Cancer precision medicine through spatially informative single cell image and transcriptomics data analysis

通过空间信息单细胞图像和转录组学数据分析进行癌症精准医学

基本信息

批准号：
10754028
负责人：
Lana X Garmire
金额：
$ 32.04万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
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：
2016-09-01 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10754028
关键词：
Address Adopted Algorithms Architecture Area Artificial Intelligence Award Bioinformatics Biological Biological Assay Cancer Patient Cell Communication Cells Characteristics Chemotherapy and/or radiation Clinical Clinical Treatment Communities Complex Computer Models Computing Methodologies Cytometry Data Data Analyses Development Diagnosis Disease Engineering Epigenetic Process Foundations Funding Genes Genetic Genomics Head Hematoxylin and Eosin Staining Method Heterogeneity Histopathology Human Image Image Cytometry Imaging Techniques In Situ Investigation Link Malignant Neoplasms Methods Mission Neck Neural Network Simulation Patient-Focused Outcomes Patients Pharmaceutical Preparations Pharmacotherapy Physiologic pulse Population Positioning Attribute Precision therapeutics Printing Productivity Prognosis Property Proxy Publications Recommendation Research Research Personnel Resistance development Sampling Scientist Series Stains Technology Therapeutic Therapeutic Intervention Tissues Treatment outcome Work accurate diagnosis cancer cell cancer diagnosis cancer heterogeneity cancer therapy career cellular imaging clinical application computer framework cost drug repurposing effective therapy frontier gene environment interaction genomic platform graph neural network improved innovation insight multi-scale modeling multimodality multiscale data novel novel drug combination novel therapeutics outcome prediction patient prognosis population health precision drugs precision oncology response single-cell RNA sequencing sound success technology platform transcriptomics transfer learning tumor tumor heterogeneity tumor microenvironment

项目摘要

Abstract Human cancers are highly heterogeneous, arising from genetic, epigenetic, genomic, and gene environment interactions. Studying single-cell cancer heterogeneity is essential for effective diagnosis, prognosis and development of personalized anti-cancer therapy. However, single-cell level tumor heterogeneity in situ with the original spatial context is not addressed until very recently, with development of new frontier technological platforms such as spatial transcriptomics (ST) and single cell imaging mass cytometry (IMC). Due to complexity of these new spatial data types, computation is a major bottle neck to bring these technologies to precision therapeutic interventions in the clinical space. In this project, we take a three-pronged approach to propose a series of novel computational methods that will harness the power of spatially informative omics and imaging data, for drug treatment and cancer patient prognosis predictions. Building upon a previously highly productive R01 project, we aim to continue investigations in single cell research, with a new focus on single-cell spatial data analysis. First, we will develop a novel personalized drug repurposing algorithm called STADS using cancer spatial transcriptomics data. Next, build a new computational model STimpute to impute spatial transcriptomics data from easily accessible histopathology image data, using transfer learning and graph neural network (GNN) models. STimpute will allow predictions of drugs from histopathology data, by using imputed ST data as the proxy input of STADS. Lastly, we will build a new computational framework scImageProg to predict patient survival at the population level from the single-cell image cytometry data, accomplishing multi-scale modeling to link single-cell data to population health. The work will be expected to have transformative clinical impacts from various cutting-edge spatially informative and complex genomics and imaging data types.

抽象的人类癌症是高度异质的，是由遗传，表观遗传，基因组和基因环境引起的互动。研究单细胞癌异质性对于有效诊断，预后和开发个性化的抗癌疗法。但是，单细胞水平肿瘤异质性原位与随着新的Frontier技术的发展，原始的空间环境直到最近才解决诸如空间转录组学（ST）和单细胞成像质量细胞术（IMC）之类的平台。由于复杂性在这些新的空间数据类型中，计算是将这些技术精确的主要瓶颈临床空间中的治疗干预措施。在这个项目中，我们采用三管齐下的方法来提出一系列新型计算方法，这些方法将利用空间信息丰富的OMICS和Imaging的力量用于药物治疗和癌症患者预后预测的数据。以先前高产的为基础 R01项目，我们旨在继续进行单细胞研究的调查，新的重点是单细胞空间数据分析。首先，我们将开发一种新型的个性化药物重新利用算法，称为Stads使用癌症空间转录组学数据。接下来，构建一种新的计算模型刺激以估算空间使用转移学习和图形神经，来自易于访问的组织病理学图像数据的转录组学数据网络（GNN）模型。 Stimpute将使用估算的ST来预测组织病理学数据的药物数据作为Stads的代理输入。最后，我们将建立一个新的计算框架scimageprog来预测从单细胞图像细胞仪数据中，患者在人群水平上的生存，完成多尺度建模将单电池数据与人群健康联系起来。预计这项工作将具有变革性的临床来自各种尖端的空间信息和复杂基因组学和成像数据类型的影响。