Single-cell multi-omics and machine learning for dissecting stemness in cancer

Cancer stem cells (CSCs) are a subpopulation of tumor cells with self-renewal capacity and the ability to drive tumor growth, metastasis, and relapse. They are widely recognized as major contributors to therapeutic resistance. Despite extensive efforts to characterize and target CSCs, their elusive nature continues to drive therapeutic resistance and relapse in epithelial malignancies. Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of tumor biology. It enables high-resolution profiling of rare subpopulations (<5%) and reveals the functional heterogeneity that contributes to treatment failure. In this review, we discuss evolving evidence for a paradigm shift, enabled by rapidly advancing single-cell technologies, from a static, marker-based definition of CSCs to a dynamic and functional perspective. We explore how trajectory inference and spatial transcriptomics redefine stemness by context-dependent dynamic-state modelling. We also highlight emerging platforms, including artificial intelligence-driven predictive modelling, multi-omics integration, and functional CRISPR screens. These approaches have the potential to uncover new vulnerabilities in CSC populations. Together, these advances should lead to new precision medicine strategies for disrupting CSC plasticity, niche adaptation, and immune evasion.