Ahmet Acar, Daniel Nichol, Javier Fernandez-Mateos, George D. Cresswell, Iros Barozzi, Sung Pil Hong, Nicholas Trahearn, Inmaculada Spiteri, Mark Stubbs, Rosemary Burke, Adam Stewart, Giulio Caravagna, Benjamin Werner, Georgios Vlachogiannis, Carlo C. Maley, Luca Magnani, Nicola Valeri, Udai Banerji, Andrea SottorivaRead the manuscript
Figure 1: Temporal frequencies of the floating barcodes in three independent evolutionary replicates under exposure to the MEK inhibitor trametinib. Coloured squares indicate the frequency of unique barcodes. Barcode frequencies showed clear temporal dynamics, conserved evolutionary dynamics between replicates, and a close correspondence between the final floating barcode frequencies and associated frequencies of barcodes deriving from harvested live cells at the final time point.To track this evolution at weekly time points we relied on floating DNA present in the cell culture media. We found that barcodes could be extracted from the milieu of cell-free DNA comprising DNA released by dying cells. These barcodes where then sequenced to provide weekly measurements of clonal composition. This seems paradoxical at first, why should the dead cells give information about resistant subclones? The answer lies in the population dynamics. As resistant subclones expand in frequency, cell-free DNA arising from the natural turnover of resistant cells outnumbers that from dying drug-sensitive cells that are diminishing in frequency. The results are striking (Figure 1). We see the same barcode-tagged resistant subclones arising between replicates, with very similar temporal dynamics. Furthermore, the final time point of media-derived barcode frequencies closely matches that sequenced from the live cells, confirming that the media-derived barcode frequencies serve as an accurate proxy of the live population composition. These results demonstrate repeatable evolutionary steering of a genetically heterogeneous cancer cell-line. The capacity to track clonal frequencies through time also suggests that this experimental system may prove well suited to the in vitro study of adaptive therapy10, or cancer evolutionary game theory11, where the dynamics of relative clonal frequencies are critical. Having demonstrated evolutionary steering, we sought to identify second-line drug collateral sensitivities through two contrasting approaches. First, we employed single-cell molecular profiling to help identify emergent molecular characteristics that might be exploited to yield collateral sensitivity. Second, we collaborated with colleagues in the CRUK Cancer Therapeutics Unit at the ICR to perform a high-throughput screen of 485 second-line compounds. Both approaches identified potential collaterally sensitive follow-ups for gefitinib or trametinib. Taken together our results indicate the potential for repeatable evolutionary steering towards collateral sensitivity in highly heterogeneous cancer populations. The robustness of these collaterally sensitive drug combinations will need be validated through further experiments. This work relied on an untested combination of large-population cell culture without serial passaging, high concentration drug exposures, lentiviral barcoding and sequencing, and DNA extraction from cell culture media. As such, we faced uncertainty about the validity of a potentially long and expensive experimental design and needed confidence in our approach from the outset. This confidence was found through a closely integrated mathematical modelling and wet lab biology approach. Each step of the experiment design, including the lentiviral barcoding process, population expansions, splitting into evolutionary replicates, and drug concentration calculations, was first validated in silico by mirroring the experimental through simulation with mathematical and computational models. This process gave us confidence that the clonal composition could be accurately identified from the DNA sequencing of barcodes if our hypotheses were indeed correct. Ultimately, this study demonstrates that close integration of mathematical modelling and experimental biology can greatly improve experimental design and yield results that either field alone would struggle to generate.
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