Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor (TKI) in EGFR-mutant advanced NSCLC

Yongchang Zhang; Liang Zeng; Xiangyu Zhang; Yizhi Li; Lingli Liu; Qinqin Xu; Haiyan Yang; Wenjuan Jiang; Analyn Lizaso; Luting Qiu; Ting Hou; Jun Liu; Ling Peng; Nong Yang

doi:10.1186/s12916-021-02118-x

Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor (TKI) in EGFR-mutant advanced NSCLC

BMC Med. 2021 Oct 19;19(1):245. doi: 10.1186/s12916-021-02118-x.

Authors

Yongchang Zhang^#^{1

2

3}, Liang Zeng^#⁴, Xiangyu Zhang^#⁴, Yizhi Li^#⁴, Lingli Liu^#^{4

5}, Qinqin Xu⁶, Haiyan Yang⁴, Wenjuan Jiang⁴, Analyn Lizaso⁷, Luting Qiu⁷, Ting Hou⁷, Jun Liu⁸, Ling Peng⁹, Nong Yang^{10

11}

Affiliations

¹ Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, China. zhangyongchang@csu.edu.cn.
² Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. zhangyongchang@csu.edu.cn.
³ Guangdong Lung Cancer Institute, Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China. zhangyongchang@csu.edu.cn.
⁴ Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, China.
⁵ Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
⁶ Department of Medical Oncology, Qinghai Provincial People's Hospital, Xining, 810000, China.
⁷ Burning Rock Biotech, Guangzhou, 510300, China.
⁸ Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
⁹ Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Hangzhou, 310003, Zhejiang, China.
¹⁰ Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, China. yangnong0217@163.com.
¹¹ Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China. yangnong0217@163.com.

^# Contributed equally.

Abstract

Background: The combination of bevacizumab and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) could prolong progression-free survival (PFS) in patients with EGFR-mutant advanced non-small-cell lung cancer (NSCLC). Our study investigated the clinical and molecular factors that affect the efficacy of first-generation EGFR-TKI with or without bevacizumab and identify the subset of patients who can benefit from combination therapy.

Methods: Our study included 318 patients with EGFR-mutant locally advanced/advanced NSCLC treated with either first-generation EGFR-TKI combined with bevacizumab (A+T; n = 159) or EGFR-TKI monotherapy (T; n = 159). Two nomogram models to predict PFS and overall survival (OS), respectively, were constructed using two factors that impact EGFR-TKI efficacy: metastatic site and presence of concurrent mutations. The study cohort was stratified into 2 cohorts for training (n = 176) and validation (n = 142) of the nomogram model. Using the median score from the nomogram, the patients were stratified into two groups to analyze their survival outcome.

Results: The A+T group had significantly longer PFS (14.0 vs. 10.5 months; p < 0.001) and OS (37.0 vs. 26.0 months; p = 0.042) than the T group. Among the patients with concurrent mutations in tumor suppressor genes, those in the A+T group had significantly longer PFS and OS than the T group (PFS 14.5 vs. 8.0 months, p < 0.001; OS 39.0 vs. 20.0 months, p = 0.003). The higher scores from the nomograms were associated with the presence of brain/liver/pleural metastasis or concomitant gene mutations, which indicated a higher likelihood of shorter PFS and OS. The validation of the nomogram revealed that patients with lower scores had significantly longer PFS for the T group than those with higher scores (15.0 vs. 9.0 months, p = 0.002), but not for the A+T group (15.9 vs. 13.9 months, p = 0.256).

Conclusions: Using a nomogram, our study demonstrated that the addition of bevacizumab may enhance the therapeutic effectiveness of EGFR-TKI by overcoming the negative impact of certain clinical and molecular factors on the efficacy of EGFR-TKI.

Keywords: Advanced NSCLC; Bevacizumab combined with EGFR-TKI; Clinical features; Molecular features; Prediction Model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bevacizumab
Carcinoma, Non-Small-Cell Lung* / drug therapy
Carcinoma, Non-Small-Cell Lung* / genetics
ErbB Receptors / genetics
Humans
Lung Neoplasms* / drug therapy
Lung Neoplasms* / genetics
Mutation
Nomograms
Protein Kinase Inhibitors / therapeutic use

Substances

Protein Kinase Inhibitors
Bevacizumab
EGFR protein, human
ErbB Receptors