Detection of EGFR Mutations Using Bronchial Washing-Derived Extracellular Vesicles in Patients with Non-Small-Cell Lung Carcinoma.
Juhee ParkChaeeun LeeJung Seop EomMi Hyun KimYoon-Kyoung ChoPublished in: Cancers (2020)
The detection of epidermal growth factor receptor (EGFR) mutation, based on tissue biopsy samples, provides a valuable guideline for the prognosis and precision medicine in patients with lung cancer. In this study, we aimed to examine minimally invasive bronchial washing (BW)-derived extracellular vesicles (EVs) for EGFR mutation analysis in patients with lung cancer. A lab-on-a-disc equipped with a filter with 20-nm pore diameter, Exo-Disc, was used to enrich EVs in BW samples. The overall detection sensitivity of EGFR mutations in 55 BW-derived samples was 89.7% and 31.0% for EV-derived DNA (EV-DNA) and EV-excluded cell free-DNA (EV-X-cfDNA), respectively, with 100% specificity. The detection rate of T790M in 13 matched samples was 61.5%, 10.0%, and 30.8% from BW-derived EV-DNA, plasma-derived cfDNA, and tissue samples, respectively. The acquisition of T790M resistance mutation was detected earlier in BW-derived EVs than plasma or tissue samples. The longitudinal analysis of BW-derived EVs showed excellent correlation with the disease progression measured by CT images. The EGFR mutations can be readily detected in BW-derived EVs, which demonstrates their clinical potential as a liquid-biopsy sample that may aid precise management, including assessment of the treatment response and drug resistance in patients with lung cancer.
Keyphrases
- epidermal growth factor receptor
- small cell lung cancer
- tyrosine kinase
- advanced non small cell lung cancer
- computed tomography
- magnetic resonance imaging
- circulating tumor
- magnetic resonance
- photodynamic therapy
- cell free
- machine learning
- real time pcr
- climate change
- convolutional neural network
- bone marrow
- risk assessment
- positron emission tomography
- ionic liquid
- fine needle aspiration
- contrast enhanced
- optical coherence tomography