Neural programs of therapy resistance in cancer: A mechanism-first framework for therapeutic targeting
Peng-Yu Zhao1, Wioletta Rozpędek-Kamińska2, Zhe-Sheng Chen3
1Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Abstract:
Neural regulation of cancer therapy resistance is established unevenly across tumor types, signal sources, and treatments. This review proposes an evidence-calibrated framework that evaluates each candidate mechanism through its source, signal or activity pattern, receptor, target cell, and treatment context. We organize candidate neural contributions into four author-defined, overlapping analytical programs: survival and damage tolerance; cell-state plasticity and drug-tolerant persistence; neuroimmune suppression; and metabolic, vascular, and stromal adaptation. These categories facilitate comparison rather than denote equally established mechanisms: survival and selected neuroimmune routes have more direct treatment-linked support than many vascular and stromal hypotheses. Local innervation is distinguished from systemic neuroendocrine signaling and non-neuronal ligand production, while circuit integration in brain tumors is separated from predominantly paracrine and perineural interactions in peripheral cancers. An explicit hierarchy differentiates prospective biomarker-supported clinical benefit (Level 1), early interventional pharmacodynamic evidence (Level 2), treatment-specific preclinical intervention evidence (Level 3), and association (Level 4). Source resolution and rescue are assessed separately within each study. Therapy-associated sprouting, receptor adaptation, and cancer-induced nerve injury are evaluated as distinct processes; current evidence does not establish treatment-induced neural rewiring as a universal resistance driver. Study-level comparisons, model-selection criteria, and operational biomarker examples identify where neural targeting is supported, where only pathway-level dependence is demonstrated, and where additional causal validation is required. The framework is intended to improve selection and interpretation of mechanism-matched combinations, rather than infer oncologic efficacy from the availability of neuromodulatory drugs.
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