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Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting‐edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody–drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood–brain barrier to achieve high intracranial concentrations, including small‐molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.
Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting‐edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody–drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood–brain barrier to achieve high intracranial concentrations, including small‐molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.
The molecular classification of breast cancer plays a pivotal role in developing personalized treatment strategies, with the aim of improving therapeutic outcomes. Despite significant advancements, current diagnostic workflows are hindered by several challenges, including pre-analytical variability, interpretive ambiguity, and inconsistencies in threshold definitions, particularly in cases involving human epidermal growth factor receptor 2 (HER2)-low breast cancer. In this context, liquid biopsy technologies have emerged as promising tools for refining breast cancer diagnostics. Techniques such as circulating tumor DNA and circulating tumor cell analysis provide a non-invasive approach to assessing tumor-associated biomarkers. These methodologies are particularly advantageous for analyzing low-abundance materials, such as formalin-fixed paraffin-embedded samples and liquid biopsies, thus enhancing the precision of molecular classification and informing more targeted therapeutic decisions for breast cancer patients. This review aims to explore the potential of liquid biopsy in addressing the limitations of current diagnostic practices, with a specific focus on its application in HER2-low breast cancer. Furthermore, it advocates for a transition toward high-throughput RNA-based screening and quantification, which may address critical unmet clinical needs.
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