To improve cancer treatments, personalized medicine approaches have aimed to identify exactly which mutations are driving tumor development in a given patient and specifically target these... Show moreTo improve cancer treatments, personalized medicine approaches have aimed to identify exactly which mutations are driving tumor development in a given patient and specifically target these mutations using precision therapies. However, one of the main challenges of this approach is identifying which mutations are true drivers, as tumors typically contain many additional passenger mutations that do not actually contribute to tumor development. Besides this, many patients often relapse after prolonged treatment due to the emergence of acquired resistance, limiting the clinical effectiveness of targeted treatments. In this thesis, we focussed on using genetically engineered mouse models to identify candidate cancer genes and therapy resistance mechanisms in two different breast cancers: invasive lobular carcinoma (ILC) and triple-negative breast cancer (TNBC). For ILC, we used transposon-based insertional mutagenesis (TIM) to uncover several novel cancer genes driving ILC development. Besides this, we also developed a novel computational approach (IM-Fusion) for improving the discovery of cancer genes from TIM screens and explored mechanisms of resistance in Fgfr2-driven ILC. For TNBC, we used CRISPR-based iterative mouse modeling combined with comparative oncogenomics to identify novel drivers of BRCA1-deficient TNBC. Finally, using combined in-vivo/in-vitro screens, we identified Parg as a driver of treatment resistance in BRCA2-deficient TNBC. Show less
