MST1 mediates doxorubicin-induced cardiomyopathy by SIRT3 downregulation.

Heart failure is a major side effect of doxorubicin (DOX) treatment in patients with cancer. However, the mechanisms underlying the development of DOX-induced heart failure need to be addressed. This study aims to test whether the serine/threonine kinase MST1, a major Hippo pathway component, contributes to the development of DOX-induced myocardial injury. C57BL/6J WT mice and mice with cardiomyocyte-specific dominant-negative MST1 (kinase-dead) overexpression received three weekly injections of DOX, reaching a final cumulative dose of 18 mg/kg. Echocardiographic, histological and biochemical analyses were performed six weeks after the first DOX administration. The effects of MST1 inhibition on DOX-induced cardiomyocyte injury were also tested in vitro. MST1 signaling was significantly activated in cardiomyocytes in response to DOX treatment in vitro and in vivo. Wild-type (WT) mice treated with DOX developed cardiac dysfunction and mitochondrial abnormalities. However, these detrimental effects were abolished in mice with cardiomyocyte-specific overexpression of dominant-negative MST1 (DN-MST1) or treated with XMU-MP-1, a specific MST1 inhibitor, indicating that MST1 inhibition attenuates DOX-induced cardiac dysfunction. DOX treatment led to a significant downregulation of cardiac levels of SIRT3, a deacetylase involved in mitochondrial protection, in WT mice, which was rescued by MST1 inhibition. Pharmacological inhibition of SIRT3 blunted the protective effects of MST1 inhibition, indicating that SIRT3 downregulation mediates the cytotoxic effects of MST1 activation in response to DOX treatment. Finally, we found a significant upregulation of MST1 and downregulation of SIRT3 levels in human myocardial tissue of cancer patients treated with DOX. In summary, MST1 contributes to DOX-induced cardiomyopathy through SIRT3 downregulation.

Advances in breast cancer risk modeling: integrating clinics, imaging, pathology and artificial intelligence for personalized risk assessment.

Breast cancer risk models represent the likelihood of developing breast cancer based on risk factors. They enable personalized interventions to improve screening programs. Radiologists identify mammographic density as a significant risk factor and test new imaging techniques. Pathologists provide data for risk assessment. Clinicians conduct individual risk assessments and adopt prevention strategies for high-risk subjects. Tumor genetic testing guides personalized screening and treatment decisions. Artificial intelligence in mammography integrates imaging, clinical, genetic and pathological data to develop risk models. Emerging imaging technologies, genetic testing and molecular profiling improve risk model accuracy. The complexity of the disease, limited data availability and model inputs are discussed. A multidisciplinary approach is essential for earlier detection and improved outcomes.