Lung cancer is the leading cause of mortality from cancer in the USA and globally. Mutations in one gene, EGFR (epidermal growth factor receptor) contribute to oncogenesis in approximately 10–20% of lung adenocarcinomas, which is the most common form of lung cancer. Tumors with EGFR mutations are sensitive to treatment with the tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib; but, after an initial response, these tumors develop drug resistance. The molecular events that cause TKI resistance are known in 60% of cases, offering targets for the development of second-line drugs, but are not known in the remaining 40% of cases.

Here, the authors follow the development of acquired resistance to TKIs in mouse models of lung adenocarcinoma. They previously developed transgenic mice that develop lung adenocarcinomas as a result of expression of either one of the two most common lung cancer-associated EGFR alleles. Mice with lung tumors identified using magnetic resonance imaging responded dramatically to treatment with erlotinib. After multiple rounds of erlotinib exposure, some of tumor-bearing mice exhibited drug-resistant tumors, about a quarter of which resulted from the same secondary events that are observed in human tumors that become TKI resistant. These findings establish this model as a reliable setting in which to study the mechanisms of drug resistance because it recapitulates the situation observed in patients.

Most of the tumors in these mouse models become resistant to TKIs by unknown mechanisms, and the models can be used to identify novel ways in which the tumors escape treatment. This is especially useful because it is often difficult to obtain adequate samples of TKI-resistant tumors from patients for thorough molecular studies. Studies of drug-resistant mouse tumors using high-throughput sequencing, comparative genomic hybridization, and expression profiling should identify mechanisms that make tumors drug resistant. Once novel mechanisms of resistance have been identified, these mouse models can be used as preclinical systems to evaluate therapeutic strategies to combat drug-resistant disease.