Exosomal proteins as potential diagnostic markers in advanced non-small cell lung carcinoma

Abstract

Background: Lung cancer is one of the leading causes of cancer-related death. At the time of diagnosis, more than half of the patients will have disseminated disease and, yet, diagnosing can be challenging. New methods are desired to improve the diagnostic work-up. Exosomes are cell-derived vesicles displaying various proteins on their membrane surfaces. In addition, they are readily available in blood samples where they constitute potential biomarkers of human diseases, such as cancer. Here, we examine the potential of distinguishing non-small cell lung carcinoma (NSCLC) patients from control subjects based on the differential display of exosomal protein markers.

Methods: Plasma was isolated from 109 NSCLC patients with advanced stage (IIIa-IV) disease and 110 matched control subjects initially suspected of having cancer, but diagnosed to be cancer free. The Extracellular Vesicle Array (EV Array) was used to phenotype exosomes directly from the plasma samples. The array contained 37 antibodies targeting lung cancer-related proteins and was used to capture exosomes, which were visualised with a cocktail of biotin-conjugated CD9, CD63 and CD81 antibodies.

Results: The EV Array analysis was capable of detecting and phenotyping exosomes in all samples from only 10 µL of unpurified plasma. Multivariate analysis using the Random Forests method produced a combined 30-marker model separating the two patient groups with an area under the curve of 0.83, CI: 0.77-0.90. The 30-marker model has a sensitivity of 0.75 and a specificity of 0.76, and it classifies patients with 75.3% accuracy.

Conclusion: The EV Array technique is a simple, minimal-invasive tool with potential to identify lung cancer patients.

Keywords: EV Array; NSCLC; exosomes; extracellular vesicles; lung cancer; phenotyping; plasma; protein microarray.

Fig. 1

Hierarchical cluster analysis. Two groups of markers show co-variance both in the control group and in the cancer group (marked with boxes). a) Heat map illustration of all markers in the control group. b) Heat map illustration of all markers in the cancer group.

Fig. 2

EV Array signal intensities for selected antigens. a) The EV Array signal intensities for the exosomal markers CD9, CD63 and CD81 displayed in box plots. The co-variation of the signal intensities across the patient samples can be seen in Fig. 1 and Supplementary Fig. 2. b) Box plot of a group of antigens (Flotilin-1, HER4, EGFRvIII, N-Cadherin and CD163) showing a high degree of co-variation (see Fig. 1). *p<0.05; ***p<0.001; ****p<0.0001; ns=not significant.

Fig. 3

Normalisation of the data to the total amount of signal. a) The signals for all analytes were summed for each individual patient and plotted; controls indicated with green and cancer with red. For each individual patient the expression of the analytes were calculated as percentage of the total signal. The pie charts illustrate an example of the normalised data for a patient in each group with a total amount of signal of ~40. Highlighted is the expression of CD9, CD63 and CD81. b) and c) Box plot of the relative expression of markers from Figure 2a and b in percentage (in relation to the total sum of exosomal signal). *p <0.05; **p<0.01; ****p<0.0001; ns=not significant.

Fig. 4

Multivariate analysis by Random Forests using the EV Array measurements of the exosomal antigens. Random Forests ROC curves generated by the cross validation performance. The area under curve (AUC) for top 3-, 5-, 10-, and 30-marker panels are given together with the 95% confidence interval.

Fig. 5

Multivariate analysis by Random Forests using the EV Array measurements of the exosomal antigens. The mean average importance to the classification model using the 30-marker panel illustrated in Fig. 4 for each of the analysed exosomal antigens, the normalised values (indicated by “*”) and their internal relations (indicated by “/”). The top 10 ranking did not change between the models including 3-, 5-, 10- or 30-markers and the markers included in each model are visualised by the coloured lines. Colours refer to the number of variables showed in Fig. 4.