Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

Abstract

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.

Keywords: TNBC; artificial intelligence-AI; classification; clustering; epigenetics; molecular subtype of breast cancer; precision medicine; triple-negative breast cancer.

Figure 1

Illustrative representation of current subtypes and the future of subsetting in TNBC. (A) Left panel: Summary of TNBC classification methods described and their subtypes. Right panel: Representation of the similitude between the different classification systems that reported comparisons with existing methods. Ribbons represent the partial overlap between different subtypes. Ribbons referring to strong overlaps are shown in purple. (B) Left panel: Schematic representation of the different layers of information to construct the definitive TNBC subtypes. This includes clinical, molecular, and histological data. Middle plot: Representation of application of artificial intelligence (AI) algorithm to integrate diverse datasets and construct TNBC subtypes. Right panel: Schematic correlation plot representing consensus integrative TNBC subtypes. TNBC stratification can be applied to improve subtype-specific therapies.