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胶质瘤免疫微环境成分计算器（GIMiCC）：一种从 DNA 甲基化微阵列数据估计十八种细胞类型比例的方法。

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.

机构信息

Integrative Neuroscience at Dartmouth, Guarini School of Graduate and Advanced Studies at Dartmouth College, Hanover, NH, USA.

Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA.

出版信息

Acta Neuropathol Commun. 2024 Oct 28;12(1):170. doi: 10.1186/s40478-024-01874-0.

DOI:10.1186/s40478-024-01874-0
PMID:39468647
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11514818/
Abstract

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

摘要

用于胶质瘤微环境细胞分型的可扩展平台可以改善肿瘤亚型分类和免疫景观检测，因为人们仍在不断寻求和评估成功的免疫治疗策略。已经开发出用于肿瘤亚型分子分类的 DNA 甲基化 (DNAm) 生物标志物用于临床应用。然而，预测肿瘤细胞景观的工具尚不完善或不易获得。我们开发了Glioma Immune Microenvironment Composition Calculator (GIMiCC)，这是一种使用 DNAm 数据对胶质瘤中的细胞类型进行去卷积的方法。使用来自 17 种分离细胞类型的数据，我们在选定基因组区域的生物学背景下描述了去卷积库的推导，并使用独立数据集验证了去卷积结果。我们利用 GIMiCC 来说明基于 DNAm 的免疫成分估计值在临床上是相关的，并且具有潜在的临床应用可扩展性。此外，我们利用 GIMiCC 来识别与高免疫浸润相关的组成无关的 DNAm 改变。我们未来的工作旨在优化 GIMiCC 并推进胶质瘤的临床评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/7f81f41c0e1e/40478_2024_1874_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/7f81f41c0e1e/40478_2024_1874_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/6f9b35916b17/40478_2024_1874_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/36ce64658045/40478_2024_1874_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/182189ceb981/40478_2024_1874_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/8224902fee3c/40478_2024_1874_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/2e585c570923/40478_2024_1874_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/f06f501e9fe1/40478_2024_1874_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/443b972304a0/40478_2024_1874_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c191/11514818/7f81f41c0e1e/40478_2024_1874_Fig8_HTML.jpg

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本文引用的文献

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Neurooncol Adv. 2023 Nov 1;5(1):vdad142. doi: 10.1093/noajnl/vdad142. eCollection 2023 Jan-Dec.
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Molecular and clinical characterization of ANG expression in gliomas and its association with tumor-related immune response.胶质瘤中ANG表达的分子与临床特征及其与肿瘤相关免疫反应的关联
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Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation.
使用DNA甲基化进行分层反卷积以实现人类大脑中广泛的细胞类型解析
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