Abstract

Background Inter-tumor heterogeneity poses significant challenges for precision therapy in thyroid cancer (TC). The conventional organoid models are limited by inefficiency and poor physiological relevance. Methods We developed droplet-engineered organoids (DEOs) using microfluidic 3D bioprinting to rapidly generate patient-derived TC models. These DEOs were characterized via histology, whole-exome and RNA sequencing, and utilized for drug sensitivity testing and metastasis modeling. Results DEOs were generated within 10 days, exhibiting superior uniformity (CV: 2.54%) and a high success rate (76%). They faithfully recapitulated the histopathological architecture, genomic landscape (92% driver gene concordance), and native immune microenvironment (CD3+/CD56+/CD68+/α-SMA+) of parental tumors. Drug screening revealed patient-specific heterogeneity, accurately mirroring clinical responses, including cisplatin sensitivity and anti-PD-1 resistance. We established a novel TC and lung organoids co-culture model, which could be used to study the TC lung metastasis. Crucially, transcriptomics identified stage-specific maturation driven by NF-κB signaling. Pharmacological inhibition of NF-κB synergistically enhanced the efficacy of dasatinib, anti-PD-1, and paclitaxel, with combination index (CI) values of 0.58, 0.45, and 0.80, respectively. Conclusions Our microfluidic platform enables rapid, high-fidelity modeling of TC, offering a scalable and physiologically relevant tool for mechanistic studies, drug screening, and personalized therapy prediction, with highly promising translational potential.