All three projects in this Consortium require advanced biostatistics, informatics and data management techniques, both for data analysis and for tracking of results obtained with samples shared across projects. Led by Michael Bittner at TGen and Jeffrey Idle at the University of Bern, Core E will provide a variety of statistical and analytical support to ail three Projects, as well as to the Pilot Projects. Core E will also provide central, secure hosting for data exchange among the Consortium members. We will provide the following services for the three projects: 1. General statistical support for the three research projects. Core C (Irradiation) and the Pilot projects, in terms of experimental design and data analysis. Monte-Cario and non parametric approaches are commonly applied here. 2. Data viewing and analytical distribution testing to identify univariate trends among potential genomic or metabolomic biomarkers in cells as they react to radiation, to find independently informative, radiation damage biomarkers that may be used to gauge the level of severity of damage for a given individual exposed to a particular dose and type of radiation. 3. Multivariate analysis to identify groups of genomic or metabolomic biomarkers that act collaboratively to carry out the cellular response to radiation. 4. Random forests machine learning and self-organizing map analysis. 5. Contextual analysis to develop genomic or metabolomic biomarker panels that are specific for particular cell types, dose scenarios, or population subgroups. 6. A common secure data-hosting facility so that the considerable amount of data to be shared, due to the use of shared biological materials, can be readily exchanged. In summary. Core E will continue to provide the bioinformatics that will be crucial to the success of the CMCR program, and will provide statistical support in terms of experimental design and data analysis for all Consortium members.

Public Health Relevance

The bioinformatics and statistical support provided by this Core will be necessary for the development of the most robust possible biodosimetry applications in each of the projects. The integration of data across projects has the further potential to provide mechanistic insight into radiation response and radiation sensitivity

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZAI1-KS-I)
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Columbia University (N.Y.)
New York
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Sprung, Carl N; Ivashkevich, Alesia; Forrester, Helen B et al. (2015) Oxidative DNA damage caused by inflammation may link to stress-induced non-targeted effects. Cancer Lett 356:72-81
Shuryak, Igor; Lubin, Jay H; Brenner, David J (2014) Potential for adult-based epidemiological studies to characterize overall cancer risks associated with a lifetime of CT scans. Radiat Res 181:584-91
Turner, Helen C; Sharma, P; Perrier, J R et al. (2014) The RABiT: high-throughput technology for assessing global DSB repair. Radiat Environ Biophys 53:265-72
Repin, Mikhail; Turner, Helen C; Garty, Guy et al. (2014) Next generation platforms for high-throughput biodosimetry. Radiat Prot Dosimetry 159:105-10
Laiakis, Evagelia C; Mak, Tytus D; Anizan, Sebastien et al. (2014) Development of a metabolomic radiation signature in urine from patients undergoing total body irradiation. Radiat Res 181:350-61
Goudarzi, Maryam; Weber, Waylon; Mak, Tytus D et al. (2014) Development of urinary biomarkers for internal exposure by cesium-137 using a metabolomics approach in mice. Radiat Res 181:54-64
Forrester, Helen B; Li, Jason; Leong, Trevor et al. (2014) Identification of a radiation sensitivity gene expression profile in primary fibroblasts derived from patients who developed radiotherapy-induced fibrosis. Radiother Oncol 111:186-93
Forrester, Helen B; Sprung, Carl N (2014) Intragenic controls utilizing radiation-induced alternative transcript regions improves gene expression biodosimetry. Radiat Res 181:314-23
Luo, Xiuquan; Suzuki, Masatoshi; Ghandhi, Shanaz A et al. (2014) ATM regulates insulin-like growth factor 1-secretory clusterin (IGF-1-sCLU) expression that protects cells against senescence. PLoS One 9:e99983
Paul, Sunirmal; Ghandhi, Shanaz A; Weber, Waylon et al. (2014) Gene expression response of mice after a single dose of 137CS as an internal emitter. Radiat Res 182:380-9

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