Precision Oncology in the Era of Genomics and Artificial Intelligence

Abstract

Cancer patient care classically represents proper diagnosis, designing appropriate therapeutics and clinical management protocols. Concept of precision medicine emerged in conjuncture to personalized medicine when subpopulations reasonably differ in disease risks, prognosis, and treatment response due to interpersonal differences in disease biology. Precision oncology aims to tailor medical decisions and interventions to optimize clinical guidance on survival benefits or quality of life for each patient by utilizing person’s characteristics such as clinicopathology, mutational load, biochemical test profiles, specific protein expressions, pharmacogenomics, and pharmacokinetics–pharmacodynamics to determine risk prediction, treatment planning, and best treatment efficacy. Artificial intelligence (AI), i.e., the ability of a machine to learn and recognizing patterns from multidimensional large datasets, has vast use in health care, and most recently has been in use to generate algorithms from complex inputs to improvise the traditional approach of cancer diagnostics or therapy. AI in superseding the benefits of classical genetic marker panels, enabling the analysis of large-scale multiomic data and the development of sophisticated predictive models, and extending its applicability to several aspects such as cancer screening, patient stratification, as well as in clinical managements. The integration of genomic profile with AI becomes a crucial predictive tool to analyze how an individual’s unique genetic makeup influences disease susceptibility and treatment outcomes. Convergence of AI and multimodal data driven by genomics has revolutionized precision oncology, ultimately reshaping the landscape and horizon of patient care as well as uncovering new opportunities for better understanding of cancer biology.

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