ArticleActa neuropathologica communications2024
Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.
Article in Acta neuropathologica communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- DNA methylation-based immune cell profiling in mouse blood using MouseRS-CMD.Genome biology · 2026Article
- Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution.Communications biology · 2026Article
- Rethinking tumor purity estimation in the era of AI.Neuro-oncology · 2025Article
- Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data.Acta neuropathologica communications · 2024Article
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9 authors.
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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.
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