Clinical implementations of spatial transcriptomics in tumors

肿瘤空间转录组学的临床应用

基本信息

批准号：
10117213
负责人：
Orr Ashenberg
金额：
$ 43.18万
依托单位：
BROAD INSTITUTE, INC.
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：
2020-03-02 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10117213
关键词：
Affect Antineoplastic Agents Automation Bar Codes Biological Assay Biological Markers Cancer Biology Cell Communication Cell Nucleus Cells Chemistry Clinic Clinical Clinical Pathology Clinical Protocols Collection Complex Computational algorithm Computer Analysis Data Data Analyses Detergents Development Diagnostic Disease Progression Ecosystem Environment Equipment Freezing Gene Expression Profile Generations Genes Genomics Histologic Human In Situ Individual Joints Libraries Location Malignant - descriptor Masks Messenger RNA Methods Microscopy Modification Molecular Molecular Biology Neighborhoods Non-Malignant Organ Paraffin Embedding Pathologist Pathology Production Protocols documentation Quality Control RNA Research Personnel Resistance Resolution Sampling Scientist Slide Small Nuclear RNA Specimen Surface Technology Therapeutic Time Tissues Training Validation analysis pipeline cancer genomics cell type clinical implementation clinical practice computational pipelines computerized tools experience genome-wide genomic platform genomic profiles indexing neoplastic cell new technology novel novel strategies novel therapeutics oligo (dT)personalized diagnostics precision medicine precision oncology profiles in patients prognostic prospective response sample collection single-cell RNA sequencing success targeted sequencing tool transcriptome transcriptome sequencing transcriptomics tumor tumor progression

项目摘要

Abstract Tumors reside within a complex multicellular ecosystem comprised of malignant and non- malignant cells, where interacting cells and molecules are organized in space and time. The diversity of these cells and their interactions affect cancer progression and drug response and resistance, and present opportunities for more precise diagnostics and therapeutics. In this proposal, we will develop Slide-seq, a novel spatial transcriptomic method, into a high- resolution spatial genomics platform for cancer precision medicine through a set of robust protocols, pipelines and computational algorithms. Our tools will allow pathologists to apply Slide-seq on a broad range of tumor specimens in the clinic with standard equipment and minimal training. Our novel computational pipelines will allow the seamless integration of molecular, cellular and histological understanding in tumors: they will enable the spatial localization of cell types within complex tumor environments, the identification of spatially varying gene expression patterns driven by pathology, as well as the organization of cellular niches. Applying these approaches will revolutionize our ability to discover changes in tumor spatial and molecular organization during disease progression and treatment, provide new biomarkers for diagnostics and prognostics, and highlight new therapeutic avenues.

抽象的肿瘤存在于由恶性和非恶性肿瘤组成的复杂的多细胞生态系统中。恶性细胞，相互作用的细胞和分子在空间和时间上组织起来。这这些细胞的多样性及其相互作用影响癌症进展和药物反应耐药性，并为更精确的诊断和治疗提供了机会。在这个提案中，我们将把 Slide-seq 这种新颖的空间转录组学方法发展成一种高通过一套强大的解决癌症精准医学的空间基因组学平台协议、管道和计算算法。我们的工具将允许病理学家应用使用标准设备在诊所对各种肿瘤标本进行幻灯片测序最少的培训。我们新颖的计算管道将允许无缝集成对肿瘤的分子、细胞和组织学理解：它们将使空间复杂肿瘤环境中细胞类型的定位，空间变化的识别由病理学以及细胞生态位的组织驱动的基因表达模式。应用这些方法将彻底改变我们发现肿瘤空间和变化的能力疾病进展和治疗期间的分子组织，为以下疾病提供新的生物标志物诊断和预后，并强调新的治疗途径。