{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:19:42Z","timestamp":1787530782423,"version":"build-2736575974"},"reference-count":35,"publisher":"SAGE Publications","issue":"17-18","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stem Cells and Development"],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:p>Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor\u2013stromal\u2013immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.<\/jats:p>","DOI":"10.1177\/15473287261475554","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:52:00Z","timestamp":1786701120000},"page":"325-338","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology\u2014Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer"],"prefix":"10.1177","volume":"35","author":[{"given":"Keira Noelle","family":"Zhong","sequence":"first","affiliation":[{"name":"USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katelyn S.","family":"Ge","sequence":"additional","affiliation":[{"name":"Cornell University, Irvine, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry Michael","family":"Lee","sequence":"additional","affiliation":[{"name":"IIA, Irvine, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9699-9204","authenticated-orcid":false,"given":"Shengwen Calvin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurology, Irvine School of Medicine, University of California, Orange, California, USA."},{"name":"CHOC Children\u2019s Research Institute, Children\u2019s Hospital of Orange County (CHOC\u00ae), Rady Children\u2019s Health Orange County (RCHOC\u00ae), Orange, California, 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