Evidence map›Paper›PMID 26933807›Full record

ArticleOncotarget2016

Optimized selection of three major EGFR-TKIs in advanced EGFR-positive non-small cell lung cancer: a network meta-analysis.

Yaxiong Zhang, Jin Sheng, Yunpeng Yang, Wenfeng Fang, Shiyang Kang, Yang He, Shaodong Hong, Jianhua Zhan, Yuanyuan Zhao, Cong Xue and 12 more

Open access · diamondAbstract readNetwork Meta-Analysis
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
4.6field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Cost-effectiveness analysis of the first-line EGFR-TKIs in patients with non-small cell lung cancer harbouring EGFR mutations.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors at 3 institutions in 1 country.

Yaxiong ZhangDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jin ShengDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yunpeng YangDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Wenfeng FangDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Shiyang KangState Key Laboratory of Oncology in South China, Guangzhou, China.
Yang HeLau Luen Hung Private Medical Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Shaodong HongDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Jianhua ZhanDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yuanyuan ZhaoDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Cong XueDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yuxiang MaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ting ZhouDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Shuxiang MaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Fangfang GaoDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Tao QinDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Zhihuang HuDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ying TianDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xue HouDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Yan HuangDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ningning ZhouDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Hongyun ZhaoDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Li ZhangDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CNState Key Laboratory of Oncology in South China

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo answer which epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) is the best choice for advanced non-small cell lung cancer (NSCLC) EGFR mutants.

results16 phase III randomized trials involving 2962 advanced NSCLC EGFR mutants were enrolled. Multiple treatment comparisons showed different EGFR-TKIs shared equivalent curative effect in terms of all outcome measures among the overall, chemo-naïve and previously treated patients. Rank probabilities showed that erlotinib and afatinib had potentially better efficacy compared with gefitinib in both of the overall and chemo-naïve patients. Potentially survival benefit of erlotinib was also observed in previously treated patients compared with gefitinib. Additionally, EGFR-TKI showed numerically greater survival benefit in 19 Del compared with chemotherapy, while it was opposite in 21 L858R. Furthermore, afatinib, erlotinib and gefitinib had high, moderate and low risk of rash & diarrhea, respectively, while the occurrence of elevated liver transaminase was more common in gefitinib.

methodsData of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and adverse events were extracted from included studies. Efficacy and toxicity of all included treatments were integrated by network meta-analyses.

conclusionOur study indicated a high efficacy-high toxicity pattern of afatinib, a high efficacy-moderate toxicity pattern of erlotinib and a medium efficacy-moderate toxicity pattern of gefitinib. Recommended EGFR-TKI should be suggested according to patients' tolerability and therapeutic efficacy in clinical practice. Moreover, the treatment for advanced EGFR-positive NSCLC might be different between 19 Del and 21 L858R.

Indexed as

Carcinoma, Non-Small-Cell LungErbB ReceptorsHumansLung NeoplasmsMutationPrognosisProtein Kinase InhibitorsEGFR protein, humanErbB ReceptorsProtein Kinase InhibitorsafatinibEGFR-TKIerlotinibgefitinibNSCLC

Identifiers

PMID26933807
PMCPMC4990517
OpenAlexW2292308194

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.