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: Inflammation scores based on general inflammation markers as leucocyte count or C-reactive protein have been evaluated as prognostic markers of inferior survival in several cancers. In small cell lung cancer (SCLC), however, inflammation scores are less studied. In the present study, we set out to perform a systematic review and meta-analysis investigating reported associations between inflammation scores and overall survival (OS) in SCLC. Methods: A literature search was performed in PubMed, Embase, Scopus, and Web of Science following the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) guidelines. Of the identified publications, only studies in English containing original data evaluating inflammation scores as a prognostic factor in SCLC patients were included. Hazard ratios (HRs) for OS were pooled in a random-effects model. Results: In total, 33 articles were included evaluating eight different inflammation scores in 7762 SCLC patients. Seven of the identified scores were based on leucocyte count. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte (PLR) ratio were the most frequently evaluated scores (NLR: n = 23; PLR: n = 22). For NLR, a meta-analysis including 16 studies demonstrated that patients with a high NLR had a significantly shorter OS compared to patients with a low NLR (pooled HR = 1.39 (95% CI, 1.23–1.56)). For PLR, an association with survival could not be confirmed in a meta-analysis performed based on eight studies (pooled HR = 1.20 (95% CI, 0.96–1.51)). Conclusions: This review identifies that inflammation scores based on general inflammation markers have some potential as prognostic biomarkers in SCLC. The meta-analyses indicated that NLR is associated with inferior OS, whereas an association between PLR and OS could not be confirmed. Thus, NLR could be a useful biomarker of OS in SCLC patients. Systematic review registration: The protocol for the study was submitted to the PROSPERO database (registration number CRD42020188553).

Background: Inflammation-scores based on general inflammation markers are suggested as prognostic markers of overall survival (OS) in lung cancer. However, whether these inflammation-scores improves the prognostication performed by well-established prognostic markers is unsettled. In a large register-based lung cancer patient cohort, nine different inflammation-scores were compared, and their ability to optimize the prognostication of OS was evaluated. Methods: Lung cancer patients diagnosed from 2009–2018 in The Central Denmark Region were identified in the Danish Lung Cancer Registry. Pre-treatment inflammation markers were extracted from the clinical laboratory information system. Prognostication of OS was evaluated by Cox proportional hazard models. Comparison of the inflammation-scores and their added value to established prognostic markers were assessed by Akaike's information criteria and Harrel's C-index. Results: In total, 5,320 patients with non-small cell lung cancer (NSCLC) and 890 patients with small cell lung cancer (SCLC) were identified. In NSCLC, the Aarhus composite biomarker score (ACBS), including albumin, C-reactive protein, neutrophil count, lymphocyte count and haemoglobin, and the neutrophil-lymphocyte-ratio (NLR) were superior. Furthermore, they improved the prognostication of OS significantly (p <0.0001) (ACBS: HR: 2.24 (95%CI: 1.97–2.54); NLR: HR: 1.58 (95%CI: 1.47 – 1.69)). In SCLC, three scores were equally superior and improved the prognostication of OS p < 0.0001): neutrophil–lymphocyte-ratio (HR:1.62 (95%CI: 1.38–1.90)), modified Glasgow Prognostic Score (mGPS) (HR:1.70 (95%CI: 1.55–1.86) and the Combined NLR and GPS (CNG) (HR:2.10 (95%CI: 1.77–2.49). Conclusions: The ACBS was the optimal score in NSCLC, whereas neutrophil–lymphocyte-ratio, mGPS and CNG were equally superior in SCLC. Additionally, these inflammation-scores all optimised the prognostication of OS and added value to well-established prognostic markers.

Background: Disease recurrence in localized lung adenocarcinoma is a major obstacle for improving the overall outcome of lung cancer. Thus, better prognostic biomarkers are needed to identify patients at risk. In order to clear cancer, immune detection of tumor cells is of vital importance. DNA-leakage into the cytosol and tumor environment is one important tumor-associated danger signal and cGAS is a pivotal DNA-sensor that detects misplaced DNA and initiates an innate immune response. In this study, we investigate the cGAS-STING-pathway expression in tumor tissue and circulating immune cells from lung adenocarcinoma patients in relation to stage of disease and overall survival (OS). Methods: Gene expression was measured using target specific droplet digital polymerase chain reaction (ddPCR) assays in a cohort of 80 patients with lung adenocarcinoma and 45 patients suspected of lung cancer, but determined to be cancer-free. The expression values were correlated to stage of disease. For further exploration of stage dependent expression, we used a publicly available gene expression data set to stratify patients by stage and correlate gene expression to OS. Results: In both tumor tissue and peripheral blood mononuclear cells (PBMCs) from cancer patients, we observed differential expression of cGAS-STING pathway components compared to cancer-free individuals. Furthermore, cGAS-STING pathway expression was elevated in PBMCs from patients with localized disease (stage I and II) compared to patients with metastatic disease (stage III and IV). Survival analysis based on publicly available gene expression data sets demonstrated a superior OS for patients with localized disease and high levels of cGAS, STING and TBK1. Conclusions: The expression of the cGAS-STING pathway is stage dependent and high expression is correlated with localized adenocarcinoma. For patients with localized disease, high cGAS, STING and TBK1 expression correlated with improved OS and may be a potential biomarker for this patient subgroup.