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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.