Three-Dimensional Microtumors for Probing Heterogeneity of Invasive Bladder Cancer

膀胱癌 癌症研究 癌症 基因敲除 恶性肿瘤 癌细胞 背景(考古学) 肿瘤异质性 表型 生物 病理 细胞培养 医学 基因 遗传学 古生物学
作者
Peter Torab,Yue Yan,Haruo Yamashita,Joshua I. Warrick,Jay D. Raman,David J. DeGraff,Pak Kin Wong
出处
期刊:Analytical Chemistry [American Chemical Society]
被引量:8
标识
DOI:10.1021/acs.analchem.0c00057
摘要

Bladder cancer is an increasingly common malignancy, and muscle invasive bladder cancer is associated with particularly high rates of morbidity and mortality. The morphologic and molecular diversity of bladder cancer poses significant challenges in elucidating the invasion mechanisms responsible for disease progression. Furthermore, conventional invasion assays do not provide a physiological context for studying bladder cancer invasion within 3D microenvironments and have limited ability to capture the contribution of cellular phenotypic heterogeneity to disease progression. Here, we describe the development of a 3D microtumor invasion model suitable for the analysis of cellular phenotypic heterogeneity in cell lines and primary tumor cells from bladder cancer patients. This model incorporates a self-assembly approach for recapitulating features of bladder cancer invasion in 3D microenvironments and probing the invasive cell subpopulations. The gene expression profiles of invading microtumors were analyzed by incorporating a gold nanorod-locked nucleic acid biosensor. The incorporation of the single cell biosensor and transient gene knockdown into the system revealed the formation of invasive leader cells with upregulated Delta-like ligand 4 (DLL4) expression as well as the role of NOTCH1-DLL4 signaling in collective bladder cancer invasion. The involvement of DLL4 expressing cells in bladder cancer invasion was also observed in patient samples obtained from transurethral resection. Collectively, our study demonstrates a 3D microtumor invasion model for investigating intracellular heterogeneity of bladder cancer invasion and analyzing patient derived samples toward personalized medicine applications.

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