Spatiotemporal heterogeneity in non-small cell lung cancer: A paradigm shift from characterization to dynamic management (Review).

Treatment of non-small cell lung cancer (NSCLC) has entered the era of precision medicine, characterized by targeted therapies and immunotherapies. However, tumor heterogeneity across spatial and temporal dimensions remains a central cause of therapeutic failure and acquired resistance. Spatial heterogeneity manifests as clonal diversity within a single tumor and between metastatic lesions, while temporal heterogeneity reflects the dynamic evolution of clonal populations under therapeutic pressure. It has proved difficult for traditional static diagnostic and therapeutic models to comprehensively capture this complexity. Advances in technologies such as single-cell sequencing, spatial transcriptomics and liquid biopsy now allow the spatiotemporal evolutionary patterns of NSCLC to be deciphered with unprecedented resolution. Based on these developments, the present review proposed that clinical management strategies need to shift from a static classification paradigm towards a new paradigm of dynamic monitoring and intervention. Emerging strategies are systematically discussed, including evolutionary trap therapy, niche intervention and adaptive therapy, which aim to achieve long-term control of disease progression by steering tumor evolutionary paths, remodeling the tumor microenvironment or leveraging competitive suppression mechanisms. Despite ongoing challenges at the technical, biological and clinical translation levels, a dynamic management framework integrating multi-omics data and intelligent algorithms represents promise for transforming NSCLC into a chronic, controllable disease.