FOUNDATION MODELS FOR COMPUTATIONAL CANCER BIOLOGY: BRIDGING MOLECULAR MECHANISMS AND CLINICAL TRANSLATION

Abstract

Foundation artificial intelligence (AI) models are transforming computational cancer biology by bridging molecular mechanisms with clinical translation through large-scale multimodal representation learning and adaptive biomedical reasoning. Traditional computational oncology has generated substantial biological insights from genomics, transcriptomics, proteomics, metabolomics, and medical imaging; however, fragmented analysis of heterogeneous biomedical data has limited comprehensive understanding of tumor evolution, therapeutic resistance, biomarker discovery, and precision medicine. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support molecular characterization, biomarker discovery, pathway analysis, prognostic prediction, therapeutic optimization, drug target identification, adaptive clinical trials, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen computational cancer biology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of foundation models for computational cancer biology, emphasizing their role in bridging molecular mechanisms with clinical translation for next-generation precision oncology.