APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring
Abstract
The detection of circulating tumor cells (CTCs) is crucial for cancer management but remains challenging due to their rarity and heterogeneity. Alkaline phosphatase (AP) activity, particularly from cancer-associated isoforms ALPP and ALPG, offers a promising functional biomarker alternative. This study developed and validated a highly sensitive assay for detecting AP-expressing circulating tumor-related materials (CTRMs). Magnetic nanoparticles were conjugated with an optimized BG2-PEG-biotin aptamer for specific CTRM capture, with systematic optimization of probe design, blocking reagents, and background suppression. The assay demonstrated excellent linearity and reproducibility (%CV = 5.6%). Clinical validation across lung cancer (LC) and other cancers revealed significantly elevated AP activity in LC (p = 0.0372) and other cancer patients (p = 0.0576) versus healthy donors. Most notably, AP activity was substantially higher in tyrosine kinase inhibitor (TKI)-resistant patients (p = 0.0001). ROC analysis showed exceptional performance for distinguishing LC from healthy donors (AUC = 0.9340) and identifying TKI resistance (AUC = 0.9198). While no significant difference was found between CTC-positive and negative groups, AP activity strongly correlated with quantitative CTC burden in LC (r = 0.485, p = 0.000) and TKI-resistant subgroups (r = 0.369, p = 0.003). Longitudinal monitoring demonstrated that dynamic AP activity changes reflected treatment response. This study establishes AP activity as a sensitive, specific functional biomarker for CTRM detection that effectively predicts TKI resistance and dynamically monitors tumor burden, highlighting its significant clinical potential for non-invasive cancer management.
Keywords
Citation Information
@article{linchen2026,
title={APtamer-Enhanced AP Assay: Dynamic Functional Profiling of Circulating Tumor Materials for Predicting TKI Resistance and Real-Time Treatment Monitoring},
author={Lin Chen and Yue Lu and Panpan Qi and Zhonglin Yang and Dongjiang Tang},
journal={Scientific Reports},
year={2026},
doi={https://doi.org/10.21203/rs.3.rs-8846023/v1}
}
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