Evidence map›Paper›PMID 34992591›Full record

ReviewFrontiers in immunology2021

Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer.

Xue-Lin Zou, Xiao-Bo Li, Hua Ke, Guang-Yan Zhang, Qing Tang, Jiao Yuan, Chen-Jiao Zhou, Ji-Liang Zhang, Rui Zhang, Wei-Yong Chen

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
2.9field-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

39 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Review
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  5. Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026
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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

10 authors at 1 institution in 1 country.

Xue-Lin ZouDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Xiao-Bo LiDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Hua KeDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Guang-Yan ZhangDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Qing TangDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Jiao YuanDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Chen-Jiao ZhouDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Ji-Liang ZhangDepartment of Oncology, Chengdu Seventh People's Hospital, Chengdu, China.
Rui ZhangDepartment of Thoracic Surgery, Chengdu Seventh People's Hospital, Chengdu, China.
Wei-Yong ChenDepartment of Respiratory Medicine, Chengdu Seventh People's Hospital, Chengdu, China.
Seventh People's Hospital of Shanghai · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoint inhibitors (ICIs) have made great progress in the field of tumors and have become a promising direction of tumor treatment. With advancements in genomics and bioinformatics technology, it is possible to individually analyze the neoantigens produced by somatic mutations of each patient. Neoantigen load (NAL), a promising biomarker for predicting the efficacy of ICIs, has been extensively studied. This article reviews the research progress on NAL as a biomarker for predicting the anti-tumor effects of ICI. First, we provide a definition of NAL, and summarize the detection methods, and their relationship with tumor mutation burden. In addition, we describe the common genomic sources of NAL. Finally, we review the predictive value of NAL as a tumor prediction marker based on various clinical studies. This review focuses on the predictive ability of NAL's ICI efficacy against tumors. In melanoma, lung cancer, and gynecological tumors, NAL can be considered a predictor of treatment efficacy. In contrast, the use of NAL for urinary system and liver tumors requires further research. When NAL alone is insufficient to predict efficacy, its combination with other indicators can improve prediction efficiency. Evaluating the response of predictive biomarkers before the treatment initiation is essential for guiding the clinical treatment of cancer. The predictive power of NAL has great potential; however, it needs to be based on more accurate sequencing platforms and technologies.

Indexed as

Antigens, NeoplasmDNA, NeoplasmDNA RepairDrug Resistance, NeoplasmEpitopesHumansImmune Checkpoint InhibitorsImmunotherapyMutationNeoplasm ProteinsNeoplasmsPrognosisTreatment OutcomeAntigens, NeoplasmDNA, NeoplasmEpitopesImmune Checkpoint InhibitorsNeoplasm Proteinsbiomarkercancerimmune checkpoint inhibitorneoantigen loadprognostic value

Identifiers

PMID34992591
PMCPMC8724026
OpenAlexW4200002229

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.