GBMdeconvoluteR可以从大块脑胶质瘤转录组数据中准确推断出肿瘤和免疫细胞的比例。
GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.
发表日期：2023 Jan 23
作者：
Shoaib Ajaib, Disha Lodha, Steven Pollock, Gemma Hemmings, Martina A Finetti, Arief Gusnanto, Aruna Chakrabarty, Azzam Ismail, Erica Wilson, Frederick S Varn, Bethany Hunter, Andrew Filby, Asa A Brockman, David McDonald, Roel G W Verhaak, Rebecca A Ihrie, Lucy F Stead
来源：
NEURO-ONCOLOGY
摘要：
在规模上表征和量化脑胶质母细胞瘤(GBM)肿瘤内的细胞类型将有助于更好地理解细胞景观与肿瘤表型或临床相关性之间的关联。我们旨在开发一种工具，通过批量RNA测序数据将GBM肿瘤微环境中的免疫和肿瘤细胞解混。我们发展了一种IDH野生型(IDHwt)GBM特异的单个免疫细胞参考，包括B细胞、T细胞、NK细胞、微胶质细胞、肿瘤相关巨噬细胞、单核细胞、肥皂泡细胞和DC细胞。我们将其与现有的肿瘤单细胞类型参考一起使用，用于星形胶质细胞瘤样、少突胶质细胞和神经祖细胞瘤样和间充质GBM癌细胞，创建基于标记和基因签名矩阵的混合反演工具。我们对十个IDHwt GBM样本进行了单细胞分辨率成像质谱细胞学(IMC)测试，包括五对原发肿瘤和复发肿瘤，以确定哪种解混方法表现最佳。使用GBM组织特异性标记的标记反演在免疫细胞和癌细胞方面都最为准确，因此我们将该方法打包为GBMdeconvoluteR。我们将GBMdeconvoluteR应用于癌症基因组图谱的GBM RNAseq数据，重现了多组学单细胞研究的最新发现，涉及间充质GBM癌细胞与淋巴细胞和髓系细胞之间的关联。此外，我们进一步拓展了这一发现，表明在预后更差的患者中，这些关联性更强。GBMdeconvoluteR可准确量化IDHwt GBM批量RNA测序数据中的免疫和肿瘤细胞比例，可在此处访问：https : // gbmdeconvoluter.leeds.ac.uk。© 作者（们）2023年。由牛津大学出版社代表神经肿瘤学学会出版。
Characterising and quantifying cell types within glioblastoma (GBM) tumours at scale will facilitate a better understanding of the association between the cellular landscape and tumour phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumour microenvironment from bulk RNA sequencing data.We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T cells, NK cells, microglia, tumour 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 tumours, to determine which deconvolution approach performed best.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.GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https : // gbmdeconvoluter.leeds.ac.uk.© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.