胶质瘤免疫微环境组成计算器(GIMiCC):一种从 DNA 甲基化芯片数据估算十八种细胞类型比例的方法。

IF 6.2 2区 医学 Q1 NEUROSCIENCES
Steven C Pike, John K Wiencke, Ze Zhang, Annette M Molinaro, Helen M Hansen, Devin C Koestler, Brock C Christensen, Karl T Kelsey, Lucas A Salas
{"title":"胶质瘤免疫微环境组成计算器(GIMiCC):一种从 DNA 甲基化芯片数据估算十八种细胞类型比例的方法。","authors":"Steven C Pike, John K Wiencke, Ze Zhang, Annette M Molinaro, Helen M Hansen, Devin C Koestler, Brock C Christensen, Karl T Kelsey, Lucas A Salas","doi":"10.1186/s40478-024-01874-0","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":6914,"journal":{"name":"Acta Neuropathologica Communications","volume":"12 1","pages":"170"},"PeriodicalIF":6.2000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514818/pdf/","citationCount":"0","resultStr":"{\"title\":\"Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.\",\"authors\":\"Steven C Pike, John K Wiencke, Ze Zhang, Annette M Molinaro, Helen M Hansen, Devin C Koestler, Brock C Christensen, Karl T Kelsey, Lucas A Salas\",\"doi\":\"10.1186/s40478-024-01874-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":6914,\"journal\":{\"name\":\"Acta Neuropathologica Communications\",\"volume\":\"12 1\",\"pages\":\"170\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514818/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Neuropathologica Communications\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s40478-024-01874-0\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Neuropathologica Communications","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40478-024-01874-0","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

随着人们不断寻找和评估成功的免疫疗法策略,一个可扩展的胶质瘤微环境细胞分型平台可以改善肿瘤亚型和免疫格局检测。用于肿瘤亚型分子分类的 DNA 甲基化(DNAm)生物标记物已开发出供临床使用。然而,预测肿瘤细胞景观的工具还没有明确定义,也不容易获得。我们开发了胶质瘤免疫微环境组成计算器(GIMiCC),这是一种利用DNAm数据解构胶质瘤细胞类型的方法。我们利用来自 17 种分离细胞类型的数据,描述了在选定基因组区域的生物学背景下衍生出的解卷积库,并利用独立数据集验证了解卷积结果。我们利用 GIMiCC 说明了基于 DNAm 的免疫组成估计值与临床相关,并可扩展到潜在的临床应用中。此外,我们还利用 GIMiCC 确定了与高免疫浸润相关的不依赖于组成的 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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Acta Neuropathologica Communications
Acta Neuropathologica Communications Medicine-Pathology and Forensic Medicine
CiteScore
11.20
自引率
2.80%
发文量
162
审稿时长
8 weeks
期刊介绍: "Acta Neuropathologica Communications (ANC)" is a peer-reviewed journal that specializes in the rapid publication of research articles focused on the mechanisms underlying neurological diseases. The journal emphasizes the use of molecular, cellular, and morphological techniques applied to experimental or human tissues to investigate the pathogenesis of neurological disorders. ANC is committed to a fast-track publication process, aiming to publish accepted manuscripts within two months of submission. This expedited timeline is designed to ensure that the latest findings in neuroscience and pathology are disseminated quickly to the scientific community, fostering rapid advancements in the field of neurology and neuroscience. The journal's focus on cutting-edge research and its swift publication schedule make it a valuable resource for researchers, clinicians, and other professionals interested in the study and treatment of neurological conditions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信