A matrix metalloproteinase biosensor-functionalized metastasis-on-a-chip platform for evaluating adrenocortical carcinoma progression

用于评估肾上腺皮质癌进展的基质金属蛋白酶生物传感器功能化转移芯片平台

• 批准号: 10576037
• 负责人: Priya Harakh Dedhia
• 金额: $ 18.09万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-21 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576037
• 关键词: 3-Dimensional; Address; Adrenocortical carcinoma; Animal Model; Biological; Biological Models; Biology; Biosensing Techniques; Biosensor; Cell Cycle; Cell Cycle Regulation; Cell Line; Cell Proliferation; Cell Separation; Cell Survival; Cells; Circulation; Clinical; Complex; Confocal Microscopy; Data; Devices; Disease; Disease Progression; Distant; Drug resistance; Event; Extracellular Matrix; Failure; Foundations; Frequencies; Future; Heterogeneity; Human; Hydrogels; IGF2 gene; In Vitro; Individual; Intervention; Kinetics; Label; Liver; Lung; Malignant Neoplasms; Matrix Metalloproteinases; Mediating; Metastatic Carcinoma; Microfluidic Microchips; Microfluidics; Microscopy; Modeling; Neoplasm Metastasis; Nonmetastatic; Oncogenic; Organoids; Pathway interactions; Patients; Peptides; Pharmaceutical Preparations; Pre-Clinical Model; Prediction of Response to Therapy; Primary Neoplasm; Proliferating; Property; Research; Resistance; Retinoblastoma Protein; Role; Sampling; Signal Pathway; Signal Transduction; Signaling Protein; Site; Sorting; Survival Rate; System; TP53 gene; Technology; Therapeutic Intervention; Time; Tissues; Tumor Cell Invasion; Work; beta catenin; cell motility; cell type; drug sensitivity; effective therapy; fluid flow; imaging Segmentation; in vivo; inhibitor; insight; migration; mouse model; mutational status; neoplastic cell; pre-clinical research; preclinical study; single-cell RNA sequencing; targeted treatment; therapeutic target; therapeutically effective; transcriptomics; tumor; tumor heterogeneity; tumor progression

Adrenocortical carcinoma (ACC) is an aggressive malignancy with a poor 5-year survival rate of 6% for patients with metastatic disease. There are no targeted therapies for these patients. Unfortunately, current treatments only slow disease progression for an average of 5 months, after which drug resistance quickly develops. As such, there is a critical need to identify new targets of intervention and develop new treatments for ACC. ACC can be characterized by significant heterogeneity both intertumorally between patients and intratumorally within an individual tumor, making choosing an effective therapeutic strategy challenging. Several mechanistic pathways have been identified that may correlate with disease progression: IGF2, Wnt/β-catenin (Wnt), and cell cycle regulating pathways such as p53/retinoblastoma protein (Rb) are dysregulated in 90% of ACC, and correlate with metastatic disease. Unfortunately, the intratumoral heterogeneity of IGF2, Wnt, and p53/Rb dysregulation and its contributions to ACC tumor progression is unknown, although each of these pathways has been implicated or correlated with tumor invasion, matrix metalloproteinase (MMP) expression, or metastasis in other cancers. This lack of understanding can partially be attributed to the lack of appropriate preclinical research models. Most attempts to generate ACC models have been unsuccessful. The small number of existing models do not adequately reflect the oncogenic signaling pathways and intratumoral heterogeneity of human ACC. A human-based ACC model system permitting functional and transcriptomic characterization of tumor subpopulations following metastasis does not exist. To address these critical research gaps, we developed the first patient-derived organoids (PTOs) from patient samples as well as an ACC metastasis-on-a-chip (MOC) platform. Our MOC platform is an in vitro microfluidic system that incorporates tumor organoids, recirculating fluid flow, and downstream tissue organoids, which can recapitulate aspects of metastasis from a primary tumor site to metastatic sites. In addition, we have developed MMP peptide biosensor technology that integrates into our organoids, enabling near-real time observation of MMP-mediated tumor cell invasion. We will deploy this platform to sort tumor cells into metastatic/motile versus non-metastatic/less motile subpopulations for analysis of expression of dysregulated pathways in ACC (IGF2, Wnt, and p53/Rb) and subpopulation heterogeneity determined by single cell RNA sequencing. We hypothesize that IGF2, Wnt, and cell cycle dysregulation correlates with increased metastasis kinetics and MMP activity in ACC PTOs deployed in our MOC platform. Towards this hypothesis: Aim 1 will delineate metastasis kinetics, MMP activity, and proliferation of ACC PTO cells; Aim 2 will define ACC intratumoral heterogeneity of critical oncogenic pathway dysregulation using single cell-RNA-sequencing; Aim 3 will determine the relative importance of each driver pathway on metastatic potential through drug-based inhibition. Upon completion of this project, we aim to both better understand ACC disease biology and have an established model of ACC that can be deployed for preclinical studies.

肾上腺皮质癌 (ACC) 是一种侵袭性恶性肿瘤，患者 5 年生存率仅为 6% 不幸的是，目前没有针对这些患者的靶向治疗。 平均仅减缓疾病进展5个月，此后耐药性迅速发展。 因此，迫切需要确定 ACC 的新干预目标并开发新的治疗方法。 其特点是患者之间的瘤间和瘤内的显着异质性。 个体肿瘤，使得选择有效的治疗策略具有挑战性。 已确定可能与疾病进展相关的途径：IGF2、Wnt/β-连环蛋白 (Wnt) 和细胞 90% 的 ACC 中循环调节途径如 p53/视网膜母细胞瘤蛋白 (Rb) 失调，并且 不幸的是，IGF2、Wnt 和 p53/Rb 的瘤内异质性与转移性疾病相关。 失调及其对 ACC 肿瘤进展的贡献尚不清楚，尽管这些途径中的每一个都具有 与肿瘤侵袭、基质金属蛋白酶（MMP）表达或转移有关或相关 这种缺乏了解的部分原因是缺乏适当的临床前研究。 大多数生成 ACC 模型的尝试都没有成功。现有模型数量很少。 不能充分反映人类 ACC 的致癌信号通路和瘤内异质性。 基于人的 ACC 模型系统允许肿瘤的功能和转录组表征 为了解决这些关键的研究空白，我们开发了转移后的亚群。 第一个来自患者样本的患者源性类器官 (PTO) 以及 ACC 芯片转移 (MOC) 我们的 MOC 平台是一个体外微流体系统，包含肿瘤类器官、循环系统。 流体流动和下游组织类器官，可以概括原发肿瘤转移的各个方面 此外，我们还开发了可集成到转移位点的 MMP 肽生物传感器技术。 我们的类器官，能够近实时观察 MMP 介导的肿瘤细胞侵袭。 将肿瘤细胞分为转移性/活动性与非转移性/活动性较差的亚群以进行分析的平台 ACC 中失调途径的表达（IGF2、Wnt 和 p53/Rb）和亚群异质性 我们通过单细胞 RNA 测序确定了 IGF2、Wnt 和细胞周期失调。 与我们的 MOC 平台中部署的 ACC PTO 中转移动力学和 MMP 活性增加相关。 针对这一假设：目标 1 将描述 ACC PTO 的转移动力学、MMP 活性和增殖 目标 2 将使用单个细胞来定义关键致癌途径失调的 ACC 瘤内异质性 细胞 RNA 测序；目标 3 将确定每个驱动通路对转移潜力的相对重要性 完成该项目后，我们的目标是更好地了解 ACC 疾病。 生物学并拥有可用于临床前研究的已建立的 ACC 模型。