Integrating cancer genomics and spatial architecture of tumor infiltrating lymphocytes

整合癌症基因组学和肿瘤浸润淋巴细胞的空间结构

基本信息

批准号：
10637960
负责人：
Hanlee P Ji
金额：
$ 44.52万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-11 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10637960
关键词：
Address Architecture Area Biopsy Brain Cancer Patient Cells Characteristics Classification Clinical Computing Methodologies Consumption Data Data Set Disease Endothelial Cells Epithelium Evaluation Fibroblasts Gene Expression Genomics Guidelines Head and Neck Cancer Hematoxylin and Eosin Staining Method Histopathology Image Image Analysis Immune Immune response Immunohistochemistry Immunologic Markers Immunotherapy Joints Label Learning Lung Lymphocyte Macrophage Malignant Neoplasms Manuals Maps Measures Methods Modeling Molecular Mutation Observer Variation Outcome Pathologic Pathologist Pathology Patients Performance Phenotype Population Predictive Value Procedures Proxy Regional Cancer Reproducibility Research Resolution Sampling Spatial Distribution Stains Standardization Structure Testing The Cancer Genome Atlas Time Tissue Fixation Training Tumor-Infiltrating Lymphocytes Validation automated analysis cancer genomics cancer imaging cancer immunotherapy cancer type cell type cohort computer framework convolutional neural network data exchange deep learning deep learning model digital genomic data geometric structure imaging modality imaging system immune checkpoint immune checkpoint blockade innovation insertion/deletion mutation microscopic imaging molecular marker multiplexed imaging neoantigens nonsynonymous mutation pathology imaging patient prognosis prognostic value response spatial relationship tumor tumor heterogeneity tumor microenvironment

项目摘要

ABSTRACT Tumor infiltrating lymphocytes (TILs) are an important component of the immune cells that reside in the tumor microenvironment (TME). The type and number of TILs in the TME have an impact on overall survival and are an indicator of response to immunotherapy. Despite their importance as an indicator of a patient’s immune response to cancer, there are multiple challenges for analyzing TILS from large population data sets involving thousands of samples. There is a lack of methods that can automate an analysis of histopathologic images for different features such as the spatial distribution of TILs, their topological interactions with their neighboring cells in the TME and their association with specific clinical outcomes. Even more challenging is integrating TIL metrics with cancer genomic data. Most other methods provide qualitive metrics of TILs and frequently rely on manual inspection from pathologists – this approach lacks scalability and is subject to observer bias. To address these challenges, we developed a computational framework that uses a deep learning model to identify multiple cell types from histopathology images. The major innovation of our approach is molecular label transferring that annotates tens of thousands of small areas extracted from histopathology images without manual inspections. This approach is highly accurate, efficient, scalable and readily automated for the analysis of millions of images. The objective of this project is to address a key challenge in the application of deep learning to histopathological image: large number of labeled images as training data set. We have three specific aims to 1) identify spatial quantification of TILs from over 10,000 histopathological images from the Cancer Genome Atlas Project; 2) correlate TIL metrics with clonal tumor mutation burden (TMB); 3) determine association of TILs with immune checkpoint blockade responses. This research is significant because our approach enables for a comprehensive characterization of TILs from histopathological images at cellular level, using data that is commonly accessible in clinical settings and can be readily integrated with cancer genomic data.

抽象的肿瘤浸润淋巴细胞（TILS）是驻留在肿瘤中的免疫细胞的重要组成部分微环境（TME）。 TME中的TIL的类型和数量对整体生存有影响，并且是对免疫疗法的反应指标。尽管它们是患者免疫的重要性对癌症的反应，分析大量人口数据集的TIL存在许多挑战成千上万的样本。缺乏可以自动化组织病理学图像分析的方法不同的特征，例如TIL的空间分布，它们与邻近细胞的拓扑相互作用在TME及其与特定临床结果的关联中。更大的挑战是整合TIL指标伴有癌症基因组数据。大多数其他方法都提供了泰尔的重要指标，并且经常依靠手册病理学家的检查 - 这种方法缺乏可伸缩性，并且会受到观察者偏见的影响。解决这些挑战，我们开发了一个计算框架，该框架使用深度学习模型来识别多个单元格组织病理学图像的类型。我们方法的主要创新是分子标签转移，以注释从没有手动检查的没有组织病理学图像中提取的成千上万个小区域。这种方法是高度准确，高效，可扩展的，并且容易自动化，以分析数百万图像。该项目的目的是应对深入学习的关键挑战组织病理学图像：大量标记的图像作为训练数据集。我们有三个针对1的特定目标）从癌症基因组地图集的10,000多个组织病理学图像中确定tils的空间定量项目; 2）将TIL指标与克隆肿瘤突变（TMB）相关； 3）确定泰尔与免疫检查点封锁响应。这项研究很重要，因为我们的方法使使用数据的数据，从细胞级别的组织病理学图像对TIL的全面表征通常可以在临床环境中访问，并且可以很容易地与癌症基因组数据融为一体。