ARTIFICIAL INTELLIGENCE IN INTEGRATIVE GENOMIC PROFILING FOR PERSONALIZED CANCER THERAPEUTICS

Abstract

Personalized cancer therapy requires a comprehensive understanding of the multi-layered molecular alterations that drive tumor behavior, yet traditional genomic approaches often fail to capture the full spectrum of biological complexity. This study presents an artificial intelligence–powered framework for integrative genomic profiling that unifies whole-genome sequencing data, transcriptomic signatures, DNA methylation patterns, and proteomic expression profiles to generate clinically actionable therapeutic insights. Using a hybrid deep-learning architecture combined with graph-based biological interaction models, the system learned latent multi-omics representations that accurately predicted therapeutic sensitivity, pathway dysregulation, and drug–target compatibility across diverse cancer subtypes. Experimental results demonstrated strong predictive performance and high stability across cross-validation cohorts, while multi-omics correlation analyses revealed key regulatory mechanisms underlying treatment responsiveness. Interpretability assessments by oncology experts confirmed that the AI system prioritized biologically meaningful features and aligned with known oncogenic pathways, therapeutic biomarkers, and resistance mechanisms. Although the study acknowledges certain limitations—including variability in sequencing depth, incomplete proteomic coverage, and sample imbalance—the overall findings highlight the promising potential of AI-driven genomic integration for enhancing precision oncology. By offering deeper molecular insight, enabling more accurate drug-response prediction, and supporting clinician decision-making, this approach establishes a scalable and clinically impactful foundation for next-generation personalized cancer therapeutics.