Profiling of tumour-immune heterogeneity in renal cell carcinoma: Next-generation radiogenomic biomarkers for immune checkpoint inhibition

近年来，随着免疫检查点信号通路的确定以及免疫检查点抑制剂(如PD-1抗体nivolumab)对其治疗的调节，转移性肾细胞癌(RCC)的全身治疗方法发生了革命性的变化。患者亚组治疗反应失败和治疗过程中获得性耐药是检查点抑制剂治疗成功的主要障碍。在缺乏可靠的生物标志物的情况下，确定可能从检查站封锁中受益的患者并确定治疗反应的标准是治疗计划中的主要挑战。已建立的放射学标准主要基于肿瘤大小的演变，可能不能恰当地描述免疫治疗的复杂反应。拟议项目的目标是使用定量成像参数使RCC在免疫检查点阻断期间的病理形态特征以及遗传、细胞和分子生物学过程“可见”。为了改进治疗计划，将定义可靠的、非侵入性的生物标志物来预测和监测治疗，试图预先选择最有可能从检查点抑制剂治疗中受益的患者。在一种前瞻性的、多模式的方法中，肾细胞癌在免疫治疗过程中复杂而不同的肿瘤免疫微环境的特征是深入的转录学、蛋白质组学、基因组和病理学分析。这一全面的生物数据集将与定量成像参数(放射基因组学方法)相关联。基于多参数磁共振成像，结合CT扫描，将应用生物信息学算法在高通量过程中自动提取和分析可访问的图像信息的整体。通过对复杂的生物学和影像数据的相关分析，将肾细胞癌组织及其免疫微环境的分子、遗传和病理特征与定量影像特征联系起来，建立放射基因组图谱。基于影像的虚拟活检方法将为预测和监测免疫检查点抑制剂治疗的反应提供大量非侵入性的候选生物标志物。最后，本研究中新生物标志物的发现有助于为肾癌量身定做个性化的免疫治疗策略，有可能改善这种疾病的预后。

The systemic treatment approach to metastatic renal cell carcinoma (RCC) has been revolutionized in recent years by the identification of immune checkpoint signalling pathways and their therapeutic modulation by immune-checkpoint inhibitors, such as the PD-1 antibody nivolumab. Failure of treatment response in patient subgroups and acquired drug resistance in the course of therapy represent major barriers to successful checkpoint inhibitor treatment. In the absence of reliable biomarkers, identifying patients who may benefit from checkpoint blockade and defining criteria for treatment response are major challenges in the treatment planning. Established radiological criteria, which are primarily based on the evolution of tumour size, may not appropriately characterize the complex response to immunotherapy.The aims of the proposed project are to render pathomorphological characteristics as well as genetic, cellular and molecular biology processes during immune checkpoint blockage in RCCs "visible" using quantitative imaging parameters. For improved treatment planning, reliable, non-invasive biomarkers will be defined to predict and monitor treatment in an attempt to preselect patients who are most likely to benefit from checkpoint inhibitor therapy.In a prospective, multimodal approach, the complex and heterogeneous tumour immune microenvironment of RCC during immunotherapy is characterized by in-depth transcriptomic, proteomic, genomic, and pathological analysis. This comprehensive biologic dataset will be correlated with quantitative imaging parameters (radiogenomics approach). Based on multiparametric MRI, in combination with CT scans, bioinformatics algorithms will be applied to extract and analyze the totality of accessible image information in an automated manner in a high-throughput process. Following correlative analysis of the complex biological and imaging data, a radiogenomic map will be developed linking molecular, genetic and pathological features of RCC tumour tissue and its immune microenvironment with quantitative imaging features.The proposed imaging-based virtual biopsy approach will provide a large set of noninvasive, candidate biomarkers for predicting and monitoring response to immune checkpoint inhibitor therapy. Finally, the discovery of novel biomarkers in this study can help to tailor personalized immunotherapeutic strategies for RCC, with the potential to improve the prognosis of this disease.