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Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor
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...
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Published in: | BMC medicine 2021-10, Vol.19 (1) |
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creator | Li, Yizhi Xu, Qinqin Jiang, Wenjuan Zeng, Liang Liu, Lingli Qiu, Luting Hou, Ting Yang, Nong Yang, Haiyan Zhang, Xiangyu Zhang, Yongchang Lizaso, Analyn Peng, Ling Liu, Jun |
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. Keywords: Clinical features, Molecular features, Prediction Model, Bevacizumab combined with EGFR-TKI, Advanced NSCLC |
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fullrecord | <record><control><sourceid>gale</sourceid><recordid>TN_cdi_gale_healthsolutions_A681635101</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A681635101</galeid><sourcerecordid>A681635101</sourcerecordid><originalsourceid>FETCH-gale_healthsolutions_A6816351013</originalsourceid><addsrcrecordid>eNqNjb1OAzEQhE-ISISfd9iKztI5yeVoUZRAjeijtW99t8E_aO0DwZvwthiJgpJqZqRvZs6ape43WvWt7s7_-IvmMudT2666vt8sm6-d58gWPWAcICRPdvYo4AjLLKQMZhogppBGwVCBgTy4JPAqNLAtHEcwFMlxAScp1PCGlj_ngAZsCoZj7b9zmcCx5KLGCgsWThH2D4cnVT4k5QrBC8f6BRwnNlySXDcLhz7Tza9eNbeH_fPuUY3o6TgR-jLl5OefqXy8397p7brTrV7_G_wGcqBelQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor</title><source>PubMed Central(OpenAccess)</source><source>ProQuest - Publicly Available Content Database</source><creator>Li, Yizhi ; Xu, Qinqin ; Jiang, Wenjuan ; Zeng, Liang ; Liu, Lingli ; Qiu, Luting ; Hou, Ting ; Yang, Nong ; Yang, Haiyan ; Zhang, Xiangyu ; Zhang, Yongchang ; Lizaso, Analyn ; Peng, Ling ; Liu, Jun</creator><creatorcontrib>Li, Yizhi ; Xu, Qinqin ; Jiang, Wenjuan ; Zeng, Liang ; Liu, Lingli ; Qiu, Luting ; Hou, Ting ; Yang, Nong ; Yang, Haiyan ; Zhang, Xiangyu ; Zhang, Yongchang ; Lizaso, Analyn ; Peng, Ling ; Liu, Jun</creatorcontrib><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. Keywords: Clinical features, Molecular features, Prediction Model, Bevacizumab combined with EGFR-TKI, Advanced NSCLC</description><identifier>ISSN: 1741-7015</identifier><identifier>EISSN: 1741-7015</identifier><language>eng</language><publisher>BioMed Central Ltd</publisher><subject>Drug therapy ; Genetic aspects ; Lung cancer, Non-small cell ; Nomography (Mathematics) ; Patient outcomes</subject><ispartof>BMC medicine, 2021-10, Vol.19 (1)</ispartof><rights>COPYRIGHT 2021 BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Li, Yizhi</creatorcontrib><creatorcontrib>Xu, Qinqin</creatorcontrib><creatorcontrib>Jiang, Wenjuan</creatorcontrib><creatorcontrib>Zeng, Liang</creatorcontrib><creatorcontrib>Liu, Lingli</creatorcontrib><creatorcontrib>Qiu, Luting</creatorcontrib><creatorcontrib>Hou, Ting</creatorcontrib><creatorcontrib>Yang, Nong</creatorcontrib><creatorcontrib>Yang, Haiyan</creatorcontrib><creatorcontrib>Zhang, Xiangyu</creatorcontrib><creatorcontrib>Zhang, Yongchang</creatorcontrib><creatorcontrib>Lizaso, Analyn</creatorcontrib><creatorcontrib>Peng, Ling</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><title>Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor</title><title>BMC medicine</title><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. Keywords: Clinical features, Molecular features, Prediction Model, Bevacizumab combined with EGFR-TKI, Advanced NSCLC</description><subject>Drug therapy</subject><subject>Genetic