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00925njm 22002777a 4500 dc Ensenyat-Mendez, Miquel author Llinàs-Arias, Pere author Orozco, Javier IJ author Iñiguez-Muñoz, Sandra author Salomon, Matthew P author Sese, Borja author DiNome, Maggie L author Marzese, Diego M author 2021-06-16 Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes. Ensenyat-Mendez M, Llinas-Arias P, Orozco JIJ, Iniguez-Munoz S, Salomon MP, Sese B, et al. Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer. Front Oncol. 2021 Jun 16;11:681476. 2234-943X https://hdl.handle.net/20.500.13003/19518 https://hdl.handle.net/20.500.12105/23202 34221999 10.3389/fonc.2021.681476 Frontiers in Oncology 2-s2.0-85112841206 668117300001 L635392346 Triple-negative breast cancer TNBC Molecular subtype of breast cancer Epigenetics Clustering Artificial intelligence-AI Classification Precision medicine Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer