Researchers compared a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers to a traditional risk prediction model and current US screening criteria to see which performed better. This investigation offered a proof of principle in demonstrating that improved lung cancer risk assessment and assistance in defining eligibility for computed tomography screening may be had by a panel of circulating protein biomarkers.

Methods

• Researchers developed a biomarker risk score based on four proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]).
• To develop this risk score, they used prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year of blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET).
• They performed a blind validation of the biomarker score using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS).
• They assessed model validity in discriminating between future lung cancer cases and controls.
• To reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity), discrimination estimates were weighted.

Results

• They found that an AUC of 0.83 (95% CI, 0.76-0.90) was offered by an integrated risk prediction model that combined smoking exposure with the biomarker score vs an AUC of 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P=.003 for difference in AUC).
• They also found that, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 vs 0.43 for the smoking model, at an overall specificity of 0.83.
• Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the observed specificity yielded by the integrated risk prediction model was 0.95, relative to 0.86 for the smoking model.