Cancer drug response and resistance: molecular mechanisms and combating strategies.

Authors

Xiaoxia Cheng, Miaochun Xu, Juncheng Wei, Canhui Cao

Published in

Signal transduction and targeted therapy. Volume 11. Issue 1. Aug 03, 2026. Epub Aug 03, 2026.

Abstract

Despite remarkable advances in cancer drug treatment, including chemotherapy, targeted therapy, and immunotherapy, therapeutic resistance remains a formidable clinical barrier, limiting durable responses and long-term survival. Drug resistance can be broadly categorized as intrinsic, where tumors fail to respond to initial treatment, or acquired, which emerges during or after therapy due to adaptive or evolutionary processes. A comprehensive understanding of the multifactorial and dynamic nature of resistance is essential for improving treatment efficacy. In this review, we systematically examine the molecular and cellular determinants of drug response and resistance across 22 cancer types, highlighting key resistance mechanisms such as compensatory pathway activation, phenotypic plasticity, immune evasion, enhanced DNA damage repair, and the survival of drug-tolerant persister cells. These mechanisms are further contextualized across major therapeutic modalities, supported by clinical trials. We also present emerging strategies to overcome resistance, including rational drug combinations, novel agents, microbiome modulation, adaptive and intermittent therapies and advanced drug delivery systems, each illustrated with representative clinical studies. Moreover, we discuss cutting-edge tools that are revolutionizing resistance research, including single-cell and spatial multiomic profiling, patient-derived tumor organoid and xenograft (PDO/PDX) models, and artificial intelligence (AI)-powered predictive analytics. By integrating insights across molecular, cellular, and clinical dimensions, this review offers a strategic framework for understanding and tackling cancer drug resistance, with important translational implications for the future of precision oncology.

PMID:
42543381
Bibliographic data and abstract were imported from PubMed on 03 Aug 2026.

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