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

• 批准号: 10409385
• 负责人: SOFIA DIANA MERAJVER
• 金额: $ 38.84万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10409385
• 关键词: 3-Dimensional; Address; Advanced Development; Animals; Artificial Intelligence; Astrocytes; Behavior; Biological Assay; Blood; Blood - brain barrier anatomy; Brain; Cells; Cellular Morphology; Communities; Complication; Computer software; Development; Devices; Disseminated Malignant Neoplasm; Event; Extracellular Matrix; Extravasation; Future; Generations; Genomics; Goals; Grant; Human; Image; In Vitro; Individual; Investigation; Lead; Length; Lesion; Libraries; Malignant Neoplasms; Measures; Membrane; Metastatic malignant neoplasm to brain; Microfluidics; Microglia; Micrometastasis; Modeling; Molecular; Molecular Profiling; Neoplasm Metastasis; Normal Cell; Pathway interactions; Patients; Pericytes; Phenotype; Physiological; Prevention; Primary Neoplasm; Problem Solving; Process; Proteomics; Pump; Quality of life; Recovery; Reproducibility; Research; Savings; Site; Standardization; Syringes; System; Technology; Therapeutic; Three-Dimensional Image; Three-dimensional analysis; Tumor-Derived; Validation; Work; advanced breast cancer; base; brain endothelial cell; cancer cell; classification algorithm; confocal imaging; design; experience; experimental study; flexibility; genomic signature; hydrodynamic flow; improved; in vitro Model; in vivo; indexing; live cell imaging; machine learning classifier; malignant breast neoplasm; matrigel; mouse model; multiple omics; neoplastic cell; novel strategies; organ on a chip; personalized medicine; predictive marker; preservation; prevent; success; targeted treatment; therapeutic evaluation; tool; tumor; tumor microenvironment; tumor progression

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.

摘要 从原发肿瘤部位转移到脑是晚期乳腺癌最致命的并发症 全世界大约20%的乳腺癌患者都有这种经历。目前没有办法 检测肿瘤是否具有脑转移潜力，没有预测未来成功转移的标志物， 没有针对任何相关过程的治疗方法。这些差距很难弥合，因为缺乏 一种技术，可以通过基于其大脑对癌细胞进行分类来阐明潜在机制 转移潜能目前的体内鼠模型表现转移缓慢，并且不具有肿瘤转移。 捕获单细胞形态和动力学的能力;并且目前的体外模型是短寿命的， 缺乏软件支持，因此，我们提出了一种体外血脑生态位（µm-BBN）芯片来测量 癌细胞和正常细胞之间以及癌细胞之间在通过 并给他们分配脑转移潜能。此外，我们建议捕获细胞， 通过BBN进行进一步分析。 该系统由µm-BBN、集成式压电泵和控制器、自动表型分析 软件和分类算法。µm-BBN有两个腔室，形成一个血管（人脑 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） 通过涂覆有Matrigel的5µm多孔膜分离。目标是在设备中培养癌细胞 使用集成泵可持续使用长达20天。扩展以前的工作，细胞表型和 将记录患者细胞库的迁移行为。表型测量的生成 单个癌细胞、微转移和肿瘤微环境将能够自动分析 肿瘤细胞的转移特征培养后，细胞将被回收并固定或分析以产生 多组学读出能够实现单细胞和亚细胞的互补细胞和分子特征， 转移性癌细胞群。 这项工作将有助于更好地了解脑转移的潜在机制， 在其下游，在一组更强大的靶向途径中预防脑转移。