2019/01/24 by Niraj Kumar, Kumar, Niraj, Gwendolyn M. Cramer +9
Biochemistry, Genetics and Molecular Biology · Mathematics · #Evolution and Genetic Dynamics #FOS: Biological sciences #Gene Regulatory Network Analysis #Mathematical Biology Tumor Growth #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.1901.08635
openalex publication_date 2019/01/24 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
Phenotypic heterogeneity in cancer cells is widely observed and is often\nlinked to drug resistance. In several cases, such heterogeneity in drug\nsensitivity of tumors is driven by stochastic and reversible acquisition of a\ndrug tolerant phenotype by individual cells even in an isogenic population.\nAccumulating evidence further suggests that cell-fate transitions such as the\nepithelial to mesenchymal transition (EMT) are associated with drug resistance.\nIn this study, we analyze stochastic models of phenotypic switching to provide\na framework for analyzing cell-fate transitions such as EMT as a source of\nphenotypic variability in drug sensitivity. Motivated by our cell-culture based\nexperimental observations connecting phenotypic switching in EMT and drug\nresistance, we analyze a coarse-grained model of phenotypic switching between\ntwo states in the presence of cytotoxic stress from chemotherapy. We derive\nanalytical results for time-dependent probability distributions that provide\ninsights into the rates of phenotypic switching and characterize initial\nphenotypic heterogeneity of cancer cells. The results obtained can also shed\nlight on fundamental questions relating to adaptation and selection scenarios\nin tumor response to cytotoxic therapy.\n