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Updated: Nov 20, 2025
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Adjuvant and Neoadjuvant Treatment of Triple-Negative Breast Cancer With Chemotherapy
Antonio Marra1, Giuseppe Curigliano
1From the Division of Early Drug Development for Innovative Therapies, European Institute of Oncology (IEO) IRCCS; and Department of Oncology and Haemato-Oncology, University of Milano, Milan, Italy.
Abstract:
Triple-negative breast cancer (TNBC) accounts for 15% to 20% of all invasive breast carcinomas and is defined by the lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Although TNBC is characterized by high rates of disease recurrence and worse survival, it is significantly more sensitive to chemotherapy as compared with other breast cancer subtypes. Accordingly, despite great efforts in the genomic characterization of TNBC, chemotherapy still represents the cornerstone of treatment. For the majority of patients with early-stage TNBC, sequential anthracycline- and taxane-based neoadjuvant chemotherapy (NACT) represents the standard therapeutic approach, with pathological complete response that strongly correlates with long-term survival outcomes. However, some issues about the optimal neoadjuvant regimen, as well as the effective role of chemotherapy in patients with residual disease after NACT, are still debated. Herein, we will review the current evidences that guide the use of (neo)adjuvant chemotherapy in patients with early-stage TNBC. Furthermore, we will discuss current controversies, including the incorporation of platinum compounds to the neoadjuvant backbone and the optimal treatment for patients with residual disease after NACT. Lastly, we will outline potential future directions that can guide treatment escalation and de-escalation, as well as the development of new therapies. In our view, the application of multi-omics technologies, liquid biopsy assays, and machine learning algorithms are strongly warranted to pave the way toward personalized anticancer treatment for early-stage TNBC.
Insights
Triple-negative breast cancer (TNBC) is aggressive but chemotherapy-sensitive. Current neoadjuvant chemotherapy (NACT) is standard, but optimal regimens and residual disease treatment require further research for personalized TNBC therapy.
Area of Science:
- Oncology
- Genomics
- Translational Medicine
Background:
- Triple-negative breast cancer (TNBC) comprises 15-20% of invasive breast carcinomas and lacks ER, PR, and HER2 expression.
- TNBC exhibits high recurrence rates and poorer survival but shows greater sensitivity to chemotherapy compared to other subtypes.
- Chemotherapy remains the primary treatment modality for TNBC, despite extensive genomic characterization efforts.
Purpose of the Study:
- To review current evidence guiding neoadjuvant and adjuvant chemotherapy in early-stage TNBC.
- To discuss ongoing controversies regarding neoadjuvant chemotherapy regimens, including platinum incorporation and treatment of residual disease.
- To outline future directions for personalized treatment strategies in early-stage TNBC.
Main Methods:
- Review of current clinical evidence and treatment guidelines for early-stage TNBC.
- Discussion of controversies in neoadjuvant chemotherapy (NACT) protocols.
- Exploration of emerging technologies for personalized treatment approaches.
Main Results:
- Sequential anthracycline- and taxane-based NACT is the standard for early-stage TNBC, with pathological complete response correlating with survival.
- Debates persist regarding optimal NACT regimens and the role of chemotherapy in managing residual disease post-NACT.
- Multi-omics, liquid biopsy, and machine learning show promise for tailoring anticancer therapies.
Conclusions:
- Chemotherapy is central to early-stage TNBC management, with NACT being the standard approach.
- Further research is needed to optimize NACT and define the best strategies for patients with residual disease.
- Integrating advanced technologies like multi-omics and machine learning is crucial for advancing personalized TNBC treatment.
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