Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

• Main Authors: Alessia Mondello (Author), Michele Dal Bo (Author), Giuseppe Toffoli (Author), Maurizio Polano (Author)
• Format: Book
• Published: Frontiers Media S.A., 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1663-9812; 10.3389/fphar.2023.1260276
• Keywords: pharmacogenomics; machine learning; omics; targeted therapy; drug toxicity; drug efficacy; Therapeutics. Pharmacology; RM1-950
• Source: Frontiers in Pharmacology, Vol 14 (2024)

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.