QUANTITATIVE ONCOLOGY Paul Spellman, PhD and Joe Gray, PhD, Program Co-Leaders ABSTRACT The Quantitative Oncology (QO) Program is a multi-disciplinary research program that was formed to facilitate the development and application of advanced measurement capabilities from omics to imaging, combined with computational techniques, to improve cancer management. The goal of the QO program is to enable quantitative understanding of the behavior of cancerous cells and tissues as they evolve, respond to therapy, and interact with their microenvironments. The QO program facilitates collaborative science organized around three research themes: 1) Imaging ? focuses on improving the understanding of cancer by analyzing components of tumors on scales from angstrom to centimeters, including proteins to cells to tissues and using this data to inform diagnostics and therapies; 2) Omics ? employing and improving tools to analyze genomes, transcriptomes, and proteomes to enhance our understanding of cancer; and 3) Systems Biology - focuses on elucidation of the emergent properties of cancer-related molecular networks, the molecular and cellular phenotypes they regulate and the evolution/adaptation of these systems during cancer development and treatment. The QO Program is co-led by Paul Spellman, PhD, an expert in the application of translational cancer genomics and systems biology to cancer detection and classification, and Joe Gray, PhD, an expert in systems biomedicine and imaging technologies, with an emphasis on breast and pancreatic cancer. The QO program has 28 members who are drawn from seven basic science departments and four clinical departments in the OHSU School of Medicine, and the Pacific Northwest National Laboratory (PNNL). Annual direct cost funding as of January 2016 amounted to $10,011,427 (total cost), of which $1,833,130 (total cost) was from the NCI and $6,229,652 (total cost) was peer-reviewed. The discoveries made in this program have resulted in 251 publications, of which 31.5% are intra-programmatic collaborations, 43.8% are inter-programmatic collaborations, and 71.3% are inter- institutional.
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