(PQ7) Quantitative in vivo optical imaging of tumor heterogeneity

 描述(申请人提供)：这个项目的目标是解决PQ7：可以开发什么体内成像方法来确定和记录导致肿瘤及其微环境异质性的每种不同细胞类型的身份、数量和位置？肿瘤细胞对治疗的反应以及微环境中细胞(如成纤维细胞、免疫细胞)的亲肿瘤行为与抗肿瘤行为存在异质性。然而，缺乏能够量化这种异质性的体内成像方法，从而限制了我们对细胞水平肿瘤行为的理解，并限制了我们开发改进的癌症治疗方法的能力。细胞代谢提供了对个体细胞行为的动态洞察。我们和其他人已经证明，肿瘤细胞的代谢反映了抗癌药物的反应，而肿瘤细胞为了抵抗药物治疗而改变了他们的代谢活动。肿瘤相关成纤维细胞的新陈代谢也支持恶性肿瘤，通过提供营养物质来驱动肿瘤生长。此外，免疫细胞的代谢反映了它们的亲肿瘤和抗肿瘤行为，M1和M2样巨噬细胞之间、CD4+和CD8+T细胞之间以及CD4+T细胞亚群之间的代谢活动发生了明显的变化。由于代谢在维持肿瘤及其微环境中的关键作用，破坏肿瘤细胞和微环境中肿瘤细胞代谢的药物已被FDA批准与标准疗法相结合用于乳腺癌的治疗。这项建议的目标是开发和验证光学代谢成像(OMI)，以量化体内肿瘤细胞、成纤维细胞、巨噬细胞和T细胞群体内的动态代谢异质性。多光子显微镜将在整个治疗过程中解析多瘤中T(PYMT)小鼠乳腺癌模型中的单个细胞。来自代谢辅酶NADH和FAD的自发荧光将量化细胞代谢，并报告细胞群体内的代谢异质性。具体地说，OMI将测量每个电池的光氧化还原比(NADH的荧光强度除以FAD的荧光强度)，这反映了电池中的氧化还原平衡。OMI还量化了NADH和FAD的荧光寿命，这反映了这些分子的酶结合活性。我们已发表的工作和初步数据表明，OMI对体内肿瘤的异质性药物反应敏感，OMI可以区分肿瘤细胞和免疫细胞的亚型。这项拟议的工作将通过体外流式细胞术和荧光细胞表面标记的体内成像来验证肿瘤异质性的体内OMI。建议的AIMS将检验这样一个假设，即在体内OMI可以记录导致肿瘤及其微环境代谢异质性的不同细胞类型的身份、数量和位置。这些工具将能够纵向量化肿瘤细胞、成纤维细胞、巨噬细胞和T细胞体内代谢的异质性。从这些测量中获得的见解可以用来开发改进的癌症治疗方法，在单细胞水平上对抗肿瘤，以实现无缓解生存。

 DESCRIPTION (provided by applicant): The goal of this project is to address PQ7: What in vivo imaging methods can be developed to determine and record the identity, quantity, and location of each of the different cell types that contribute to the heterogeneity of a tumor and it microenvironment? Heterogeneity exists in tumor cell response to treatment, and in the pro-tumor vs. anti-tumor behavior of the cells in the microenvironment (e.g. fibroblasts, immune cells). However, there are a lack of in vivo imaging methods that can quantify this heterogeneity, thus limiting our understanding of cellular-level tumor behavior, and limiting our ability to develop improved cancer treatments. Cellular metabolism provides dynamic insight into individual cell behavior. We and others have shown that tumor cell metabolism reflects anti-cancer drug response, and tumor cells alter their metabolic activities in order to resist drug treatment. The metabolism of tumor-associated fibroblasts also support malignancy, by supplying nutrients to drive tumor growth. Additionally, the metabolism of immune cells is reflective of their pro- and anti-tumor behavior, with distinct changes in metabolic activities between M1-like and M2-like macrophages, between CD4+ and CD8+ T cells, and within the CD4+ T cell subset. Due to the key role of metabolism in maintaining the tumor and its microenvironment, drugs that disrupt the metabolism of tumor cells and cells in the microenvironment have been FDA approved for breast cancer treatment in combination with standard therapies. The goal of this proposal is to develop and validate optical metabolic imaging (OMI) to quantify dynamic metabolic heterogeneity within the tumor cell, fibroblast, macrophage, and T cell populations in tumors in vivo. Multiphoton microscopy will resolve individual cells within the Polyoma middle T (PyMT) mouse model of breast cancer throughout a treatment time-course. Autofluorescence from the metabolic co-enzymes NADH and FAD will quantify cellular metabolism, and report on metabolic heterogeneity within cell populations. Specifically, OMI will measure the optical "redox ratio" of each cell (fluorescence intensity of NADH divided by that of FAD), which reflects redox balance in a cell. OMI also quantifies the fluorescence lifetimes of NADH and FAD, which reflect the enzyme binding activity of these molecules. Our published work and preliminary data demonstrate that OMI is sensitive to heterogeneous drug response in tumors in vivo, and OMI can distinguish sub-types of tumor cells and immune cells. The proposed work will validate in vivo OMI of tumor heterogeneity with ex vivo flow cytometry and in vivo imaging of fluorescent cell surface markers. The proposed aims will test the hypothesis that in vivo OMI can record the identity, quantity, and location of different cell types that contribute to the metabolic heterogeneity of a tumor and its microenvironment. These tools will enable longitudinal quantification of the in vivo metabolic heterogeneity of tumor cells, fibroblasts, macrophages, and T- cells. The insights gained from these measurements can be used to develop improved cancer treatments that combat tumors on a single-cell level in order to achieve remission-free survival.