ERI: A New Spatial Image Analysis of the Tumor Microenvironment for Cancer Immunotherapy

ERI：用于癌症免疫治疗的肿瘤微环境的新空间图像分析

• 批准号: 2138260
• 负责人: Sanghoon Lee
• 金额: $ 19.92万
• 依托单位: Marshall University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138260&HistoricalAwards=false
• 关键词: ERI; New; Spatial; Image; Analysis

Cancer immunotherapy uses the power of the body’s immune system against cancer cells. Recent progress has shed light on cancer initiation and response, with key therapies showing significant effectiveness in cancer patients. However, response to cancer immunotherapy is highly dependent on interaction between cancer cells and their microenvironment. This Engineering Research Initiation (ERI) award will tackle an important challenge — exploring, analyzing, and interpreting the complex interactions in the tumor microenvironment with AI-based approaches. As part of this project, K-12, undergraduate, and graduate students will be introduced to biomedical engineering concepts through synergistic research and educational activities. The long-term research goal of this project is to discover statistically significant cancer biomarkers that will aid researchers in determining the prognosis of cancer patients. Towards this goal, this project aims to uncover the quantitative spatial relationship between heterogeneous cancer-associated populations using a two-pass pipeline design to achieve two objectives: (i) to characterize different cell types such as tumor, tumor-infiltrating lymphocytes, and stroma with deep-learned features extracted from whole slide images and (ii) to investigate spatial correlations between heterogeneous cancer-associated populations with statistically significant immune cells. Unlike conventional histopathological spatial image analysis methods that classify image regions into two or more categories, the proposed method overlays the results of the classification from heterogeneous cancer-associated populations to proactively determine the spatial correlations of different cancer-associated populations cells in both the high level of infiltrating immune cells and the low level of infiltrating immune cells. The proposed research will broadly benefit AI-based biomarker development, investigation of multiple types of cancer, and interpretation of spatial variation in other research fields. The outcomes of this project are expected to yield new knowledge to help predict response to cancer immunotherapy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

癌症免疫疗法利用人体对癌细胞的免疫系统的力量。最近的进步揭示了癌症的启动和反应，关键疗法在癌症患者中表现出显着的有效性。然而，对癌症免疫疗法的反应高度取决于癌细胞及其微环境之间的相互作用。该工程研究启动（ERI）奖将应对一个重要的挑战 - 探索，分析和解释肿瘤微环境中的复杂相互作用，并采用基于AI的方法。作为该项目的一部分，K-12，本科生和研究生将通过协同研究和教育活动介绍生物医学工程概念。该项目的长期研究目标是发现具有统计学意义的癌症生物标志物，这将有助于研究人员确定癌症患者的预后。为了实现这一目标，该项目旨在使用两次通道的管道设计来揭示异质性癌症相关人群之间的定量空间关系，以实现两个目标：（i）表征不同的细胞类型：肿瘤，肿瘤浸润型淋巴细胞，肿瘤浸润淋巴细胞，以及从slide corne in II）中提取的特征（II）的较深型杂种（II）的杂种（II）的杂种（II）的杂种（II），以ii）的propecor（II）的杂种（II）进行了研究。具有统计学意义的免疫细胞。与将图像区域分为两个或多个类别的常规历史空间图像分析方法不同，该建议的方法覆盖了异构癌症相关种群的分类结果，以主动确定不同癌症相关群体的空间相关性在高水平的炎性炎性免疫接种水平和低水平的免疫接种量的空间相关性。拟议的研究将广泛地有益于基于AI的生物标志物发展，对多种类型的癌症的研究以及对其他研究领域空间变化的解释。预计该项目的结果将产生新知识，以帮助预测对癌症免疫疗法的反应。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是珍贵的支持。