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应用贝叶斯网络分析对流式细胞术数据进行癌症免疫治疗反应的剖析。

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data.

机构信息

City of Hope National Medical Center, Department of Computational and Quantitative Medicine, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA.

City of Hope National Medical Center, Department of Immuno-Oncology, Beckman Research Institute, 1500 East Duarte Road, Duarte, CA 91010, USA.

出版信息

Int J Mol Sci. 2021 Feb 26;22(5):2316. doi: 10.3390/ijms22052316.

DOI:10.3390/ijms22052316
PMID:33652558
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7956201/
Abstract

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.

摘要

癌症免疫疗法，特别是免疫检查点阻断，已被发现对转移性癌症的治疗有效。然而，只有一部分患者能取得临床缓解。阐明预测持续临床缓解的预处理生物标志物是一个主要的研究重点。另一个研究重点是评估应答者与无应答者治疗前后免疫系统的变化。我们的研究小组一直在研究免疫网络，因为它可以准确反映全身的免疫状态。使用来自胃食管腺癌 (GEA) 患者外周血单核细胞 (PBMC) 的免疫细胞谱的流式细胞术 (FACS) 数据来分析该环境下免疫网络的变化。在这里，我们描述了一种使用系统生物学/机器学习技术和概念对 FACS 数据进行二次分析的新型计算流程。该流程以免疫网络的比较贝叶斯网络分析为中心，能够检测到传统方法（如 FlowJo 手动门控）可能遗漏的强信号。计划进行未来的研究来验证和跟踪与通过该计算流程确定的临床缓解相关的免疫生物标志物（及其组合/相互作用）。

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