@article{RN6050,
   author = {Ajaib, S. and Lodha, D. and Pollock, S. and Hemmings, G. and Finetti, M. A. and Gusnanto, A. and Chakrabarty, A. and Ismail, A. and Wilson, E. and Varn, F. S. and Hunter, B. and Filby, A. and Brockman, A. A. and McDonald, D. and Verhaak, R. G. W. and Ihrie, R. A. and Stead, L. F.},
   title = {GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data},
   journal = {Neuro Oncol},
   volume = {25},
   number = {7},
   pages = {1236-1248},
   note = {1523-5866
Ajaib, Shoaib
Lodha, Disha
Pollock, Steven
Hemmings, Gemma
Finetti, Martina A
Gusnanto, Arief
Chakrabarty, Aruna
Ismail, Azzam
Wilson, Erica
Varn, Frederick S
Hunter, Bethany
Filby, Andrew
Brockman, Asa A
McDonald, David
Verhaak, Roel G W
Ihrie, Rebecca A
Stead, Lucy F
Orcid: 0000-0002-9550-4150
MR/T020504/1/MRC_/Medical Research Council/United Kingdom
R01 NS118580/NS/NINDS NIH HHS/United States
U54 CA217450/CA/NCI NIH HHS/United States
R01NS118580/NH/NIH HHS/United States
Journal Article
England
2023/01/24
Neuro Oncol. 2023 Jul 6;25(7):1236-1248. doi: 10.1093/neuonc/noad021.},
   abstract = {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.},
   keywords = {Humans
*Glioblastoma/pathology
Transcriptome
*Brain Neoplasms/pathology
Gene Expression Profiling/methods
Microglia/metabolism
Tumor Microenvironment
deconvolution
glioblastoma
immune
neoplastic
transcriptomics},
   ISSN = {1522-8517 (Print)
1522-8517},
   DOI = {10.1093/neuonc/noad021},
   year = {2023},
   type = {Journal Article}
}

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F."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Ajaib"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Lodha"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Pollock"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Hemmings"],"firstnames":["G."],"suffixes":[]},{"propositions":[],"lastnames":["Finetti"],"firstnames":["M.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Gusnanto"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Chakrabarty"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Ismail"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Wilson"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Varn"],"firstnames":["F.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Hunter"],"firstnames":["B."],"suffixes":[]},{"propositions":[],"lastnames":["Filby"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Brockman"],"firstnames":["A.","A."],"suffixes":[]},{"propositions":[],"lastnames":["McDonald"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Verhaak"],"firstnames":["R.","G.","W."],"suffixes":[]},{"propositions":[],"lastnames":["Ihrie"],"firstnames":["R.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Stead"],"firstnames":["L.","F."],"suffixes":[]}],"title":"GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data","journal":"Neuro Oncol","volume":"25","number":"7","pages":"1236-1248","note":"1523-5866 Ajaib, Shoaib Lodha, Disha Pollock, Steven Hemmings, Gemma Finetti, Martina A Gusnanto, Arief Chakrabarty, Aruna Ismail, Azzam Wilson, Erica Varn, Frederick S Hunter, Bethany Filby, Andrew Brockman, Asa A McDonald, David Verhaak, Roel G W Ihrie, Rebecca A Stead, Lucy F Orcid: 0000-0002-9550-4150 MR/T020504/1/MRC_/Medical Research Council/United Kingdom R01 NS118580/NS/NINDS NIH HHS/United States U54 CA217450/CA/NCI NIH HHS/United States R01NS118580/NH/NIH HHS/United States Journal Article England 2023/01/24 Neuro Oncol. 2023 Jul 6;25(7):1236-1248. doi: 10.1093/neuonc/noad021.","abstract":"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.","keywords":"Humans *Glioblastoma/pathology Transcriptome *Brain Neoplasms/pathology Gene Expression Profiling/methods Microglia/metabolism Tumor Microenvironment deconvolution glioblastoma immune neoplastic transcriptomics","issn":"1522-8517 (Print) 1522-8517","doi":"10.1093/neuonc/noad021","year":"2023","bibtex":"@article{RN6050,\n author = {Ajaib, S. and Lodha, D. and Pollock, S. and Hemmings, G. and Finetti, M. A. and Gusnanto, A. and Chakrabarty, A. and Ismail, A. and Wilson, E. and Varn, F. S. and Hunter, B. and Filby, A. and Brockman, A. A. and McDonald, D. and Verhaak, R. G. W. and Ihrie, R. A. and Stead, L. F.},\n title = {GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data},\n journal = {Neuro Oncol},\n volume = {25},\n number = {7},\n pages = {1236-1248},\n note = {1523-5866\nAjaib, Shoaib\nLodha, Disha\nPollock, Steven\nHemmings, Gemma\nFinetti, Martina A\nGusnanto, Arief\nChakrabarty, Aruna\nIsmail, Azzam\nWilson, Erica\nVarn, Frederick S\nHunter, Bethany\nFilby, Andrew\nBrockman, Asa A\nMcDonald, David\nVerhaak, Roel G W\nIhrie, Rebecca A\nStead, Lucy F\nOrcid: 0000-0002-9550-4150\nMR/T020504/1/MRC_/Medical Research Council/United Kingdom\nR01 NS118580/NS/NINDS NIH HHS/United States\nU54 CA217450/CA/NCI NIH HHS/United States\nR01NS118580/NH/NIH HHS/United States\nJournal Article\nEngland\n2023/01/24\nNeuro Oncol. 2023 Jul 6;25(7):1236-1248. doi: 10.1093/neuonc/noad021.},\n abstract = {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.},\n keywords = {Humans\n*Glioblastoma/pathology\nTranscriptome\n*Brain Neoplasms/pathology\nGene Expression Profiling/methods\nMicroglia/metabolism\nTumor Microenvironment\ndeconvolution\nglioblastoma\nimmune\nneoplastic\ntranscriptomics},\n ISSN = {1522-8517 (Print)\n1522-8517},\n DOI = {10.1093/neuonc/noad021},\n year = {2023},\n type = {Journal Article}\n}\n\n","author_short":["Ajaib, S.","Lodha, D.","Pollock, S.","Hemmings, G.","Finetti, M. A.","Gusnanto, A.","Chakrabarty, A.","Ismail, A.","Wilson, E.","Varn, F. S.","Hunter, B.","Filby, A.","Brockman, A. A.","McDonald, D.","Verhaak, R. G. W.","Ihrie, R. A.","Stead, L. 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