New proteomic signature in circulating extracellular vesicles from tumor-draining vein of lung adenocarcinomas patients

Identification of noninvasive prognostic biomarkers, allowing monitoring of frequently developed relapse in patients with locally advanced non-small cell lung cancer (NSCLC), still of primary importance. Tumor-draining vein (TDV) plasma samples, are known to be enriched in circulating cancer biomarkers compared to samples from peripheral vein (PV). Thus, we thought to investigate the proteomic profile of extracellular vesicles (EVs) from TDV compared to those from PV plasma samples of patients operated for NSCLC.

Purified EVs from TDV and PV plasma samples were characterized for their size distribution and concentration using nanoparticles tracking analysis (NTA). Proteomic profiling of TDV-derived EVs and PV-derived EVs were further done using mass spectrometry (nanoLC-MS/MS) analysis. In parallel, proteomic profile of tumoral and non-tumoral adjacent counterpart tissues from patients with NSCLC were investigated.

Twenty patients with NSCLC, treated by surgery with curative intent, were enrolled in this study. We showed that EVs from TDV plasma samples were significantly smaller than those from PV plasma samples. Interestingly, the concentration of TDV-derived EVs were significantly higher than PV-derived EVs. However, EVs concentration and size were not associated with tumor size or other clinical characteristics. Proteomic profiling showed that 9 of the 10 most overexpressed proteins in EVs from TDV samples compared to those from PV, were associated with lung cancer diagnosis and prognosis. Remarkably, 1 protein (SRPRB) was commonly upregulated in lung tumor tissues (as compared to non-tumoral counterparts) and in TDV-derived EVs (as compared to PV-derived EVs). In contrast, 12 proteins were found to be upregulated in TDV-derived EVs and downregulated in tumor tissues.

In conclusion, all of these identified proteins, carried by EVs from TDV plasma samples, might represent promising novel biomarkers for NSCLC prognosis and predicting recurrences at early stages.