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Program Information

Modeling Cancer Complexity

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R Austin
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E Koay
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R Rabadan

R Austin1*, E Koay2*, R Rabadan3*, (1) Princeton University, Princeton, NJ, (2) MD Anderson, Houston, TX, (3) Columbia University, New York, NY


WE-D-BRB-0 (Wednesday, July 15, 2015) 11:00 AM - 12:15 PM Room: Ballroom B

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis.

Learning Objectives:
1. To learn about treatment response heterogeneity
2. To learn about tumor evolutionary capabilities
3. To learn about mathematical tools that link physical sciences with experimental and clinical data

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