Optimization of Dosing for EGFR-Mutant Non–Small Cell Lung Cancer with Evolutionary Cancer Modeling

Predictive models of EGFR-mutant tumor behavior point to alternative drug dosing strategies to prevent and treat acquired resistance. Harnessing Evolution to Improve Lung Cancer Therapy Like any organism under severe evolutionary pressure, a few select members of a cancer cell population acquire molecular changes that strengthen the clan’s chances of survival. Therapeutic drugs exert a powerful selective force on characteristically compliant cancer cells, as the common recurrence of drug-resistant cancers testifies. To learn how to better fight the potent forces of evolution, Chmielecki et al. examined the behavior of non–small cell lung cancer (NSCLC) before and after the cells acquire resistance to targeted therapy, which inevitably they do. The growth characteristics of these cells were consistent with patient tumor behavior, enabling construction of a mathematical model that predicted alternative therapeutic strategies to delay the development of drug-resistant cancer cells. The authors made paired isogenic cell lines that were sensitive and resistant to afatinib and erlotinib—drugs used to treat NSCLC that are directed against the epidermal growth factor receptor (EGFR) tyrosine kinase, which is activated in a subset of NSCLCs. To the authors’ surprise, the drug-resistant cells grew more slowly than their sensitive counterparts, and resistance was not maintained in the absence of selection. Multiple clinical observations corroborated these findings. For example, patients with resistant tumors showed a slow course of disease progression, and patients with acquired resistance have re-responded to tyrosine kinase inhibitor (TKI) therapy after a drug holiday. The authors then constructed an evolutionary mathematical model of tumor behavior based on the differential growth rates of TKI-sensitive and TKI-resistant cells in heterogeneous tumor cell populations. Understanding the growth dynamics of how tumors behave allowed the authors to calculate what would happen under different treatment regimes. Their models predicted that continuous administration of a low-dose EGFR TKI combined with high-dose pulses of an EGFR TKI should delay the onset of resistance. Subsequent cellular studies bore out this prediction. Modeling also indicated the wisdom of prolonging treatment with the EGFR TKI after the development of resistance to prevent fast overgrowth by the sensitive cells, a result also born out in vitro and in vivo. Ultimate proof will have to come from patients. Clinical trials based on these alternative dosing strategies will be the true test of the utility of evolutionary mathematical modeling in designing cancer treatments. If they prove beneficial, individual models based on the characteristics of diverse cancer cell types could offer clues for designing optimal treatment strategies. Non–small cell lung cancers (NSCLCs) that harbor mutations within the epidermal growth factor receptor (EGFR) gene are sensitive to the tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib. Unfortunately, all patients treated with these drugs will acquire resistance, most commonly as a result of a secondary mutation within EGFR (T790M). Because both drugs were developed to target wild-type EGFR, we hypothesized that current dosing schedules were not optimized for mutant EGFR or to prevent resistance. To investigate this further, we developed isogenic TKI-sensitive and TKI-resistant pairs of cell lines that mimic the behavior of human tumors. We determined that the drug-sensitive and drug-resistant EGFR-mutant cells exhibited differential growth kinetics, with the drug-resistant cells showing slower growth. We incorporated these data into evolutionary mathematical cancer models with constraints derived from clinical data sets. This modeling predicted alternative therapeutic strategies that could prolong the clinical benefit of TKIs against EGFR-mutant NSCLCs by delaying the development of resistance.