Evidence map›Paper›PMID 31270941›Full record

ArticleCancer medicine2019

Comprehensive analysis of potential immunotherapy genomic biomarkers in 1000 Chinese patients with cancer.

Yuan-Sheng Zang, Chun Dai, Xiaoman Xu, Xin Cai, Guan Wang, Jinwang Wei, Angela Wu, Wending Sun, Shunchang Jiao, Qiang Xu

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 2 pooled it
6.5field-weighted citation impact, top 3% 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

50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.

  1. Pooled it
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  13. ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023
    Article
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  15. Paraganglioma of the Head and Neck: A Review.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2023
    Review
  16. Esophagogastric Cancers: Integrating Immunotherapy Therapy Into Current Practice.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022
    Review
  17. Metabolic activity viaBMC cancer · 2022
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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 3 institutions in 1 country.

Yuan-Sheng ZangDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Chun DaiGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Xiaoman XuGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Xin CaiGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Guan WangGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Jinwang WeiGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Angela WuGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Wending SunGenomiCare Biotechnology Co. Ltd., Shanghai, China.
Shunchang JiaoDepartment of Medical Oncology, Chinese PLA General Hospital, Beijing, China.
Qiang XuGenomiCare Biotechnology Co. Ltd., Shanghai, China.ORCID 0000-0003-1563-579X
GenomiCare (China) · CNChinese PLA General Hospital · CNSecond Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor mutation burden (TMB), DNA mismatch repair deficiency (dMMR), microsatellite instability (MSI), and PD-L1 amplification (PD-L1 AMP) may predict the efficacy of the PD-1/PD-L1 blockade. With the broadening landscape of immunotherapy use, it is important to identify patients who are likely to benefit from the therapy. This study aimed to characterize the distributions of these biomarkers and explore the relationships among these biomarkers for Chinese patients with cancer.

methodsIn this study, we examined the aforementioned biomarkers in more than 1000 Chinese patients with cancer. These biomarkers were determined based on whole-exome sequencing (WES) of tumor/blood samples.

resultsOf the 953 samples from Chinese cancer patients assessed in this study, 35% exhibited high TMB (TMB-H), 4% were positive for high MSI (MSI-H), dMMR occurred in 0.53%, and PD-L1 AMP was positive in 3.79%. We found higher rates of TMB-H among hepatocellular carcinoma, breast cancer, and esophageal cancer patients than was reported for The Cancer Genome Atlas (TCGA) data. Lung cancer patients with EGFR mutations had significantly lower TMB values than those with wild-type EGFR, and increased TMB was significantly associated with dMMR in colorectal cancer (CRC). The frequency of tumors with MSI-H was the highest in CRC and gastric cancer. PD-L1 AMP occurred most frequently in lung squamous cell carcinoma and HER2-positive breast cancer. While MSI and dMMR are associated with higher mutational loads, correlations between TMB-H and other biomarkers, between MSI-H and dMMR, and between PD-L1 AMP and other biomarkers were low, indicating different underlying causes of the four biomarkers.

conclusionThe results reveal the frequency of these biomarkers in different malignancies, with potential implications for PD-1/PD-L1 blockade use for Chinese patients with cancer.

Indexed as

Asian PeopleB7-H1 AntigenBiomarkers, TumorErbB ReceptorsExome SequencingFemaleGene AmplificationHigh-Throughput Nucleotide SequencingHumansImmunotherapyMaleMicrosatellite InstabilityMutationNeoplasmsPrecision MedicineB7-H1 AntigenBiomarkers, TumorCD274 protein, humanEGFR protein, humanErbB ReceptorsChinese patients with cancerdMMRMSIPD-L1 amplificationTMBWES

Identifiers

PMID31270941
PMCPMC6712454
OpenAlexW2954591958

What OpenQuestion holds

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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.