Abstract 377: Molecular features-based model for predicting benefit from Bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor

Introduction: Two phase 3 clinical studies, NEJ026 and CTONG1509, have consistently demonstrated better anti-tumor activity of adding bevacizumab to erlotinib regimen in the treatment of patients with epidermal growth factor receptor (EGFR)-mutant advanced non-squamous non-small-cell lung cancer (NSCLC); however, the subset of patients that benefits more with the addition of bevacizumab to their EGFR-TKI regimen still remains to be identified. In this study, we aimed to investigate the clinical and molecular factors that affect the efficacy of first-generation EGFR-TKI regimen with or without bevacizumab as first-line treatment of patients with EGFR-mutant NSCLC. Methods: A total of 176 patients with EGFR-mutant stage IIIB-IV relapsed or metastatic NSCLC and received first-generation EGFR-TKI gefitinib or erlotinib monotherapy (T; n=88) or combined with bevacizumab (A+T; n=88) were included in our study. The two groups had comparable baseline clinical characteristics. Retrospective analysis was performed on clinical data, survival outcomes, and mutation profiles at baseline and progression. A nomogram model to predict PFS was constructed with the following variables identified from Cox multivariate analysis: treatment group (A+T) and mutations in EGFR L858R, exon 19 deletion, TP53, CDK4, FGF3, and NFkB signaling pathway. Another nomogram was constructed to predict overall survival with treatment group (A+T) and mutations in TP53 signaling pathway as the variables. Results: As compared to the T group, progression-free survival (PFS) was significantly longer among patients who received A+T and harbored concurrent EGFR amplification (16.1 vs 9.0 months; HR: 0.267, 95%CI: 0.110-0.651; p=0.004), and hotspot and loss-of-function mutations in TP53 (16.1 vs 8.0 months; HR: 0.215, 95%CI: 0.108-0.426; p Conclusions: Our study demonstrated the clinical value of the nomogram model in predicting the subset of patients who benefits from first-generation EGFR-TKI regimen with or without bevacizumab. Citation Format: Yongchang Zhang, Analyn Lizaso. Molecular features-based model for predicting benefit from Bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 377.