Integrating Genomic Data with Deep Learning for Personalized Cancer Treatment

Cancer remains a significant global health burden, with its heterogeneous genetic and molecular etiology complicating effective treatment. Precision medicine introduces a transformative paradigm by leveraging patients’ genomic profiles to improve individualized treatment response predictions and optimize therapeutic strategies. Integrating genomic data with deep learning (DL) has emerged as a promising approach to advancing personalized cancer care. DL’s capacity to process high-dimensional datasets, uncover intricate patterns, and predict actionable outcomes makes it a potent tool in oncology. This review explores DL’s applications in genomic data analysis for cancer treatment, focusing on biomarker discovery, drug response prediction, and multi-omics integration. Challenges, including data heterogeneity, interpretability, and ethical considerations, are critically examined. A proposed framework for integrating multi-modal data highlights its potential to enhance clinical decision-making. This study underscores the significant promise of DL in reshaping cancer treatment paradigms, emphasizing the importance of robust validation in real-world settings.