The impact of deep learning and omics data in transforming precision therapy for brain cancer

Abstract

The combination of AI with high-throughput genomics has revolutionized oncology, particularly in the case of brain cancer, a diagnostically complicated and heterogeneous tumor. With the explosion of omics data and computational power, deep learning (DL) has evolved as a powerful computational approach for the interpretation of the molecular topography of brain tumors. Utilizing mono-omics and multi-omics data, including gene expression, somatic mutations, DNA methylation, copy number alterations, and miRNA profiles, deep learning algorithms can detect complex, nonlinear patterns underlying tumor biology and clinical behavior. Several DL architectures have been used in recent studies for classification, biomarker discovery, and subtype prediction in brain cancer. The combination of multi-omics data has been especially useful, since oncogenic changes arise at several molecular levels; therefore, the omission of any specific omics aspect might undermine diagnostic accuracy and therapeutic prediction. Here, in this review, DL-based approaches to various omics modalities are reviewed, covering the tools constructed, architectures, and performance levels, and then delving into integrative multi-omics frameworks that maximize the accuracy and interpretability of diagnostic models. It also discusses the significance of AI-based approaches in advancing personalized treatment for brain cancer, emphasizing their capacity to predict patient-specific drug responses. Notwithstanding these breakthroughs, some major challenges remain, which include data heterogeneity, explainability of models, computational requirements, and ethical issues related to the use of genomic data. Together, the review highlights the potential of DL and multi-omics integration in advancing precision oncology for brain cancer by enhancing diagnostic precision, prognostic accuracy, and personalized therapy development.