Workshop: Exploring Quantitative Approaches to Clinical Cancer Data; February 13-18, 2015, Princeton, New Jersey

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The past five to fifteen years have seen remarkable advances in the technologies applied to biological problems. This has resulted in quantitative measurements of genomes, RNA species, protein functions and modifications, and epigenetic regulation of cellular functions. Extremely large databases of information have been assembled using these new technologies. Among the most rapid areas of biology to apply these technologies has occurred in cancer research. Quantitative based analysis is becoming increasingly important to understand the causes, pathways, diagnosis, prognosis and treatment of cancer. Commonly, biologists and clinicians lack the extensive skills in mathematics, physics, computer science, and chemistry that are required for the sophisticated levels of analysis that are needed to extract useful information from these large datasets. Quantitative researchers from an array of disciplines have entered the field of biology to explore these basic science challenges, but they often have little or no prior training in the field of cancer biology, and little understanding of the important biological questions in the field. The benefits of collaboration between these two groups are starting to be addressed and observed in cancer research, but the pace and full promise of synergistic collaboration remains a challenge. SU2C will bring together theoretical physicists, mathematicians, and computer scientists with strong backgrounds in biological research and clinical oncologists, two groups whose research fields do not traditionally intersect, in order to develop a research project integrating mathematical and computational approaches and clinical cancer research. SU2C will organize an Ideas Lab to bring these researchers to a residential research setting for a multi-day exchange of ideas, methods, and knowledge, to develop such integrated research projects. The goal is to assemble 3 to 4 teams of clinical oncologists and quantitative scientists to address important problems or questions that arise from clinical cancer research data. These approaches will be articulated in short preliminary proposals by teams or groups of scientists at the Ideas Lab. The proposals will then be refined and additional members of a team may be asked to participate. The proposals may then be submitted for possible funding from public and private sources. Thus, the Ideas Lab has both an intellectual and potential practical set of outcomes, resulting in the best ideas and quantitative approaches being explored employing clinical research data.Bringing together highly accomplished individuals from the two groups outlined above, for the purpose of jointly educating both groups and developing research projects at the convergence of quantitative and clinical cancer research, can be expected to lead to the development of novel approaches to fundamental science questions in cancer research. This effort has the potential to create a new field of research that can address our understanding of disease origin, progression, diagnosis, prognosis, treatment and the outcome of disease in patients. This, in turn, has the potential to lower the costs to society of developing effective therapeutic treatments, of enhancing quality of life and the outcome for cancer patients. The types of benefit that may be realized include the possibility of: 1) refining the technologicaltools to generate robust quantitative biological data; 2) capturing and representing high dimensional genomic data using rigorous mathematical approaches - these representations can help to identify markers to stratify patients and to identify potential therapeutic targets; 3) integrating different types of large scale cancer data; 4) generating tools to identify potential therapeutic targets in large datasets beyond recurrence of single gene alterations; for instance, identifying synergistic activities between different genes using large scale genomic data, and 5) providing quantitative testable models about the evolution of tumors using genomic data; in particular, reconstructing the evolutionary history of tumors from longitudinal and cross sectional genomic data.