RT Journal Article
SR Electronic
T1 Exploiting evolutionary herding to control drug resistance in cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 566950
DO 10.1101/566950
A1 Ahmet Acar
A1 Daniel Nichol
A1 Javier Fernandez-Mateos
A1 George D. Cresswell
A1 Iros Barozzi
A1 Sung Pil Hong
A1 Inmaculada Spiteri
A1 Mark Stubbs
A1 Rosemary Burke
A1 Adam Stewart
A1 Georgios Vlachogiannis
A1 Carlo C. Maley
A1 Luca Magnani
A1 Nicola Valeri
A1 Udai Banerji
A1 Andrea Sottoriva
YR 2019
UL http://biorxiv.org/content/early/2019/03/04/566950.abstract
AB Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called ‘evolutionary herding’ that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 108–109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.