Development and validation of a model to predict tyrosine kinase inhibitor-sensitive EGFR mutations of non-small cell lung cancer based on multi-institutional data.

BACKGROUND

Non-small cell lung cancer (NSCLC) with different EGFR mutation types shows distinct sensitivity to tyrosine kinase inhibitors (TKIs). This study developed a patho-clinical profile-based prediction model of TKI-sensitive EGFR mutations.

METHODS

The records of 1121 Chinese patients diagnosed with NSCLC from November 2008 to October 2014 (the development set) were reviewed. Multivariate logistic regression was conducted to identify any association between potential predictors and the classic sensitive EGFR mutations (exon 19 deletion and exon 21 L858R point mutation). A prediction index was created by assigning weighted scores to each factor proportional to a regression coefficient. Validation was made in an independent cohort consisting of 864 patients who were consecutively enrolled between November 2014 and January 2017 (the validation set).

RESULTS

Seven independent predictors were identified: gender (female vs. male), adenocarcinoma (yes vs. no), smoking history (no vs. yes), N stage (N+ vs. N0), M stage (M1 vs. M0), brain metastasis (yes vs. no), and elevated Cyfra 21-1 (no vs. yes). Each was assigned a number of points. In the validation set, the area under curve of the prediction index appeared as 0.698 (95% confidence interval 0.663-0.733). The sensitivity, specificity, positive and negative predictive values, and concordance were 95.0%, 32.3%, 61.4%, 85.1%, and 65.6%, respectively.

CONCLUSION

We developed a patho-clinical profile-based model for predicting TKI-sensitive EGFR mutations. Our model may represent a noninvasive, economical choice for clinicians to inform TKI therapy.