The need for novel biomarkers that could aid in non-small cell lung cancer (NSCLC) detection, together with the relevance of Matrix Metalloproteases (MMPs) -1, -2, -7, -9 and -10 in lung tumorigenesis, prompted us to assess the diagnostic usefulness of these MMPs and the Tissue Inhibitor of Metalloproteinase (TIMP) -1 in NSCLC patients. Markers were evaluated in an initial study cohort (19 NSCLC cases and 19 healthy controls). Those that better performed were analyzed in a larger sample including patients with benign lung diseases. Serum MMPs and TIMP-1 were determined by multiplexed immunoassays. Logistic regression was employed for multivariate analysis of biomarker combinations. MMPs and TIMP-1 were elevated in the serum of NSCLC patients compared to healthy controls. MMP-1, -7 and -9 performed at best and were further evaluated in the sample including benign pathologies, corroborating the superiority of MMP-9 in NSCLC discrimination, also at early-stage NSCLC. The optimal diagnostic value was obtained with the model including MMP-9, gender, age and smoking history, that demonstrated an AUC of 0.787, 85.54% sensitivity and 64.89% specificity. Our results suggest that MMP-9 is a potential biomarker for NSCLC diagnosis and its combined measurement with other biomarkers could improve NSCLC detection.

Staging of mediastinal lymph nodes in non-small cell lung cancer (NSCLC) is mandatory. The maximum Standard Uptake Value (SUVmax) obtained using F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) is the best non-invasive technique available for this evaluation, but its performance varies from center to center. The aim of the present study was to identify FDG-PET predictors of mediastinal malignancy that are able to minimize intercenter variability and improve the selection of subsequent staging procedures. A multicenter study of NSCLC patients staged through FDG-PET and endobronchial ultrasonography with needle aspiration (EBUS-NA) was performed using therapeutic surgery with systematic nodal dissection as gold standard. Intercenter variability and predictive power for mediastinal malignancy of different FDG-PET measures were assessed, as well as the role of these measures for selecting additional staging procedures. One hundred and twenty-one NSCLC patients, of whom 94 (72%) had ≥1 hypermetabolic spots in the mediastinum, were included in the study. Mean SUVmax of the primary tumor was 12.3 (SD 6.3), and median SUVmax of the highest hypermetabolic spots in the mediastinum was 3.9 (IQR 2.4-7). Variability of FDG-PET measures between hospitals was statistically significant (p = 0.016 and p < 0.001 respectively), but lost significance when SUVmax in the mediastinum was expressed as a ratio or a subtraction from the primary tumor (SUVmax mediastinum/tumor, p = 0.083; and SUVmax mediastinum - tumor, p = 0.428 respectively). SUVmax mediastinum/tumor showed higher accuracy in the ROC analysis (AUC 0.77 CI 0.68-0.85, p < 0.001), and showed predictive power for mediastinal malignancy when using a 0.4 cutoff (OR 6.62, 95%CI 2.98-14.69). Sensitivities and negative predictive values of clinical staging through EBUS-NA attained values ranging between 57% and 92% after FDG-PET, which improved with additional techniques when the tumor had a diameter >3 cm and/or a SUVmax mediastinum/tumor ratio >0.4. The SUVmax mediastinum/tumor ratio is a good predictor of regional tumor extension in NSCLC. This measure is not influenced by intercenter variability and has an accuracy of over 70% for the identification of malignancy when using a 0.4 cutoff.

Different methodological approaches are available to assess DNA methylation biomarkers. In this study, we evaluated two sodium bisulfite conversion-dependent methods, namely pyrosequencing and methylation-specific qPCR (MS-qPCR), with the aim of measuring the closeness of agreement of methylation values between these two methods and its effect when setting a cut-off. Methylation of tumor suppressor gene p16/INK4A was evaluated in 80 lung cancer patients from which cytological lymph node samples were obtained. Cluster analyses were used to establish methylated and unmethylated groups for each method. Agreement and concordance between pyrosequencing and MS-qPCR was evaluated with Pearson’s correlation, Bland–Altman, Cohen’s kappa index and ROC curve analyses. Based on these analyses, cut-offs were derived for MS-qPCR. An acceptable correlation (Pearson’s R2 = 0.738) was found between pyrosequencing (PYRmean) and MS-qPCR (NMP; normalized methylation percentage), providing similar clinical results when categorizing data as binary using cluster analysis. Compared to pyrosequencing, MS-qPCR tended to underestimate methylation for values between 0 and 15%, while for methylation >30% overestimation was observed. The estimated cut-off for MS-qPCR data based on cluster analysis, kappa-index agreement and ROC curve analysis were much lower than that derived from pyrosequencing. In conclusion, our results indicate that independently of the approach used for estimating the cut-off, the methylation percentage obtained through MS-qPCR is lower than that calculated for pyrosequencing. These differences in data and therefore in the cut-off should be examined when using methylation biomarkers in the clinical practice.

Objective: Although the importance of assessing inspiratory flow in the selection of treatments for chronic obstructive pulmonary disease (COPD) is understood, evaluation of this factor is not yet widespread or standardized. The objective of the present work was to evaluate the peak inspiratory flow (PIF) of patients with COPD and to explore the variables associated with a suboptimal PIF. Methods: An observational, cross-sectional study was carried out at specialized nursing consultations over a period of 6 months. We collected clinical data as well as data on symptoms, treatment adherence, and patient satisfaction with their inhalers via questionnaires. PIF was determined using the In-Check Dial G16^® device (Clement Clarke International, Ltd., Harlow, UK). In each case, the PIF was considered suboptimal when it was off-target for any of the prescribed inhalers. The association with suboptimal PIF was evaluated using multivariate logistic regression and the results were expressed as the odds ratio (OR) with 95% confidence interval (CI). Results: A total of 122 COPD patients were included in this study, of whom 34 (27.9%) had suboptimal PIF. A total of 229 inhalers were tested, of which 186 (81.2%) were dry powder devices. The multivariate analysis found an association between suboptimal PIF and age (OR = 1.072; 95% CI (1.019, 1.128); p = 0.007) and forced vital capacity (OR = 0.961; 95% CI (0.933, 0.989); p = 0.006). Conclusions: About a third of patients in complex specialized COPD care have suboptimal PIFs, which is related to age and forced vital capacity.