RESUMO
Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.
Assuntos
Epigênese Genética , Heterogeneidade Genética , Modelos Biológicos , Neoplasias/genética , Neoplasias/patologia , Antineoplásicos/farmacologia , Morte Celular/efeitos dos fármacos , Linhagem Celular Tumoral , Simulação por Computador , Epigênese Genética/efeitos dos fármacos , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Heterogeneidade Genética/efeitos dos fármacos , Genoma Humano , Humanos , Fenótipo , Processos Estocásticos , Transcriptoma/efeitos dos fármacos , Transcriptoma/genéticaRESUMO
In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.
Assuntos
Proliferação de Células/efeitos dos fármacos , Descoberta de Drogas/métodos , Modelos Teóricos , Biologia Molecular/métodos , Bibliotecas de Moléculas Pequenas/farmacologia , Linhagem Celular Tumoral , Simulação por Computador , Relação Dose-Resposta a Droga , Humanos , Microscopia de Fluorescência , Sensibilidade e Especificidade , Bibliotecas de Moléculas Pequenas/química , Fatores de TempoRESUMO
The dynamics of heterogeneous clonal lineages within a cell population, in aggregate, shape both normal and pathological biological processes. Studies of clonality typically relate the fitness of clones to their relative abundance, thus requiring long-term experiments and limiting conclusions about the heterogeneity of clonal fitness in response to perturbation. We present, for the first time, a method that enables a dynamic, global picture of clonal fitness within a mammalian cell population. This novel assay allows facile comparison of the structure of clonal fitness in a cell population across many perturbations. By utilizing high-throughput imaging, our methodology provides ample statistical power to define clonal fitness dynamically and to visualize the structure of perturbation-induced clonal fitness within a cell population. We envision that this technique will be a powerful tool to investigate heterogeneity in biological processes involving cell proliferation, including development and drug response.
Assuntos
Proliferação de Células/fisiologia , Técnicas de Cultura de Células , Linhagem Celular , Proliferação de Células/efeitos dos fármacos , Proliferação de Células/genética , Células Clonais , Cicloeximida/farmacologia , Regulação da Expressão Gênica , Aptidão Genética , Humanos , Inibidores da Síntese de Proteínas/farmacologiaRESUMO
We present an integrated method that uses extended time-lapse automated imaging to quantify the dynamics of cell proliferation. Cell counts are fit with a quiescence-growth model that estimates rates of cell division, entry into quiescence and death. The model is constrained with rates extracted experimentally from the behavior of tracked single cells over time. We visualize the output of the analysis in fractional proliferation graphs, which deconvolve dynamic proliferative responses to perturbations into the relative contributions of dividing, quiescent (nondividing) and dead cells. The method reveals that the response of 'oncogene-addicted' human cancer cells to tyrosine kinase inhibitors is a composite of altered rates of division, death and entry into quiescence, a finding that challenges the notion that such cells simply die in response to oncogene-targeted therapy.