Liu Lunan, Wang Huishu, Chen Ruiqi, Song Yujing, Wei William, Baek David, Gillin Mahan, Kurabayashi Katsuo, Chen Weiqiang
Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA.
Lab Chip. 2025 May 16. doi: 10.1039/d4lc01043d.
Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity and patient specificity in human cancer. Cancer-on-a-chip (CoC) microphysiological systems can closely mimic the complex anatomical features and microenvironment interactions in an actual tumor, enabling more accurate disease modeling and therapy testing. This review article concisely summarizes and highlights the state-of-the-art progresses in CoC development for modeling critical TME compartments including the tumor vasculature, stromal and immune niche, as well as its applications in therapying screening. Current dilemma in cancer therapy development demonstrates that future preclinical models should reflect patient specific pathophysiology and heterogeneity with high accuracy and enable high-throughput screening for anticancer drug discovery and development. Therefore, CoC should be evolved as well. We explore future directions and discuss the pathway to develop the next generation of CoC models for precision cancer medicine, such as patient-derived chip, organoids-on-a-chip, and multi-organs-on-a-chip with high fidelity. We also discuss how the integration of sensors and microenvironmental control modules can provide a more comprehensive investigation of disease mechanisms and therapies. Next, we outline the roadmap of future standardization and translation of CoC technology toward real-world applications in pharmaceutical development and clinical settings for precision cancer medicine and the practical challenges and ethical concerns. Finally, we overview how applying advanced artificial intelligence tools and computational models could exploit CoC-derived data and augment the analytical ability of CoC.
尽管许多癌症疗法在临床前研究中显示出强大的疗效，但在临床试验中却失败了。关键原因之一是所采用的临床前模型无法重现复杂的肿瘤微环境（TME），也无法反映人类癌症中的异质性和患者特异性。芯片上的癌症（CoC）微生理系统可以紧密模拟实际肿瘤中的复杂解剖特征和微环境相互作用，从而实现更准确的疾病建模和治疗测试。这篇综述文章简要总结并强调了CoC在模拟关键TME区室（包括肿瘤脉管系统、基质和免疫微环境）方面的最新进展，以及其在治疗筛选中的应用。癌症治疗开发中的当前困境表明，未来的临床前模型应高精度反映患者特异性病理生理学和异质性，并能够进行高通量筛选以发现和开发抗癌药物。因此，CoC也应不断发展。我们探索了未来的方向，并讨论了开发用于精准癌症医学的下一代CoC模型的途径，例如患者来源芯片、芯片上的类器官以及高保真的多器官芯片。我们还讨论了传感器和微环境控制模块的整合如何能够更全面地研究疾病机制和治疗方法。接下来，我们概述了CoC技术未来标准化和转化为精准癌症医学药物开发和临床环境中的实际应用的路线图，以及实际挑战和伦理问题。最后，我们概述了应用先进的人工智能工具和计算模型如何利用CoC衍生的数据并增强CoC的分析能力。
