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

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/.