In resected non-small cell lung cancer (NSCLC), post-surgical recurrence occurs in around 40% of patients, highlighting the necessity to identify relapse biomarkers. An analysis of the extracellular vesicle (EV) cargo from a pulmonary tumor-draining vein (TDV) can grant biomarker identification. We studied the pulmonary TDV EV-miRNAome to identify relapse biomarkers in a two-phase study (screening and validation). In the screening phase, a 17-miRNA relapse signature was identified in 18 selected patients by small RNAseq. The most expressed miRNA from the signature (EV-miR-203a-3p) was chosen for further validation. Pulmonary TDV EV-miR-203a-3p was studied by qRT-PCR in a validation cohort of 70 patients, where it was found to be upregulated in relapsed patients (p = 0.0194) and in patients with cancer spread to nearby lymph nodes (N+ patients) (p = 0.0396). The ROC curve analysis showed that TDV EV-miR-203a-3p was able to predict relapses with a sensitivity of 88% (AUC: 0.67; p = 0.022). Moreover, patients with high TDV EV-miR-203a-3p had a shorter time to relapse than patients with low levels (43.6 vs. 97.6 months; p = 0.00703). The multivariate analysis showed that EV-miR-203a-3p was an independent, predictive and prognostic post-surgical relapse biomarker. In conclusion, pulmonary TDV EV-miR-203a-3p is a promising new relapse biomarker for resected NSCLC patients.

Introduction: The robotic-assisted (RATS) lobectomy learning curve is usually measured compared to an established videothoracoscopic (VATS) surgery program. The objective of our study is to compare the learning curves of both techniques. Methods: We performed an intention-to-treat analysis comparing the RATS vs. VATS lobectomies. Surgical time, conversions, complications, number of lymph nodes (LNs) and lymph node stations harvested, chest drainage duration, length of stay, readmissions, and 90-day mortality were compared between both groups. The learning curve was assessed using the CUSUM method. Results: RATS cases (30) and VATS cases (35) displayed no significant differences. The RATS learning curve was completed after 23 procedures while the VATS curve required 28 interventions. Complications appeared in four RATS procedures and in eight VATS patients. No differences in the number of LNs and harvested LN stations were reported. Four patients were readmitted in the RATS group, and eight in the VATS group. No 90-day postoperative mortality was observed in either group. The RATS group reported fewer chest tube days (3 (2–5) vs. 5 (4–5.8), p = 0.005) and hospital days (4 (3–6) vs. 5 (4–6), p = 0.023). Conclusions: The RATS curve appears shorter than the VATS curve. RATS lobectomies resulted in reduced chest tube duration and length of stay during the learning time period.

In resected non-small cell lung cancer (NSCLC), postsurgical recurrence is the major factor affecting long-term survival. The identification of biomarkers in extracellular vesicles (EV) obtained from serial blood samples after surgery could enhance early detection of relapse and improve NSCLC outcome. Since EV cargo contains long non-coding RNAs (lncRNAs), we aimed to analyze whether the oncogenic lncRNA HOTTIP, which higher expression in tumor tissue was related to worse outcome in NSCLC, could be detected in EV from NSCLC patients and serve as recurrence biomarker. After purification of EVs by ultracentrifugation in 52 serial samples from 18 NSCLC patients, RNA was isolated and HOTTIP was quantified by Real time PCR. We observed that patients that relapsed after surgery displayed increased postsurgical EV HOTTIP levels in comparison with presurgical levels. In the relapsed patients with several samples available between surgery and relapse, we observed an increment in the EV HOTTIP levels when approaching to relapse, which indicated its potential utility for monitoring disease recurrence. When we focused in EV HOTTIP levels in the first post-surgical sample, we observed that the detection of an increment of the expression levels in comparison to presurgical sample, predicted recurrence with high sensitivity (85.7%) and specificity (90.9%) and that patients had shorter time to relapse and shorter overall survival. In conclusion, our pilot study showed that EV HOTTIP is a potential biomarker for monitoring disease recurrence after surgery in NSCLC.