Computational Modeling of Cancer Biology

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DESCRIPTION (provided by applicant): This proposal is a "Planning Grant" to develop a premier multi-disciplinary program in computational modeling of cancer biology at Stanford University. We will assemble a team of cancer biologists, oncologists, engineers, mathematicians, computer scientists and statisticians to work jointly toward identifying the molecular mechanisms that drive the transformation of low grade malignancy to high grade malignancy. Little is known about this neoplastic transformation process, yet whole genome expression data is available on disease pre- and post-transformation. The analysis of such vast amounts of data requires new computational tools of the type that we propose in this application. By making use of our sophisticated mathematical models and new computational methods, we aim to identify key regulatory networks and signaling pathways that underlie the neoplastic transformation process in humans and mice from whole genome expression data. Our study in humans will focus on the transformation of follicular lymphoma to high grade lymphoma using gene expression data derived from tissue that is correlated to clinical events of disease progression and patient outcomes. Our study in transgenic mice will focus on the transformation of tumors from dependence to independence of MYC, the initiating oncogene. The mouse model not only gives us a valuable experimental system for validation but also focuses on a specific oncogene that has been highly implicated in the transformation of human follicular lymphoma. By studying the human and mouse system in parallel (first separately and then in combination) with the use of new computational methods, we hope to gain robust insights on fundamental regulatory and signaling processes that underlie the neoplastic transformation. Discovering these mechanisms can eventually lead to the development of molecularly targeted therapies that may ultimately reduce cancer mortality. By the end of the "Planning Grant" we fully expect a major expansion of our computational cancer biology program.