Clinical implementation of single cell tumor transcriptome analysis

 描述(申请人提供)：癌症基因组技术和计算分析的快速发展产生了许多基本的癌症发现，从而在不到十年的时间里改变了科学和临床的格局。肿瘤基因组图谱揭示的基因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展示非DNA编码的药物靶点或肿瘤介导的耐药机制。此外，大量肿瘤样本的分析方法只提供平均信号，不能反映不同的肿瘤成分和单个细胞群体或细胞的内在异质性。新兴的单细胞图谱技术，如单细胞转录组分析，可以克服几个挑战，并提供大量的翻译发现机会。我们最近为单细胞RNA-SEQ在患者来源的肿瘤样本中的应用提供了概念验证。为了将这项技术更广泛地应用于翻译肿瘤学领域，我们建议创建、优化和实施单细胞RNA-SEQ平台，该平台可以作为临床肿瘤学领域的翻译工具部署。在初步研究中，我们对几种黑色素瘤肿瘤的约300个单细胞进行了测序，其中包括癌症和相应的肿瘤浸润性细胞(TIL)。转录组分析显示黑色素瘤的关键标记，如MITF和Sox10的表达，以及在部分黑色素瘤细胞中出现的干细胞特征。T细胞的表达反映了一系列的功能状态，从幼稚到高度无能(“疲惫”)的细胞。这些初步结果表明，我们已经为端到端(诊所到工作台)的工作流创建了每个必要的组件，从而产生了有意义的单细胞数据。这项研究的目标是优化当前的协议，以创建标准操作程序(SOP)，并将各个组件合并到标准化的工作流程中。从我们的临床同事那里，我们将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，从每种样本类型中提取分解的单个细胞，并使用最先进的单细胞RNA-Seq来分析每个细胞。现有的计算管道将得到优化，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。在这个项目完成后，我们希望创建一个强大的、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，并使整个肿瘤学社区都可以使用这项技术。

 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.