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 治疗前后的微环境。项目2的总体目标是确定哪些功能 肿瘤及其微环境使其对ICI或靶向疗法的反应或合并。我们 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， 基质和免疫电池以及构成肿瘤微环境的固体和ECM成分 （TME）。这将使用标准福尔马林固定的高度多路复用荧光成像来完成 石蜡包裹的（FFPE）组织和肿瘤样品与单细胞RNA测序（SCRNA）结合使用。到 提供有关变量之间因果关系的见解，我们将分析在不同点收集的样本 在这种情况下，最常见的是在治疗前和治疗时以及耐药性疾病时进行活检。 在ICI诱导的皮肤毒性中，我们将进行局部介入研究（例如，用视视视网麻样式治疗） 然后进行活检以确定治疗的有效性并检验有关免疫的特定假设 细胞稳态分别在皮肤中。该项目中的许多工作将是假设的，并且将是 在项目1和3中，与细胞和小鼠中的假设检验研究紧密整合。 AIM 5.1将重点介绍用于从 福尔马林固定的石蜡嵌入（FFPE）组织和肿瘤样品使用基于组织的循环 免疫荧光（T-Cycif）。 AIM 5.2将整合高维T-Cycif成像和单细胞RNA 测序以产生有关单细胞肿瘤，基质和免疫细胞的组成和状态的数据 分辨率（“深肿瘤表型”）。 AIM 5.3将使用深层表型分析BRAFV600E的肿瘤 接受BRAF和MEK抑制剂或ICIS治疗的患者，无论BRAF突变如何 地位。在治疗前后收集的活检以及进展时，将用于识别 与恶性细胞，TME和免疫球体队列的变化有关，并有可能预测， 治疗反应和耐药性。 AIM 5.4将分析ICI对皮肤居民T细胞的影响和 将广告回答与它们相似的特发性条件进行比较；分析局部对视网膜类似的反应 类固醇将为皮肤中的免疫稳态提供新的见解。 AIM 5.5将识别功能 与（并最终预测）对ICI在脑肿瘤中的特殊反应有关，并提供有关 可以在贝叶斯自适应临床试验中评估的生物标志物。 AIM 5.6将调查连接 免疫浸润与三重阴性乳腺癌中固有或药物诱导的基因组不稳定性之间 （TNBC）。 AIM 5.7将使用A整合有关肿瘤表型，药物干预和临床反应的数据 受监督和无监督的机器学习方法，包括基于网络先验的方法， 并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。