Clinical implementation of single cell tumor transcriptome analysis

单细胞肿瘤转录组分析的临床实施

• 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
• 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

 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（由申请人提供）：癌症基因组技术和计算分析的快速发展已经产生了许多基本的癌症发现，从而在不到十年的时间内改变了科学和临床景观。肿瘤基因组分析揭示的遗传改变越来越多地指导癌症的诊断、治疗和研究。尽管取得了这些进展，但许多基因组技术无法证明非DNA编码的药物靶点或肿瘤介导的耐药机制。此外，大量肿瘤样品的分析方法仅提供不反映不同肿瘤组分和个体细胞群或细胞的内在异质性的平均特征。 新兴的单细胞分析技术，如单细胞转录组分析，可以克服一些挑战，并提供大量的翻译发现机会。我们最近提供了在患者来源的肿瘤样品中应用单细胞RNA-seq的概念验证。为了在转化肿瘤学竞技场中更广泛地应用该技术，我们建议创建、优化和实施单细胞RNA-seq平台，该平台可以作为转化工具部署在临床肿瘤学竞技场中。在初步研究中，我们对来自几种黑色素瘤肿瘤的约300个单细胞进行了测序，包括癌症和相应的肿瘤浸润细胞（TIL）。转录组分析揭示了黑色素瘤的关键标志物，如MITF和SOX 10的表达，以及黑色素瘤细胞部分中出现的干性特征。T细胞表达反映了一系列功能状态，从幼稚细胞到高度无反应性（“衰竭”）细胞。这些初步结果表明，我们已经为端到端（临床到工作台）工作流程创建了每个必要的组件，从而产生了有意义的单细胞数据。 本研究的目标是优化当前的协议，以创建标准操作程序（SOP），并将各个组件合并为标准化的工作流程。我们将从临床同事那里收到来自不同肿瘤类型的临床标本（肿瘤、活检、恶性积液），包括黑色素瘤、肺癌、乳腺癌和卵巢癌。我们将部署实验方案，从每种样品类型中提取分解的单个细胞，并使用最先进的单细胞RNA-Seq对每个细胞进行分析。现有的计算管道将被优化，以最好地服务于高通量单细胞分析，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强大的，多组件的工作流程，用于适用于癌症和样本类型的单细胞转录组分析，并使整个肿瘤学界都能使用该技术。