Prediction of biomarkers of therapeutic effects of patients with lung adenocarcinoma treated with gefitinib based on progression-free-survival by metabolomic fingerprinting

• Biomarkers were found out to indicate what kind of lung cancer patients who is suitable for treating with gefitinib.
• 19 biomarkers were statistically significant predictors of PFS.
• Cox regression analysis was employed to get rid of the influences of individual differences

Lung carcinoma is one of the most frequently diagnosed malignancy and threats human life and health. In clinical practice, gefitinib, one of the most well-known epidermal growth factor receptor tyrosine kinase inhibitors, was frequently used in the treatment of non-small cell lung carcinoma. However, this drug is not useful for all non-small cell patients. In this study, the biomarkers were found out to predict the therapeutic effects of gefitinib for lung carcinoma patients. Serum samples were collected from patients with advanced lung adenocarcinoma. The ultra-high performance liquid chromatography (UHPLC)-quadrupole-time of flight mass spectrometry (Q-TOF MS) was conducted to obtain the metabolic data for each patient. Partial least squares-discriminate analysis (PLS-DA) was performed to indicate the differences between metabolites of patients, and Cox proportional hazards regression analysis was used to eliminate the interference of the patient’s gender, age, smoking history and disease stage. Thus, differential biomarkers were found. The combination of these biomarkers was statistically significant predictors based on progression-free survival. If these biomarkers can be further confirmed by the clinic, it could suggest the proper therapeutic schedule, and help to reduce patients’ economic burden and medication side effects.

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