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Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

ABSTRACT: We subject multiple versions and single cell-derived sublines of an archetypal non-small cell lung cancer cell line to experimental scrutiny at the genomic, transcriptomic, and cell population levels. We find evidence of genetic variability among cell line versions and one subline. Additional sublines show no genetic variation but epigenetic differences are detected at the transcriptomic level. Stochastic simulations of subline growth dynamics confirm that one subline likely contains at least two genetic states while all others are isogenic. Overall, our results substantiate a view of intratumoral heterogeneity in which different genetic states give rise to multiple epigenetic “basins of attraction,” across which cells can transition driven by stochastic factors such as gene expression noise and asymmetric cell division. Deconvolving intratumoral heterogeneity into genetic, epigenetic, and stochastic components provides a lens through which to view tumor drug response and acquired treatment resistance that may lead to novel therapeutic strategies in the future.

ORGANISM(S): Homo sapiens

PROVIDER: GSE150025 | GEO | 2020/05/08

REPOSITORIES: GEO

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Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

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