- Assistant Professor of Oncology
Departments / Divisions
- Oncology - Biostatistics and Bioinformatics
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Quantitative Proteomics; RNA-Sequencing; eQTL Analysis; Integration across different genomic data types; Reproducible Research
Dr. Kammers has advanced training in biostatistics and bioinformatics and his work is fundamentally motivated by applications to real-life genomic research questions through close collaborations involving researchers from a variety of scientific backgrounds. He was a Postdoctoral Fellow under the supervision of Drs. Jeffrey Leek and Ingo Ruczinski in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health after he had obtained his Ph.D. in Statistics from the University of Dortmund, Germany.
During his doctoral studies, he developed new methodological approaches for modeling survival data in the presence of high-dimensional covariates with the goal of improving prediction accuracy and interpretability through the integration of gene expression data and prior biological knowledge on groups of genes. These approaches were subsequently applied to microarray data of individuals with breast cancer to identify gene groups associated with survival times. The results showed improved prediction performance compared to classical models using clinical information or gene expression measurements as covariates.
His current research focuses on developing new statistical methods and software tools for the integrative analysis of high-throughput genomic data, including sequencing and proteomic data (e.g. RNA-sequencing, DNA genotype, and iTRAQ/TMT proteomics data). He develops methods for pre-processing such genomic data, and software to reproducibly execute these protocols. Currently, the joint analysis of RNA-sequencing data and DNA genotypes (delineated by genomic arrays or next generation sequencing) to detect patterns of transcript expression related to specific genetic variants - known as eQTLs, or expression quantitative trait loci - is one of his main research areas. In the future, he will extend these methods to also incorporate methylation and proteomic data in our quest to fully understand the underlying biology.
Dr. Kammers is very also interested in helping to bridge the sometimes existing gap between statisticians and scientists, developing easy to use tools based on new or existing sound statistical principles. For example, he has developed open source software for the normalization of isobaric mass labeled proteomic data, with subsequent inference based on moderated test statistics.
Kammers K, Taub MA, Ruczinski I, Martin J, Yanek LR, Frazee A, Gao Y, Hoyle D, Faraday N, Becker DM, Cheng L, Wang ZZ, Leek JT, Becker LC, Mathias RA. Integrity of induced pluripotent stem cell (iPSC) derived megakaryocytes as assessed by genetic and transcriptomic analysis. PLoS One. 2017;12(1):e0167794. doi: 10.1371/journal.pone.0167794.
Wilky BA, Kim C, McCarty G, Montgomery EA, Kammers K, Cole RN, Raman V, Loeb D. RNA Helicase DDX3 - A novel therapeutic target in Ewing sarcoma. Oncogene. 2016;35(20):2574-83. doi: 10.1038/onc.2015.336.
Kammers K, Cole RN, Tiengwe C, Ruczinski. Detecting significant changes in protein abundance. EuPA Open Proteom. 2015;7:11-9. doi: 10.1016/j.euprot.2015.02.002
Kammers K, Lang M, Hengstler JG, Schmidt M, Rahnenführer J. Survival models with preclustered gene groups as covariates. BMC Bioinformatics 2011;12:478. doi: 10.1186/1471-2105-12-478.
Kammers K, Foster DB, Ruczinski I. Analysis of proteomic data. In Agnetti G, Lindsey ML, Foster DB (eds). Manual of Cardiovascular Proteomics. Springer International Publishing, Cham, Switzerland, 2016. doi: 10.1007/978-3-319-31828-8.