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BRAF is a serine/threonine kinase that is found mutated in 7% of all human cancers. It is even more frequently mutated in melanoma and thyroid cancer. BRAFT1799A is the most common mutation found in patients. Based on this mutation we have developed in the lab several models of thyroid cancer in mice that we use as pre-clinical platform for testing of novel therapeutic approaches. Papillary thyroid carcinoma, the most frequent form of thyroid cancer, is cured in more than 93% of the cases while anaplastic thyroid carcinomas have a survival average below 6 months. During the last decades, the incidence of thyroid cancer has been increasing steadily and it is now the most prevalent form of endocrine cancer. Interestingly, many papillary thyroid cancer patients present a stable disease for years before presenting a sudden decline and progression to anaplastic thyroid cancer.Despite tremendous advances in molecular thyroid pathology over the last years, the mechanisms and genes that underlie tumor progression from papillary thyroid carcinoma to anaplastic thyroid carcinoma, which can also arise de novo, are not well understood. There is a clear lack of knowledge around the intimate mechanisms that are promoting a manageable disease to become a highly lethal threat. In contrast to overall medical advances, the treatment and prognosis of ATC has not significantly changed during the last 40 years. It is obvious that deeper insights of genes involved in tumor formation, progression and maintenance to provide novel strategies to target this cancer are urgently needed. In this grant, we want to use our pre-clinical mouse model to understand better the intimate mechanisms driving tumor changes to a more aggressive form of the disease using two axes: 1- Reverse genetics by using Notch-related modified genes to enhance or block the pathway in order to evaluate the importance of this signaling pathway in thyroid cancers and 2- Forward genetics by using a gene expression screening from mouse and human tumors to uncover new genes involved in cancer progression. To that end, we will use our mouse model, in silico analysis from publicly available databases and patient derived samples. In both cases, we aim at unveiling new markers for prognosis or eventually new potential targets for drug therapies.This project will bring a better understanding of anaplastic thyroid cancer and the driving forces behind the disease progression. The data from this work done in our pre-clinical models will generate substantial information for clinicians in order to improve patients’ care, either by improving prognosis with the discovery of early warning signs of tumor progression or by validating drug targets and treatment that could be readily translated into clinical practice.