A unifying framework disentangles genetic, epigenetic, and stochastic sources of drug-response variability in anin vitromodel of tumor heterogeneity

Abstract

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 of targeted therapy response is observed. Additional genetic mutations can only partially explain this variability, leading to consideration of non-genetic factors, such as "stem-like" and "mesenchymal" phenotypic states, as critical contributors to tumor relapse and resistance. Here, we show that both genetic and non-genetic factors contribute to targeted drug-response variability in an experimental tumor heterogeneity model based on multiple versions and clonal sublines of PC9, the archetypal EGFR-mutant non-small cell lung cancer cell line. We observe significant drugresponse variability across PC9 cell line versions, among sublines, and within sublines. To disentangle genetic, epigenetic, and stochastic components underlying this variability, we adopt a theoretical framework whereby distinct 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. Using mutational impact analysis, single-cell differential gene expression, and semantic similarity of gene ontology terms to connect genomics and transcriptomics, we establish a baseline of genetic differences explaining drug-response variability across PC9 cell line versions. In contrast, with the same approach, we conclude that in all but one of the clonal sublines, drug-response variability is due to epigenetic rather than genetic differences. Finally, using a clonal drug-response assay and stochastic simulations, we attribute drug-response variability within sublines to intracellular stochastic fluctuations and confirm that one subline likely contains a genetic resistance mutation that emerged in the absence of selective pressures. We propose that a theoretical framework deconvolving the complex interplay among genetic, epigenetic, and stochastic sources of intratumoral heterogeneity will lead to novel therapeutic strategies to combat tumor relapse and resistance.A theoretical framework for understanding the connections between genetic and non-genetic sources of tumor heterogeneity is the "epigenetic landscape," which was proposed by Waddington over 50 years ago 34 but has received renewed attention recently 16,17,35,36 . In analogy to the potential energy landscape of physical chemistry, Waddington posited that a cellular state can be assigned a "quasi-potential" energy and placed within a landscape where local minima correspond to cellular phenotypes. Phenotypic state transitions occur when cells traverse the barriers separating adjacent basins, driven by intrinsic or extrinsic sources of noise 37,38 . Importantly, the topography of the epigenetic landscape depends on a complex set of biochemical interactions within (and possibly spanning 39 ) cells and the values of associated rate parameters 40,41 . Many of these parameters depend strongly on protein stru...

Bivalent chromatin as a therapeutic target in cancer: Anin silicopredictive approach for combining epigenetic drugs

“…A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Despite tumour cell heterogeneity has been recognized as a bona fide engine for drug resistance [1, 3, 76], few successful approaches [17] have been proposed aimed at formulating strategies capable of quantifying the variability associated to individual cancer cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Our current work accepts this challenge to provide a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against cancer-driving chromatin modifiers [77–79].…”

“…Non-genetic heterogeneity has two broad sources, epigenetic and stochastic, the latter being due to intrinsic factors such as noise in gene expression [10][11][12][13] and asymmetric cell division [14,15], and it can produce phenotype diversity even in genetically identical cells [8,9,16]. Not surprisingly, the analysis of the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human diseases including cancer has become a crucial issue to understanding drug resistance phenomena and developing new targeted agents [1,3,17].…”

“…Such transitions are driven by intrinsic or extrinsic fluctuations [29,30]. Noteworthy, the topography of such landscapes depends on a complex set of biochemical interactions and, in particular, on the values of the corresponding set of rate constants [17,[31][32][33][34]. These constants strongly depend on protein structure (e.g.…”

“…the accessibility of a binding domain), which is encoded within the DNA [32]. Thus, an epigenetic landscape emerges from a particular genotype [17], and therefore can be interpreted as a representation of the genotype-phenotype map [35][36][37][38][39][40]. Beyond their dependence on DNA sequence, in the case of biochemical reactions involving enzymatic catalysis, the associated rate constants depend also on the concentration of metabolic cofactors [33,34], which may differ among cells thus adding a further source of cell-to-cell heterogeneity.…”

“…In order to shed some light on the role of bivalent chromatin as a therapeutic target, we propose a mathematical model of a simple gene regulatory circuit with a bivalent promoter (see Figures 1(a) and (b)). Specifically, our model addresses mutual nonlinear feedbacks between marked nucleosomes and transcription factors (TFs), which drive not only the rates of both addition of new marks and TF synthesis [41][42][43][44][45], but also the heterogeneity of cellular populations [17,33,34,46,47].…”