A key challenge posed by personalized medicine is the ability to identify actionable mutations to guide treatment decisions and maximize clinical benefit for patients. Large scale efforts to sequence human tumors have revealed significant heterogeneity and complexity in the patterns of somatic mutations found in solid tumors. While some cancers are driven by single oncogenes that are amenable to pharmacologic inhibition, many solid tumors harbor an array of low prevalence mutations in genes encoding proteins of ambiguous biological function. One approach to unraveling this complexity is to build model systems that faithfully recapitulate more complex genetic alterations found in human tumors. Ideally we would be able to model individual patient tumors in order to perform rapid in vivo characterization of response to a panel of drugs. This approach would identify personalized drug-tumor combinations for patients based on their specific mutation profile. Clinical management of patients with advanced thyroid cancer presents unique challenges in integrating genetic characterization of tumors with choice of therapy. Translocations involving the RET gene occur in a significant fraction of papillary thyroid carcinomas, and in radiation induced thyroid cancers. Patients with RET translocations also harbor other mutations, including mutations in the TERT promoter and mutations in TP53, yet the contribution of these to tumor initiation and response to therapy has not been well characterized. In this proposal we present proof-of-concept data that thyroid carcinoma from individual patients can be genetically characterized and modeled in vivo using zebrafish, all on a timescale compatible with clinical decision making. We demonstrate that common oncogenes identified in human thyroid cancer lead to proliferative and neoplastic phenotypes in zebrafish thyroid. These findings prompt us to propose a platform in which we will build personalized models of thyroid cancer using transgenic approaches in zebrafish. We will examine patient specific genetic mutations identified from a personalized medicine program and determine their contribution to tumor initiation and response to treatment with tyrosine kinase inhibitors. We will use these models to identify the mechanisms of acquired resistance to tyrosine kinase inhibitors in thyroid cancer. These studies target a critical unmet need, as we lack a firm scientific basis for selecting initial therapy, or therapy after the emergence of acquired resistance to kinase inhibitors in patients with thyroid cancer. Using novel transgenic approaches in zebrafish we will develop a platform for patient specific models of thyroid carcinoma in order to personalize therapeutic decisions.