Abstract
Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.
Highlights
Phenotypic plasticity is a widespread phenomenon across the tree of life
Phenotypic switching is a source for non-genetic heterogeneity in cancer beyond Cancer Stem Cells hierarchies (Flavahan et al 2017; Marusyk et al 2012; Brock et al 2009)
Beyond the well-known plasticity related to the Epithelial-Mesenchymal transition driving metastatic release (Kalluri and Weinberg 2009; Yeung and Yang 2017), more complex architectures with more than two switching phenotypes in place are being uncovered across cancers
Summary
Phenotypic plasticity is a widespread phenomenon across the tree of life. From bacteria to multicellular development, epigenetic pathways generate a population of diverse phenotypes from homogeneous, stable genomes (Sultan 2000; Piggliuci 2001; Margueron and Reinberg 2010; Balalszi et al 2011). Phenotypic switching (PHS) is a stochastic phenomenon known to maintain population diversity in unicellular organisms as a means to survive in fluctuating environments (Kussell and Leibler 2005; Balaban et al 2004) This mechanism can be found to boost non-genetic heterogeneity in a special multicellular context: cancer cell populations (Flavahan et al 2017). Tumor relapse after therapy is usually acknowledged to be a consequence of pre-existing or acquired resistance mutations, present in a given subclone that survives and repopulates the tumor (see e.g., Diaz 2012) This image is often correct, yet further mechanisms in many therapeutic settings, from stem cell senescence (Jordan et al 2006) to immunological editing (Sharma et al 2017) prove that a wider scope is key when trying to understand therapeutic failure. The model allows in particular to analyze the rise of Transition Therapy: Tackling the Ecology of Tumor
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