Evidence map›Paper›PMID 33542239›Full record

ArticleNPJ genomic medicine2021

Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma.

Chuanliang Cui, Canqiang Xu, Wenxian Yang, Zhihong Chi, Xinan Sheng, Lu Si, Yihong Xie, Jinyu Yu, Shun Wang, Rongshan Yu and 2 more

Open access · goldAbstract read
In one paragraph

Article in NPJ genomic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed
7.8field-weighted citation impact, top 2% 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

84 citing papers in PubMed, 112 citations in OpenAlex.

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  10. DamageZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026
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24 more citing papers are in PubMed but not listed here.

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

12 authors at 2 institutions in 1 country.

Chuanliang Cui *Peking University Cancer Hospital and Institute, Beijing, China.
Canqiang Xu *Aginome-XMU Joint lab, Xiamen University, Xiamen, China.
Wenxian YangAginome-XMU Joint lab, Xiamen University, Xiamen, China.ORCID http://orcid.org/0000-0002-5349-9680
Zhihong ChiPeking University Cancer Hospital and Institute, Beijing, China.
Xinan ShengPeking University Cancer Hospital and Institute, Beijing, China.ORCID http://orcid.org/0000-0001-9359-0975
Lu SiPeking University Cancer Hospital and Institute, Beijing, China.
Yihong XiePeking University Cancer Hospital and Institute, Beijing, China.
Jinyu YuPeking University Cancer Hospital and Institute, Beijing, China.
Shun WangDepartment of Computer Science, School of Informatics, Xiamen University, Xiamen, China.
Rongshan YuAginome-XMU Joint lab, Xiamen University, Xiamen, China. rsyu@xmu.edu.cn.ORCID http://orcid.org/0000-0003-2179-173X
Jun GuoPeking University Cancer Hospital and Institute, Beijing, China. guoj307@126.com.ORCID http://orcid.org/0000-0003-1759-0986
Yan KongPeking University Cancer Hospital and Institute, Beijing, China. k-yan08@163.com.ORCID http://orcid.org/0000-0002-2266-7872
Peking University · CNXiamen University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81672696National Natural Science Foundation of China (National Science Foundation of China) 81772912National Natural Science Foundation of China (National Science Foundation of China) 81972557Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) 7202022
6 · The paper itself

Abstract

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumor microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analyzing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 54) and anti-CTLA-4 (n = 42), as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive biomarker that complements existing GEP signatures for immunotherapy.

Identifiers

PMID33542239
PMCPMC7862369
OpenAlexW3128727304

What OpenQuestion holds

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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.