Personalized Organoid-Chip Model For Drug Testing in Pancreatic Cancer

摘要 为了改善癌症患者的治疗效果，下一代癌症治疗剂应结合联合收割机 有针对性的治疗，能够预测一个人的肿瘤的可能性，以受益于给定的 治疗该项目的重点是推进基于个体肿瘤的个性化平台 这不仅是由癌上皮细胞，但整个肿瘤微环境决定的行为 （TME）。TME在疾病进展或甚至治疗药物反应中的作用在各种研究中得到了很好的证实。 预后不良的癌症，包括胰腺导管腺癌（PDA），5年生存率 低于10%。在这里，结合3D患者源性类器官（PDO）和微流体技术，我们将 开发一种新的肿瘤芯片设备，有可能产生个性化的治疗反应预测 离体。在目标1中，使用多个组织和功能读数，我们将表明我们的多细胞平台 它概括了肿瘤及其微环境。在目标2中，我们将展示我们的系统作为 个体化离体平台，以功能性地表征肿瘤对常规和新型抗肿瘤药物的反应。 治疗我们的长期目标是优化患者特异性离体药物反应测定技术， 它可以用于快速、准确和可靠地告知个体化治疗决策。作为服务的证明 概念，该项目涉及开发个性化的胰腺癌模型;然而，这项技术可以 扩展到具有丰富TME的其他实体瘤类型（即，乳腺癌）以改善分子和/或细胞 癌症生物学和治疗学的表征。

ABSTRACT In order to improve cancer patient outcomes, the next generation of cancer therapeutics should combine targeted treatments with the ability to predict the likelihood of an individual’s tumor to benefit from a given treatment. The focus of this project is on advancing a personalized platform based on the individual’s tumor behavior which is determined not only by the cancerous epithelium but the entire tumor microenvironment (TME). The role of TME in disease progression or even therapeutic drug response is well established in variety of cancers with poor outcome including pancreatic ductal adenocarcinoma (PDA) with a five year survival rate of less than 10%. Here, combining 3D patient-derived organoids (PDO) and microfluidic technologies, we will develop a novel tumor-chip device with the potential to generate individualized treatment response predictions ex vivo. In Aim 1, using multiple tissue and functional read outs we will show that our multicellular platform recapitulates the tumor and its microenvironment. In Aim 2, we will show the utility of our system as an individualized ex vivo platform to functionally characterize the tumor response to conventional and novel therapies. Our long-term goal is to optimize patient-specific ex vivo drug response assay technology such that it may be used to rapidly, accurately, and reliably inform individualized treatment decisions. As a proof of concept, the project involves developing a personalized pancreatic cancer model; however this technology can be extended to other solid tumor types with rich TME (i.e., breast cancer) to improve molecular and/or cellular characterizations of cancer biology and therapeutics.