Importance: There is an urgent need to improve lung cancer risk assessment as current screening criteria miss a large proportion of cases.
Objective: To determine if a panel of selected circulating protein biomarkers can contribute to lung cancer risk assessment and outperform current US screening criteria.
Design, Setting and Participants: Pre-diagnostic samples from ever-smoking cases diagnosed within one year of blood collection and smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk-score based on 4 proteins (CA125, CEA, CYFRA 21-1 and Pro-SFTPB). The biomarker score was subsequently validated blindly using absolute risk-estimates in ever-smoking cases diagnosed within one year of blood collection and matched controls from two large European population-based cohorts; the European Prospective Investigation into Cancer and nutrition (EPIC) study and the Northern Sweden Health and Disease Study (NSHDS).
Main Outcome and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under receiver-operating characteristics curve [AUC], sensitivity and specificity).
Results: In the validation study, an integrated risk-prediction model combining smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI: 0.76-0.90) compared to 0.73 (95% CI: 0.64-0.82) for a model based on smoking exposure alone (P=0.003 for difference in AUC). At an overall specificity of 0.83 based on the USPSTF screening criteria, the sensitivity of the integrated risk-prediction model (biomarker) model was 0.63 compared to 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42 (USPSTF), the integrated risk-prediction model yielded a specificity of 0.95 compared to 0.86 for the smoking model.
Conclusions and Relevance: This study provided a proof-of-principle in demonstrating that a panel of circulating protein biomarkers can improve lung cancer risk assessment and may be used to define eligibility for CT-screening.