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.

抽象的肿瘤位于一个复杂的多细胞生态系统中，该系统由恶性和非 - 恶性细胞，其中相互作用的细胞和分子在空间和时间上组织。这这些细胞及其相互作用的多样性会影响癌症的进展和药物反应，并且抵抗，并为更精确的诊断和治疗学提供了机会。在这个提案，我们将开发一种新型的空间转录法幻灯片序列，以高于通过一组健壮的癌症精确医学的分辨率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申请标准设备和最小的培训。我们新颖的计算管道将允许无缝集成肿瘤中的分子，细胞和组织学理解：它们将启用空间在复杂肿瘤环境中的细胞类型的定位，鉴定空间变化由病理学驱动的基因表达模式以及细胞壁细分的组织。应用这些方法将彻底改变我们发现肿瘤空间变化的能力和疾病进展和治疗过程中的分子组织为新的生物标志物提供诊断和预后，并突出显示新的治疗途径。