The Barrett's Esophagus Translational Research Network (BETRNet) has been proposed to reduce the incidence, morbidity, and mortality of esophageal adenocarcinoma (EA), through answering key questions related to the progression of this disease, especially in the premalignant stage. The Vanderbilt-lngram Cancer Center BETRNet Coordinating Center (VICC-BETRNetCC) will have a central role to play in facilitating these scientific advances, by achieving the following specific aims: 1. Ensure smooth functioning of the entire BETRNet, especially with regard to interaction among research centers. To achieve this goal, the coordinating center will provide leadership, logistical, and administrative support for cross-network communication and interaction;establish BETRNet working groups and subcommittees to provide expertise in various areas relevant to the needs of BETRNet investigators;and facilitate communication between BETRNet research centers and the NCI. 2. Facilitate data collection, management, analysis, and dissemination across the BETRNet. The coordinating center wiil provide infrastructure for data collection and storage, at the silver or gold level of caBIG compatibility. The center also will develop mechanisms to ensure that institutions across the BETRNet remain informed of the data and resources available within the network, as these data and resources continue to be developed through trans-disciplinary research. Moreover, the coordinating center will be available to assist with study design and statistical analysis across the research network, especially with regard to high-dimensional omics studies, including the latest technologies (e.g., RNAseq). 3. Develop a multi-institutional patient registry to house clinical, longitudinal, and epidemiological data, as well as datasets relevant to biospecimens available at each BETRNet site. 4. Develop and apply evaluation metrics for the BETRNet. Results of this evaluation will allow for mid-course corrections, to help maximize research productivity and efficiency within the BETRNet initiative.

Public Health Relevance

Although EA is a relatively uncommon cancer, the incidence of this disease is increasing faster than that of any other cancer in the United States;moreover, at the time of detection, most patients have incurable disease. The alarming increase in EA, along with the poor prognosis, demand improved patient management and development of preventive measures. Advances in these areas will have significant benefit in reducing morbidity and mortality, as well as reducing the public health costs of managing EA at advanced stages.

Agency
National Institute of Health (NIH)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA163056-04
Application #
8719948
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Yassin, Rihab R,
Project Start
Project End
Budget Start
Budget End
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
City
Nashville
State
TN
Country
United States
Zip Code
37212
Wang, Jing; Liu, Qi; Sun, Jingchun et al. (2016) Disrupted cooperation between transcription factors across diverse cancer types. BMC Genomics 17:560
Liu, Qi; Zhong, Xue; Madison, Blair B et al. (2015) Assessing Computational Steps for CLIP-Seq Data Analysis. Biomed Res Int 2015:196082
Wang, Jing; Liu, Qi; Shyr, Yu (2015) Dysregulated transcription across diverse cancer types reveals the importance of RNA-binding protein in carcinogenesis. BMC Genomics 16 Suppl 7:S5
Wang, Jing; Raskin, Leon; Samuels, David C et al. (2015) Genome measures used for quality control are dependent on gene function and ancestry. Bioinformatics 31:318-23
Abrams, Julian A; Appelman, Henry D; Beer, David G et al. (2014) Barrett's Esophagus Translational Research Network (BETRNet): the pivotal role of multi-institutional collaboration in esophageal adenocarcinoma research. Gastroenterology 146:1586-90
Zhao, Shilin; Guo, Yan; Sheng, Quanhu et al. (2014) Advanced heat map and clustering analysis using heatmap3. Biomed Res Int 2014:986048
Sheng, Quanhu; Shyr, Yu; Chen, Xi (2014) DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis. BMC Bioinformatics 15:323
Guo, Yan; Zhao, Shilin; Su, Pei-Fang et al. (2014) Statistical strategies for microRNAseq batch effect reduction. Transl Cancer Res 3:260-265
Guo, Yan; He, Jing; Zhao, Shilin et al. (2014) Illumina human exome genotyping array clustering and quality control. Nat Protoc 9:2643-62
Li, Chung-I; Su, Pei-Fang; Guo, Yan et al. (2013) Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution. Int J Comput Biol Drug Des 6:358-75

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