Research Information System
CANCER RESEARCH, vol.85, no.15, pp.2921-2938, 2025 (SCI-Expanded, Scopus)
Cancer drug resistance is multifactorial, driven by heritable (epi)genetic changes but also by phenotypic plasticity. In this study, we dissected the drivers of resistance by perturbing organoids derived from patients with colorectal cancer longitudinally with drugs in sequence. Combined longitudinal lineage tracking, single-cell multiomics analysis, evolutionary modeling, and machine learning revealed that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory of drug resistance was encoded as a heritable epigenetic configuration from which multiple transcriptional programs could run, supporting a one-to-many (epi)genotype-to-phenotype map that explains how clonal expansions and plasticity manifest together. This epigenetic landscape may ensure drug-resistant subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapy resistance was instead entirely driven by transient phenotypic plasticity rather than stable clonal selection. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programs. Collectively, these data show how genetic and epigenetic alterations are selected to engender a "permissive epigenome" that enables phenotypic plasticity.Significance: Drug resistance is driven by genetic-epigenetic memory that enables cancer cells to adopt multiple phenotypic states depending on environmental conditions, supporting integration of evolutionary principles into biomarker discovery and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.Significance: Drug resistance is driven by genetic-epigenetic memory that enables cancer cells to adopt multiple phenotypic states depending on environmental conditions, supporting integration of evolutionary principles into biomarker discovery and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.