Gene Expression Data Analysis of Persister Cancer Cells



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Many cancer patients experience recurrence of the disease within months or years after the first treatment. Recent studies have shown that many of these relapses can arise due to the presence of drug-tolerant persister cells. Persisters are a subpopulation of transiently drug-tolerant cells that can enter a state of negligible growth, through reversible, non-mutational mechanisms. These cells have shown to have unique but conserved metabolic mechanisms that are essential for their survival. In this study we analyzed publicly release data from the Connectivity Map (CMap) at the Broad Institute, a genome-scale library of cellular signatures that catalogs transcriptional responses to chemical, genetic, and disease perturbation. The perturbational data is generated using the L1000 assay and was used to screen for metabolic pathways associated with persister formation and survival. By using one of the most high-level, interpreted and general-purpose dynamic programming languages, Python, we identified specific metabolic pathways active in persisters.