Biology and Management of Patients With Triple-Negative Breast Cancer
Open accessAbstract
Triple-negative breast cancer (TNBC) accounts for 15% of all breast cancers and is associated with poor long-term outcomes compared with other breast cancer subtypes. Because of the lack of approved targeted therapy, at present chemotherapy remains the mainstay of treatment for early and advanced disease. TNBC is enriched for germline BRCA mutation, providing a foundation for the use of this as a biomarker to identify patients suitable for treatment with DNA-damaging agents. Inherited and acquired defects in homologous recombination DNA repair, a phenotype termed "BRCAness," may be present in a large proportion of TNBC cases, making it an attractive selection and response biomarker for DNA-damaging therapy. Triple-negative breast cancer is a diverse entity for which additional subclassifications are needed. Increasing understanding of biologic heterogeneity of TNBC has provided insight into identifying potentially effective systemic therapies, including cytotoxic and targeted agents. Numerous experimental approaches are under way, and several encouraging drug classes, such as immune checkpoint inhibitors, poly(ADP-ribose) polymerase inhibitors, platinum agents, phosphatidylinositol-3-kinase pathway inhibitors, and androgen receptor inhibitors, are being investigated in TNBC. Molecular biomarker-based patient selection in early-phase trials has the potential to accelerate development of effective therapies for this aggressive breast cancer subtype. TNBC is a complex disease, and it is likely that several different targeted approaches will be needed to make meaningful strides in improving the outcomes.
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The findings expand the number of TNBC patients eligible for treatment strategies targeting DNA repair mechanisms such as PARP inhibitors.
“Critically, it expands the number of TNBC patients eligible for treatment strategies targeting DNA repair mechanisms such as PARP inhibitors22,27.”
From Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study (2019) · cites this paper as [22]
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Flagged first, then by citations
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- Authors
- Sharma, P.
- Affiliations
- University of Kansas Medical Center (US)
- Corresponding author
- Sharma, Priyanka
- ORCID
- Sharma, Priyanka
- Venue
- The Oncologist
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-07-11
- Volume
- 21
- Issue
- 9
- Pages
- 1050-1062
- ISSN
- 1083-7159 (ISSN-L)
- 1549-490X
- PMC
- PMC5016071
- OpenAlex
- W2469627334
- Cited by
- 294
- References
- 135
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 32 · 2023: 33 · 2022: 50 · 2021: 52 · 2020: 31 · 2019: 21 · 2018: 19 · 2017: 13 · 2016: 6
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Oncologist (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Health Sciences › Medicine › Oncology › PARP inhibition in cancer therapy
- Keywords
- triple-negative breast cancer, germline BRCA1/2 mutation, BRCAness, homologous recombination deficiency, DNA-damaging agents, platinum agents, PARP inhibitors, immune checkpoint inhibitors, PI3K inhibitors, androgen receptor antagonists, molecular subtyping, targeted therapy, biomarker-based patient selection, chemotherapy
- MeSH terms
- Poly(ADP-ribose) Polymerase Inhibitors, DNA Damage, Female, Humans, Biomarkers, Tumor, Germ-Line Mutation, BRCA1 Protein, Angiogenesis Inhibitors, BRCA2 Protein, Molecular Targeted Therapy, Triple Negative Breast Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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