Evidence map›Paper›PMID 34663309›Full record

ArticleBMC medicine2021

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 and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  4. Review
  5. Observational
  6. Review
  7. Article
  8. Targeted therapies for cancer.BMC medicine · 2022
    Article
  9. Article
  10. Article
  11. Review
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

14 authors at 7 institutions in 1 country.

Yongchang Zhang *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.
Liang Zeng *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.
Xiangyu Zhang *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.
Yizhi Li *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.
Lingli Liu *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.
Qinqin XuDepartment of Medical Oncology, Qinghai Provincial People's Hospital, Xining, 810000, China.
Haiyan YangDepartment 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.
Wenjuan JiangDepartment 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.
Analyn LizasoBurning Rock Biotech, Guangzhou, 510300, China.
Luting QiuBurning Rock Biotech, Guangzhou, 510300, China.
Ting HouBurning Rock Biotech, Guangzhou, 510300, China.
Jun LiuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Ling PengDepartment of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Hangzhou, 310003, Zhejiang, China.
Nong YangDepartment 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.
Central South University · CNBurning Rock Biotech (China) · CNHunan Cancer Hospital · CNUniversity of South China · CNHuazhong University of Science and Technology · CNQinghai Provincial Peoples Hospital · CNZhejiang Provincial People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsOur 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.

resultsThe 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).

conclusionsUsing 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.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsBevacizumabErbB ReceptorsHumansMutationNomogramsProtein Kinase InhibitorsBevacizumabEGFR protein, humanErbB ReceptorsProtein Kinase InhibitorsAdvanced NSCLCBevacizumab combined with EGFR-TKIClinical featuresMolecular featuresPrediction Model

Identifiers

PMID34663309
PMCPMC8525046
OpenAlexW3207524449

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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.