Identifying cancer cell-state transitions from multimodal single-cell data

Abstract Phenotypic plasticity allows cancer cells to evade therapy, yet the transient nature of state transitions has made their molecular drivers difficult to define. Here, we present a single-cell framework that leverages the temporal delays between mRNA and protein accumulation to directly capture cells undergoing phenotypic switching. Applying this strategy to the K562 leukemia model, which alternates between CD24- and stem-like CD24+ states, we identify transitioning cells and derive a transcriptional signature linking cell-cycle progression and mitochondrial remodeling to plasticity. Genome-wide CRISPR screening confirms key regulators of plasticity, including BCR–ABL1 and mitochondrial homeostasis genes. We summarize the transition-associated program into a score that predicts imatinib response in chronic myeloid leukemia, stratifies survival in acute myeloid leukemia, and retains prognostic value across 31 TCGA tumor types. Spatial transcriptomics reveals localized plasticity hotspots in solid tumors. Together, this framework exposes the molecular basis of cancer plasticity and enables its quantification across tumors. Competing Interest Statement The authors have declared no competing interest.