DeepMoDRP: A Multi-Omics-Based Deep Learning Framework for Drug Response Prediction in Brain Cancer

Considering the limited efficacy of existing pharmacotherapies for brain tumors, the development of accurate predictive models is essential for advancing neuro-oncology treatment strategies. In this article, we introduce a drug response prediction model, DeepMoDRP, specifically designed for brain cancer. This model integrates genomic, transcriptomic, and epigenomic data from various brain tumor cell lines, including low-grade glioma, glioblastoma multiforme, and diffuse large B-cell lymphoma. To address the high-dimensional complexity inherent in gene expression and copy number variations within cell line data, we have integrated sparse autoencoders (AEs) and denoising AEs to reduce noise and redundancy. Meanwhile, one-dimensional convolutional neural networks are utilized to process the low-dimensional mutation and DNA methylation data. Additionally, a multiscale graph neural network is implemented to handle the drug-related data. Finally, fully connected networks are employed to generate predictions of drug responses. A series of experiments were conducted utilizing a brain tumor dataset that was extracted and curated from public databases. The experimental results demonstrate that the proposed DeepMoDRP outperforms the performance of state-of-the-art pan-cancer baseline models in predicting drug responses for brain tumors. The downstream analysis indicates that the DeepMoDRP holds significant promise for the treatment of brain tumors.