Evidence map›Paper›PMID 34733843›Full record

ArticleFrontiers in cell and developmental biology2021

Predictive Value of Max's Giant Associated Protein Mutation in Outcomes of Lung Adenocarcinoma Patients Treated With Immune Checkpoint Inhibitors.

Yan Qu, Chao Wang, Lihui Liu, Sini Li, Xue Zhang, Zixiao Ma, Hua Bai, Jie Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 52% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. In vivo CRISPR screens identifyProceedings of the National Academy of Sciences of the United States of America · 2024
    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 1 institution in 1 country.

Yan QuState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chao WangState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lihui LiuState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Sini LiState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xue ZhangState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zixiao MaState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Hua BaiState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jie WangState Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment with immune checkpoint inhibitors (ICIs) has considerably improved prognosis in multiple cancers. However, regardless of PD-L1 expression and TMB, better predictive biomarkers are required to identify ICI-responsive patients. We analyzed a pan-cancer cohort as the discovery cohort to identify the role of Max's giant associated protein (MGA) mutation in the outcome of ICI treatment in different types of cancers. A pooled lung adenocarcinoma (LUAD) cohort was considered as the validation cohort. Another two LUAD cohorts who received conventional treatment were included for prognostic analysis and mechanism exploration. In the discovery cohort, MGA mutation was a favorable survival biomarker for patients with LUAD than in those with other types of cancers. MGA mutation was positively correlated with the TMB score. The results of the validation cohort were consistent with those of the discovery cohort. Patients with MGA mutation in the TMB-low subgroup had longer survival. Two LUAD cohorts who received standard treatment showed that the MGA mutation was not a prognostic biomarker for standard treatment. Mechanically, we found that the co-mutant genes did not affect the prognostic role of MGA mutation. Gene-set enrichment analysis revealed that genes belonging to the immunodeficiency pathway were enriched in the MGA wild-type group in LUAD. Moreover, activated NK cells were more enriched in the MGA mutant LUAD group. In conclusion, our results demonstrated that MGA mutation was an independent predictive biomarker for ICI therapy. These results may provide a novel insight into identifying potential patients with LUAD for ICI therapy.

Indexed as

immunotherapylung adenocarcinomaMGA mutationpan-cancerpredictive biomarker

Identifiers

PMID34733843
PMCPMC8558674
OpenAlexW3207951415

What OpenQuestion holds

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LicenceCC BY
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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.