The projects proposed in the UTSW Kidney Cancer SPORE encompass a wide spectrum of kidney cancer translational research activities, including studies in cell lines, animal models, clinically- and molecularly- annotated tumor samples, and clinical studies. These studies will generate many different types of data, including clinical, biochemical, immunohistochemical, gene expression, sequencing, etc. Proper experimental design and valid data analysis require comprehensive biostatistics and bioinformatics expertise. For example, several biomarker studies are proposed in this SPORE. Statistical rigor is needed in every phase of the biomarker development process including biomarker measurement, preliminary data analysis, external validation, etc. Core C (Data Analytics Core) functions as a centralized research design and data analysis support team, bringing together expertise and intellectual resources campus-wide in biostatistics, bioinformatics, clinical trials, and data management for the SPORE PIs. Core C is based on the Cancer Center Support Grant (CCSG) core infrastructure and all Core C personnel are part of the UTSW CCSG grant. Core C members (biostatisticians, bioinformaticians, database specialists) play integrated roles in all SPORE projects, the DRP, and CEP. Each SPORE project is supported by two designated Core C members ensuring statistical rigor for all research design and data analysis. The project-designated Core C members are further assisted by other Core C members with consideration given to bioinformatics and database requirements. Integration of Core C biostatisticians into SPORE projects has maximized biostatistical expertise on the SPORE research, providing opportunities for our biostatisticians to contribute each step along the research process, from preliminary data analysis to hypothesis formulation to study design. In addition to the following four specific aims, Core C also has the capability and research plan for new biostatistics and bioinformatics method development that will directly enhance SPORE projects, as well as DRP and CEP projects.
Aim 1 (Biostatistics). To provide state-of-the-art statistical design and analysis plans required to address the specific aims of translational and basic science research projects, and clinical trial protocols.
Aim 2 (Biostatistics). To perform proper, innovative statistical analysis for all SPORE Projects. To ensure that the results of all Projects are based on well-designed experiments, appropriately interpreted, and to assist in the preparation of manuscripts describing these results.
Aim 3 (Bioinformatics). To ensure that all high-throughput experiments (genomic, proteomic, and metabolomic) are well designed and the data are properly processed with adequate quality control, and properly analyzed using valid and innovative bioinformatics methods.
Aim 4 (Database). To develop and maintain a web-accessible site for data integration and storage linked to an extensive live tissue repository of frozen samples, tumorgrafts and cell lines to support SPORE investigators and, more broadly, the research community.
The Data Analytics Core (Core C) ensures that all SPORE experiments are properly designed, the data collected are safely stored and HIPAA compliant, properly analyzed, and made available to other SPORE investigators and the research community as a whole in order to further the ultimate goal of promoting collaboration and accelerating the translation of knowledge from the research lab into the clinic.
|Kay, Fernando U; Canvasser, Noah E; Xi, Yin et al. (2018) Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. Radiology 287:543-553|
|Chen, Xi; Zhou, Zhiguo; Hannan, Raquibul et al. (2018) Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model. Phys Med Biol 63:215008|
|Puertollano, Rosa; Ferguson, Shawn M; Brugarolas, James et al. (2018) The complex relationship between TFEB transcription factor phosphorylation and subcellular localization. EMBO J 37:|
|Chen, Kenneth S; Stroup, Emily K; Budhipramono, Albert et al. (2018) Mutations in microRNA processing genes in Wilms tumors derepress the IGF2 regulator PLAG1. Genes Dev 32:996-1007|
|O'Kelly, Devin; Zhou, Heling; Mason, Ralph P (2018) Tomographic breathing detection: a method to noninvasively assess in situ respiratory dynamics. J Biomed Opt 23:1-6|
|Wang, Xinzeng; Pirasteh, Ali; Brugarolas, James et al. (2018) Whole-body MRI for metastatic cancer detection using T2 -weighted imaging with fat and fluid suppression. Magn Reson Med 80:1402-1415|
|Courtney, Kevin D; Infante, Jeffrey R; Lam, Elaine T et al. (2018) Phase I Dose-Escalation Trial of PT2385, a First-in-Class Hypoxia-Inducible Factor-2? Antagonist in Patients With Previously Treated Advanced Clear Cell Renal Cell Carcinoma. J Clin Oncol 36:867-874|
|Krajewski, Katherine M; Pedrosa, Ivan (2018) Imaging Advances in the Management of Kidney Cancer. J Clin Oncol :JCO2018791236|
|Carbone, Michele; Amelio, Ivano; Affar, El Bachir et al. (2018) Consensus report of the 8 and 9th Weinman Symposia on Gene x Environment Interaction in carcinogenesis: novel opportunities for precision medicine. Cell Death Differ 25:1885-1904|
|Kay, Fernando U; Pedrosa, Ivan (2018) Imaging of Solid Renal Masses. Urol Clin North Am 45:311-330|
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