RNA-sequencing in non-small cell lung cancer shows gene downregulation of therapeutic targets in tumor tissue compared to non-malignant lung tissue - Kobe Reynders; Els Wauters; Matthieu Moisse; Herbert Decaluwé; Paul De Leyn; Stéphanie Peeters; Maarten Lambrecht; Kristiaan Nackaerts; Christophe Dooms; Wim Janssens; Johan Vansteenkiste; Diether Lambrechts; et al. (Radiation Oncology, 2018)
Abstract Background Gene expression of specific therapeutic targets in non-malignant lung tissue might play an important role in optimizing targeted therapies. This study aims to identify different expression patterns of fifteen genes important for targeted therapy in non-small cell lung cancer (NSCLC). Methods We prospectively collected tissue of NSCLC and non-malignant lung tissue from 25 primary resected patients. RNA-sequencing and 450 K methylation array profiling was applied to both NSCLC and non-malignant lung tissue and data were analyzed for 14 target genes. We analyzed differential expression and methylation as well as expression according to patient characteristics like smoking status, histology, age, chronic obstructive pulmonary disease, C-reactive protein (CRP) and gender. TCGA data served as a validation set. Results Nineteen men and 6 women were included. Important targets like PD-L2 (p = 0.035), VEGFR2 (p < 0.001) and VEGFR3 (p < 0.001) were downregulated (respective fold changes = 1.8, 3.1, 2.7, 3.5) in tumor compared to non-malignant lung tissue. The TCGA set confirmed these findings almost universally. PD-L1 (p < 0.001) became also significantly downregulated in the TCGA set. In NSCLC, MUC1 (p = 0.003) showed a higher expression in patients with a CRP < 5 mg/L compared to > 5 mg/L. In the TCGA data but not in our primary data, PD-L1 & 2 were both borderline more expressed in tumors of active smokers vs. tumors of ex-smokers (p = 0.044 and 0.052). Conclusions Our results suggest a lower PD-L1 & 2 and VEGFR expression in NSCLC vs. non-malignant lung tissue. Specific patient characteristics did not seem to change the overall expression differences as they were in line with the overall results. This information may contribute to the optimization of targeted treatments.