![]() ![]() However, it is becoming increasingly apparent that genetics alone cannot fully explain the wide ranges of responses observed in patient populations to anticancer therapies. Indeed, genomic instability is a hallmark of cancer and is considered to be the primary source of this genetic heterogeneity. Genetic differences among cancer cells within and across tumors have long been appreciated. Uniform Manifold Approximation and Projection UMI,Ĭancer is a complex and dynamic disease characterized by intertumoral and intratumoral heterogeneities that have been implicated in treatment avoidance and acquired resistance to therapy. ![]() T-distributed Stochastic Neighbor Embedding UMAP, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.įluorescence-activated cell sorting GATK, Kirschstein National Research Service Award (NRSA, F31-CA221147) and Chemical-Biology Interface Training Grant (T32-GM0650) L.A.H., Vanderbilt Biomedical Informatics Training Program (NLM 5T15-LM007450-14), Quantitative Systems Biology Center at Vanderbilt, and National Cancer Institute (NCI) Transition Career Development Award to Promote Diversity (K22-CA237857-01A1) D.R.T., Lung Cancer Research Foundation (LCRF, UALC 13020513) and NIH Research Specialist Award (1R50CA243783) P.L.F., NIH NRSA (F31-CA165840) C.J.R., Vanderbilt Trans-Institutional Programs Grant: Understanding the Complexity of Life One Cell at a Time V.Q., NIH Clinical and Translational Science Award (U54-CA113007) Sequencing studies were supported by the Vanderbilt Institute for Clinical and Translational Research (VICTR, Voucher VR52385). The codes used to generate model simulations and analyze experimental data are publicly available via GitHub ( /QuLab-VU/GES_2021).įunding: This work was supported by the following funding sources: C.E.H., National Institutes of Health (NIH) Ruth L. Additional experimental data are available on Github ( /QuLab-VU/GES_2021). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The sequencing datasets generated in this study can be found in the gene expression omnibus (GEO GSE150084) and sequence read archive (SRA PRJNA631050 and PRJNA632351). Received: Accepted: MaPublished: June 1, 2021Ĭopyright: © 2021 Hayford et al. Siegal, New York University, UNITED STATES 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.Ĭitation: Hayford CE, Tyson DR, Robbins CJ III, Frick PL, Quaranta V, Harris LA (2021) An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. 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. 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. ![]() 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. 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. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. The existence of additional genomic alterations among tumor cells can only partially explain this variability. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. ![]()
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