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首页> 外文会议>IEEE EMBS International Conference on Biomedical amp;amp;amp;amp;amp;amp; Health Informatics >An image informatics pipeline for imaging mass cytometry to characterize the immune landscape in pre- and on-treatment immune therapy and its application in recurrent platinium-resistant epithelial ovarian cancer
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An image informatics pipeline for imaging mass cytometry to characterize the immune landscape in pre- and on-treatment immune therapy and its application in recurrent platinium-resistant epithelial ovarian cancer

机译：用于成像质量细胞术的图像信息管道，在治疗前和治疗前免疫治疗中的免疫景观及其在复发性镀铂上皮性卵巢癌中的应用

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摘要

Imaging mass cytometry (IMC) visualizes thirty or more protein markers simultaneously at subcellular resolution in the spatial context of the tissue microenvironment, enabling comprehensive analysis of cellular phenotypes and their interrelationships. There is, however, a lack of robust data analytics pipelines for integrating spatial information of complex IMC data. To fill this gap, we developed an image informatics pipeline to analyze the immune landscape and spatial interactions between different cell types of the tumor tissues of pre- and on-treatment cancer patients and applied the technology to study tissue samples of advanced epithelial ovarian cancer (EOC) patients. Immunotherapy targeting CTLA4 and PD1 immune checkpoint pathways provides new strategies for EOC. We analyzed tissue samples from a clinical trial testing Durvalumab and Tremelimumab administered in combination vs. Tremelimumab alone in recurrent platinum-resistant EOC patients. Our results show that IMC reveals the immune cell diversity of the EOC tumor ecosystem. The numbers of CD8+ T cells increased while a subtype of tumor cells decreased in on-treatment samples. CD8+ T cells and FoxP3+ cells increased most strongly in the patients who had best response to the treatment. We also developed algorithms to visualize the overall proximity and spatial correlation between any two cell types in the patient tissue.
机译：成像质量细胞术（IMC）在组织微环境的空间背景下以亚细胞分辨率在亚细胞分辨率下同时观察到的三十或更多蛋白标记，从而能够综合分析细胞表型及其相互关系。然而，缺乏稳健的数据分析管道，用于积分复杂IMC数据的空间信息。为了填补这种差距，我们开发了一种图像信息管道，分析了预治疗癌症患者的肿瘤组织的不同细胞类型之间的免疫景观和空间相互作用，并应用了先进上皮卵巢癌组织样本的技术研究（ EOC）患者。靶向CTLA4和PD1免疫检查点途径的免疫疗法为EOC提供了新的策略。我们分析了从临床试验中分析了组织样本Durvalumab和Tremetimumab，单独在复发性铂抗性EoC患者中组合施用。我们的结果表明，IMC揭示了EOC肿瘤生态系统的免疫细胞多样性。 CD8 + T细胞的数量增加，而在治疗样品中肿瘤细胞的亚型降低。 CD8 + T细胞和FoxP3 +细胞在最适合对待治疗的患者中增加最强烈。我们还开发了算法以可视化患者组织中的任何两种细胞类型之间的整体接近度和空间相关性。

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