Evidence map›Paper›PMID 34233686›Full record

ArticleCancer cell international2021

Post treatment NLR is a predictor of response to immune checkpoint inhibitor therapy in patients with esophageal squamous cell carcinoma.

Xianbin Wu, Runkun Han, Yanping Zhong, Nuoqing Weng, Ao Zhang

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 6 pooled it
3.1field-weighted citation impact, top 6% 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

36 citing papers in PubMed, 6 syntheses or guidelines pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Xianbin Wu *Department of Gastrointestinal Surgery, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China.
Runkun Han *Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Yanping ZhongDepartment of Health Management, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Nuoqing WengDepartment of Gastrointestinal Surgery, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China. wengnq3@mail.sysu.edu.cn.
Ao ZhangDepartment of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. zhangao@sysucc.org.cn.ORCID http://orcid.org/0000-0001-7379-6992
Sun Yat-sen University · CN

Funding

Futian District Health Public Welfare Scientific Research Project FTWS2020011National Postdoctoral Program for Innovative Talents BX20200399research initiation fund of the eighth affiliated hospital of sun yat-sen university GCCRCYJ011special fund for outstanding youth reserve talent program of the eighth affiliated hospital of sun yat-sen university FBJQ2019006the china postdoctoral science foundation 2021M693653
6 · The paper itself

Abstract

backgroundIn view of the fact that peripheral blood parameters have been reported as predictors of immunotherapy to various cancers, this study aimed to determine the predictors of response to anti-programmed death-1 (anti-PD-1) therapy in patients with esophageal squamous cell carcinoma (ESCC) from peripheral blood parameters.

methodsA retrospective analysis was conducted to investigate the predictive value of peripheral blood parameters including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR) and systemic immune-inflammation index (SII) in the response to anti-PD-1 antibody treatment. 119 ESCC patients receiving combined treatment including anti-PD-1 antibody were enrolled in this study.

resultsThe median progression-free survival (PFS) of all ESCC patients was 3.73 months. PFS rate in ESCC patients with low NLR at 6 weeks post treatment was higher than patients with high NLR (HR = 2.097, 95% CI 0.996-4.417, P = 0.027). However, PFS rate in ESCC patients with low NLR at baseline (HR = 1.060, 95% CI 0.524-2.146, P = 0.869) or 3 weeks post treatment (HR = 1.293, 95% CI 0.628-2.663, P = 0.459) was comparable with high NLR. And no statistically different was found in PFS rate between low PLR and high PLR at baseline (HR = 0.786, 95% CI 0.389-1.589, P = 0.469), 3 weeks post treatment (HR = 0.767, 95% CI 0.379-1.552, P = 0.452) or 6 weeks post treatment (HR = 1.272, 95% CI 0.624-2.594, P = 0.488) in ESCC patients. PFS rate was also comparable between low MLR and high MLR at baseline (HR = 0.826, 95% CI 0.408-1.670, P = 0.587), 3 weeks post treatment (HR = 1.209, 95% CI 0.590-2.475, P = 0.580) or 6 weeks post treatment (HR = 1.199, 95% CI 0.586-2.454, P = 0.596). PFS rate was similar between patients with low SII and high SII at baseline (HR = 1.120, 95% CI 0.554-2.264, P = 0.749), 3 weeks post treatment (HR = 1.022, 95% CI 0.500-2.089, P = 0.951) and 6 weeks post treatment (HR = 1.759, 95% CI 0.851-3.635, P = 0.097).

conclusionsNLR at 6 weeks post treatment is a predictor of the response to anti-PD-1 treatment in patients with ESCC.

Indexed as

Anti-PD-1 treatmentEsophageal squamous cell carcinomaNeutrophil-to-lymphocyte ratioProgression-free survival

Identifiers

PMID34233686
PMCPMC8262036
OpenAlexW3180897560

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.