Precision Oncology 2.0: From Molecular Insights to Personalized Therapeutics

Abstract

Precision oncology has evolved beyond conventional genomic medicine into an integrated, data-driven discipline that combines molecular biology, artificial intelligence (AI), multi-omics technologies, advanced imaging, and computational medicine to deliver highly personalized cancer care. While early precision oncology primarily focused on identifying actionable genomic mutations for targeted therapy, emerging technologies now enable comprehensive characterization of tumor biology through the integration of genomics, epigenomics, transcriptomics, proteomics, metabolomics, radiomics, digital pathology, liquid biopsy, and real-world clinical data. Artificial intelligence has become the computational engine that transforms these complex datasets into clinically actionable knowledge through machine learning, deep learning, multimodal learning, graph neural networks, transformer architectures, and foundation models. These innovations are improving cancer diagnosis, prognostic prediction, biomarker discovery, therapeutic selection, immunotherapy response assessment, adaptive radiation planning, drug development, and long-term survivorship management. In addition, advances in spatial biology, single-cell sequencing, digital twins, explainable AI, federated learning, and intelligent clinical decision-support systems are redefining precision oncology as a continuously learning healthcare ecosystem. Despite remarkable progress, challenges remain regarding data harmonization, interoperability, computational scalability, cybersecurity, transparency, ethical governance, regulatory approval, and equitable clinical implementation. This review discusses the evolution of Precision Oncology 2.0, highlighting emerging technologies that are transforming molecular insights into individualized therapeutic strategies for the next generation of cancer care.[1]