Facing the challenge posed by aging population in developed countries urgently requires novel diagnostic and therapeutic molecules. Non-small cell lung cancer (NSLC), which is affecting elderly patients at the significant rate, would benefit from such new biomarkers. We aimed here at identifying novel therapeutic targets for elderly patients diagnosed with NSCLC.
We have used NextSeq500 system to sequence the transcriptome of 30 tumors from NSCLC patients (17 adenocarcinomas (ADC) and 13 squamous-cell lung cancer (SQC)). The patients were classified into 2 groups according to the overall survival (poor and good) and 3 groups according to age: 40-64 (9 cases), 65-74 (10 cases), ≥ 75 (11 cases). The groups comprised of patients with different tumor stage, gender, and smoking status, however the proportions of these variables were similar among them. Genes with modulated expression were selected using DEG elimination strategy DEGES/edgeR.
A total of 194 genes (68 for ADC, 43 for SQC and 83 in common) were significantly up-regulated in association with aging (q < 0.05). Noteworthy were genes encoding for cancer antigens such as melanoma antigen, notably MAGEB10, cancer/testis antigen family, CXorf61 and CEACAM8. Further genes of interest are immune checkpoint regulators such as PD1, CTLA4, CD276, and VTCN1, which also showed an increased expression in elderly NSCLC patients. Further analysis evidenced 88 genes (23 for ADC, 14 for SQC and 51 in common) that were found to be significantly up-regulated in patients with poor prognosis (q < 0.05). Of these, 33 correlated positively also with the increase in age. Of interest is a cluster of 4 genes encoding neuroactive ligand-receptors such as ADRA2A, GABRB2, GRID2 and GRIK3 as well as PTH2 and SCG5 encoding neuropeptides.
Our data suggest that immunotherapies targeting cancer antigens might be effective in elderly NSCLC patients and that neuroendocrine genes could represent useful prognostic markers. We are undertaking further validations using IHC/RT-PCR on more elderly patients (N > 100) in order to corroborate our findings.
Clinical trial identification
All authors have declared no conflicts of interest.