Discovering Network-Based Drivers of Single-Cell Transcriptional State in Tumor Immune Microenvironment to Reveal Immuno-Therapeutic Targets and Treatment Synergies

发现肿瘤免疫微环境中基于网络的单细胞转录状态驱动因素，以揭示免疫治疗靶点和治疗协同作用

• 批准号: 10376033
• 负责人: Aleksandar Zoran Obradovic
• 金额: $ 5.18万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376033
• 关键词: Academic Medical Centers; Aftercare; Algorithmic Analysis; Algorithms; Androgens; Binding; Bioinformatics; CD4 Positive T Lymphocytes; Carcinoma; Cell Count; Cell surface; Cells; Characteristics; Clinical; Clinical Oncology; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Combination immunotherapy; Combined Modality Therapy; Conventional (Clear Cell) Renal Cell Carcinoma; Data; Data Set; Databases; Development; Dropout; Evaluation; FDA approved; Flow Cytometry; Future; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Human; Immune; Immune checkpoint inhibitor; Immunotherapeutic agent; Immunotherapy; Individual; Infiltration; Inflammatory; Joints; Knock-out; Ligands; Malignant Neoplasms; Malignant neoplasm of prostate; Manuals; Mentors; Mining; Minority; Modality; Network-based; Outcome; Patients; Peripheral; Pharmaceutical Preparations; Phenotype; Physicians; Population; Prediction of Response to Therapy; Prostate Adenocarcinoma; Proteins; Proteomics; Publishing; Regulatory T-Lymphocyte; Renal Cell Carcinoma; Renal carcinoma; Research; Resistance; Resolution; Reverse engineering; Sampling; Scientist; Solid Neoplasm; Systems Biology; T-Lymphocyte; Th1 Cells; Therapeutic; Tissue-Specific Gene Expression; Tissues; Translating; Tumor Cell Line; Tumor-infiltrating immune cells; Up-Regulation; Validation; analysis pipeline; androgen deprivation therapy; anti-CTLA4; anti-PD-1; base; cancer immunotherapy; cancer therapy; career; cell type; checkpoint therapy; clinically relevant; conventional therapy; cytotoxic CD8 T cells; density; deprivation; druggable target; experience; follow-up; genetic regulatory protein; immunotherapy clinical trials; improved; inhibitor; knockout gene; melanoma; mouse model; neoplastic cell; new therapeutic target; novel; predicting response; programs; protein biomarkers; receptor; resistance mechanism; responders and non-responders; response; response biomarker; screening; single cell analysis; single-cell RNA sequencing; synergism; targeted treatment; therapy development; therapy resistant; tool; transcription regulatory network; transcriptome sequencing; treatment effect; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; tumor-immune system interactions

Project Summary/Abstract: Solid tumors consist not only of tumor cells, but also of immune cell types infiltrating the tumor micro- environment. Traditional approaches to cancer therapy have focused on killing tumor cells directly, but recent immune checkpoint inhibitor therapies have instead aimed to activate anti-tumor immune cells in the tissue. Immunotherapy has been transformative in clinical oncology over the past several years, but biomarkers of response are limited and effect of treatment on tumor micro-environment is incompletely understood. This has motivated efforts by Drake lab and others to better profile immune cell types in tumors under various treatment conditions, aiming to reveal novel therapy targets and identify improved predictors of treatment response. Our group has considerable experience applying high-throughput single-cell RNA sequencing (scRNA-Seq) to profile tumor micro-environment with full transcriptional resolution at the level of individual cells. We hypothesize that profiling the tumor microenvironment at single-cell level and applying an advanced network-based analysis pipeline to treatment-naïve and immunotherapy-treated tumors will improve characterization of the transcriptional program in tumor-infiltrating immune cell types, their association with outcome, and their clinically relevant interactions with tumor cells. Aim 1) Despite high resolution, scRNA-Seq data are typically sparse, with a minority of genes detected in any given cell. We aim to develop a powerful set of tools originating in the Califano Lab for network-based inference of regulatory protein activity in single-cell data, mitigating gene expression dropout and providing a scalable pipeline for inference of cell populations, tumor-immune interactions, and regulatory proteins differentially activated in distinct cell states. We validate this pipeline by comparison to markers concurrently profiled by flow cytometry in a dataset of clear cell renal carcinoma (ccRCC) patients. Aim 2) We will specifically leverage our novel analysis pipeline to interrogate drivers of tumor-infiltrating regulatory T-cells, an immunosuppressive population induced by multiple conventional treatment modalities, including androgen deprivation therapy in prostate cancer. We will validate predicted tumor-infiltration drivers by CRISPR knockout screen and apply an advanced transcriptional perturbation screen to identify drugs which invert the tumor-specific Treg signature. These are expected to serve as prime candidates for future combination immunotherapy studies. Aim 3) We will identify changes in micro-environment induced by immunotherapy in responders and non-responders across two clinical trials of immunotherapy plus androgen deprivation in prostate cancer and one trial of anti-PD1 plus anti-IL1b in ccRCC, identifying potentially novel therapeutic targets. In addition, we will apply our newly developed analytic pipeline to published scRNA-Seq datasets to identify predictors of treatment response in melanoma. With joint guidance from experienced mentors in Immunotherapy and Computational Systems Biology in the setting of Columbia University Medical Center, this project will prepare the trainee for a career as a physician-scientist with a unique background in translational bioinformatics research.

