Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding

Frederick J. H. Whiting (), Maximilian Mossner, Calum Gabbutt, Christopher Kimberley, Chris P. Barnes, Ann-Marie Baker, Erika Yara-Romero, Andrea Sottoriva, Richard A. Nichols () and Trevor A. Graham ()
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Frederick J. H. Whiting: Institute of Cancer Research
Maximilian Mossner: Institute of Cancer Research
Calum Gabbutt: Institute of Cancer Research
Christopher Kimberley: Queen Mary University of London
Chris P. Barnes: University College London
Ann-Marie Baker: Institute of Cancer Research
Erika Yara-Romero: Institute of Cancer Research
Andrea Sottoriva: Institute of Cancer Research
Richard A. Nichols: Queen Mary University of London
Trevor A. Graham: Institute of Cancer Research

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract Cancer treatment frequently fails due to the evolution of drug-resistant cell phenotypes driven by genetic or non-genetic changes. The origin, timing, and rate of spread of these adaptations are critical for understanding drug resistance mechanisms but remain challenging to observe directly. We present a mathematical framework to infer drug resistance dynamics from genetic lineage tracing and population size data without direct measurement of resistance phenotypes. Simulation experiments demonstrate that the framework accurately recovers ground-truth evolutionary dynamics. Experimental evolution to 5-Fu chemotherapy in colorectal cancer cell lines SW620 and HCT116 validates the framework. In SW620 cells, a stable pre-existing resistant subpopulation was inferred, whereas in HCT116 cells, resistance emerged through phenotypic switching into a slow-growing resistant state with stochastic progression to full resistance. Functional assays, including scRNA-seq and scDNA-seq, validate these distinct evolutionary routes. This framework facilitates rapid characterisation of resistance mechanisms across diverse experimental settings.

Date: 2025
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DOI: 10.1038/s41467-025-59479-7

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