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

描述（由申请人提供）：由于没有可用的靶向治疗，基底样乳腺癌是预后不良的特别侵袭性癌症。基底样乳腺癌在其微环境中具有独特的基质-上皮相互作用。定量3D组织培养测定的可用性来测量基底样细胞系模型在其微环境中的反应，可能会导致这种疾病的新治疗选择。特别是，间充质细胞运动性是癌症迁移和转移的必要前体，但缺乏定量和高通量的方法来研究组织培养中的运动性。我们将采用光学相干断层扫描（OCT）进行高帧率、非侵入性、体积成像，以量化乳腺类器官组织培养物的运动性和形态发生。我们的长期假设是，通过OCT获得的乳腺类器官运动的空间模式和频率依赖性与转移潜力相关，并且3D培养中的运动表型是筛选治疗剂的体内相关指标。 我们的第一个具体目标将是确定与形态发生和恶性相关的运动表型。这将使用高帧率OCT进行，以监测由与成纤维细胞共培养的乳腺上皮细胞（MEC）运动性引起的0.002 - 100 Hz波段的波动。我们将量化基底样上皮细胞类型的能动性，将正常的癌前病变细胞与浸润性基底样癌细胞进行比较，作为3D共培养中成纤维细胞密度的函数。高光谱（运动谱加空间）成像数据将采用先进技术进行可视化，以在表面和体积上显示多个标量场。这些运动数据将与基因表达谱结合使用，以确定乳腺癌恶性肿瘤的运动为基础的表型。 我们的第二个具体目标将是量化当暴露于抗癌治疗时基底MEC中运动性的抑制。我们假设基质成纤维细胞通过c-Met受体通过肝细胞生长因子（HGF）信号传导促进基底样癌细胞运动。采用目标1中确定的运动表型组，我们将研究基底样MEC对抗HGF或其他c-Met抑制剂的反应。重要的是，高分辨率OCT可能能够分辨MEC类器官内细胞的异质反应，识别对治疗无反应的运动细胞。 在本提案结束时，我们将（1）开发一种定量和自动化工具，用于测量3D组织培养物中乳腺细胞的运动性，（2）鉴定癌症相关运动性表型，以及（3）应用这些工具预测疗效。 基底细胞样乳腺癌的潜在治疗方法。这将构成用于临床前测试的高通量微量测定的新工具，为治疗开发提供定量靶点，以及对肿瘤微环境的新的基本见解。

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.