The tumor microenvironment is an integral player in cancer initiation, tumor progression, response and resistance to anti‐cancer therapy. Understanding the complex interactions of tumor immune architecture (referred to as ‘immune contexture’) has therefore become increasingly desirable to guide our approach to patient selection, clinical trial design, combination therapies, and patient management. Quantitative image analysis based on multiplexed fluorescence immunohistochemistry and deep learning technologies are rapidly developing to enable researchers to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response. Herein, we discuss current developments in multiplexed fluorescence immunohistochemistry for immune contexture analysis, and their application in immuno‐oncology, and discuss challenges to effectively use this technology in clinical settings. We also present a multiplexed image analysis workflow to analyse fluorescence multiplexed stained tumor sections using the Vectra Automated Digital Pathology System together with FCS express flow cytometry software. The benefit of this strategy is that the spectral unmixing accurately generates and analyses complex arrays of multiple biomarkers, which can be helpful for diagnosis, risk stratification, and guiding clinical management of oncology patients.

中文翻译：

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免疫肿瘤学中的免疫情况分析：多重荧光免疫组织化学的应用和挑战

肿瘤微环境是癌症发生，肿瘤进展，反应和抗癌治疗耐药性的重要组成部分。因此，越来越需要了解肿瘤免疫体系结构（称为“免疫背景”）的复杂相互作用，以指导我们进行患者选择，临床试验设计，联合疗法和患者管理的方法。基于多重荧光免疫组织化学和深度学习技术的定量图像分析正在迅速发展，以使研究人员能够询问来自肿瘤微环境的复杂信息并找到对治疗反应的预测见解。在这里，我们讨论了用于免疫组织学分析的多重荧光免疫组织化学的最新进展及其在免疫肿瘤学中的应用，并讨论在临床环境中有效使用该技术的挑战。我们还提出了一种多重图像分析工作流程，以使用Vectra自动数字病理系统和FCS Express流式细胞仪软件分析荧光多重染色的肿瘤切片。这种策略的好处是，光谱分解可以准确地生成并分析多种生物标志物的复杂阵列，这有助于诊断，风险分层以及指导肿瘤患者的临床管理。