012 issue of Cancer Epi Bio Prev and described by the editor in that issue's highlights.

    • Covered as a press release in Dartmouth Now, Dartmouth's source of news and noteworthy research discoveries.

    • Described in an article published in The Dartmouth, the nation's oldest college newspaper.

    • Listed as an example of research among the top reasons prospective MPH students should consider Dartmouth College.

    • Featured in Epigenie, a repository of highlights in epigenetics research.   


2010 - Insight offers new angle of attack on variety of brain tumors


Book Chapters

1. Koestler DC.  Semi-supervised methods for analyzing high-dimensional genomic data.  Statistical Diagnostics of Cancer: Analyzing High-Dimensional Genomic Data.   Dehmer M (Editor) and Emmert-Streib F (Series Editor). Wiley-Blackwell (2013).  ISBN: 978-3-527-33262-5

2. Koestler DC and Houseman EA. Model based clustering analysis of DNA methylation array data.  Computational and Statistical Epigenomics. Teschendorff A (Editor) Springer: Translational Bioinformatics Series. ISBN 978-94-017-9926-3

3. Graw S and Koestler DC. Book Review: A Primer of Human Genetics.  Reviews of Books and Teaching Materials, The American Statistician, 70:1, 120-126, DOI: 10.1080/00031305.2016.1140432.  2016. (link to article)


Select Publications

1.   Grieshober L, Graw S, Barnett MJ, Thornquist MD, Goodman GE, Chen C, Koestler DC*, Marsit CJ*, Doherty JA*: Methylation-derived Neutrophil-to-Lymphocyte Ratio and Lung Cancer Risk in Heavy Smokers. Cancer Prev Res (Phila) 2018, 11:727-734.

2.   Salas LA, Wiencke JK, Koestler DC, Zhang Z, Christensen BC, Kelsey KT: Tracing human stem cell lineage during development using DNA methylation. Genome Res 2018, 28:1285-1295.

3.   Salas LA*, Koestler DC*, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, Christensen BC: An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 2018, 19:64.

4.   Kim S, Eliot M, Koestler DC, Wu WC, Kelsey KT: Association of Neutrophil-to-Lymphocyte Ratio With Mortality and Cardiovascular Disease in the Jackson Heart Study and Modification by the Duffy Antigen Variant. JAMA Cardiol 2018, 3:455-462

5.   Zheng SC, Beck S, Jaffe AE, Koestler DC, Hansen KD, Houseman AE, Irizarry RA, Teschendorff AE: Correcting for cell-type heterogeneity in epigenome-wide association studies: revisiting previous analyses. Nat Methods 2017, 14:216-217.

6.   Wiencke JK*, Koestler DC*, Salas LA, Wiemels JL, Roy RP, Hansen HM, Rice T, McCoy LS, Bracci PM, Molinaro AM, et al: Immunomethylomic approach to explore the blood neutrophil lymphocyte ratio (NLR) in glioma survival. Clin Epigenetics 2017, 9:10.

7.   Koestler DC*, Usset J*, Christensen BC, Marsit CJ, Karagas MR, Kelsey KT, Wiencke JK: DNA Methylation-Derived Neutrophil-to-Lymphocyte Ratio: An Epigenetic Tool to Explore Cancer Inflammation and Outcomes. Cancer Epidemiol Biomarkers Prev 2017, 26:328-338.

8.   Koestler DC, Jones MJ, Usset J, Christensen BC, Butler RA, Kobor MS, Wiencke JK, Kelsey KT: Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinformatics 2016, 17:120.

9.   Koestler DC, Marsit CJ, Christensen BC, Kelsey KT, Houseman EA: A recursively partitioned mixture model for clustering time-course gene expression data. Transl Cancer Res 2014, 3:217-232.

10.   Koestler DC, Chalise P, Cicek MS, Cunningham JM, Armasu S, Larson MC, Chien J, Block M, Kalli KR, Sellers TA, et al: Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer. BMC Med Genomics 2014, 7:8.

11.   Koestler DC, Jones MJ, Kobor MS: The era of integrative genomics: more data or better methods? Epigenomics 2014, 6:463-467.

12.   Koestler DC, Li J, Baron JA, Tsongalis GJ, Butterly LF, Goodrich M, Lesseur C, Karagas MR, Marsit CJ, Moore JH, et al: Distinct patterns of DNA methylation in conventional adenomas involving the right and left colon. Mod Pathol 2014, 27:145-155.

13.   Wilhelm-Benartzi CS*, Koestler DC*, Karagas MR, Flanagan JM, Christensen BC, Kelsey KT, Marsit CJ, Houseman EA, Brown R: Review of processing and analysis methods for DNA methylation array data. Br J Cancer 2013, 109:1394-1402.

14.   Koestler DC*, Christensen B*, Karagas MR, Marsit CJ, Langevin SM, Kelsey KT, Wiencke JK, Houseman EA: Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis. Epigenetics 2013, 8:816-826.

15.   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. Environ Health Perspect 2013, 121:971-977.

16.   Koestler DC, Christensen BC, Marsit CJ, Kelsey KT, Houseman EA: Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures. Stat Appl Genet Mol Biol 2013, 12:225-240.

17.   Koestler DC, Marsit CJ, Christensen BC, Accomando W, Langevin SM, Houseman EA, Nelson HH, Karagas MR, Wiencke JK, Kelsey KT: Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers. Cancer Epidemiol Biomarkers Prev 2012, 21:1293-1302.

18.   Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT: DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 2012, 13:86.

19.   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. J Clin Oncol 2011, 29:1133-1139.

20.   Koestler DC, Marsit CJ, Christensen BC, Karagas MR, Bueno R, Sugarbaker DJ, Kelsey KT, Houseman EA: Semi-supervised recursively partitioned mixture models for identifying cancer subtypes. Bioinformatics 2010, 26:2578-2585.

* Signifies equal contribution

Last modified: Nov 30, 2018


Devin C. Koestler, Ph.D.
Associate Professor, Department of Biostatistics & Data Science
Associate Director, Biostatistics and Informatics Shared Resource, The University of Kansas Cancer Center
Satellite Director, Bioinformatics, Kansas-INBRE (K-INBRE)

P: 913-588-4703
F: 913-588-0252