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Updated: Sep 11, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
Drug resistance in breast cancer: Mechanisms and strategies for management
Guo-Yu Wu1, Ming-Zhu Xiao2, Wei-Chao Hao3
1The First Affiliated Hospital, Guangdong Pharmaceutical University, Guangzhou, China; School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China; Center for Drug Research and Development, Guangdong Pharmaceutical University, Guangzhou, China; NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangdong Pharmaceutical University, Guangzhou, China; Guangdong Provincial Key Laboratory for Research and Evaluation of Pharmaceutical Preparations, Guangdong Pharmaceutical University, Guangzhou, China.
Abstract:
Therapeutic resistance remains a significant challenge in breast cancer treatment, often driven by factors such as genetic mutations, dysregulation of receptors and signaling pathways, alterations in drug metabolism and transport, cellular heterogeneity, and modifications in the tumor microenvironment. As a highly heterogeneous and complex disease, breast cancer exhibits distinct molecular and histopathological characteristics, necessitating tailored therapeutic approaches. This article reviews recent advancements in understanding therapy resistance across four major subtypes - Luminal A, Luminal B, HER2-enriched, and triple-negative breast cancer (TNBC) - and explores potential strategies to overcome resistance, providing insights into developing novel therapeutic interventions. Notably, TNBC patients have limited treatment options, with chemotherapy remaining the standard approach and immunotherapy emerging as an adjunct strategy. We concisely overview key mechanisms contributing to therapy resistance and discuss innovative therapeutic strategies, including combination regimens, molecularly targeted therapies, photodynamic therapy, and ferroptosis-inducing treatments. Additionally, we highlight recent advancements in multi-omics data integration and artificial intelligence-driven approaches in breast cancer research. Future efforts should focus on refining predictive models, optimizing combination therapies, and leveraging artificial intelligence to enhance treatment efficacy, ultimately overcoming resistance and improving long-term outcomes for breast cancer patients.
Insights
Therapeutic resistance in breast cancer is a major hurdle, but new strategies like combination therapies and AI are showing promise. Overcoming resistance is key to improving patient outcomes.
Area of Science:
- Oncology
- Cancer Research
- Pharmacology
Background:
- Therapeutic resistance is a significant challenge in breast cancer treatment.
- Breast cancer is a complex disease with distinct subtypes requiring tailored therapies.
- Triple-negative breast cancer (TNBC) has limited treatment options, with chemotherapy as standard care.
Purpose of the Study:
- To review advancements in understanding therapy resistance across major breast cancer subtypes.
- To explore strategies for overcoming resistance and developing novel therapeutic interventions.
- To highlight the role of multi-omics and artificial intelligence in breast cancer research.
Main Methods:
- Review of recent scientific literature on breast cancer therapy resistance mechanisms.
- Analysis of therapeutic strategies including combination regimens, targeted therapies, photodynamic therapy, and ferroptosis induction.
- Examination of advancements in multi-omics data integration and artificial intelligence (AI).
Main Results:
- Key mechanisms of therapy resistance identified across Luminal A, Luminal B, HER2-enriched, and TNBC subtypes.
- Innovative strategies such as combination therapies and AI-driven approaches show potential for overcoming resistance.
- Immunotherapy is emerging as an adjunct strategy for TNBC treatment.
Conclusions:
- Tailored therapeutic approaches are essential for managing breast cancer subtypes.
- Future research should focus on refining predictive models and optimizing combination therapies.
- Leveraging AI and multi-omics data integration can enhance treatment efficacy and improve patient outcomes.
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