Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype to treat due to its aggressive characteristics and low response to the existing clinical therapies. Distant... Show moreTriple-negative breast cancer (TNBC) is the most challenging breast cancer subtype to treat due to its aggressive characteristics and low response to the existing clinical therapies. Distant metastasis is the main cause of death of TNBC patients. Better understanding of the mechanisms underlying TNBC metastasis may lead to new strategies of early diagnosis and more efficient treatment. In our study, we uncovered that the autophagy receptor optineurin (OPTN) plays an unexpected role in TNBC metastasis. Data mining of publicly available data bases revealed that the mRNA level of OPTN in TNBC patients positively correlates with relapse free and distance metastasis free survival. Importantly, in vitro and in vivo models demonstrated that OPTN suppresses TNBC metastasis. Mechanistically, OPTN inhibited the pro-oncogenic transforming growth factor-beta (TGF beta) signaling in TNBC cells by interacting with TGF beta type I receptor (T beta RI) and promoting its ubiquitination for degradation. Consistent with our experimental findings, the clinical TNBC samples displayed a negative correlation between OPTN mRNA expression and TGF beta gene response signature and expression of proto-typic TGF beta target genes. Altogether, our study demonstrates that OPTN is a negative regulator for TGF beta receptor/SMAD signaling and suppresses metastasis in TNBC. Show less
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
