突变
液体活检
表皮生长因子受体
生物标志物
T790米
外体
拉曼光谱
计算生物学
生物
分子生物学
化学
基因
微泡
受体
癌症
生物化学
遗传学
小RNA
光学
物理
克拉斯
作者
Seungmin Kim,Byeong Hyeon Choi,Hyunku Shin,Kihun Kwon,Sung Yong Lee,Hyun Bin Yoon,Hyun Koo Kim,Yeonho Choi
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2023-06-06
卷期号:8 (6): 2391-2400
被引量:12
标识
DOI:10.1021/acssensors.3c00681
摘要
Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.
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