Artificial intelligence-assisted genome editing in cancer therapy

Abstract

Cancer remains a major global health challenge due to its genetic complexity, tumor heterogeneity, and the development of therapeutic resistance.Conventional treatment modalities, including surgery, chemotherapy, immunotherapy, and radiotherapy, often fail to completely eradicate malignant cells and may cause significant systemic toxicity.Genome editing technologies, particularly clustered regularly interspaced short palindromic repeats Cas 9-based systems, have emerged as powerful tools for precise modification of oncogenes, tumor suppressor genes, and regulatory genomic elements, offering new opportunities for targeted cancer therapy.However, challenges such as offtarget effects, variable editing efficiency, and genomic complexity continue to limit their clinical translation.The rapid advancement of artificial intelligence (AI), especially machine learning and deep learning approaches, is transforming genome engineering strategies in oncology.AI-driven platforms enable optimized guide RNA design, improved off-target prediction, efficient clustered regularly interspaced short palindromic repeats screening analysis, and the development of improved base and prime editing systems, thereby increasing the precision and therapeutic potential of genome editing technologies.This review highlights the emerging convergence of AI and genome editing in cancer therapeutics, discussing current applications, technological advances, and translational challenges.The integration of AI-guided genome editing platforms holds significant promise for developing safer, more precise, and personalized cancer treatment strategies.