Journal
FRONTIERS IN ONCOLOGY
Volume 11, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.681476
Keywords
triple-negative breast cancer; TNBC; molecular subtype of breast cancer; epigenetics; clustering; artificial intelligence-AI; classification; precision medicine
Categories
Funding
- Instituto de la Salud Carlos III [CP17/00188, I19/01514]
- Institut d'Investigacio Sanitaria Illes Balears (IdISBa) FUTURMed FOLIUM program
- Associates for Breast and Prostate Cancer Studies (ABCs) Foundation
- Fashion Footwear Association of New York (FFANY) Foundation
- Asociacion Espanola Contra el Cancer (AECC) Foundation
- Balearic Islands Government Margalida Comas program
- Fundacion Francisco Cobos
- UCLA Breast Cancer Epigenetics Research Program
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TNBC is a highly heterogeneous disease lacking targeted treatment options, emphasizing the need for improved stratification systems. New methodologies such as microarray technology and high-throughput sequencing have allowed for the characterization of TNBC subtypes, but challenges remain in integrating diverse molecular data and implementing cost-effective classification methods. The application of artificial intelligence in translational oncology shows promise in identifying definitive TNBC subtypes.
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.
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