GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data. Neuro Oncol. 2023;25(7):1236-48.
JGM, Humans, Glioblastoma, Transcriptome, Brain Neoplasms, Gene Expression Profiling, Microglia, Tumor Microenvironment
Neuro Oncol. 2023;25(7):1236-48.
This work was supported by grants from UK Research and Innovation [MR/T020504/1 to LFS], the Integrated Biological Imaging Network [IBIN4LS to LFS], Yorkshire’s Brain Tumour Charity and OSCARs Paediatric Brain Tumour Charity [Joint Infrastructure funding to LFS], the British Neuropathology Society [Small Grant Award to SA], and Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the de- volved administrations, and leading medical research charities. Work in the Ihrie lab is supported by the US National Institutes of Health [R01NS118580 and a supplement to U54CA217450 to RAI], the Ben & Catherine Ivy Foundation [RAI], and a gift from the Michael David Greene Brain Cancer Fund at the Vanderbilt– Ingram Cancer Center [RAI].
BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data.
METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best.
RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis.
CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.