Genome-wide molecular measurements of tumor specimens have the potential to provide a wealth of information about the tumor. These measurements can be combined with histological measurements and patient outcome to better understand the molecular mechanisms affecting tumor development, progression, and response or resistance to therapy. However, the analysis involved in such comparisons can be time-consuming and require specialized expertise. We developed a system to manage multiple cancer gene expression data sets, and a web-based tool to quickly analyze this data to perform routine comparisons between gene expression levels and clinical measurements.

The OCELOT (Online Cancer Expression analysis Tool) is an open-source system with two modules: First, the data management module, which is accessible only to the system managers, provides tools to import gene expression data from GEO or from other sources, and to map the associated clinical data to specific clinical terms. Second, the analytical module, which can be open to the public, provides the user with statistically sound tools to assess the association between a particular gene and outcome using Kaplan-Meier survival analysis or ROC curves, or to compare gene expression among various groups, or to compare one gene to another gene. OCELOT performs these tasks in a simple and secure manner, and it can be readily deployed on a basic web server.

Although OCELOT is general enough to work with data from any source, we have focused in particular on renal cancer to develop OCELOT-Renal, a realization of OCELOT with data sets chosen for their relevance to PREDICT research and to renal cancer research in general.

OCELOT-Renal can be accessed here: www.cbs.dtu.dk/services/ocelot-1.0/renal/