代谢物
肺癌
癌症研究
医学
癌症
肿瘤科
内科学
作者
Lucksamon Thamlikitkul,Siriphan Manocheewa,Suphitcha Limjiasahapong,Kwanjeera Wanichthanarak,Atikorn Panya,Naravat Poungvarin,Yotsawat Pomyen,Sakda Khoomrung
出处
期刊:Cancer Research
[American Association for Cancer Research]
日期:2024-03-22
卷期号:84 (6_Supplement): 1068-1068
标识
DOI:10.1158/1538-7445.am2024-1068
摘要
Abstract Background: Lung cancer is the leading cause of cancer death. More than half of the patients are diagnosed in the advanced stage for which the 5-year survival is below 10%. Lung cancer screening using low-dose CT scan reduced lung cancer mortality among heavy smokers. However, no standard screening method exists for non-smokers. Up to 35% and 90% of male and female lung cancer patients in Southeast Asia, respectively, do not smoke. Half of NSCLC patients in this region harbor EGFR mutations, which is common in women and non-smokers. Thus, we sought to explore novel biomarkers that help to identify lung cancer patients and those who have EGFR mutation. Metabolic reprogramming is one of the hallmarks of cancer. It leads to changes in metabolite levels in the blood. Hence, we conducted plasma metabolomic analysis to identify potential metabolite biomarkers in lung cancer patients. Methods: Plasma samples were collected from 69 lung cancer patients at Siriraj hospital and 10 healthy individuals during 2018-2022. Plasma metabolites from 29 treatment-naïve EGFR-mutated NSCLC patients, 30 treatment-naïve EGFR-wild-type NSCLC patients, 10 treatment-resistant EGFR T790M-mutated NSCLC patients, and 10 healthy individuals were profiled using GC-MS and untargeted LC-MS with Metlin database matching. EGFR mutation was detected from plasma ctDNA using real-time PCR. Negative plasma EGFR mutation results were confirmed with corresponding tumor tissues. Metabolomics data were processed by the metabox R package. Results: Among 114 candidate metabolites that passed the variable importance in projection (VIP) cutoff of 1.5, the top ten metabolites that differentiate NSCLC patients with EGFR mutation from those without were shown in Table 1. Many of them can also separate between NSCLC patients and healthy individuals. Table 1. ROC curve analysis Discriminant metabolites (Mutated EGFR vs Wild-type EGFR NSCLC) AUC Accuracy Sensitivity Specificity PG(a-13:0/i-12:0) 0.8000 0.7833 0.8333 0.7333 Nervonic ceramide 0.7736 0.6772 0.7259 0.6333 Valine 0.7678 0.7298 0.7259 0.7333 L-Histidinol 0.7644 0.7281 0.7222 0.7333 Leucine 0.7563 0.7632 0.7259 0.8000 Tryptophan 0.7425 0.6289 0.5593 0.7000 8-methylcaffeine 0.6793 0.6096 0.6222 0.6000 Formic acid 0.6759 0.6447 0.5889 0.7000 PC(20:0/24:0) 0.6506 0.6614 0.8259 0.5000 Sucrose 0.6471 0.6596 0.7593 0.5667 Discriminant metabolites (NSCLC patients vs Healthy individuals) AUC Accuracy Sensitivity Specificity Tryptophan 0.9986 0.9867 1.0000 0.9848 PC(20:0/24:0) 0.8609 0.9505 0.6111 1.0000 L-Histidinol 0.8572 0.9625 0.7778 0.9861 Formic acid 0.6696 0.9262 0.6667 0.9704 Conclusions: Plasma L-Histidinol, tryptophan, PC(20:0/24:0), and formic acid are potential metabolite biomarkers for NSCLC patients harboring EGFR mutation. Citation Format: Lucksamon Thamlikitkul, Siriphan Manocheewa, Suphitcha Limjiasahapong, Kwanjeera Wanichthanarak, Atikorn Panya, Naravat Poungvarin, Yotsawat Pomyen, Sakda Khoomrung. Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1068.
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