免疫肿瘤异质性和先进免疫疗法的诊断分析

Ralf Huss, Christoph Schmid, Mael Manesse, Jeppe Thagaard, Bruno Maerkl
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引用次数: 3

摘要

免疫疗法已经改变了我们在各个阶段治疗癌症的方式。对个体肿瘤标本中免疫系统的了解指导了免疫调节剂的选择,如免疫检查点抑制剂单独或与其他靶向、调节或释放患者免疫系统的治疗剂联合使用。尽管组织病理学诊断相似,但每个肿瘤在原发部位和转移部位都是独特的,也取决于以前的治疗方案或遗传改变,如染色体不稳定性或获得性突变。PD‐1/PD‐L1抑制剂的临床应用已经需要在不同适应症的不同阈值的不同细胞(单独活的肿瘤细胞或与免疫细胞联合或单独免疫细胞)中评估其靶分子。无论如何,当免疫效应细胞(如肿瘤浸润淋巴细胞)在空间上接近而不受其他体液或细胞调节机制的抑制时,检查点抑制剂显示出最佳的总体反应率。因此,富含免疫细胞的肿瘤(“热肿瘤”)通常对免疫调节剂反应强烈,而其他免疫耗尽或免疫排除的肿瘤区域反应较弱,需要替代治疗方案,如修饰的免疫效应细胞或免疫刺激剂,例如溶瘤病毒。在这里,我们总结了通过应用多重原位技术和应用机器学习工具来理解整个肿瘤异质性及其环境的相关性,以及所有免疫和肿瘤细胞的上下文关系和空间量化以及个体癌症的遗传背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies

Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies

Immunotherapies have changed the way how we treat cancer at all stages. The understanding of the immune system in individual tumor specimens guides the selection of immune-modulating agents such as immune checkpoint inhibitors alone or in combination with other therapeutic agents that target, modulate or unleash the patient's immune system. Despite the similar histopathological diagnosis, each tumor is unique at its primary site and site of metastasis, also depending on previous treatment regimens or genetic alterations, such as chromosomal instability or acquired mutations. The clinically well-established use of PD-1/PD-L1 inhibitors already requires the assessment of its target molecules in different cells (viable tumor cells alone or in combination with immune cells or immune cells alone) with different thresholds in various indications. Anyhow, checkpoint inhibitors show the best overall response rate when immune effector cells like tumor-infiltrating lymphocytes are in close spatial proximity without being suppressed by other humoral or cellular regulatory mechanisms. Therefore, immune cell-rich tumors (“hot tumors”) are usually quite reactive to immune-modulating agents, whereas other immune-depleted or immune-excluded tumor areas are less responsive and require alternative treatment regimens such as modified immune effectors cells or immune-stimulating agents, for example, oncolytic viruses. Here, we summarize the relevance to understand the entire tumor heterogeneity and its environment, the contextual relationship and spatial quantification of all immune and tumor cells along with the genetic background of the individual cancer through the application of multiplex in-situ technologies and the application of machine learning tools.

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