Predicting glioblastoma gene expression therapy response with machine learning

Abstract AIMS The rapid recurrence of therapy-resistant glioblastoma leaves patients with a dismal 2-year survival rate of just 16%, highlighting the need to develop treatments that combat or prevent glioblastoma recurrence. Recently, glioblastoma patients have been stratified by their gene expression response to therapy. Specifically, genes with JARID2 binding sites in their promoter (JBS-genes) either become up- or down-regulated on progression from the primary to the recurrent tumour. Importantly, these two responses appear to employ different mechanisms of therapy resistance. Using gene expression data from primary tumours, we aim to predict the direction of JBS- gene dysregulation. Ultimately, this prediction could guide treatment decisions that prevent the acquisition of therapy resistance in glioblastoma. METHOD We used gene expression data from 84 primary glioblastoma tumours that had been labelled as either ‘Up’ or ‘Down’ responders based on the direction of JBS-gene dysregulation in matched recurrent tumours. We converted the RNA-sequencing data from the primary tumours to single-sample pathway scores, which were used to train several binary classification machine learning models. RESULTS A ridge regression model achieved an accuracy of 73.9% in predicting the direction of JBS-gene dysregulation in an independent set of 23 primary glioblastoma tumours. CONCLUSIONS Our classifier performs better than random chance demonstrating that gene expression data is informative and molecular features influence this gene expression response to therapy. However, there remains scope for improvement. This suggests further studies may benefit from integrating multiple data types such as genomics, epigenomics, tumour architecture, or clinical variables.