Editor's Summary Harnessing Evolution to Improve Lung Cancer Therapy

strategies. models based on the characteristics of diverse cancer cell types could offer clues for designing optimal treatment utility of evolutionary mathematical modeling in designing cancer treatments. If they prove beneficial, individual will have to come from patients. Clinical trials based on these alternative dosing strategies will be the true test of the resistance to prevent fast overgrowth by the sensitive cells, a result also born out in vitro and in vivo. Ultimate proof prediction. Modeling also indicated the wisdom of prolonging treatment with the EGFR TKI after the development of with high-dose pulses of an EGFR TKI should delay the onset of resistance. Subsequent cellular studies bore out this combineddifferent treatment regimes. Their models predicted that continuous administration of a low-dose EGFR TKI underUnderstanding the growth dynamics of how tumors behave allowed the authors to calculate what would happen based on the differential growth rates of TKI-sensitive and TKI-resistant cells in heterogeneous tumor cell populations. therapy after a drug holiday. The authors then constructed an evolutionary mathematical model of tumor behavior disease progression, and patients with acquired resistance have re-responded to tyrosine kinase inhibitor (TKI) observations corroborated these findings. For example, patients with resistant tumors showed a slow course of their sensitive counterparts, and resistance was not maintained in the absence of selection. Multiple clinical which is activated in a subset of NSCLCs. To the authors ' surprise, the drug-resistant cells grew more slowly tha