GBMPurity: A machine learning tool for estimating glioblastoma tumor purity from bulk RNA-sequencing data

被引:0
|
作者
Thomas, Morgan P. H. [1 ,2 ]
Ajaib, Shoaib [2 ]
Tanner, Georgette [2 ]
Bulpitt, Andrew J. [1 ]
Stead, Lucy F. [2 ]
机构
[1] Univ Leeds, Sch Comp Sci, Leeds, England
[2] Univ Leeds, Leeds Inst Med Res St Jamess, Leeds, England
基金
英国科研创新办公室;
关键词
deconvolution; glioblastoma; transcriptomics; tumor microenvironment; tumor purity; EVOLUTION; SUBTYPES; ATLAS;
D O I
10.1093/neuonc/noaf026
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumor purity are nonspecific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM.Methods We developed GBMPurity, a deep learning model specifically designed to estimate the purity of IDH-wild type primary GBM from bulk RNA-sequencing (RNA-seq) data. The model was trained using simulated pseudobulk tumors of known purity from labeled single-cell data acquired from the GBmap resource. The performance of GBMPurity was evaluated and compared to several existing tools using independent datasets.Results GBMPurity outperformed existing tools, achieving a mean absolute error of 0.15 and a concordance correlation coefficient of 0.88 on validation datasets. We demonstrate the utility of GBMPurity through inference on bulk RNA-seq samples and observe reduced purity of the proneural molecular subtype relative to the classical, attributed to the increased presence of healthy brain cells.Conclusions GBMPurity provides a reliable and accessible tool for estimating tumor purity from bulk RNA-seq data, enhancing the interpretation of bulk RNA-seq data and offering valuable insights into GBM biology. To facilitate the use of this model by the wider research community, GBMPurity is available as a web-based tool at: https://gbmdeconvoluter.leeds.ac.uk/.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] REPAC: analysis of alternative polyadenylation from RNA-sequencing data
    Imada, Eddie L.
    Wilks, Christopher
    Langmead, Ben
    Marchionni, Luigi
    GENOME BIOLOGY, 2023, 24 (01)
  • [22] Identifying transposon insertions and their effects from RNA-sequencing data
    de Ruiter, Julian R.
    Kas, Sjors M.
    Schut, Eva
    Adams, David J.
    Koudijs, Marco J.
    Wessels, Lodewyk F. A.
    Jonkers, Jos
    NUCLEIC ACIDS RESEARCH, 2017, 45 (12) : 7064 - 7077
  • [23] Prediction of tumor purity from gene expression data using machine learning
    Koo, Bonil
    Rhee, Je-Keun
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (06)
  • [24] Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response
    Mestdagh, Pieter
    Van Dam, Pieter-Jan
    De Baene, Frederick
    Fierro, Carolina
    Van Hoof, Elise
    Riviere, Emmanuel
    Dangreau, Hanne
    Van Cauwenberge, Reindert
    Vandesompele, Jo
    CANCER RESEARCH, 2024, 84 (06)
  • [26] Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data
    Zhang, Junpeng
    Liu, Lin
    Wei, Xuemei
    Zhao, Chunwen
    Luo, Yanbi
    Li, Jiuyong
    Le, Thuc Duy
    BMC BIOLOGY, 2024, 22 (01)
  • [27] Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data
    Lijia Yu
    Yue Cao
    Jean Y. H. Yang
    Pengyi Yang
    Genome Biology, 23
  • [28] Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data
    Yu, Lijia
    Cao, Yue
    Yang, Jean Y. H.
    Yang, Pengyi
    GENOME BIOLOGY, 2022, 23 (01)
  • [29] SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
    Assefa, Alemu Takele
    Vandesompele, Jo
    Thas, Olivier
    BIOINFORMATICS, 2020, 36 (10) : 3276 - 3278
  • [30] Bulk and single-cell RNA-sequencing analyses along with abundant machine learning methods identify a novel monocyte signature in SKCM
    Liu, Yuyao
    Zhang, Haoxue
    Mao, Yan
    Shi, Yangyang
    Wang, Xu
    Shi, Shaomin
    Hu, Delin
    Liu, Shengxiu
    FRONTIERS IN IMMUNOLOGY, 2023, 14