项目摘要/摘要： 实体瘤不仅由肿瘤细胞组成，还由浸润肿瘤微结构的免疫细胞类型组成。 传统的癌症治疗方法主要集中于直接杀死肿瘤细胞，但近年来 相反，免疫检查点抑制剂疗法的目的是激活组织中的抗肿瘤免疫细胞。 在过去的几年里，免疫疗法在临床肿瘤学中发生了变革，但免疫疗法的生物标志物 反应有限，治疗对肿瘤微环境的影响尚不完全清楚。 受到 Drake 实验室和其他人的激励，以更好地分析各种治疗下肿瘤中的免疫细胞类型 条件，旨在揭示新的治疗目标并确定治疗反应的改进预测因子。 团队在应用高通量单细胞 RNA 测序 (scRNA-Seq) 进行分析方面拥有丰富的经验 在单个细胞水平上具有完全转录分辨率的肿瘤微环境。 在单细胞水平上分析肿瘤微环境并应用先进的基于网络的分析 未经治疗和免疫疗法治疗的肿瘤的管道将改善肿瘤的表征 肿瘤浸润免疫细胞类型中的转录程序、它们与结果的关联以及它们的临床 目标 1) 尽管分辨率很高，但 scRNA-Seq 数据通常很稀疏，并且 我们的目标是开发一套源自 Califano 的强大工具。 基于网络推断单细胞数据中调节蛋白活性、减轻基因表达的实验室 退出并提供可扩展的管道来推断细胞群、肿瘤免疫相互作用以及 我们通过比较来验证该管道在不同细胞状态下的不同激活。 通过流式细胞术在透明细胞肾癌 (ccRCC) 患者数据集中同时分析标记物。 2）我们将专门利用我们新颖的分析管道来询问肿瘤浸润监管的驱动因素 T 细胞，由多种常规治疗方式诱导的免疫抑制群体，包括 我们将通过 CRISPR 验证预测的肿瘤浸润驱动因素。 敲除筛选并应用先进的转录扰动筛选来识别逆转转录的药物 肿瘤特异性 Treg 特征有望成为未来组合的主要候选者。 目标 3）我们将确定免疫疗法引起的微环境变化。 前列腺免疫疗法加雄激素剥夺的两项临床试验中的应答者和非应答者 癌症和 ccRCC 中抗 PD1 加抗 IL1b 的一项试验，确定了潜在的新治疗靶点。 此外，我们将把我们新开发的分析流程应用于已发布的 scRNA-Seq 数据集，以识别 在免疫疗法经验丰富的导师的共同指导下，黑色素瘤治疗反应的预测因素。 和计算系统生物学在哥伦比亚大学医学中心的背景下，该项目将准备 具有转化生物信息学研究独特背景的医师科学家职业的实习生。