Uncovering the molecular networks underlying non-genetic heterogeneity in cancer cell populations

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PROJECT SUMMARY Tumor heterogeneity is a major contributor to variable response and treatment failure in cancer patients. Usually, heterogeneity in cancer is thought of in terms of resistance-conferring genetic mutations that pre- exist or emerge during treatment. However, recent studies, including our own, increasingly point to non- genetic sources of heterogeneity as critical factors in the early stages of tumor response. Non-genetic mechanisms are known to underlie cellular processes such as stem cell differentiation and epithelial-to- mesenchymal transitions. In bacteria, isogenic cell populations have been shown to diversify in the absence of perturbations (e.g., drugs) into a variety of cellular phenotypes, each with differential fitness to potential stressors. This “bet hedging” strategy increases the odds that a portion of the population will survive a future, unknown challenge. We, and others, have recently hypothesized that cancer cells employ a similar survival strategy to withstand the initial onslaught of anticancer drugs. So-called “drug tolerant” cells may persist within a patient for extended periods of time before acquiring genetic resistance mutations that lead to tumor recurrence. The objective of this proposal is to uncover the molecular factors that control non-genetic heterogeneity in cancer cell populations using a combined computational and experimental approach. In Aim 1, I propose to construct a detailed kinetic model of the biochemical signaling networks that control division and death decisions in individual cancer cells. It is well established that complex biochemical networks can give rise to multiple stable equilibrium states, known as “attractors.” Each attractor corresponds to a cellular phenotype and can be conceptualized as a basin within an “epigenetic landscape.” Cells can transition between phenotypes with rates dependent upon the depths of the basins and the heights of the barriers separating them. Using a dynamical systems analysis approach, I will mathematically solve for the epigenetic landscape of the biochemical division/death model and quantify molecule signatures for all attractors. In Aim 2, using BRAF-mutant melanoma and EGFR- mutant lung cancer as in vitro model systems, I will use clonal and single-cell RNA sequencing and chromatin accessibility sequencing (ATAC-seq) to enumerate the number and molecular signatures of non-genetic phenotypic states. I will also utilize whole-exome sequencing to establish the non-genetic nature of the phenotypes and immunocompromised mouse models to validate model predictions. Differences between the experimental and in silico molecular signatures will lead to model refinement and further experimentation. Quantifying the epigenetic landscapes of cancer cells will lay the groundwork for novel therapies based on rationally modifying the landscape to favor phenotypes with increased drug sensitivity, an approach termed “targeted landscaping.” This would reduce the size of the drug-tolerant pool and delay, perhaps indefinitely, the acquisition of genetic resistance mutations and tumor recurrence.