Clinical implementations of spatial transcriptomics in tumors

肿瘤空间转录组学的临床应用

• 批准号: 10350620
• 负责人: Orr Ashenberg
• 金额: $ 42.31万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 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/10350620
• 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

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.

摘要 肿瘤存在于一个复杂的多细胞生态系统中，该生态系统由恶性肿瘤和非恶性肿瘤组成 恶性细胞，相互作用的细胞和分子在空间和时间上被组织起来。这个 这些细胞的多样性及其相互作用影响癌症的进展和药物反应 抗药性，并为更精确的诊断和治疗提供了机会。在这 提议，我们将把Slide-Seq这一新的空间转录方法发展成一种高效的 分辨率空间基因组学平台通过一套稳健的癌症精准医学 协议、管道和计算算法。我们的工具将允许病理学家应用 使用标准设备对临床上广泛的肿瘤标本进行玻片排序 最低限度的训练。我们的新型计算管道将允许无缝集成 对肿瘤的分子、细胞和组织学的理解：它们将使空间 复杂肿瘤环境中细胞类型的定位、空间变异的鉴定 由病理学驱动的基因表达模式，以及细胞生态位的组织。 应用这些方法将彻底改变我们发现肿瘤空间和空间变化的能力 疾病进展和治疗过程中的分子组织，为疾病提供新的生物标志物 诊断和预后，并突出新的治疗途径。