e15550 Background: Exosomes are endosome-derived nano size (30-150 nm) extracellular microvesicles released from many cell types including cancer cells and encapsulated by cell membrane play a key role for cell to cell communication. Use of exosomes as a biomarkers in lung cancer is a rising nanotechnology in a liquid biopsy. We explored a role of an exosome specific marker and the relationship between pathological stage of lung cancer patients who underwent surgery and lung cancer specific exosome markers. Methods: Conditioned medium from non-small cell lung cancer (NSCLC) cells and human pulmonary alveolar endothelial cell (HPAEpiC) was collected and exosomes were isolated by size exclusion chromatography. Proteomics analysis was performed to investigate lung cancer specific proteins. Written informed consent was obtained from all human subjects (17 controls and 54 patients), approved by the Korea University Guro Hospital Institutional Review Bored. Plasma exosomes were isolated using dual size exclusion chromatography. We validated proteomics results with lung cancer exosome-specific protein 1 (LESP-1) ELISA assay and western blot assay in lung cancer patients with healthy control. Cancer cell lines with pCMV-CD63-GFP were transduced by lentiviral vectors containing LESP-1 shRNA. The distribution of GFP+ exosomes and cell migration were examined by immunocytochemistry (ICC) and cell migration assay. Results: We identified LESP1 by the proteomics analysis of exosomes from NSCLC cell lines, but not in HPAEpiC cell. Level of LESP1 was dramatically increased in exosomes from NSCLC cell lines and from NSCLC patients. LESP-1 concentration increased in lung cancer patients than healthy controls ( p < 0.0001) and increased according to the grade of lung cancer stage in peripheral blood ( p < 0.0001). Western blot results confirmed the presence of the LESP1 with higher intensity band at each grade of lung cancer stage than healthy controls. Interestingly, we found that the number of GFP+ exosomes was decreased and cell migration was inhibited when the LESP1 was suppressed in NSCLC cells. Conclusions: The LESP-1 in exosomes was highly expressed in blood plasma of lung cancer patients, and the exosome release and cancer cell migration was inhibited by the LESP-1, which suggest that LESP-1 could be a feasible factor for diagnosis and treatment of non-small cell lung cancer.

e15532 Background: Lung cancer has a high mortality rate because of belated diagnosis at advanced stages beyond the treatable condition. Early detection of lung cancer can improve the survival rate. A liquid biopsy that detects tumor-related biomarkers in body fluids has a great potential for the purpose. Particularly, tumor-derived exosomes in blood have been proposed as a promising biomarker. The tumor-derived exosomes carry molecules of their parental cells; thus, they provide information about the tumor in the body. Unfortunately, exosomal markers conducive to the early detection of lung cancer are still obscure. Therefore, using the molecular fingerprint of exosomes markers can be useful to detect the tumor exosomes. Raman spectroscopy is one of the representative methods for the purpose. However, because the exosomes have a heterogeneous composition in blood, interpreting their spectroscopic signals is hard. Thus, we utilized a deep learning approach to analyze the spectroscopic signal of the exosomes for liquid biopsy of lung cancer. Methods: The basic concept was to evaluate how much the exosomes in human plasma resemble cancer cell exosomes. As a proof of concept, exosomes of 43 non-small cell lung cancer (NSCLC) adenocarcinoma patients and 20 healthy controls were isolated from plasma of peripheral blood. Also, cell exosomes were isolated from culture media of adenocarcinoma cell lines and a human pulmonary alveolar epithelial cell line. Then, the spectroscopic signals were detected using surface-enhanced Raman spectroscopy (SERS). Further, the deep learning algorithm was employed to classify the signals. Then, we calculated the relative similarity to cancerous exosomes against human plasma exosomes. Results: Our method was able to classify cancer and normal cell exosomes with 95% accuracy. Also, Raman signals of cancer patients’ exosomes were more similar to the cancer cell exosomes than those of healthy controls. Notably, the similarity was proportional to cancer stages. Importantly, our method even detected stage I patients. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was 0.912 for stage I and II, and 0.910 for stage I. Conclusions: We reported a novel diagnostic method using deep learning analysis against spectroscopic signals of circulating exosomes. Our method that evaluates the similarity to cancer exosomes accurately identified lung cancer patients, even stage I with high accuracy.