Phenotypic sorting of cancer cells to study the role and control of cell stiffness in the in vivo metastatic cascade

项目摘要/摘要 大多数与癌症相关的死亡是由于癌细胞从 肿瘤的原发部位转移到身体的远处。细胞的机械性能，包括硬度，与 肿瘤细胞的迁移和转移潜能。然而，以往的研究受到观察细胞的限制。 力学是转移的影响，而不是潜在的驱动力。如果细胞硬度之间的因果联系 体内转移潜能可以建立，细胞力学的直接调节可以构成 减缓或阻止癌细胞转移扩散的治疗策略。 这项研究的长期目标是将体外研究的细胞行为和机械性能联系起来 以体内转移表型确定细胞机械型和转移的治疗控制点。一个 基于微流控刚性的细胞分选装置将用于产生僵硬和柔软的细胞亚群 比较细胞硬度对原位乳腺癌不同转移阶段的影响 老鼠模型。初步实验已经建立了我们成功地对细胞进行分类的能力 生物物理特性。此外，莱因哈特-金实验室在进行体外和体内比较方面具有独特的优势 了解转移的细胞行为。这个项目将探索细胞之间的因果联系的问题 僵硬和细胞转移潜能：1)通过以下途径确定癌细胞机械型的遗传力 基于微流控硬度的细胞分选：2)研究细胞硬度在体内多个阶段的作用 3)探索细胞硬度的遗传控制点和表观遗传控制点以减少转移 散开。首先，建立的微流控装置将被用于重复分选四种乳腺癌细胞株 机械亚群，跟踪细胞在分裂和传代后是保持僵硬还是柔软。这个 然后将分选的种群注射到小鼠的乳房脂肪垫中，形成原位乳腺癌 监测细胞硬度对转移瘤形成影响的模型及研究 转移级联的每一步体内和体外模型的硬度。最后，多重组学分析将 用来理解导致每个机械亚群的潜在分子机制。 拟议的研究结果将第一次显示细胞之间的因果关系 机械特性以及它们在体内成功地穿过转移级联的每一步的能力。这个 申请者的长期职业目标是成为转移性癌症领域的领先研究人员和专家 生物学。该奖学金将帮助申请者增强她以前在微流体和原子力方面的专业知识。 与癌症机械生物学专家一起培训癌症和转移的体内模型的显微镜， 辛西娅·莱因哈特-金博士，作为一名研究转移的独立研究员，这些技能对她来说将是无价的 在一个临床相关的模型中。

PROJECT SUMMARY/ABSTRACT A majority of cancer-related deaths are the result of the metastatic spread of cancer cells from their primary tumor location to distant sites in the body. Cell mechanical properties, including stiffness, are related to the migratory and metastatic potential of tumor cells. However, previous studies are limited by observing cell mechanics as an effect rather than as a potential driving force of metastasis. If a causal link between cell stiffness and metastatic potential in vivo can be established, direct modulation of cell mechanics could constitute a therapeutic strategy to slow or stop the metastatic spread of cancer cells. The long term goal of this research is to connect cell behaviors and mechanical properties studied in vitro with in vivo metastatic phenotypes to identify therapeutic control points of cell mechanotype and metastasis. A microfluidic stiffness-based cell sorting device will be used to generate stiff and soft cell subpopulations to compare the effect of cell stiffness on the various stages of metastasis that occur in an orthotopic breast cancer mouse model. Preliminary experiments have established our ability to successfully sort cells based on several biophysical properties. Additionally, the Reinhart-King lab is uniquely positioned to compare in vitro and in vivo cell behaviors to understand metastasis. This project will explore the question of the causal link between cell stiffness and cell metastatic potential by 1) determining the heritability of cancer cell mechanotypes through microfluidic stiffness-based cell sorting, 2) investigating the role of cell stiffness on multiple stages of in vivo metastasis, and 3) exploring the genetic and epigenetic control points of cell stiffness for abatement of metastatic spread. First, the established microfluidic device will be used to repeatedly sort four breast cancer cell lines into mechanical subpopulations, tracking whether cells remain stiff or soft after cell division and passaging. The sorted populations will then be injected into the mammary fat pad of a mouse to form an orthotopic breast cancer model monitoring the effect of cell stiffness on metastatic tumor formation as well as studying the effect of stiffness of in vivo and in vitro models of each step of the metastatic cascade. Finally, multi-omics analyses will be used to understand the underlying molecular mechanisms that result in each mechanical subpopulation. The result of the proposed study will show, for the first time, the causal relationship between cell mechanical properties and their ability to successfully traverse each step in the metastatic cascade in vivo. The applicant’s long-term career goal is to become a leading researcher and expert in the field of metastatic cancer biology. The fellowship will help the applicant augment her previous expertise in microfluidics and atomic force microscopy with training in in vivo models of cancer and metastasis with an expert in cancer mechanobiology, Dr. Cynthia Reinhart-King, skills that will be invaluable to her as an independent researcher studying metastasis in a clinically relevant model.