The impact of microenvironmental heterogeneity on the evolution of drug resistance in cancer cells

Shannon M. Mumenthaler, Jasmine Foo, Nathan C. Choi, Nicholas Heise, Kevin Leder, David B. Agus, William Pao, Franziska Michor, Parag Mallick

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.

Original languageEnglish (US)
Pages (from-to)19-31
Number of pages13
JournalCancer Informatics
Volume14
DOIs
StatePublished - Mar 12 2015

Keywords

  • Cancer
  • Drug resistance
  • Evolutionary modeling
  • Microenvironment

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