Evidence map›Paper›PMID 36428744›Full record

ArticleCancers2022

Maximum Somatic Allele Frequency-Adjusted Blood-Based Tumor Mutational Burden Predicts the Efficacy of Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer.

Yiting Dong, Yixiang Zhu, Minglei Zhuo, Xiaomin Chen, Yinpeng Xie, Jianchun Duan, Hua Bai, Shiguang Hao, Zicheng Yu, Yuting Yi and 6 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

9 citing papers in PubMed, 10 citations in OpenAlex.

  1. Trial
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  4. Review
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  8. Article
  9. Biomarkers for Immunotherapy in Driver-Gene-Negative Advanced NSCLC.International journal of molecular sciences · 2023
    Review
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

16 authors at 3 institutions in 1 country.

Yiting DongState 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 & Peking Union Medical College, Beijing 100021, China.
Yixiang ZhuState 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 & Peking Union Medical College, Beijing 100021, China.
Minglei ZhuoKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department I of Thoracic Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Xiaomin ChenGeneplus-Shenzhen, Shenzhen 518118, China.
Yinpeng XieGeneplus-Shenzhen, Shenzhen 518118, China.ORCID 0000-0002-4897-9612
Jianchun DuanState 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 & Peking Union Medical College, Beijing 100021, China.ORCID 0000-0003-3300-1707
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 & Peking Union Medical College, Beijing 100021, China.ORCID 0000-0002-3179-8137
Shiguang HaoGeneplus-Beijing, Beijing 102200, China.
Zicheng YuGeneplus-Shenzhen, Shenzhen 518118, China.
Yuting YiGeneplus-Beijing, Beijing 102200, China.
Yanfang GuanGeneplus-Beijing, Beijing 102200, China.
Jie YuanGeneplus-Shenzhen, Shenzhen 518118, China.
Xuefeng XiaGeneplus-Beijing, Beijing 102200, China.
Xin YiGeneplus-Beijing, Beijing 102200, 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 & Peking Union Medical College, Beijing 100021, China.
Zhijie 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 & Peking Union Medical College, Beijing 100021, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNAnnoroad Gene Technology (China) · CNPeking University · CN

Funding

Beijing Municipal Administration of Hospitals Incubating Program PX2020045Chinese Academy of Medical Sciences Key lab of translational research on lung cancer 2018PT31035National Natural Science Foundation of China 81871889National Natural Science Foundation of China 82072586National Natural Science Foundation of China 82141117The Capital health research and development of special fund 2022-2-1023
6 · The paper itself

Abstract

introductionRecent studies exhibited the unstable prediction ability of blood-based tumor mutational burden (bTMB) when predicting the response of immune checkpoint inhibitors (ICIs) therapy in patients with non-small cell lung cancer (NSCLC). Circulating tumor DNA (ctDNA) abundance, usually represented by maximum somatic allele frequency (MSAF), was one possible confounding factor influencing bTMB ability in ICIs response prediction.

methodsMSAF-adjusted bTMB (Ma-bTMB) was established and validated in patients with advanced NSCLC among Geneplus Cancer Genome Database (GCGD,

resultsMSAF demonstrated a modest positive correlation with bTMB and a negative one with survival benefit. Improved survival outcomes of ICIs therapy have been observed among patients with high-Ma-bTMB compared to those with low-Ma-bTMB in Zhuo and Wang cohorts. In addition, compared to low-Ma-bTMB, high-Ma-bTMB was associated with more positive clinical benefits from ICIs therapy than chemotherapy both in POPLAR and OAK cohorts. Further exploration suggested that Ma-bTMB could precisely identify more potential ICIs beneficiaries compared to bTMB and LAF-bTMB, complementary to PD-L1 expression.

conclusionsWe developed Ma-bTMB, a convenient, readily available, non-invasive predictive biomarker effectively differentiates beneficiaries of ICIs therapy in advanced NSCLC, warranting future clinical trials.

Indexed as

ICIsimmune checkpoint inhibitorsmaximum somatic allele frequencyMSAFnon-small-cell lung cancerNSCLCTMBtumor mutation burden

Identifiers

PMID36428744
PMCPMC9688065
OpenAlexW4309459724

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.