Evidence map›Paper›PMID 29758930›Full record

ArticleCancer biomarkers : section A of Disease markers2018

The potential predictive value of circulating immune cell ratio and tumor marker in atezolizumab treated advanced non-small cell lung cancer patients.

Minglei Zhuo, Hanxiao Chen, Tianzhuo Zhang, Xue Yang, Jia Zhong, Yuyan Wang, Tongtong An, Meina Wu, Ziping Wang, Jing Huang and 1 more

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.2field-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

20 citing papers in PubMed, 52 citations in OpenAlex.

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  18. Immune biomarkers of response to immune-checkpoint inhibitors in head and neck squamous cell carcinoma.Annals of oncology : official journal of the European Society for Medical Oncology · 2019
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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

11 authors at 3 institutions in 1 country.

Minglei ZhuoKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Hanxiao ChenKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Tianzhuo ZhangDepartment of Immunology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
Xue YangKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Jia ZhongKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Yuyan WangKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Tongtong AnKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Meina WuKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Ziping WangKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Jing HuangDepartment of Immunology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
Jun ZhaoKey Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China.
Peking University · CNMinistry of Education of the People's Republic of China · CNPeking University Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe PD-L1 antibody atezolizumab has shown promising efficacy in patients with advanced non-small cell lung cancer. But the predictive marker of clinical benefit has not been identified.

objectiveThis study aimed to search for potential predictive factors in circulating blood of patients receiving atezolizumab.

methodsTen patients diagnosed with advanced non-small cell lung cancer were enrolled in this open-label observing study. Circulating immune cells and plasma tumor markers were examined in peripheral blood from these patients before and after atezolizumab treatment respectively. Relation between changes in circulating factors and anti-tumor efficacy were analyzed.

resultsBlood routine test showed that atezolizumab therapy induced slightly elevation of white blood cells count generally. The lymphocyte ratio was increased slightly in disease controlled patients but decreased prominently in disease progressed patients in response to atezolizumab therapy. Flow cytometric analysis revealed changes in percentage of various immune cell types, including CD4+ T cell, CD8+ T cell, myeloid-derived suppressor cell, regulatory T cell and PD-1 expressing T cell after atezolizumab. Levels of plasma tumor marker CEA, CA125 and CA199 were also altered after anti-PD-L1 therapy. In comparison with baseline, the disease progressed patients showed sharp increase in tumor marker levels, while those disease controlled patients were seen with decreased regulatory T cell and myeloid-derived suppressor cell ratios.

conclusionsThe circulating immune cell ratios and plasma tumor marker levels were related with clinical efficacy of atezolizumab therapy. These factors could be potential predictive marker for anti-PD-L1 therapy in advanced non-small cell lung cancer.

Indexed as

Biomarkers, TumorAgedAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedAntineoplastic AgentsCarcinoma, Non-Small-Cell LungCell Line, TumorFemaleFlow CytometryHumansImmunohistochemistryLung NeoplasmsMaleMiddle AgedNeoplasm MetastasisNeoplasm StagingAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedAntineoplastic AgentsatezolizumabBiomarkers, TumorCirculating immune cellimmunotherapynon-small cell lung cancerPD-L1tumor marker

Identifiers

PMID29758930
PMCPMC13078483
OpenAlexW2803072541

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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.