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朝着系统免疫学方法的方向,以揭示对癌症免疫疗法的反应。

Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy.

机构信息

Tumor Immunology Unit, Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy.

出版信息

Front Immunol. 2020 Oct 22;11:582744. doi: 10.3389/fimmu.2020.582744. eCollection 2020.

Abstract

Immunotherapy, particularly immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, holds a great promise against cancer. These treatments have markedly improved survival in solid as well as in hematologic tumors previously considered incurable. However, durable responses occur in a fraction of patients, and existing biomarkers ( PD-L1) have shown limited prediction power. This scenario highlights the need to dissect the complex interplay between immune and tumor cells to identify reliable biomarkers of response to be used for patients' selection. In this context, systems immunology represents indeed the new frontier to address important clinical challenges in biomarker discovery. Through the integration of multiple layers of data obtained with several high-throughput approaches, systems immunology may give insights on the vast range of inter-individual differences and on the influences of genes and factors that cooperatively shape the individual immune response to a given treatment. In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. We also highlight the need of favoring the collaboration of researchers with complementary expertise and of integrating multi-omics data into biological networks with the final goal of developing accurate markers of therapeutic response.

摘要

免疫疗法,特别是免疫检查点阻断和嵌合抗原受体 (CAR)-T 细胞疗法,为癌症治疗带来了巨大的希望。这些治疗方法显著提高了实体瘤和以前被认为无法治愈的血液肿瘤患者的生存率。然而,持久的反应仅发生在一部分患者中,而现有的生物标志物(如 PD-L1)显示出有限的预测能力。这种情况凸显了需要剖析免疫细胞和肿瘤细胞之间的复杂相互作用,以确定可靠的反应生物标志物,用于患者选择。在这种情况下,系统免疫学确实代表了解决生物标志物发现中重要临床挑战的新前沿。通过整合使用多种高通量方法获得的多个层次的数据,系统免疫学可以深入了解个体之间广泛的差异,以及基因和因素的影响,这些因素共同塑造了个体对特定治疗的免疫反应。在这篇综述中,我们概述了当前的高通量方法学,包括基因组学、表观基因组学、转录组学、代谢组学、蛋白质组学和多参数表型分析,这些方法适用于系统免疫学,以及数据整合和生物学解释的关键步骤。此外,我们回顾了最近的研究,这些研究使用多组学技术来描述反应机制,并确定针对检查点抑制剂、CAR-T 细胞疗法、树突细胞和肽基癌症疫苗的强大反应生物标志物。我们还强调了需要促进具有互补专业知识的研究人员之间的合作,并将多组学数据整合到生物网络中,最终目标是开发治疗反应的准确标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/744e/7649803/faa4d526e0dc/fimmu-11-582744-g001.jpg

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