Advanced development and validation of an in vitro platform to phenotype brain metastatic tumor cells using artificial intelligence

使用人工智能对脑转移肿瘤细胞进行表型分析的体外平台的高级开发和验证

基本信息

批准号：
10630975
负责人：
Jianping Fu
金额：
$ 38.07万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

项目摘要

Abstract Metastasis from the primary tumor site to the brain is the most lethal complication of advanced breast cancer and is experienced by approximately 20% of breast cancers worldwide. There is at present no approach to detect if a tumor has brain metastatic potential, no markers that predict successful future metastasis, and thus no therapies to target any of the processes involved. These gaps are difficult to bridge due to a lack of technology that can elucidate the underlying mechanisms by classifying a cancer cell based on its brain metastatic potential. Current in vivo murine models are slow to manifest metastasis and do not have the capability of capturing single cell morphology and dynamics; and current in vitro models are short lived and lack software support therefore, we propose an in vitro blood brain niche (µm-BBN) on-a-chip to measure the phenotypic differences between cancer cells and normal cells and amongst cancer cells as they transit through the model niche and to assign them a brain metastatic potential. Moreover, we propose capturing the cells that transit through the BBN for further analysis. This system is composed of the µm-BBN, an integrated piezo pump and controller, automated phenotyping software and a classification algorithm. The µm-BBN has two chambers which form a vessel (human brain endothelial cells and brain stroma (ECM and Normal Human Astrocytes (NHA), Microglia and Pericytes) separated by a 5µm porous membrane coated with Matrigel. The goal is to culture cancer cells in the device for up to 20 days using the integrated pump. Expanding on previous work, the cellular phenotype and migratory behavior of a library of patient cells will be recorded. Generation of phenotypic measures for individual cancer cells, micro-metastasis and the tumor micro-environment will enable automated profiling of the metastatic signature of tumor cells. After culture the cells will be recovered and fixed or analyzed to create a multi-omic readout enabling a complimentary cellular and molecular signature for single cells and sub- populations of metastatic cancer cells. This work will enable improved understanding of the underlying mechanisms of brain metastasis and, downstream from it, in a more robust set of targetable pathways for prevention of brain metastasis.

抽象的从原发性肿瘤部位到大脑的转移是晚期乳腺癌最致命的并发症在全球大约有20％的乳腺癌经历。目前没有方法检测肿瘤是否具有脑转移潜力，没有预测成功未来转移的标记，因此没有疗法来针对任何涉及的过程。由于缺乏，这些差距很难弥合可以通过基于大脑对癌细胞进行分类来阐明基本机制的技术转移潜力。当前的体内鼠模型表现出缓慢的转移，没有捕获单细胞形态和动力学的能力；当前的体外模型寿命短，因此，缺乏软件支持，我们提出了一个体外血脑脑生态位（µm-BBN），以测量癌细胞与正常细胞之间以及癌细胞之间的表型差异，它们通过模型利基并为他们分配脑转移潜力。此外，我们建议捕获细胞通过BBN过境以进行进一步分析。该系统由µM-BBN组成，一个集成的压电泵和控制器，自动表型软件和分类算法。 µm-BBN有两个形成容器（人脑）的腔室内皮细胞和脑基质（ECM和正常人星形胶质细胞（NHA），小胶质细胞和周细胞）用涂有Matrigel的5µm多孔膜分离。目的是培养设备中的癌细胞使用集成泵最多20天。扩大了以前的工作，细胞表型和将记录患者细胞库的迁徙行为。生成表型测量单个癌细胞，微征和肿瘤微环境将实现自动分析肿瘤细胞的转移特征。培养后，将回收和固定或分析细胞以创建多摩尼转移性癌细胞的种群。这项工作将使人们对脑转移的潜在机制以及，以及下游，以更强大的靶向途径进行预防脑转移。