GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.
GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.
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DOI：
10.1093/neuonc/noad021
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发表时间：
2023-07-06
期刊：
影响因子：
15.9
通讯作者：
Stead LF
中科院分区：
文献类型：
--
作者：
Ajaib S;Lodha D;Pollock S;Hemmings G;Finetti MA;Gusnanto A;Chakrabarty A;Ismail A;Wilson E;Varn FS;Hunter B;Filby A;Brockman AA;McDonald D;Verhaak RGW;Ihrie RA;Stead LF
大规模表征和定量胶质母细胞瘤（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。我们的目标是开发一种工具，从大量RNA测序数据中去卷积GBM肿瘤微环境中的免疫细胞和肿瘤细胞。我们开发了IDH野生型（IDHwt）GBM特异性单一免疫细胞参照，其由B细胞、T细胞、NK细胞、小胶质细胞、肿瘤相关巨噬细胞、单核细胞、肥大细胞和DC细胞组成。我们将其与星形胶质细胞样、少突胶质细胞样和神经元祖细胞样和间充质GBM癌细胞的现有肿瘤性单细胞类型参考一起使用，以创建基于标记和基因签名矩阵的去卷积工具。我们将单细胞分辨率成像质谱细胞术（IMC）应用于10个IDHwt GBM样本，5个配对的原发性和复发性肿瘤，以确定哪种去卷积方法表现最好。使用GBM组织特异性标记的基于标记的去卷积对于免疫细胞和癌细胞都是最准确的，因此我们将这种方法打包为GBMdeconvoluteR。我们将GBMdeconvoluteR应用于来自癌症基因组图谱的大量GBM RNAseq数据，并概括了多组学单细胞研究中关于间充质GBM癌细胞与淋巴细胞和骨髓细胞之间相关性的最新发现。此外，我们对此进行了扩展，以表明这些关联在预后较差的患者中更强。GBMdeconvoluteR可准确定量IDHwt GBM批量RNA测序数据中的免疫和肿瘤细胞比例，可在https://gbmdeconvoluter.leeds.ac.uk访问。
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. 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. 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.
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影响因子：
7.7
作者：
Racle J;de Jonge K;Baumgaertner P;Speiser DE;Gfeller D
通讯作者：
Gfeller D
影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
DOI：
10.1007/978-1-0716-0301-7_7
发表时间：
2020
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Steen CB;Liu CL;Alizadeh AA;Newman AM
通讯作者：
Newman AM
影响因子：
25
作者：
Sankowski, Roman;Bottcher, Chotima;Prinz, Marco
通讯作者：
Prinz, Marco
影响因子：
12.3
作者：
Li B;Severson E;Pignon JC;Zhao H;Li T;Novak J;Jiang P;Shen H;Aster JC;Rodig S;Signoretti S;Liu JS;Liu XS
通讯作者：
Liu XS