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.

Lung cancer screening programs with computed tomography of the chest reduce mortality by more than 20%. Yet, they have not been implemented widely because of logistic and cost implications. Here, we sought to: (1) use real-life data to compare the outcomes and cost of lung cancer patients with treated medically or surgically in our region and (2) from this data, estimate the cost–benefit ratio of a lung cancer screening program (CRIBAR) soon to be deployed in our region (Catalunya, Spain). We accessed the Catalan Health Surveillance System (CHSS) and analysed data of all patients with a first diagnosis of lung cancer between 1 January 2014 and 31 December 2016. Analysis was carried forward until 30 months (t = 30) after lung cancer diagnosis. Main results showed that: (1) surgically treated lung cancer patients have better survival and return earlier to regular home activities, use less healthcare related resources and cost less tax-payer money and (2) depending on incidence of lung cancer identified and treated in the program (1–2%), the return on investment for CRIBAR is expected to break even at 3–6 years, respectively, after its launch. Surgical treatment of lung cancer is cheaper and offers better outcomes. CRIBAR is estimated to be cost-effective soon after launch.