ArticleNature communications2025
Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- Review
- Charting single-cell lineages with synthetic and natural barcodes.Nature reviews. Genetics · 2026Review
- Evolutionary dynamics under phenotypic uncertainty.bioRxiv : the preprint server for biology · 2026Article
- Strength through diversity: how cancers thrive when clones cooperate.Molecular oncology · 2026Review
- Stage-specific evolutionary dynamics in pediatric leukemia inferred using Bayesian stochastic modeling.PloS one · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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.
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Registered trials
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