Evidence map›Paper›PMID 33489799›Full record

ArticleTranslational lung cancer research2020

Predictive values of genomic variation, tumor mutational burden, and PD-L1 expression in advanced lung squamous cell carcinoma treated with immunotherapy.

Yanjun Xu, Hui Li, Zhiyu Huang, Kaiyan Chen, Xiaoqing Yu, Jiamin Sheng, Han-Han Zhang, Yun Fan

Open access · diamondAbstract read
In one paragraph

Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
2.3field-weighted citation impact, top 9% 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Pathology oncology research : POR · 2024
    Article
  9. Article
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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 5 institutions in 1 country.

Yanjun XuDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Hui LiDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Zhiyu HuangDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Kaiyan ChenDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Xiaoqing YuDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Jiamin ShengDepartment of Medical Thoracic Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Cancer Research and Basic Medical Sciences of Chinese Academy of Sciences, Hangzhou, China.
Han-Han ZhangBurning Rock Biotech, Guangzhou, China.
Yun FanWenzhou Medical University, Wenzhou, China.
Cancer Hospital of Chinese Academy of Medical Sciences · CNUniversity of Chinese Academy of Sciences · CNZhejiang Cancer Hospital · CNBurning Rock Biotech (China) · CNWenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitors (ICIs) prolong overall survival (OS) in patients with advanced lung squamous cell carcinoma (LUSC). However, predictive and prognostic factors related to ICIs in LUSC remain elusive. This study aimed to identify predictors that are related to better clinical benefit and outcomes in LUSC patients treated with immunotherapy.

methodsCapture-based targeted sequencing was performed in 64 patients with advanced LUSC who underwent immunotherapy. Tumor mutational burden (TMB) was defined as the sum of nonsynonymous single nucleotide and indel variants. Programmed cell death ligand-1 (PD-L1) expression was evaluated by immunohistochemical analysis. Clinicopathological characteristics including age, sex, performance status, smoking history, body mass index (BMI), blood fat, brain metastases, liver metastases, previous thoracic radiotherapy, and treatment lines were analyzed.

resultsThe most commonly mutated genes included

conclusionsThe predictive value of TMB is more significant in LUSC patients receiving ICI as a single agent than as a combination therapy. The combination of Eastern Cooperative Oncology Group performance status (ECOG-PS), smoking status, TMB, PD-L1, and genomic variation might be helpful for personalized immunotherapy decisions in clinical practice for advanced LUSC.

Indexed as

Genomeimmunotherapylung squamous cell carcinoma (LUSC)programmed cell death ligand-1 (PD-L1)tumor mutational burden (TMB)

Identifiers

PMID33489799
PMCPMC7815375
OpenAlexW3112125820

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.