Frontiers of Oncology: Biobanking Resources for the 21st Century

Clinical databases and the digitization of clinical information have improved significantly along with the advances in -omics technologies. Significant challenges in defining and enforcing ontologies and encouraging meta-data capture represent significant hurdles. The Cancer Biomedical Informatics Grid (CaBIG) and other tissue database projects have developed common dictionaries of terms for cancer staging and defining diagnostic subclasses of cancer; such efforts are critical to being able to query across databases. In this issue Surati and colleagues at the University of Chicago outlined their success in developing a Thoracic Oncology Database that serves as a repository for well-annotated cancer specimens combined with clinical, genomic, and proteomic data obtained from specific tumor tissue studies. Their goal was to make the database not just a repository, but also a dynamic tool to drive data mining and exploratory analysis for clinical and translational research for thoracic oncology. In the article, the investigators used non-small cell lung cancer samples from the database combined with specific proteomic analyses of these samples to examine functional relevance of protein over- and under-expression. Clinical data for 1323 patients with non-small cell lung cancer was captured and proteomic studies were performed on tissue samples from 105 patients. Initial biomarker studies identified receptor tyrosine kinase family members that were over-expressed in tumor tissues. Since clinical data and research data are present in a single database, investigators were able to powerfully address research questions or plan studies that minimize duplication, maximize the potential for valuable results and encourage additions to the database by other investigators at the University of Chicago and at other institutions. In fact the stated goals of the study, as outlined in the accompanying paper were to: (1) create a platform to house clinical, genomic, and proteomic data fr