Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, United States.
Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States.
Theranostics. 2022 May 1;12(8):3628-3636. doi: 10.7150/thno.71761. eCollection 2022.
Predicting tumor responses to adjuvant therapies can potentially help guide treatment decisions and improve patient survival. Currently, tumor pathology, histology, and molecular profiles are being integrated into personalized profiles to guide therapeutic decisions. However, it remains a grand challenge to evaluate tumor responses to immunotherapy for personalized medicine. We present a microfluidics-based mini-tumor chip approach to predict tumor responses to cancer immunotherapy in a preclinical model. By uniformly infusing dissociated tumor cells into isolated microfluidic well-arrays, 960 mini-tumors could be uniformly generated on-chip, with each well representing the tumor niche that preserves the original tumor cell composition and dynamic cell-cell interactions and autocrine/paracrine cytokines. By incorporating time-lapse live-cell imaging, our mini-tumor chip allows the investigation of dynamic immune-tumor interactions as well as their responses to cancer immunotherapy (e.g., anti-PD1 treatment) in parallel within 36 hours. Additionally, by establishing orthotopic breast tumor models with constitutive differential PD-L1 expression levels, we showed that the on-chip interrogation of the primary tumor's responses to anti-PD1 as early as 10 days post tumor inoculation could predict the tumors' responses to anti-PD1 at the endpoint of day 24. We also demonstrated the application of this mini-tumor chip to interrogate on-chip responses of primary tumor cells isolated from primary human breast and renal tumor tissues. Our approach provides a simple, quick-turnaround solution to measure tumor responses to cancer immunotherapy.
预测肿瘤对辅助治疗的反应有助于指导治疗决策并提高患者生存率。目前,肿瘤病理学、组织学和分子谱正在被整合到个性化的图谱中,以指导治疗决策。然而,评估肿瘤对免疫疗法的反应以实现个体化医学仍然是一个巨大的挑战。我们提出了一种基于微流控的迷你肿瘤芯片方法,用于预测临床前模型中肿瘤对癌症免疫疗法的反应。通过将分离的肿瘤细胞均匀注入到隔离的微流控井阵列中,可以在芯片上均匀生成 960 个迷你肿瘤,每个井代表保留原始肿瘤细胞组成和动态细胞-细胞相互作用以及自分泌/旁分泌细胞因子的肿瘤微环境。通过结合延时活细胞成像,我们的迷你肿瘤芯片允许在 36 小时内平行研究动态免疫-肿瘤相互作用及其对癌症免疫疗法(例如,抗 PD-1 治疗)的反应。此外,通过建立具有组成型差异 PD-L1 表达水平的原位乳腺癌肿瘤模型,我们表明在肿瘤接种后 10 天即可在芯片上检测原发性肿瘤对抗 PD-1 的反应,从而可以预测原发性肿瘤对抗 PD-1 的反应在第 24 天的终点。我们还展示了该迷你肿瘤芯片在检测从原发性人乳腺癌和肾肿瘤组织中分离的原发性肿瘤细胞在芯片上的反应的应用。我们的方法为测量肿瘤对癌症免疫疗法的反应提供了一种简单、快速的解决方案。