Plasticity of extrachromosomal DNA segregation during drug adaptation
Shibata, C.; Miyata, K.; Kumegawa, K.; Yang, L.; Nozawa, R.-S.; Maruyama, R.
Show abstract
Uneven segregation during mitosis is a striking feature of extrachromosomal DNA (ecDNA). Because ecDNA lacks a centromere, it is thought to segregate stochastically and randomly during cell division, thereby generating extensive intratumoral heterogeneity in genomic copy number. Several studies have reported that ecDNA copy numbers can readily change in response to drug treatment, which enables the cells to acquire drug resistance. However, the mechanisms underlying these dynamic changes remain poorly understood --particularly whether such copy-number changes result from static selection of pre-existing clones or from active reconfiguration under drug-induced stress. This key question remains unresolved, mainly due to the absence of technologies capable of tracking ecDNA copy number simultaneously across clones. To overcome this limitation, we developed a high-throughput framework that combines single-cell DNA sequencing with cellular barcoding for clonal tracking of ecDNA copy-number dynamics. The results of single-cell cloning experiments revealed that not all clones exhibit identical segregation modes even under drug-free conditions. Clonal tracking under drug treatment showed that resistant populations do not simply emerge from pre-existing clones with favorable ecDNA states; instead, some clones seemed capable of actively reconfiguring their segregation behavior, possibly involving neuron-like alternation in microtubule organization and intracellular transport pathways, to generate drug-resistant cells. These findings suggest that, although ecDNA might segregate stochastically, it may undergo nonrandom, actively regulated segregation under drug stress. This discovery allows the therapeutically targeting of ecDNA segregation mechanisms to counteract adaptive drug resistance.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.