We evaluated the real-world efficacy and side effects of afatinib as a first-line therapy for advanced EGFR mutation-positive lung adenocarcinoma. The medical records of patients receiving afatinib as a first-line therapy after National Health Insurance reimbursement between May 2014 and January 2016 were reviewed, and information on patient characteristics and treatment courses were collected consecutively. Rebiopsy tissue was collected for EGFR mutation and MET amplification analyses. MET amplification was detected by fluorescence in situ hybridization and immunohistochemistry. In total, 140 patients were enrolled (median follow-up, 18.0 months). No significant differences in side effects, treatment responses, progression-free survival, or brain metastasis control were observed between patients receiving 40 mg versus < 40 mg of afatinib during the first 6 months. Patients with significant pretreatment weight loss (> 10.0% in 6 months) had a shorter median progression-free survival. Patients with brain metastases had a poorer Eastern Cooperative Oncology Group performance status and were associated with a shorter median progression-free survival. Nine patients (32.1%) had a p.T790M mutation and only 1 patient gained MET amplifications after disease progression. Afatinib is effective as a first-line therapy for advanced EGFR mutation-positive lung adenocarcinoma. Afatinib dosage does not affect clinical efficacy and drug-related side effects.

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(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

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