Foundation Models for Translational Cancer Biology

Cancer remains a leading cause of death globally. The complexity and diversity of cancer-related datasets across different specialties pose challenges in refining precision medicine for oncology. Foundation models offer a promising solution. Trained on vast amounts of data, these models develop a broad understanding across a wide range of tasks. We examine the role of foundation models in domains relevant to cancer research, including natural language processing, computer vision, molecular biology, and cheminformatics. Through a review of state-of-the-art methods, we explore how these models have already advanced translational cancer research goals such as precision tumor classification and artificial intelligence–assisted surgery. We also discuss prospective advances in areas like early tumor detection, personalized cancer treatment, and drug discovery. This review provides researchers with a curated set of resources and methodologies, offers practitioners a deeper understanding of how these models enhance cancer care, and points to opportunities for future applications of foundation models in cancer research.