Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning

Background: Tumor evolution is driven by substantial cellular heterogeneity, yet its reconstruction remains challenging with conventional bulk sequencing approaches. Single-cell transcriptomic data provide opportunities to study clonal diversity and evolutionary dynamics, but accurate inference of clonal structure and lineage relationships from these data remains difficult. Methods: We developed single-cell reinforcement learning (RL) for evolution modeling (scRevol), an RL-based model for inferring tumor evolution from single-cell RNA sequencing (scRNA-seq) data. Using copy number variation (CNV) profiles inferred from scRNA-seq data, scRevol employs a label assignment learning strategy to generate informative embeddings, identify clonal populations, and reconstruct evolutionary trajectories. We evaluated scRevol using simulated datasets, lineage tracing data, and ovarian cancer scRNA-seq datasets. Results: In simulated datasets, scRevol showed robust intra-cluster coherence and accurately recovered lineage topology across varying levels of clonal complexity and noise. Compared with clustering baselines and existing methods for single-cell tumor evolution analysis, tscRevol achieved strong agreement with ground truth. In lineage tracing data, scRevol identified clonal groups associated with metastatic potential and revealed substantial metastatic heterogeneity. In ovarian cancer datasets, scRevol resolved subclonal structures across primary and metastatic lesions and associated inferred clones with distinct transcriptional and pathway-level features. Conclusions: These results support scRevol as a practical framework for reconstructing tumor evolution from single-cell transcriptomic data and for characterizing clonal architecture and subclonal diversity.