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Single Cell Deconvolution of the Pancreatic Tumor Microenvironment

胰腺肿瘤微环境的单细胞反卷积

基本信息

批准号：
10318092
负责人：
Ki Oh
金额：
$ 3.83万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-09 至 2024-12-08
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10318092
关键词：
Address Algorithms Atlases Basic Science Big Data Biological Biology Cancer Biology Cell Communication Cells Clinical Clinical Trials Coculture Techniques Combined Modality Therapy Communities Complex Cultured Cells Data Data Set Desmoplastic Drug resistance Early Diagnosis Ecosystem Elements Face Fibroblast Growth Factor Fibroblasts Future Genetic Transcription Growth Heterogeneity Histology Human Hybrids Immune Immune Evasion Immune system Immunohistochemistry Immunosuppression In Situ Hybridization In Vitro Insulin-Like Growth Factor I Intuition Lead Light Logistics Lymphocyte Malignant - descriptor Malignant Neoplasms Malignant neoplasm of pancreas Maps Measurement Meta-Analysis Microdissection Minor Molecular Analysis Molecular Profiling Mus Myelogenous Nature Oncology Online Systems Organoids Outcome Pancreatic Ductal Adenocarcinoma Pathology Pathway interactions Patient-Focused Outcomes Patients Pharmaceutical Preparations Phenotype Play Population Production Prognosis Proteins Reproducibility Research Research Personnel Resistance Resolution Resources Role Sampling Signal Transduction Solid Source Stromal Cells TGFB1 gene Testing Therapeutic Therapeutic Intervention Time Tissues Translational Research Tumor Subtype Validation Work Xenograft procedure bioinformatics resource cancer heterogeneity cancer type cell type clinical prognosis cloud based computer infrastructure data exploration data visualization experimental study genetic signature high dimensionality improved in vivo insight laser capture microdissection molecular subtypes multidimensional data multimodality neoplastic neoplastic cell novel pancreatic ductal adenocarcinoma model pancreatic neoplasm paracrine patient stratification personalized medicine precision medicine prognostic single cell analysis single cell sequencing single-cell RNA sequencing standard care therapeutic target therapeutically effective transcriptome sequencing transcriptomics treatment group treatment response tumor tumor microenvironment virtual wasting

项目摘要

Project Summary/Abstract: Pancreatic ductal adenocarcinoma (PDAC) maintains its status as one of most lethal solid cancers with a 5-year survival of 8%. Minor improvements have been attributed to early detection, but the vast majority of patients face a grim prognosis without effective therapeutic intervention. Molecular analysis of patient samples has often been confounded by mixed biological samples, leading to reproducibility challenges. Previously, our lab performed virtual microdissection on bulk RNA-seq patient samples establishing robust prognostic gene signatures describing an aggressive basal-like and drug-responsive classical tumor subtypes highlighting the importance of cancer heterogeneity across patients. Using these signatures as patient classifiers has been an important utility in preliminary clinical trials and therapeutic profiling of patient derived organoids. Building evidence suggests patient unique TME composition impacts PDAC progression and resistance to standard treatments. While patient tissue characterization with bulk measurements has provided key insights into cancer biology, parsing the complex tumor microenvironments requires higher resolution due to the widespread stromal involvement and sparse neoplastic populations. Single-cell sequencing delivers the analytical power to help identify variable TME elements between patients that lead to the distinct prognostic and therapeutic responses. Thus, understanding the extent and role of TME heterogeneity in PDAC across patient tumor subtypes is paramount to widen the door for personalized medicine in oncology. In this proposal, I will establish a comprehensive single-cell atlas of human PDAC TME to significantly lower the barrier between researchers and complex single-cell transcriptomics data to explore novel prognostic and synergistic therapeutic targets. I will use local and public single cell RNA-seq data of PDAC tissue to investigate the extent and role of cellular heterogeneity across patient tumor subtypes. Specifically, I will define molecular signatures and map out the interactome of functional cell types within stromal, lymphocytic, myeloid populations at unprecedented spatial resolution. Ultimately, by integrating high-dimensional data from single-cell RNA-seq and Spatial Transcriptomics, this work will shed light on the intricate tissue pathology while laying down a broad framework for understanding multi-axis cell interactions behind progression and resistance in diverse cancer types.

项目总结/文摘: