Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding
Cancer treatment frequently fails due to the evolution of drug resistant cell phenotypes caused by underlying genetic or non-genetic changes. The origin of these adaptations, their timing and rate of spread is key information for distinguishing the mechanism(s) of drug resistance, yet the dynamics cannot be observed directly. Here, we construct a mathematical framework to infer the dynamics of drug resistance without the need for direct measurement of the resistance phenotype using only genetic lineage tracing and population size data. The veracity of the framework is demonstrated through experimental evolution to 5-Fu chemotherapy in two common colorectal cancer cell lines: SW620 and HCT116. In SW620 cells, a stable pre-existing resistant subpopulation was inferred. In HCT116 cells resistance emerged through phenotypic switching into a slow growing resistant state with stochastic exiting into a fully resistant phenotype. Extensive functional assays, including scRNA-seq and scDNA-seq confirmed these distinct evolutionary routes and their molecular nature. Our mathematical framework can be extended to diverse experimental designs to infer the evolutionary dynamics of cancer cell therapy resistance evolution from readily obtained experimental data, enabling more rapid characterisation of resistance mechanisms.