Abstract
Traditional frameworks view cancer progression through genetic mutation and clonal selection. However, emerging long-read epigenomic studies reveal that stochastic epigenetic alterations introduce an underappreciated evolutionary layer governing cellular adaptation. Nanopore methylome analyses of acute myeloid leukemia (AML) demonstrate that extensive hypermethylation in CpG-poor regions and heightened epigenetic entropy accompany relapse and transcription factor network rewiring. This perspective article introduces "Epigenetic Climate Change," a framework positioning methylation entropy as a cellular climate indicator and artificial intelligence as a predictive forecasting engine. We propose the Epigenetic Climate Index (ECI) to operationalize system stability, integrating nanopore methylomics and graph-based machine learning to forecast disease trajectories—such as drug resistance and relapse—before irreversible clinical transitions occur.
