Clinical implementation of single cell tumor transcriptome analysis

单细胞肿瘤转录组分析的临床实施

基本信息

批准号：
9272844
负责人：
BRUCE E. JOHNSON
金额：
$ 43.83万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-05-12 至 2019-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9272844
关键词：
Alpha Cell Archives Area Asses Biological Biopsy Biopsy Specimen Cell Extracts Cells Characteristics Clinical Clinical Oncology Clinical Research Clinical Trials Community Clinical Oncology Program Computational Biology Computer Analysis Computing Methodologies Core Biopsy DNA DNA Sequence Alteration Data Data Set Development Diagnosis Drug Exposure Drug Targeting Drug effect disorder Drug resistance Empiricism Epigenetic Process Evolution Excision Fine needle aspiration biopsy Fine-needle biopsy Gene Expression Gene Targeting Genes Genetic Genetic Transcription Genome Genomics Goals Heterogeneity Immune Immunotherapy Individual Investigation Knowledge Lesion Lymphocytic Infiltrate Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of ovary Massive Parallel Sequencing Measurement Measures Mediating Melanoma Cell Methods Molecular Molecular Profiling Mutation Operative Surgical Procedures Pathway interactions Patients Pharmaceutical Preparations Pharmacodynamics Population Procedures Property Protocols documentation Quality Control RNA Regimen Relapse Research Sampling Series Specimen Standardization T-Lymphocyte Technology Therapeutic Tissues Transcript Tumor Tissue Tumor-Derived Validation anti-cancer therapeutic base bench to bedside cancer genome cancer therapy cell type cost design effusion exhaust exome genomic profiles improved insight lymphoid neoplasm malignant breast neoplasm melanoma neoplastic cell new technology non-genetic oncology patient stratification precision medicine precision oncology public health relevance resistance mechanism response single cell analysis stemness tool transcriptome transcriptome sequencing treatment response treatment strategy tumor tumor heterogeneity tumor microenvironment

项目摘要

DESCRIPTION (provided by applicant): The rapid evolution of cancer genome technology and computational analysis has engendered many fundamental cancer discoveries, thus transforming the scientific and clinical landscape in less than a decade. Increasingly, genetic alterations revealed by tumor genomic profiling guide diagnosis, treatment, and investigation in cancer. Despite these advances, many genomic technologies are unable to demonstrate drug targets or tumor-mediated drug resistance mechanisms that are not DNA-encoded. Furthermore, profiling approaches of bulk tumor samples only provide average signatures that do not reflect different tumor components and intrinsic heterogeneity of individual cell populations or cells. Emerging single-cell profiling technologies such as single-cell transcriptome analysis could overcome several challenges and provide a plethora of translational discovery opportunities. We recently provided proof- of-concept for application of single-cell RNA-seq in patient-derived tumor samples. To apply this technology more broadly in the translational oncology arena, we propose to create, optimize and implement a single-cell RNA-seq platform that can be deployed as a translational tool in the clinical oncology arena. In preliminary studies, we sequenced ~300 single cells from several melanoma tumors, including cancer and corresponding tumor-infiltrating cells (TILs). Transcriptome analysis revealed expression of key markers of melanoma, such as MITF and SOX10, and a stem-ness signature occurring in a fraction of melanoma cells. T cells expression reflected a spectrum of functional states, ranging from naïve to highly anergic (`exhausted') cells. These preliminary results demonstrate that we have created each necessary component for an end-to-end (clinic-to- bench) workflow, which yields meaningful single-cell data. The goal of this research is to optimize current protocols to create standard operating procedures (SOPs) and merge individual components into a standardized workflow. From our clinical colleagues, we will receive clinical specimens (tumors, biopsies, malignant effusions) from diverse tumor types, including melanoma, lung, breast and ovarian cancer. We will deploy experimental protocols to extract disaggregated individual cells from each sample type and state-of-the-art single cell RNA-Seq to profile each cell. Existing computational pipelines will be optimized to best serve high-throughput single-cell analysis, and once standardized will be made publicly available via an existing online platform. Upon completion of this project, we expect to have created a robust, multi-component workflow for single-cell transcriptome analysis applicable across cancer and sample types, and to render this technology accessible to the entire oncology community.

描述（由适用提供）：癌症基因组技术和计算分析的快速演变造成了许多基本的癌症发现，从而改变了不到十年的科学和临床景观。肿瘤基因组分析指导诊断，治疗和癌症研究表明的遗传改变越来越多。尽管有这些进展，但许多基因组技术仍无法证明未经DNA编码的药物靶标或肿瘤介导的耐药性机制。此外，散装肿瘤样品的分析方法仅提供平均签名，这些签名不反映单个细胞群体或细胞的不同肿瘤成分和内在异质性。新兴的单细胞分析技术（例如单细胞转录组分析）可以克服几个挑战，并提供大量的转化发现机会。我们最近提供了用于在患者衍生的肿瘤样品中应用单细胞RNA-Seq的概念概念。为了在翻译肿瘤学领域更广泛地应用这项技术，我们建议创建，优化和实施一个单细胞RNA-seq平台，该平台可以在临床肿瘤学领域中作为转化工具部署。在初步研究中，我们对来自几个黑色素瘤肿瘤的300个单细胞进行了测序，包括癌症和相应的肿瘤浸润细胞（TILS）。转录组分析表明，黑色素瘤和SOX10等黑色素瘤的关键标记表达，以及在黑色素瘤细胞中的茎度特征表达。 T细胞的表达反映了功能状态的光谱，从天真到高度厌食（耗尽”）细胞。这些初步结果表明，我们为端到端（诊所至基础）工作流创建了每个必要组件，从而产生有意义的单细胞数据。这项研究的目的是优化当前协议以创建标准操作程序（SOP）并将单个组件合并为标准化的工作流程。从我们的临床同事中，我们将从潜水肿瘤类型（包括黑色素瘤，肺，乳腺癌和卵巢癌）中获得临床规格（肿瘤，活检，恶性积液）。我们将部署实验方案，以从每种样本类型和最先进的单细胞RNA-Seq提取分开的单个单元格以介绍每个细胞。现有的计算管道将被优化，以最好地提供高通量单细胞分析，一旦标准化将通过现有的在线平台公开提供。该项目完成后，我们预计将创建一个适用于跨癌症和样本类型的单细胞转录组分析的强大的多组分工作流程，并使整个肿瘤学社区都可以访问这项技术。