Anti-cancer drug resistance driven by tumor-intrinsic and tumor microenvironment (TME) derived factors underpins cancer therapy efficacy. While the gold-standard tissue biopsies are limited by invasiveness, spatiotemporal heterogeneity, and poor repeatability, and thereby failing on continuous therapeutic monitoring, liquid biopsy functions as the counterpart to assist tumor-informed biomolecule detection in peripheral blood and other biofluids, enables real-time non-invasive tracking of molecular alterations in situ, converting drug resistance monitoring from a post-hoc clinical event into a predictable and actionable molecular signal. This has become a core technology for precision oncology. This review systematically summarizes the latest advances in detection technologies for key liquid biopsy-based biomarkers, elaborating their technical principles, clinical utility, and limitations, and compares the characteristics of diverse biofluid samples in drug resistance-relevant research. It also details the translational applications of these biomarkers in monitoring drug resistance across major malignancies, including resistance mutation detection, clonal evolution tracking, minimal residual disease (MDR) assessment, and adaptive therapy guidance. More importantly, this review addresses critical clinical translation challenges, including pre-analytical standardization, sensitivity-specificity trade-offs, multi-biomarker data interpretability, and outlines future development directions stressing the needs of multi-modalmodule based detection, technological standardization, translational research integration, personalized liquid biopsy panels, AI-assisted data mining, and novel technical direction such as Proximity Barcoding Assay for single-exosome profiling. This work provides a comprehensive mechanistic and technical framework for liquid biopsy-guided monitoring of anti-cancer drug resistance, highlighting its potential to enable early detection of drug resistance and timely treatment adjustments, thereby advancing precision cancer therapy.
Zhang et al. (Sun,) studied this question.