RESUMO
Iron catalyses the oxidation of lipids in biological membranes and promotes a form of cell death referred to as ferroptosis1-3. Identifying where this chemistry takes place in the cell can inform the design of drugs capable of inducing or inhibiting ferroptosis in various disease-relevant settings. Whereas genetic approaches have revealed underlying mechanisms of lipid peroxide detoxification1,4,5, small molecules can provide unparalleled spatiotemporal control of the chemistry at work6. Here, we show that the ferroptosis inhibitor liproxstatin-1 (Lip-1) exerts a protective activity by inactivating iron in lysosomes. Based on this, we designed the bifunctional compound fentomycin that targets phospholipids at the plasma membrane and activates iron in lysosomes upon endocytosis, promoting oxidative degradation of phospholipids and ferroptosis. Fentomycin effectively kills primary sarcoma and pancreatic ductal adenocarcinoma cells. It acts as a lipolysis-targeting chimera (LIPTAC), preferentially targeting iron-rich CD44high cell-subpopulations7,8 associated with the metastatic disease and drug resistance9,10. Furthermore, we demonstrate that fentomycin also depletes CD44high cells in vivo and reduces intranodal tumour growth in an immunocompetent murine model of breast cancer metastasis. These data demonstrate that lysosomal iron triggers ferroptosis and that lysosomal iron redox chemistry can be exploited for therapeutic benefits.
RESUMO
Metastatic melanoma is a complex and deadly disease. Due to its complexity, the development of novel therapeutic strategies to inhibit metastatic melanoma remains an outstanding challenge. Our ability to study metastasis is advanced with the development of in vitro and in vivo models that better mimic the different steps of the metastatic cascade beginning from primary tumor initiation to final metastatic seeding. In this review, we provide a comprehensive summary of in vitro models, in vivo models, and in silico platforms to study the individual steps of melanoma metastasis. Furthermore, we highlight the advantages and limitations of each model and discuss the challenges of how to improve current models to enhance translation for melanoma cancer patients and future therapies.