Multiplexed drug testing of micro-dissected tumors using a microfluidic platform with integrated electrochemical aptasensors

摘要：电化学生物传感器与微电子学的结合为生物传感器的发展提供了独特的途径。 小型化、低成本系统，将生物分子传感与数字计算、编程和 沟通。在这里，我们建议将基于电化学适体的传感器(“aptasensors”)集成到一个 微流控多孔平台，可实现多时间点、高度并行的细胞死亡和细胞因子读数 药物治疗期间和治疗后完整的肿瘤活检组织的分泌物。我们的平台将允许收集 机器学习方法进行药物测试所需的大量分子数据。 抗癌药物测试-抗癌药物开发和个性化肿瘤学的核心过程-通常是 不准确和低效，因为它通常依赖于对缺乏人类的细胞培养或动物的研究 肿瘤微环境。在每年进行临床试验的约1000种药物中，只有4%的抗癌药物 通过安全性和有效性测试；超过一半的失败是由于缺乏有效性。因此，平均而言， 将一种药物推向市场需要10年时间和10亿美元的成本，这往往会导致药物的价格很高。在……里面 在过去的十年里，病人衍生的有机化合物和芯片器官等技术带来了一些希望。 然而，这些方法的吞吐量比传统细胞培养要低得多，而且通常不能 以完全重建完整组织的TME。这些限制是个性化的根本障碍 通常需要针对患者的独特TME和开发进行定制的治疗方法 针对TME的联合免疫疗法的数量正在指数级增加。因此，一个 保存人类TME的不同药物测试范例是帮助转变肿瘤学的关键 进入一个更负担得起和快速发展的治疗阶段。 Folch和Gujral实验室开发了一种完整的组织微流控药物测试平台，该平台基于定期- 用组织机械切割的大小、长方体形状的显微解剖组织(称为“长方体”) 直升机。在不到一个小时的时间里，可以从大约1厘米的固体中生产出10,000多个长方体(大约400微米宽) 肿瘤。长方体永远不会分离，并保留了大部分天然的TME(例如，免疫细胞和 微血管系统)。该平台是一种用户友好的多井设备，可以微流体捕获和选择性地 治疗一大堆长方体。在这里，我们建议将电化学适配器集成到我们的长方体中 实现对分泌化合物(细胞因子或细胞死亡)的自动化、多时间点监测的平台 指示器)，以及从大型长方体阵列直接实施电子读数。 作为概念验证，我们将使用96孔格式的小鼠肿瘤和患者样本的长方体。我们会 使用结直肠癌(CRC)肝转移的小鼠模型和患者样本。使用机器学习 技术，我们将在鼠标长方体上实现概念验证药物屏幕和概念验证 长方体患者联合免疫治疗药物评价。

ABSTRACT: The integration of electrochemical biosensors with microelectronics offers a unique avenue for miniaturized, low-cost systems that merge biomolecular sensing with digital computing, programming, and communication. Here we propose to integrate electrochemical aptamer-based sensors (“aptasensors”) into a microfluidic multi-well platform to enable multi-time-point, highly parallel readouts of cell death and cytokine secretion from intact tumor biopsies during and after drug treatment. Our platform will allow for gathering the large amounts of molecular data that are needed for machine learning approaches to drug testing. Cancer drug testing – a central process in cancer drug development and personalized oncology – is often inaccurate and inefficient because it typically relies on studies in cell cultures or animals that lack the human tumor microenvironment (TME). Only <4% of cancer drugs out of the ~1,000 drugs in clinical trials each year pass the safety and efficacy tests; more than half of the failures are due to lack of efficacy. Hence, on average, bringing a drug to market takes >10 years and costs >$1 billion, often resulting in high prices for the drugs. In the last decade, technologies such as patient-derived organoids and organs-on-chips have brought some hope. However, these approaches have much lower throughput than traditional cell cultures and are generally unable to fully recreate the TME of an intact tissue. These limitations are a fundamental hurdle for the personalization of therapies which often need to be customized to the unique TME of the patient, and also for the development of combination immunotherapies, which target the TME and are exponentially increasing in number. Thus, a different paradigm for drug testing that preserves the human TME is critically needed to help transition oncology into a stage of more affordable and rapidly evolving treatments. The Folch and Gujral labs have developed an intact-tissue microfluidic drug testing platform based on regularly- sized, cuboidal-shaped microdissected tissues (referred to as “cuboids”) that are mechanically cut with a tissue chopper. In under an hour, more than 10,000 cuboids (~400 µm-wide) can be produced from ~1 cm3 of solid tumor. The cuboids are never dissociated and retain much of the native TME (e.g. immune cells and microvasculature). The platform is a user-friendly multi-well device that can microfluidically trap and selectively treat a large array of cuboids. Here we propose to integrate electrochemical aptasensors into our cuboids platform to enable the automated, multi-time-point monitoring of secreted compounds (cytokines or cell death indicators) within the wells and the straightforward implementation of electrical readouts from large cuboid arrays. As a proof of concept, we will use cuboids from mouse tumors and patient samples in a 96-well format. We will use a mouse model and patient samples of colorectal cancer (CRC) liver metastases. Using machine learning techniques, we will implement a proof-of-concept drug screen on mouse cuboids and a proof-of-concept combination immunotherapy drug evaluation on patient cuboids.