In Silico Perspectives on Triple Negative Breast Cancer: Challenges and Progress

Triple-negative breast cancer (TNBC) represents a highly aggressive and heterogeneous subtype of breast cancer (BC), lacking estrogen, progesterone, and HER2 receptors, thereby limiting treatment options and contributing to poor prognosis. This review comprehensively explores the evolving landscape of in silico study and its role in addressing the complexities of TNBC. It highlights the integration of bioinformatics, computational modeling, and artificial intelligence in uncovering TNBC molecular signatures, drug resistance mechanisms, and potential therapeutic targets. The article examines the epidemiological trends, biological characteristics, and molecular subtypes of TNBC, as well as the challenges posed by tumor heterogeneity and treatment resistance. In silico methods, including molecular docking, machine learning, systems biology, and multi-omics approaches, are shown to enhance drug discovery, biomarker identification, and predictive modeling. Specific case studies illustrate the successful application of computational tools in repurposing drugs, designing novel therapeutics, and predicting immunotherapy outcomes. Furthermore, the review underscores the potential of AI-assisted diagnostics and personalized medicine strategies, fueled by large-scale genomic and clinical datasets. Despite challenges such as data quality and model validation, the evidence indicates that in silico approaches hold transformative potential in TNBC research and clinical practice. Future directions advocate for interdisciplinary collaboration, the integration of real-world data, and the development of robust, predictive platforms to optimize treatment strategies and enhance patient outcomes.