Quantitative Motility Phenotyping of Basal Breast Cancer in a 3D Microenvironment

描述(申请人提供)：由于没有可用的靶向治疗，基底细胞样乳腺癌是一种特别具有侵袭性的癌症，预后很差。基底样癌在其微环境中具有独特的间质-上皮相互作用。定量3D组织培养分析的有效性，以测量基底样细胞系模型在其微环境中的反应，可能会导致对这种疾病的新的治疗选择。尤其是间充质细胞的运动性是肿瘤迁移和转移的必要先兆，但在组织培养中缺乏定量和高通量的运动性研究方法。我们将使用光学相干层析成像(OCT)进行高帧速率、非侵入性的体积成像，以量化乳腺器官组织培养的运动性和形态发生。我们的长期假设是，OCT获得的乳腺器官运动的空间模式和频率依赖性与转移潜能相关，3D培养中的运动表型是筛选治疗药物的体内相关指标。我们的第一个具体目标将是确定与形态发生和恶性肿瘤相关的运动表型。这将使用高帧速率OCT来监测由于乳腺上皮细胞(MEC)与成纤维细胞共同培养而产生的0.002-100HZ频段的波动。我们将量化基底样上皮细胞类型的运动性，比较正常、癌前和浸润性基底样癌细胞，作为3D共培养中成纤维细胞密度的函数。高光谱(运动光谱加空间)成像数据将用先进的技术可视化，以在表面和体积上显示多个标量场。这些运动性数据将与基因表达谱结合使用，以确定乳腺癌恶性肿瘤基于运动性的表型。我们的第二个具体目标将是量化基础微血管内皮细胞在接受抗癌治疗时的运动性抑制。我们假设间质成纤维细胞通过c-Met受体通过肝细胞生长因子(HGF)信号来促进基底样癌细胞的运动。利用目标1中确定的一组运动表型，我们将研究基底样微血管内皮细胞对抗HGF或其他c-Met抑制剂的反应。重要的是，超高分辨率OCT可能能够解决MEC有机体内细胞的异质性反应，识别对治疗没有反应的活动细胞。在这项建议的结论中，我们将(1)开发一种定量和自动化的工具来测量3D组织培养中的乳腺细胞的运动性，(2)识别与癌症相关的运动性表型，以及(3)应用这些工具来预测疗效 一种治疗基底细胞样乳腺癌的潜在方法。这将构成高通量微量分析的新工具，用于临床前测试，为治疗开发提供定量目标，并对肿瘤微环境有新的基本见解。

DESCRIPTION (provided by applicant): With no available targeted therapies, basal-like breast cancers are particularly aggressive cancers with a poor prognosis. Basal-like breast cancers have unique stromal-epithelial interactions within their microenvironments. The availability of quantitative 3D tissue culture assays to measure the response of basal-like cell line models in their microenvironments may lead to new treatment options in this disease. In particular, mesenchymal cell motility is a necessary precursor to the migration and metastasis of cancer, but there is a lack of quantitative and high-throughput methods for studying motility in tissue cultures. We will employ Optical Coherence Tomography (OCT) to perform high frame rate, non-invasive, volumetric imaging to quantify motility and morphogenesis of mammary organoid tissue cultures. Our long-term hypothesis is that the spatial pattern and frequency-dependence of mammary organoid motility obtained by OCT is correlated with metastatic potential, and that the motility phenotype in 3D culture is an in vivo relevant metric for screening therapeutic agents. Our first specific aim will be to identify motility phenotypes associated with morphogenesis and malignancy. This will be performed using high frame rate OCT to monitor fluctuations in the 0.002 - 100 Hz band arising from the motility of mammary epithelial cells (MECs) in co-culture with fibroblasts. We will quantify the motilities of basal-like epithelial cel types, comparing normal to pre-malignant to invasive basal-like cancer cells, as a function of fibroblast density in 3D co-culture. Hyperspectral (motility spectrum plus space) imaging data will be visualized with advanced techniques to display multiple scalar fields on surfaces and in volumes. These motility data will be used in conjunction with gene expression profiles to identify motility-based phenotypes of breast cancer malignancy. Our second specific aim will be to quantify the inhibition of motility in basal MECs when exposed to anti- cancer treatments. We hypothesize that stromal fibroblasts promote basal-like cancer cell motility via hepatocyte growth factor (HGF) signaling through the c-Met receptor. Employing the panel of motility phenotypes identified in Aim 1, we will study the response of basal-like MECs to anti-HGF or other c-Met inhibitors. Importantly, ultrahigh resolution OCT may be capable of resolving the heterogeneous response of cells within MEC organoids, identifying motile cells that do not respond to treatment. At the conclusion of this proposal we will have (1) developed a quantitative and automated tool for measuring motility of breast cells in 3D tissue cultures, (2) identified cancer-relevant motility phenotypes, and (3) applied these tools to predict the efficacy of a potential treatment for basal-like breast cancer. This will constitute a new tool for high throughput micro-assays for pre-clinical testing, providing quantitative targets for treatment development, and new fundamental insight into the tumor microenvironment.