The goal of the Bioinformatics/Biostatistics core is to address the statistical and bioinformatics analysis needs of the Yale SPORE in skin cancer projects and cores. Another major goal is the maintenance and extension of a SPORE Data Management and Analysis System (DMAS) for tracking of biological specimens and processing of clinical and experimental data. A final goal of the Bioinformatics/Biostatistics Core is the provision of data compliant with the SPORE data and resource-sharing plan.
The specific aims of the Bioinformatics/Biostatistics Core are Aim 1: Maintenance and extension of a SPORE Data Management and Analysis System (DMAS) for specimen tracking, as well as storing and analyzing clinical and experimental data for all SPORE projects, and Aim 2: Bioinformatics and statistical analysis of SPORE project data.
For Aim 1, we will maintain and extend the SPORE DMAS, which currently tracks SPORE specimens in caTissue, uses caArray for storing omics data and calntegrator for data integration and dissemination. The Core also maintains a dedicated data warehouse for integrative data analysis across omics modalities. DMAS will be tightly integrated Into the SPORE specimen resource core, and will serve the data management needs of the SPORE project members. DMAS will make extensive use of existing informatics systems at Yale University and existing caBIG technology. The Core will emphasis the Interactions with the wider skin cancer community.
For Aim 2, the Bioinformatics/Biostatistics Core will address the analytic questions arising from the SPORE projects. Service provided by the Core will range from planning activities to consulting on specific analytic questions. More specifically, the Core will schedule regular meetings with the SPORE investigators, and maintain an open door policy for any bioinformatics/biostatistical questions. Since the observed data can have characteristics different from hypothesized, the Core will conduct regular interim analyses, dynamically update the power calculations, develop new statistical and bioinformatics methodology as needed, provide timely suggestions to SPORE investigators, and thus play an important role in the entire study.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
5P50CA121974-07
Application #
8557723
Study Section
Special Emphasis Panel (ZCA1-RPRB-0)
Project Start
Project End
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
7
Fiscal Year
2013
Total Cost
$165,478
Indirect Cost
Name
Yale University
Department
Type
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Ferrucci, Leah M; Cartmel, Brenda; Clare, Rachel A et al. (2018) Cross-sectional assessment of ultraviolet radiation-related behaviors among young people after a diagnosis of melanoma or basal cell carcinoma. J Am Acad Dermatol 79:149-152
Arbesman, Joshua; Ravichandran, Sairekha; Funchain, Pauline et al. (2018) Melanoma cases demonstrate increased carrier frequency of phenylketonuria/hyperphenylalanemia mutations. Pigment Cell Melanoma Res 31:529-533
Perry, Curtis J; Muñoz-Rojas, Andrés R; Meeth, Katrina M et al. (2018) Myeloid-targeted immunotherapies act in synergy to induce inflammation and antitumor immunity. J Exp Med 215:877-893
Liu, Xiaoni; Zhang, Shang-Min; McGeary, Meaghan K et al. (2018) KDM5B Promotes Drug Resistance by Regulating Melanoma Propagating Cell Subpopulations. Mol Cancer Ther :
Sulkowski, Parker L; Scanlon, Susan E; Oeck, Sebastian et al. (2018) PTEN Regulates Nonhomologous End Joining By Epigenetic Induction of NHEJ1/XLF. Mol Cancer Res 16:1241-1254
Chen, Ling; Azuma, Takeshi; Yu, Weiwei et al. (2018) B7-H1 maintains the polyclonal T cell response by protecting dendritic cells from cytotoxic T lymphocyte destruction. Proc Natl Acad Sci U S A 115:3126-3131
Krauthammer, Michael (2018) Unraveling the etiology of primary malignant melanoma of the esophagus. J Thorac Dis 10:S1074-S1075
Das, Rituparna; Bar, Noffar; Ferreira, Michelle et al. (2018) Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J Clin Invest 128:715-720
Miller, Chad J; Muftuoglu, Yagmur; Turk, Benjamin E (2017) A high throughput assay to identify substrate-selective inhibitors of the ERK protein kinases. Biochem Pharmacol 142:39-45
Kluger, Harriet M; Zito, Christopher R; Turcu, Gabriela et al. (2017) PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clin Cancer Res 23:4270-4279

Showing the most recent 10 out of 172 publications