ArticleScientific data2024
Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.
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.
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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.
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Who cites it
3 citing papers in PubMed.
- Bidirectional coupling among EMT, AXL-RB1 signaling and lineage switch drives resistance to osimertinib and worse clinical outcomes in NSCLC.bioRxiv : the preprint server for biology · 2026Article
- Epidermal growth factor receptor tyrosine kinase inhibitor for the treatment of non-small cell lung cancer in the past 30 years (1997-2026).Chinese medical journal · 2026Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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