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

近年来，转移性肾细胞癌（RCC）的全身治疗方法已经发生了革命性变化，通过识别免疫检查点信号传导途径及其免疫检查点抑制剂（如PD-1抗体nivolumab）的治疗调节。患者亚组中治疗应答失败和治疗过程中获得性耐药是成功进行检查点抑制剂治疗的主要障碍。在缺乏可靠的生物标志物的情况下，识别可能从检查点阻断中获益的患者并定义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，主要是基于肿瘤大小的演变，可能无法适当地表征免疫治疗的复杂反应，拟议项目的目的是使病理形态学特征以及遗传，细胞和分子生物学过程中免疫检查点阻断在RCC“可见”使用定量成像参数。为了改善治疗计划，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therapy.In前瞻性，多模式的方法，免疫治疗过程中RCC的复杂和异质性肿瘤免疫微环境的特点是通过深入的转录组学，蛋白质组学，基因组学和病理学分析。该综合生物数据集将与定量成像参数（放射基因组学方法）相关。基于多参数MRI，结合CT扫描，将应用生物信息学算法以高通量过程中自动化的方式提取和分析所有可访问的图像信息。在对复杂的生物学和成像数据进行相关分析后，将开发一个放射基因组图谱，将RCC肿瘤组织及其免疫微环境的分子，遗传和病理特征与定量成像特征联系起来，提出的基于成像的虚拟活检方法将提供一个大的非侵入性候选生物标志物，用于预测和监测免疫检查点抑制剂治疗的反应。最后，在这项研究中发现的新生物标志物可以帮助为RCC定制个性化的免疫策略，并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.