Precision Medicine driving progress in Triple Negative breast cancer: biomarkers and AI-enabled approaches
Researchers are making progress in treating Triple Negative breast cancer using precision medicine, biomarkers, and AI-enabled approaches. This approach aims to improve patient selection for therapies and accelerate the translation of data-rich insights into improved outcomes for patients with TNBC.
Triple Negative breast cancer is a challenging subtype of breast cancer. Recent advances have improved outcomes for some patients, but durable benefit is still unevenly distributed. Precision medicine depends on the discovery and validation of biomarkers and analytic frameworks. Biomarker science and AI-enabled approaches are accelerating progress in TNBC. Researchers are focusing on early-stage and advanced/metastatic clinical settings, from risk stratification to therapy selection and response monitoring. The integration of advanced computational methods, including machine learning and multimodal AI, is translating complex data into clinically actionable insight.
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