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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

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 tha...

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Autores principales: Zhang, Yongchang, Zeng, Liang, Zhang, Xiangyu, Li, Yizhi, Liu, Lingli, Xu, Qinqin, Yang, Haiyan, Jiang, Wenjuan, Lizaso, Analyn, Qiu, Luting, Hou, Ting, Liu, Jun, Peng, Ling, Yang, Nong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525046/
https://www.ncbi.nlm.nih.gov/pubmed/34663309
http://dx.doi.org/10.1186/s12916-021-02118-x
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author Zhang, Yongchang
Zeng, Liang
Zhang, Xiangyu
Li, Yizhi
Liu, Lingli
Xu, Qinqin
Yang, Haiyan
Jiang, Wenjuan
Lizaso, Analyn
Qiu, Luting
Hou, Ting
Liu, Jun
Peng, Ling
Yang, Nong
author_facet Zhang, Yongchang
Zeng, Liang
Zhang, Xiangyu
Li, Yizhi
Liu, Lingli
Xu, Qinqin
Yang, Haiyan
Jiang, Wenjuan
Lizaso, Analyn
Qiu, Luting
Hou, Ting
Liu, Jun
Peng, Ling
Yang, Nong
author_sort Zhang, Yongchang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02118-x.
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spelling pubmed-85250462021-10-22 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 Zhang, Yongchang Zeng, Liang Zhang, Xiangyu Li, Yizhi Liu, Lingli Xu, Qinqin Yang, Haiyan Jiang, Wenjuan Lizaso, Analyn Qiu, Luting Hou, Ting Liu, Jun Peng, Ling Yang, Nong BMC Med Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02118-x. BioMed Central 2021-10-19 /pmc/articles/PMC8525046/ /pubmed/34663309 http://dx.doi.org/10.1186/s12916-021-02118-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Zhang, Yongchang
Zeng, Liang
Zhang, Xiangyu
Li, Yizhi
Liu, Lingli
Xu, Qinqin
Yang, Haiyan
Jiang, Wenjuan
Lizaso, Analyn
Qiu, Luting
Hou, Ting
Liu, Jun
Peng, Ling
Yang, Nong
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525046/
https://www.ncbi.nlm.nih.gov/pubmed/34663309
http://dx.doi.org/10.1186/s12916-021-02118-x
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