aspects</subject><subject>Lung cancer, Non-small cell</subject><subject>Nomography (Mathematics)</subject><subject>Patient outcomes</subject><issn>1741-7015</issn><issn>1741-7015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid/><recordid>eNqNjb1OAzEQhE-ISISfd9iKztI5yeVoUZRAjeijtW99t8E_aO0DwZvwthiJgpJqZqRvZs6ape43WvWt7s7_-IvmMudT2666vt8sm6-d58gWPWAcICRPdvYo4AjLLKQMZhogppBGwVCBgTy4JPAqNLAtHEcwFMlxAScp1PCGlj_ngAZsCoZj7b9zmcCx5KLGCgsWThH2D4cnVT4k5QrBC8f6BRwnNlySXDcLhz7Tza9eNbeH_fPuUY3o6TgR-jLl5OefqXy8397p7brTrV7_G_wGcqBelQ</recordid><startdate>20211019</startdate><enddate>20211019</enddate><creator>Li, Yizhi</creator><creator>Xu, Qinqin</creator><creator>Jiang, Wenjuan</creator><creator>Zeng, Liang</creator><creator>Liu, Lingli</creator><creator>Qiu, Luting</creator><creator>Hou, Ting</creator><creator>Yang, Nong</creator><creator>Yang, Haiyan</creator><creator>Zhang, Xiangyu</creator><creator>Zhang, Yongchang</creator><creator>Lizaso, Analyn</creator><creator>Peng, Ling</creator><creator>Liu, Jun</creator><general>BioMed Central Ltd</general><scope/></search><sort><creationdate>20211019</creationdate><title>Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor</title><author>Li, Yizhi ; Xu, Qinqin ; Jiang, Wenjuan ; Zeng, Liang ; Liu, Lingli ; Qiu, Luting ; Hou, Ting ; Yang, Nong ; Yang, Haiyan ; Zhang, Xiangyu ; Zhang, Yongchang ; Lizaso, Analyn ; Peng, Ling ; Liu, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-gale_healthsolutions_A6816351013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Drug therapy</topic><topic>Genetic aspects</topic><topic>Lung cancer, Non-small cell</topic><topic>Nomography (Mathematics)</topic><topic>Patient outcomes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yizhi</creatorcontrib><creatorcontrib>Xu, Qinqin</creatorcontrib><creatorcontrib>Jiang, Wenjuan</creatorcontrib><creatorcontrib>Zeng, Liang</creatorcontrib><creatorcontrib>Liu, Lingli</creatorcontrib><creatorcontrib>Qiu, Luting</creatorcontrib><creatorcontrib>Hou, Ting</creatorcontrib><creatorcontrib>Yang, Nong</creatorcontrib><creatorcontrib>Yang, Haiyan</creatorcontrib><creatorcontrib>Zhang, Xiangyu</creatorcontrib><creatorcontrib>Zhang, Yongchang</creatorcontrib><creatorcontrib>Lizaso, Analyn</creatorcontrib><creatorcontrib>Peng, Ling</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><jtitle>BMC medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yizhi</au><au>Xu, Qinqin</au><au>Jiang, Wenjuan</au><au>Zeng, Liang</au><au>Liu, Lingli</au><au>Qiu, Luting</au><au>Hou, Ting</au><au>Yang, Nong</au><au>Yang, Haiyan</au><au>Zhang, Xiangyu</au><au>Zhang, Yongchang</au><au>Lizaso, Analyn</au><au>Peng, Ling</au><au>Liu, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor</atitle><jtitle>BMC medicine</jtitle><date>2021-10-19</date><risdate>2021</risdate><volume>19</volume><issue>1</issue><issn>1741-7015</issn><eissn>1741-7015</eissn><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: Clinical features, Molecular features, Prediction Model, Bevacizumab combined with EGFR-TKI, Advanced NSCLC</abstract><pub>BioMed Central Ltd</pub></addata></record> |
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subjects | Drug therapy Genetic aspects Lung cancer, Non-small cell Nomography (Mathematics) Patient outcomes |
title | Clinical and molecular feature-based nomogram model for predicting benefit from bevacizumab combined with first-generation EGFR-tyrosine kinase inhibitor |
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