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: 10231345
• 负责人: Aleksandar Zoran Obradovic
• 金额: $ 4.6万
• 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/10231345
• 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; Engineering; 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; 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/antagonist; 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; 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，而且由免疫小球类型组成 环境。传统的癌症治疗方法集中于直接杀死肿瘤细胞，但最近 免疫检查点抑制剂疗法旨在激活组织中的抗肿瘤免疫细胞。 在过去的几年中，免疫疗法在临床肿瘤学方面一直具有变化 反应有限，治疗对肿瘤微环境的影响尚不完全了解。这就是 Drake Lab和其他人在各种治疗下的肿瘤中的免疫细胞类型中的动机努力 条件，旨在揭示新的治疗靶标并确定治疗反应的预测指标。我们的 小组考虑了应用高通量单细胞RNA测序（SCRNA-SEQ）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。我们假设这一点 在单细胞水平上分析肿瘤微环境并应用基于高级网络的分析 未经治疗和免疫治疗治疗的肿瘤的管道将改善 肿瘤浸入免疫细胞类型的转录程序，与结果的关联及其临床 与肿瘤细胞的相关相互作用。 AIM 1）尽管分辨率很高，但SCRNA-SEQ数据通常很少，有 在任何给定细胞中检测到的少数基因。我们旨在开发一套起源于加利福尼亚州的强大工具 实验室用于基于网络的单细胞数据中调节蛋白活性的推断，减轻基因表达 辍学并提供可扩展的管道来推断细胞群体，肿瘤免疫相互作用和 调节蛋白在不同的细胞状态下激活不同。我们通过相比验证该管道 在清晰细胞肾癌（CCRCC）患者数据集中流式细胞术同时介绍了标记。目的 2）我们将专门利用新的分析管道来询问肿瘤渗透调节的驱动因素 T细胞是由多种常规治疗方式引起的免疫抑制人群，包括 前列腺癌中的雄激素剥夺疗法。我们将验证CRISPR的预测肿瘤浸润驱动器 敲除屏幕并应用高级转录扰动屏幕以识别颠倒的药物 肿瘤特异性特雷格签名。预计这些将成为未来组合的主要候选人 免疫疗法研究。目标3）我们将确定免疫疗法引起的微环境的变化 在两项免疫疗法的临床试验中，反应者和无反应者以及前列腺中的雄激素剥夺 CCRC中的抗PD1和抗IL1b的癌症和一项试验，确定了潜在的新型治疗靶标。在 此外，我们将将新开发的分析管道应用于已发布的SCRNA-SEQ数据集以识别 黑色素瘤治疗反应的预测指标。在经验丰富的免疫疗法的共同指导下 和计算系统生物学在哥伦比亚大学医学中心的环境中，该项目将准备 作为身体科学家的职业学员，具有转化生物信息学研究的独特背景。