A computational framework successfully distinguishes between benign and pathogenic single amino acid substitutions in proteins, addressing a critical gap in cancer genetics. The model analyzes missense mutations—where one DNA letter change swaps a single amino acid—to predict functional disruption and oncogenic potential with high accuracy. This capability represents a significant advance in precision oncology, as missense variants comprise roughly 75% of disease-causing mutations but have been notoriously difficult to interpret. Current clinical practice often classifies these variants as "uncertain significance," leaving patients and physicians without actionable guidance. The new framework could accelerate personalized cancer treatment by rapidly identifying which mutations warrant targeted therapy versus those representing neutral variation. Beyond immediate clinical applications, this tool may enhance our understanding of protein structure-function relationships and evolutionary constraints. However, the model's performance across diverse populations and rare cancer types requires validation, and integration into clinical workflows will demand extensive regulatory review. The framework also highlights the growing role of artificial intelligence in translating genomic discoveries into patient care, potentially reducing the time from genetic testing to treatment selection from months to days.