Explores tumour evolution dynamics in clinical samples, suggesting new pathways for precision oncology.
Current methods for distinguishing neutral from selection-driven tumour evolution rely on assumptions frequently violated in clinical samples. We argue that tumour evolution is not a binary state, but a dynamic continuum shaped by genetic and non-genetic heterogeneity, spatial constraints, microenvironmental pressures and therapeutic bottlenecks. Refining evolutionary inference through integrative, longitudinal and spatially resolved analyses will be essential to align these models with therapeutic decision-making in precision oncology.
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Sud et al. (2026) studied this question.
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