Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer
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. T...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-06-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.681476/full |
| _version_ | 1818443667804782592 |
|---|---|
| author | Miquel Ensenyat-Mendez Pere Llinàs-Arias Javier I. J. Orozco Sandra Íñiguez-Muñoz Matthew P. Salomon Borja Sesé Maggie L. DiNome Diego M. Marzese |
| author_facet | Miquel Ensenyat-Mendez Pere Llinàs-Arias Javier I. J. Orozco Sandra Íñiguez-Muñoz Matthew P. Salomon Borja Sesé Maggie L. DiNome Diego M. Marzese |
| author_sort | Miquel Ensenyat-Mendez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T19:03:41Z |
| format | Article |
| id | doaj.art-02e9a0b4b26a4ea69a1f946dbd6bca0f |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-14T19:03:41Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-02e9a0b4b26a4ea69a1f946dbd6bca0f2022-12-21T22:50:54ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-06-011110.3389/fonc.2021.681476681476Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast CancerMiquel Ensenyat-Mendez0Pere Llinàs-Arias1Javier I. J. Orozco2Sandra Íñiguez-Muñoz3Matthew P. Salomon4Borja Sesé5Maggie L. DiNome6Diego M. Marzese7Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, SpainCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, SpainSaint John’s Cancer Institute, Providence Saint John’s Health Center, Santa Monica, CA, United StatesCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, SpainKeck School of Medicine, University of Southern California, Los Angeles, CA, United StatesCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, SpainDepartment of Surgery, David Geffen School of Medicine, University California Los Angeles (UCLA), Los Angeles, CA, United StatesCancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, SpainTriple-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.https://www.frontiersin.org/articles/10.3389/fonc.2021.681476/fulltriple-negative breast cancerTNBCmolecular subtype of breast cancerepigeneticsclusteringartificial intelligence-AI |
| spellingShingle | Miquel Ensenyat-Mendez Pere Llinàs-Arias Javier I. J. Orozco Sandra Íñiguez-Muñoz Matthew P. Salomon Borja Sesé Maggie L. DiNome Diego M. Marzese Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer Frontiers in Oncology triple-negative breast cancer TNBC molecular subtype of breast cancer epigenetics clustering artificial intelligence-AI |
| title | Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer |
| title_full | Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer |
| title_fullStr | Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer |
| title_full_unstemmed | Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer |
| title_short | Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer |
| title_sort | current triple negative breast cancer subtypes dissecting the most aggressive form of breast cancer |
| topic | triple-negative breast cancer TNBC molecular subtype of breast cancer epigenetics clustering artificial intelligence-AI |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.681476/full |
| work_keys_str_mv | AT miquelensenyatmendez currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT perellinasarias currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT javierijorozco currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT sandrainiguezmunoz currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT matthewpsalomon currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT borjasese currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT maggieldinome currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer AT diegommarzese currenttriplenegativebreastcancersubtypesdissectingthemostaggressiveformofbreastcancer |