Drug Resistance in Cancer: A Conceptual Shift from Single-Gene Mechanisms to Systemic Adaptive Resistance

Proposes a conceptual shift from conventional MDR models toward systemic adaptive resistance, viewing MDR as a complex and evolving biological phenomenon rather than the result of isolated molecular events. Relative to traditional explanations centered on single-gene mechanisms such as ABC transporter overexpression, drug-target mutations, bypass signaling activation, and increased DNA repair, the article argues these mechanisms alone are insufficient to explain the heterogeneity, reversibility, temporal evolution, and environmental dependence observed clinically. This is a review article; its argument rests on synthesis and conceptual reframing of existing evidence rather than new experimental data, as signaled by the phrase 'Increasing evidence suggests.'

Systematically reviews classical resistance mechanisms, including drug uptake/efflux, compensatory pathway activation, and apoptosis evasion. Integrates widely discussed classical mechanisms into a unified framework as components of systemic adaptive resistance. Review-level synthesis based on existing literature as described in the text.

Introduces and discusses non-genetic resistance mechanisms such as drug-tolerant persisters (DTPs), epithelial-mesenchymal transition (EMT), cancer stem cell (CSC) plasticity, and related factors. Places non-genetic, phenotypic-plasticity sources of resistance alongside classical genetic mechanisms, broadening the understanding of resistance origins. Review-level synthesis based on existing literature as described in the text.

Discusses interactions among these mechanisms and how their coordinated effects contribute to tumor persistence and MDR evolution, and evaluates translational barriers and emerging pharmacological strategies including longitudinal molecular monitoring and AI-assisted drug resistance prediction. Integrates mechanism interactions, translational barriers, and emerging strategies (longitudinal molecular monitoring, AI-assisted prediction) into a single analytical framework pointing toward dynamic, mechanism-informed treatment. Review-level analysis and outlook based on existing literature and the authors' proposed strategic directions.

This is a review article; its conclusions apply at the level of conceptual framework and strategic direction, intended to offer an integrative perspective for resistance researchers and drug developers. The proposed systemic adaptive resistance perspective, mechanism-interaction analysis, and strategies such as longitudinal molecular monitoring and AI-assisted drug resistance prediction are aimed at clinical and research settings where treatment must be dynamically adjusted according to the evolving resistance state.

Readers should still watch: the specific strength and sources of the 'Increasing evidence' cited in the text are not elaborated here; the causal weight and temporal ordering between non-genetic mechanisms (DTPs, EMT, CSC plasticity) and classical genetic mechanisms remain to be further clarified; and the practical feasibility and validation status of longitudinal molecular monitoring and AI-assisted drug resistance prediction in clinical settings are presented only as emerging strategies and remain open questions.