Evidence map›Paper›PMID 39695165›Full record

ArticleScientific data2024

Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.

Ning Lou, Ruyun Gao, Yuankai Shi, Xiaohong Han

Abstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

4 authors.

Ning Lou *Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College; No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Ruyun Gao *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 Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs; No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Yuankai ShiDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs; No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China. syuankai@cicams.ac.cn.
Xiaohong HanClinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College; No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. hanxiaohong@pumch.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81972805
6 · The paper itself

Abstract

Third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the latest and a vital treatment option for non-small cell lung cancer (NSCLC) patients. Although EGFR-sensitive mutations are an indication for third-generation EGFR-TKI therapy, 30% of NSCLC patients lack response and all patients inevitably progress. There is a lack of biomarkers to predict the efficacy of EGFR-TKI therapy. In this report, we performed comprehensive plasma metabolomic profiling on 186 baseline and 20 post-treatment samples, analyzing 1,019 metabolites using four ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. The dataset contains detailed clinical and metabolic information for 186 patients. Rigorous quality control measures were implemented. No significant differences in body mass index and biochemical metabolic parameters were observed between responders and non-responders. The datasets were utilized to characterize the responsive metabolic traits of third-generation EGFR-TKI therapy. All datasets are available for download on the OMIX website. We anticipate that these datasets will serve as valuable resources for future studies investigating NSCLC metabolism and for the development of personalized therapeutic strategies.

Indexed as

Carcinoma, Non-Small-Cell LungErbB ReceptorsLung NeoplasmsMetabolomicsMutationProtein Kinase InhibitorsAdolescentAdultAgedFemaleHumansMaleMiddle AgedTandem Mass SpectrometryYoung AdultEGFR protein, humanErbB ReceptorsProtein Kinase Inhibitors

Identifiers

PMID39695165
PMCPMC11655626

What OpenQuestion holds

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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.