Telomere-based risk models for the early diagnosis of lung cancer.

Background: The objective of this study was to evaluate the use of telomere length measurements as diagnostic biomarkers during early screening for lung cancer in high-risk patients. Methods: This was a prospective study of patients undergoing lung cancer diagnosis at two Spanish hospitals between April 2017 and January 2020. Telomeres from peripheral blood lymphocytes were analysed by Telomere Analysis Technology, which is based in high-throughput quantitative fluorescent in situ hybridization. Analytical predictive models were developed using Random Forest from the dataset of telomere-associated variables (TAV). Receiver Operating Characteristic curves were used to characterize model performance. Findings: From 233 patients undergoing lung cancer diagnosis, 106 patients aged 55-75 with lung cancer or lung cancer and COPD were selected. A control group (N = 453) included individuals of similar age with COPD or healthy. Telomere analysis showed that patients in the cancer cohort had a higher proportion of short telomeres compared to the control cohort. A TAV-based predictive model assuming a prevalence of 5 % of lung cancer among screened subjects showed an AUC of 0.98 %, a positive predictive value of 0.60 (95 % CI, 0.49-0.70) and a negative predictive value of 0.99 (95 % CI, 0.98-0.99) for prediction of lung cancer. Interpretation: The results of this study suggest that TAV analysis in peripheral lymphocytes can be considered a useful diagnostic tool during screening for lung cancer in high-risk patients. TAV-based models could improve the predictive power of current initial diagnostic pathways, but further work is needed to integrate them into routine clinical evaluation. Funding: Life Length SL.