Project 2: Measuring and modeling the tumor and immune microenvironment before and during therapy and at the time of drug resistance

项目摘要-项目2(AIM 5)对肿瘤和免疫进行测量和建模 治疗前后的微环境变化。项目2的总体目标是确定 肿瘤及其微环境使其对ICIS或靶向治疗单独或联合治疗有反应。我们 将在单细胞分辨率下收集关于肿瘤的身份、状态和物理排列的定量数据， 间质和免疫细胞以及构成肿瘤微环境的可溶性和细胞外基质成分 (TME)。这将使用固定标准福尔马林的高度多路复用的荧光成像来完成， 石蜡包埋(FFPE)组织和肿瘤标本结合单细胞RNA测序(ScRNA)。至 洞察变量之间的因果关系，我们将分析在不同点收集的样本 最常见的是在治疗前和治疗中以及在耐药疾病发生时进行活组织检查。在这种情况下 对于ICI引起的皮肤毒性，我们将进行局部干预研究(例如，维甲酸治疗) 然后进行活组织检查，以确定治疗的有效性，并测试关于免疫的特定假设 皮肤中的细胞动态平衡。这个项目中的大部分工作都将是假设生成的，并将 在项目1和3中，与细胞和小鼠的假设检验研究紧密结合。 Aim 5.1将重点介绍从以下位置获取20-60个通道图像的实验和计算方法 福尔马林固定石蜡包埋(FFPE)组织和肿瘤标本的组织环化 免疫荧光法(t-CycIF)。AIM 5.2将整合高维t-CycIF成像和单细胞RNA 在单细胞上产生关于肿瘤、间质和免疫细胞的组成和状态的数据的测序 分辨率(“深部肿瘤表型”)。AIM 5.3将使用深度表型分析BRAFV600E的肿瘤 接受BRAF和MEK抑制剂治疗的患者或接受ICIS治疗的患者，与BRAF突变无关 状态。在治疗前和治疗期间以及病情进展时收集的活组织检查将用于确定 与之相关的恶性细胞、TME和免疫细胞队列的变化，以及潜在的预测 治疗反应和耐药性。AIM 5.4将分析ICIS对皮肤驻留T细胞和 比较对特发性疾病的不良反应；对维甲酸局部反应的分析 类固醇将为皮肤的免疫动态平衡提供新的见解。AIM 5.5将确定功能 与脑肿瘤对ICIS的异常反应相关(并最终预测)，并提供以下数据 可以在贝叶斯适应性临床试验中评估的生物标记物。AIM 5.6将调查两者之间的联系 三阴性乳腺癌中免疫浸润与固有或药物诱导的基因组不稳定性之间的关系 (TNBC)。AIM 5.7将整合肿瘤表型、药物干预和临床反应数据 一系列监督和非监督机器学习方法，包括基于网络先验的方法， 并且还使用多视点学习框架将scRNA转录与T-CycIF图像数据相链接。

PROJECT SUMMARY – PROJECT 2 (AIM 5) Measuring and modeling the tumor and immune microenvironment before and after therapy. The overall goal of Project 2 is to determine which features of a tumor and its microenvironment make it responsive to ICIs or targeted therapies alone or in combination. We will collect quantitative data at single cell resolution on the identities, states and physical arrangement of tumor, stromal and immune cells and on soluble and ECM components that comprise the tumor microenvironment (TME). This will be accomplished using highly multiplexed fluorescence imaging of standard formalin fixed, paraffin-embedded (FFPE) tissue and tumor samples combined with single cell RNA sequencing (scRNA). To provide insight into causal relationships among variables, we will analyze samples collected at different points in time, most commonly biopsies prior to and on therapy, and at the time of drug-resistant disease. In the case of ICI-induced skin toxicities, we will perform localized interventional studies (e.g. treatment with retinoids) followed by biopsies to determine the effectiveness of treatment and to test specific hypotheses about immune cell homeostasis in skin, respectively. Much of the work in this project will be hypothesis generating and will be tightly integrated with hypothesis testing studies in cells and mice in Projects 1 and 3. Aim 5.1 will focus on experimental and computational methods for obtaining 20-60 channel images from formalin-fixed, paraffin embedded (FFPE) tissue and tumor samples using tissue-based cyclic immunofluorescence (t-CycIF). Aim 5.2 will integrate high dimensional t-CycIF imaging and single cell RNA sequencing to generate data on the composition and states of tumor, stromal and immune cells at single-cell resolution (“deep tumor phenotypes”). Aim 5.3 will use deep phenotyping to analyze tumors from BRAFV600E patients treated with BRAF and MEK inhibitors or patients treated with ICIs irrespective of the BRAF mutation status. Biopsies collected before and during therapy, and at the time of progression, will be used to identify changes in the malignant cells, TME and immune cell cohort associated with, and potentially predictive of, therapeutic response and drug resistance. Aim 5.4 will analyze the effects of ICIs on skin-resident T-cells and compare adverse responses to the idiopathic conditions they resemble; analysis of local responses to retinoids and steroids will provide new insight into immune homeostasis in the skin. Aim 5.5 will identify features associated with (and ultimately predictive of) exceptional response to ICIs in brain tumors and provide data on biomarkers that can be evaluated in Bayesian adaptive clinical trials. Aim 5.6 will investigate the connection between immune infiltration and intrinsic or drug-induced genomic instability in triple negative breast cancers (TNBC). Aim 5.7 will integrate data on tumor phenotypes, drug interventions and clinical responses using a range of supervised and unsupervised machine-learning methods, including methods based on network priors, and also link scRNA transcriptomics with t-CycIF image data using a multi-view learning framework.