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.

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