AI-Driven Multi-Omics Approaches for Deciphering Small-Cell Lung Cancer Heterogeneity Using Nucleic-Acid-Based Features

Abstract

Simple Summary The extensive heterogeneity of small-cell lung cancer (SCLC) across genomic, transcriptomic, epigenetic, proteomic, and microenvironmental levels has continued to undermine precision medicine. This review aims to help understand SCLC biology, resistance, and therapy by utilizing current literature encompassing nucleic-acid-based determinants of resistance, SCLC molecular architecture, transformation biology, and subtype evolution, integrating findings from genomic, transcriptomic, epigenetic, proteomic, imaging, and clinical studies. By leveraging AI-enabled multi-omics and radiomics, this framework has the potential to advance personalized clinical management and overcome longstanding diagnostic and therapeutic challenges in SCLC.