External Validation of Plasma Glycosaminoglycans as Biomarkers to Improve Lung Cancer Risk Stratification

Lung cancer 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). In this study, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict lung cancer independent of LLPv3 and other prespecified comorbidities.

In this retrospective cohort-based case-control study, we included patients who were suspected of having lung cancer at baseline and were either diagnosed with lung cancer (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGomes were measured at baseline and used to compute a prespecified GAGome score to discriminate lung cancer from controls. We then applied multivariable Bayesian logistic regression to evaluate the likelihood that 7 LLPv3 predictors or 14 comorbidities 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 lung cancer risk prediction in a set equivalent to an LLPv3-predicted 5-year risk of ≥1.51%.

We included 653 lung cancer and 653 controls. The AUC of the GAGome score was 0.63 (95% confidence interval, 0.62-63). None of the LLPv3 predictors or comorbidities 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%).

Plasma GAGomes identified additional lung cancer cases beyond those predicted by LLPv3 alone.

GAGomes could improve risk-stratified lung cancer if validated in a screening population.