{"title":"Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.","authors":"Ning Lou, Ruyun Gao, Yuankai Shi, Xiaohong Han","doi":"10.1038/s41597-024-04169-0","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1369"},"PeriodicalIF":5.8000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04169-0","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.