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November 17, 2025Molecular Biomedicine0 citationsOpen Access

Drug resistance in cancer: molecular mechanisms and emerging treatment strategies

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JLJinxin LiJHJiatao HuYYYiren Yang

Key Points

  • To examine the molecular mechanisms underpinning drug resistance in cancer and to propose emerging treatment strategies.
  • Systematic review of literature on drug resistance in oncology
  • Integration of findings related to genetic and epigenetic factors
  • Analysis of therapeutic strategies including synthetic lethality and metabolic targeting.
  • Exploration of advanced technologies like artificial intelligence and liquid biopsy for resistance prediction.
  • Identified key molecular mechanisms contributing to drug resistance in cancer
  • Proposed frameworks linking metabolic reprogramming and tumor ecology to resistance
  • Highlighted the role of microbiome in influencing therapy response
  • Outlined novel strategies to exploit resistance mechanisms for better therapeutic outcomes.

Abstract

Abstract Therapeutic resistance remains a defining challenge in oncology, limiting the durability of current therapies and contributing to disease relapse and poor patient outcomes. This review systematically integrates recent progress in understanding the molecular, cellular, and ecological foundations of drug resistance across chemotherapy, targeted therapy, and immunotherapy. We delineate how genetic alterations, epigenetic reprogramming, post-translational modifications, and non-coding RNA networks cooperate with metabolic reprogramming and tumor microenvironment remodeling to sustain resistant phenotypes. The influence of the microbiome is highlighted as an emerging determinant of therapeutic response through immune modulation and metabolic cross-talk. By summarizing key regulatory circuits, We establishe a unified framework linking clonal evolution, metabolic adaptability, and tumor ecological dynamics. We further synthesizes novel therapeutic strategies that convert resistance mechanisms into therapeutic vulnerabilities, including synthetic lethality approaches, metabolic targeting, and disruption of stem cell and stromal niches. Advances in single-cell and spatial omics, liquid biopsy, and artificial intelligence are emphasized as transformative tools for early detection and real-time prediction of resistance evolution. This review also identifies major translational gaps in preclinical modeling and proposes precision oncology frameworks guided by evolutionary principles. By bridging mechanistic understanding with adaptive clinical design, this work provides an integrated roadmap for overcoming therapeutic resistance and achieving sustained, long-term cancer control.

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Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/692509e8c0ce034ddc3528c0https://doi.org/10.1186/s43556-025-00352-w
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