Metastatic and triple-negative breast cancer: challenges and treatment options
Open accessAbstract
The major current conventional types of metastatic breast cancer (MBC) treatments include surgery, radiation, hormonal therapy, chemotherapy, or immunotherapy. Introducing biological drugs, targeted treatment and gene therapy can potentially reduce the mortality and improve the quality of life in patients with MBC. However, combination of several types of treatment is usually recommended. Triple negative breast cancer (TNBC) accounts for 10-20% of all cases of breast carcinoma and is characterized by the low expression of progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2). Consequently, convenient treatments used for MBC that target these receptors are not effective for TNBC which therefore requires special treatment approaches. This review discusses the occurrence of MBC, the prognosis and predictive biomarkers of MBC, and focuses on the novel advanced tactics for treatment of MBC and TNBC. Nanotechnology-based combinatorial approach for the suppression of EGFR by siRNA and gifitinib is described.
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Metastasis is the leading cause of death from breast cancer.
“Metastasis is the leading cause of death from breast cancer1, 37-39.”
From Liposomal Formulation of HIF-1α Inhibitor Echinomycin Eliminates Established Metastases of Triple-negative Breast Cancer (2020) · cites this paper as [39]
HIF-1α expression in primary tumor breast cancer cells promotes metastasis.
“It is known that HIF-1α expression in primary tumor breast cancer cells promotes metastasis, although from a clinical perspective this concept has limited value since prognosis weighs heavily on the stage of progression at time of surgical resection39.”
From Liposomal Formulation of HIF-1α Inhibitor Echinomycin Eliminates Established Metastases of Triple-negative Breast Cancer (2020) · cites this paper as [39]
The 'Metastatic breast cancer' and 'Models of breast cancer' sections discuss how primary breast tumors metastasize, but do not address whether HIF-1α expression promotes that process. Searches for HIF-1α and hypoxia-inducible factor found no passage making the attributed link; page numbers were unavailable in our copy. (LLM, conf=0.72)
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- Authors
- Al-Mahmood, S., Sapiezynski, J., Garbuzenko, O. B., & Minko, T.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Minko, Tamara
- Venue
- Drug Delivery and Translational Research
- Published
- 2018-07-05
- Volume
- 8
- Issue
- 5
- Pages
- 1483-1507
- ISSN
- 2190-393X (ISSN-L)
- 2190-3948
- PMC
- PMC6133085
- OpenAlex
- W2838505861
- Cited by
- 524
- References
- 269
- Citations by year
- 2026: 32 · 2025: 60 · 2024: 66 · 2023: 83 · 2022: 87 · 2021: 83 · 2020: 77 · 2019: 33 · 2018: 3
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Drug Delivery and Translational Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (CA138533)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Oncology › HER2/EGFR in Cancer Research
- Health Sciences › Medicine › Oncology › Cancer Treatment and Pharmacology
- Keywords
- metastatic breast cancer, triple-negative breast cancer, EGFR, siRNA, nanotechnology, combination therapy, predictive biomarkers, prognosis, targeted therapy, gene therapy, immunotherapy, hormone therapy, chemotherapy
- MeSH terms
- Gefitinib, Animals, Clinical Trials as Topic, Combined Modality Therapy, Female, Humans, Neoplasm Metastasis, Prognosis, Biomarkers, Tumor, Genetic Therapy, RNA, Small Interfering, Triple Negative Breast Neoplasms, ErbB Receptors
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.
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