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

项目总结/摘要 大多数与癌症相关的死亡是由于癌细胞从其组织中转移扩散的结果。 原发性肿瘤的位置在身体的远处。细胞的机械性能，包括刚度，与 肿瘤细胞的迁移和转移潜能。然而，以前的研究局限于观察细胞， 机制作为一种效应，而不是作为转移的潜在驱动力。如果细胞硬度和细胞生长速度 和体内转移潜力，直接调节细胞力学可以构成一个 治疗策略，以减缓或停止癌细胞的转移扩散。 本研究的长期目标是将体外研究的细胞行为和力学性能联系起来 用体内转移表型鉴定细胞机械型和转移的治疗控制点。一 基于微流体硬度的细胞分选装置将用于产生硬细胞和软细胞亚群， 比较细胞硬度对原位乳腺癌发生转移的各个阶段的影响 小鼠模型初步的实验已经建立了我们的能力，成功地分选细胞的基础上，几个 生物物理特性此外，Reinhart-King实验室在体外和体内比较方面具有独特的优势， 细胞行为来了解转移。这个项目将探讨细胞之间的因果关系的问题， 硬度和细胞转移潜力，通过1）确定癌细胞机械型的遗传性， 基于微流体刚度的细胞分选，2）研究细胞刚度在体内细胞分选的多个阶段中的作用， 3）探索细胞硬度的遗传和表观遗传控制点，以减少转移性肿瘤的发生。 传播.首先，建立的微流控装置将用于重复分选四种乳腺癌细胞系， 机械亚群，跟踪细胞分裂和传代后细胞是否保持僵硬或柔软。的 然后将分选的群体注射到小鼠的乳房脂肪垫中以形成原位乳腺癌 模型监测细胞硬度对转移性肿瘤形成的影响，以及研究 转移级联的每个步骤的体内和体外模型的刚度。最后，多组学分析将 用于理解导致每个机械亚群的潜在分子机制。 这项研究的结果将首次表明， 机械性能和它们在体内成功穿过转移级联中的每个步骤的能力。的 申请人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人增强她以前在微流体和原子力方面的专业知识 显微镜与培训在体内模型的癌症和转移与专家在癌症机械生物学， 博士Cynthia Reinhart-King，作为一名研究转移的独立研究人员， 在临床相关模型中。

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.