The overall role of the Bioinformatics Core is to provide SPORE investigators with bioinformatics support services. These services include high-throughput data (microarray and sequence) processing and analysis, often involving statistical and probabilistic analyses, developing ad hoc software and databases, and performing computer simulations. Analyses not involving high-throughput data (e.g., clinical correlations) will be performed by the SPORE Biostatistics Core.
The specific aims of the Bioinformatics Core are 1. To generate gene expression signatures from global lllumina and Affymetrix gene expression microarrays. 2. To describe the biological characteristics and prognostic ability of gene expression signatures through a wide range of functional and pathway analyses. 3. To generate copy number alteration (CNA) profiles from global ahd targeted Agilent array comparative genomic hybridization (aCGH) microarrays. 4. To design custom Agilent aCGH arrays that target specific cancer-associated genes and pathways.
The Bioinformatics Core forms an integral part of the SPORE in Prostate Cancer as it directly supports the timely conduct of research in 3 RPs. The centralization of various bioinformatics services is designed to streamline requests of SPORE investigators to Core personnel with particular areas of expertise, therefore ensuring that services are provided in an efficient manner and with the highest quality.
|Loeb, Stacy; Lilja, Hans; Vickers, Andrew (2016) Beyond prostate-specific antigen: utilizing novel strategies to screen men for prostate cancer. Curr Opin Urol 26:459-65|
|Fleshner, Katherine; Assel, Melissa; Benfante, Nicole et al. (2016) Clinical Findings and Treatment Outcomes in Patients with Extraprostatic Extension Identified on Prostate Biopsy. J Urol 196:703-8|
|Carlsson, Sigrid V; de Carvalho, Tiago M; Roobol, Monique J et al. (2016) Estimating the harms and benefits of prostate cancer screening as used in common practice versus recommended good practice: A microsimulation screening analysis. Cancer 122:3386-3393|
|Zelefsky, Michael J; Poon, Bing Ying; Eastham, James et al. (2016) Longitudinal assessment of quality of life after surgery, conformal brachytherapy, and intensity-modulated radiation therapy for prostate cancer. Radiother Oncol 118:85-91|
|Kent, Matthew; Penson, David F; Albertsen, Peter C et al. (2016) Successful external validation of a model to predict other cause mortality in localized prostate cancer. BMC Med 14:25|
|Scher, Howard I; Lu, David; Schreiber, Nicole A et al. (2016) Association of AR-V7 on Circulating Tumor Cells as a Treatment-Specific Biomarker With Outcomes and Survival in Castration-Resistant Prostate Cancer. JAMA Oncol 2:1441-1449|
|Sood, Anup; Miller, Alexandra M; Brogi, Edi et al. (2016) Multiplexed immunofluorescence delineates proteomic cancer cell states associated with metabolism. JCI Insight 1:|
|Danila, Daniel C; Samoila, Aliaksandra; Patel, Chintan et al. (2016) Clinical Validity of Detecting Circulating Tumor Cells by AdnaTest Assay Compared With Direct Detection of Tumor mRNA in Stabilized Whole Blood, as a Biomarker Predicting Overall Survival for Metastatic Castration-Resistant Prostate Cancer Patients. Cancer J 22:315-320|
|Braun, Katharina; Sjoberg, Daniel D; Vickers, Andrew J et al. (2016) A Four-kallikrein Panel Predicts High-grade Cancer on Biopsy: Independent Validation in a Community Cohort. Eur Urol 69:505-11|
|Preston, Mark A; Batista, Julie L; Wilson, Kathryn M et al. (2016) Baseline Prostate-Specific Antigen Levels in Midlife Predict Lethal Prostate Cancer. J Clin Oncol 34:2705-11|
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