External validation of plasma glycosaminoglycans as biomarkers to improve lung cancer risk stratification.

Abstract

Background: Lung cancer (LC) screening excludes individuals not considered at an increased risk for lung cancer, as predicted by risk models like the Liverpool Lung Project version 3 (LLPv3). Here, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict LC independent of LLPv3 and other pre-specified co-morbidities.

Methods: In this retrospective cohort-based case-control study, we included patients who were suspected of having LC at baseline and were either diagnosed with LC (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGome were measured at baseline and used to compute a pre-specified GAGome score to discriminate LC from controls. We then applied multivariable Bayesian logistic regression to evaluate the likelihood that 7 LLPv3 predictors or 14 co-morbidities had an effect on the GAGome score. We tested the independence of the GAGome score from LLPv3-predicted 5-year risk using the likelihood ratio test and assessed whether it improved LC risk prediction in a set equivalent to an LLPv3-predicted 5-year risk of ≥1.51%.

Results: We included 653 LC and 653 controls. The AUC of the GAGome score was 0.63 (95% CI = 0.62-63). None of the LLPv3 predictors or co-morbidities were compatible with a significant effect on the score. The GAGome score was independent of LLPv3 (p < 0.001) and improved its sensitivity (72% vs. 69%) and specificity (61% vs. 59%).

Conclusion: Plasma GAGomes identified additional LC cases beyond those predicted by LLPv3 alone.

Impact: GAGomes could improve risk-stratified LC if validated in a screening population.