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.

Background: Use of exosomes as biomarkers in non‐small cell lung cancer (NSCLC) is an intriguing approach in the liquid‐biopsy era. Exosomes are nano‐sized vesicles with membrane‐bound proteins that reflect their originating cell. Prognostic biomarkers are needed to improve patient selection for optimal treatment. We here evaluate exosomes by protein phenotyping as a prognostic biomarker in NSCLC.Methods: Exosomes from plasma of 276 NSCLC patients were phenotyped using the Extracellular Vesicle Array; 49 antibodies captured the proteins on the exosomes, and a cocktail of biotin‐conjugated antibodies binding the general exosome markers CD9, CD81 and CD63 was used to visualise the captured exosomes. For each individual membrane‐bound protein, results were analysed based on presence, in a concentration‐dependent manner, and correlated to overall survival (OS).Results: The 49 proteins attached to the exosomal membrane were evaluated. NY‐ESO‐1, EGFR, PLAP, EpCam and Alix had a significant concentration‐dependent impact on inferior OS. Due to multiple testing, NY‐ESO‐1 was the only marker that maintained a significant impact on inferior survival (hazard rate (HR) 1.78 95% (1.78–2.44); p = 0.0001) after Bonferroni correction. Results were adjusted for clinico‐pathological characteristics, stage, histology, age, sex and performance status.Conclusion: We illustrate the promising aspects associated with the use of exosomal membrane‐bound proteins as a biomarker and demonstrate that they are a strong prognostic biomarker in NSCLC.

BACKGROUND: The feasibility of monitoring epidermal growth factor receptor (EGFR) mutations in plasma DNA from patients with advanced non–small cell lung cancer (NSCLC) during treatment with erlotinib and its relation to disease progression was investigated.METHODS: The amount of EGFR‐mutant DNA was tested in plasma DNA from patients with advanced NSCLC with allele‐specific polymerase chain reaction assays. Blood samples from 23 patients with adenocarcinoma of NSCLC that carried tyrosine kinase inhibitor‐sensitizing EGFR mutations were taken immediately before treatment with erlotinib. Additional blood samples were taken at timed intervals until erlotinib treatment was withdrawn.RESULTS: The amount of plasma DNA with sensitizing EGFR mutations was found to be reduced after the first cycle of erlotinib treatment in 22 of 23 patients (96%). No patients presented with the resistant T790M mutation in the pretreatment sample, but at the time of disease progression the mutation was detected in plasma from 9 patients (39%). The quantitative data from the current study demonstrated that when a T790M mutation emerged in the blood it was accompanied by an increase in the original sensitizing EGFR mutation. When T790M was detected, it was found to be present in all subsequent blood samples from that patient. Most interestingly, the results of the current study demonstrated that monitoring the EGFR mutations in the blood allows for the detection of the T790M mutation up to 344 days before disease progression is clinically evident (range, 15‐344 days).CONCLUSIONS: The results of the current study demonstrated that serial monitoring of EGFR mutations in plasma DNA is feasible and may allow for the early detection of resistance mutations. These results warrant further studies to explore the clinical usefulness of such analysis. Cancer 2014;120:3896–3901. © 2014 The Authors. Cancer published by American Cancer Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Erlotinib (Tarceva) targets the epidermal growth factor receptor (EGFR), which is commonly overexpressed in human cancers, including lung cancer. We show that erlotinib can be labeled with [¹¹C] by reacting the normethyl precursor with [¹¹C]-methyl iodide. By using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide proliferation assay, two lung cancer cell lines (A549 and NCI358) were shown to be less sensitive to erlotinib compared with the lung cancer cell line HCC827. This correlated with higher expression and activity of the EGFR in HCC827 cells as compared with the less sensitive cell lines. Micro–positron emission tomography (PET) and biodistribution of erlotinib was performed with [¹¹C]-erlotinib in nude mice bearing xenografts of A549, NCI358, and HCC827 cells. Dynamic micro-PET showed that HCC827 tumors had the highest [¹¹C]-erlotinib uptake and retained the activity significantly longer as compared with A549 and NCI358 tumors. Biodistribution of [¹¹C]-erlotinib in the xenograft models of lung cancer showed the highest accumulation in the liver. In mice carrying the sensitive cancer cells, the accumulation of [¹¹C]-erlotinib was higher in tumors than in the other organs. In contrast, the drug accumulated to a comparable extent in tumors from the less sensitive cancer cells and the other organs. Uptake of [¹¹C]-erlotinib in the tumors was 1.6%, 0.7%, and 3.7% (percentage of injected dose/g), in A549, NCI358, and HCC827 cells, respectively. We show for the first time that [¹¹C]-erlotinib identifies erlotinib-sensitive tumors. These results pave the road for studies examining the benefit of [¹¹C]-erlotinib PET in patients with lung tumors or other tumors overexpressing EGFR. [Cancer Res 2009;69(3):873–8]