Evidence map›Paper›PMID 27618180›Full record

SynthesisPloS one2016

Adoptive Immunotherapy in Postoperative Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Yuan Zeng, Wenli Ruan, Jiaxi He, Jianrong Zhang, Wenhua Liang, Yaoqi Chen, Qihua He, Jianxing He

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

8 authors at 1 institution in 1 country.

Yuan ZengDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.ORCID http://orcid.org/0000-0002-3270-2459
Wenli RuanGuangzhou Zisheng Biotech Company Limited, No 11, Zhu Jiang East Rd, Guangzhou, 510120, Guangdong Province, PR China.
Jiaxi HeDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
Jianrong ZhangDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
Wenhua LiangDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
Yaoqi ChenDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
Qihua HeDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
Jianxing HeDepartment of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Disease & China State Key Laboratory of Respiratory Disease, No 151, Yanjiang Rd, Guangzhou, 510120, Guangdong Province, PR China.
First Affiliated Hospital of Guangzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdoptive immunotherapy (AI) has been applied in the treatment of non-small-cell lung cancer (NSCLC) patients, but the value of postoperative AI has been inconclusive largely as a result of the small number of patients included in each study. We performed a systematic review and meta-analysis to address this issue for patients with postoperative NSCLC.

methodsPubmed, Embase, Cochrane Library were searched for randomized controlled trials comparing adoptive immunotherapy with control therapies in postoperative NSCLC patients. The primary endpoint was overall survival. Hazard ratio (HR) was estimated and 95% confidence intervals (CI) were calculated using a fixed-effect model.

resultsCompared with control therapies, analyses of 4 randomized controlled trials (472 patients) showed a significant benefit of adoptive immunotherapy on survival (hazard ratio [HR] 0.61, 95% CI 0.45-0.84, p = 0.002), and a 39% reduction in the relative risk of death (no evidence of a difference between trials; p = 0.16, I² = 42%). In subgroup analyses by treatment cycles and treatment regimen, significant OS benefit was found in combination therapy of AI with chemotherapy, regardless of whether or not the treatment cycles were more than 10 cycles.

conclusionAdoptive immunotherapy has the potential to improve overall survival in postoperative NSCLC. The findings suggest this is a valid treatment option for these patients. Further randomized clinical trials are urgently needed.

Indexed as

Immunotherapy, AdoptiveCarcinoma, Non-Small-Cell LungFemaleHumansLung NeoplasmsMaleMiddle AgedPostoperative PeriodSurvival Rate

Identifiers

PMID27618180
PMCPMC5019384
OpenAlexW2519085124

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.