A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments

20 April 2024 | Yanmei Zhang, Qifan Hu, Yuquan Pei, Hao Luo, Zixuan Wang, Xinxin Xu, Qing Zhang, Jianli Dai, Qianqian Wang, Zilian Fan, Yongcong Fang, Min Ye, Binhan Li, Mailin Chen, Qi Xue, Qingfeng Zheng, Shulin Zhang, Miao Huang, Ting Zhang, Jin Gu & Zhuo Xiong
The article presents an innovative patient-specific lung cancer assembloid (LCA) model developed using droplet microfluidic technology and microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. The model also reconstructs the functional heterogeneity of cancer-associated fibroblasts (CAFs) and reflects the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting their potential for predicting personalized treatments. The study provides a valuable method for fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.The article presents an innovative patient-specific lung cancer assembloid (LCA) model developed using droplet microfluidic technology and microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. The model also reconstructs the functional heterogeneity of cancer-associated fibroblasts (CAFs) and reflects the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting their potential for predicting personalized treatments. The study provides a valuable method for fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.
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