The Clinical and Economic Impact of Inaccurate EGFR Mutation Tests in the Treatment of Metastatic Non-Small Cell Lung Cancer

Advances in personalized medicine are supported by companion diagnostic molecular tests. Testing accuracy is critical for selecting patients for optimal therapy and reducing treatment-related toxicity. We assessed the clinical and economic impact of inaccurate test results between laboratory developed tests (LDTs) and a US Food and Drug Administration (FDA)-approved test for detection of epidermal growth factor receptor (EGFR) mutations. Using a hypothetical US cohort of newly diagnosed metastatic non-small cell lung cancer (NSCLC) patients and EURTAC (erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer) clinical trial data, we developed a decision analytic model to estimate the probability of misclassification with LDTs compared to a FDA-approved test. We estimated the clinical and economic impact of inaccurate test results by quantifying progression-free and quality-adjusted progression-free life years (PFLYs, QAPFLYs) lost, and costs due to incorrect treatment. The base-case analysis estimated 2.3% (n = 1422) of 60,502 newly diagnosed metastatic NSCLC patients would be misclassified with LDTs compared to 1% (n = 577) with a FDA-approved test. An average of 477 and 194 PFLYs were lost among the misclassified patients tested with LDTs compared to the FDA-approved test, respectively. Aggregate treatment costs for patients tested with LDTs were approximately $7.3 million more than with the FDA-approved test, due to higher drug and adverse event costs among patients incorrectly treated with targeted therapy or chemotherapy, respectively. Invalid tests contributed to greater probability of patient misclassification and incorrect therapy. In conclusion, risks associated with inaccurate EGFR mutation tests pose marked clinical and economic consequences to society. Utilization of molecular diagnostic tests with demonstrated accuracy could help to maximize the potential of personalized medicine.