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

项目2：治疗前、治疗期间以及耐药时的肿瘤和免疫微环境的测量和建模

• 批准号: 10343840
• 负责人: Arlene H. Sharpe
• 金额: $ 30.14万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-08 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10343840
• 关键词: Adrenal Cortex Hormones; Adverse effects; Adverse event; Adverse reactions; Antibodies; Antigens; BRAF gene; Biological; Biological Assay; Biological Markers; Biopsy; Brain Neoplasms; Bullous Pemphigoid; CTLA4 gene; Cell Cycle; Cells; Clinical; Clinical Trials; Complement; Computing Methodologies; DNA Damage; DNA damage checkpoint; Data; Disease Progression; Disease Resistance; Drug resistance; Ecosystem; Excision; Extracellular Matrix; Formalin; Generations; Genomic Instability; Goals; Homeostasis; Human; Image; Immune; Immune System Diseases; Immune Tolerance; Immune checkpoint inhibitor; Immunofluorescence Immunologic; Immunohistochemistry; Immunologic Surveillance; Infiltration; Institutional Review Boards; Intervention; Intervention Studies; Learning; Lichen Planus; Ligands; Light; Link; MEKs; Measures; Messenger RNA; Methods; Modeling; Molecular; Mus; Non-Malignant; Paraffin Embedding; Patients; Periodicity; Pharmaceutical Preparations; Pharmacology; Phenotype; Phosphotransferases; Physiology; Prediction of Response to Therapy; Property; Proteins; Psoriasis; Reaction; Reagent; Resected; Resistance; Resolution; Retinoids; Sampling; Severities; Signal Transduction; Skin; Specimen; Steroids; Stromal Cells; Stromal Neoplasm; Supervision; System; T-Lymphocyte; TNF gene; Testing; Therapeutic; Therapeutic Intervention; Time; Time Series Analysis; Tissue Embedding; Tissues; Toxic effect; Treatment Effectiveness; Triplet Multiple Birth; Tumor Markers; Tumor-Infiltrating Lymphocytes; Tumor-infiltrating immune cells; Vitiligo; Work; algorithm development; arm; base; bevacizumab; cancer cell; cell stroma; cell type; cohort; effectiveness evaluation; fluorescence imaging; high dimensionality; human imaging; imaging modality; immune checkpoint; inhibitor; insight; machine learning method; malignant state; melanoma; mouse model; multidimensional data; mutational status; neoplastic cell; novel; open source; phenotypic data; predicting response; programmed cell death ligand 1; programmed cell death protein 1; response; response biomarker; single cell sequencing; single-cell RNA sequencing; small molecule; software development; targeted agent; targeted treatment; transcriptomics; treatment response; triple-negative invasive breast carcinoma; tumor; tumor microenvironment; tumor-immune system interactions; unsupervised learning

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.

项目摘要 – 项目 2（目标 5）测量和建模肿瘤和免疫 项目 2 的总体目标是确定治疗前后的微环境。 肿瘤及其微环境使其对单独或联合的 ICI 或靶向治疗有反应。 将以单细胞分辨率收集有关肿瘤的身份、状态和物理排列的定量数据， 基质和免疫细胞以及构成肿瘤微环境的可溶性和 ECM 成分 (TME)。这将通过标准福尔马林固定的高度多重荧光成像来完成。 石蜡包埋 (FFPE) 组织和肿瘤样本与单细胞 RNA 测序 (scRNA) 相结合。 为了深入了解变量之间的因果关系，我们将分析在不同点收集的样本 及时进行活检，最常见的是在治疗前和治疗中以及出现耐药性疾病时进行活检。 针对ICI引起的皮肤毒性，我们将进行局部介入研究（例如使用类维生素A治疗） 随后进行活检以确定治疗的有效性并测试有关免疫的具体假设 该项目的大部分工作将分别生成假设和皮肤细胞稳态。 与项目 1 和 3 中细胞和小鼠的假设检验研究紧密结合。 目标 5.1 将重点关注从以下位置获取 20-60 通道图像的实验和计算方法： 使用基于组织的循环分析福尔马林固定、石蜡包埋 (FFPE) 的组织和肿瘤样本 免疫荧光 (t-CycIF) 目标 5.2 将整合高维 t-CycIF 成像和单细胞 RNA。 测序以生成有关单细胞肿瘤、基质和免疫细胞的组成和状态的数据 分辨率（“深层肿瘤表型”）。目标 5.3 将使用深层表型分析 BRAFV600E 的肿瘤。 使用 BRAF 和 MEK 抑制剂治疗的患者或使用 ICI 治疗的患者，无论 BRAF 突变如何 治疗前、治疗期间以及进展时收集的活检将用于识别。 恶性细胞、TME 和免疫细胞群的变化与以下因素相关，并有可能预测： 目标 5.4 将分析 ICI 对皮肤驻留 T 细胞和耐药性的影响。 比较对类似特发性疾病的不良反应；分析对类维生素A的局部反应； 类固醇将为皮肤免疫稳态提供新的见解，目标 5.5 将确定特征。 与脑肿瘤中 ICI 的异常反应相关（并最终预测），并提供以下数据： 目标 5.6 将研究可在贝叶斯适应性临床试验中评估的生物标记物之间的联系。 三阴性乳腺癌中免疫浸润与内在或药物诱导的基因组不稳定性之间的关系 (TNBC) 目标 5.7 将使用以下方法整合肿瘤表型、药物干预和临床反应的数据： 一系列有监督和无监督的机器学习方法，包括基于网络先验的方法， 还使用多视图学习框架将 scRNA 转录组学与 t-CycIF 图像数据联系起来。