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.

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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