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Study Finds Panel of Circulating Protein Biomarkers Improve Lung Cancer Risk Assessment

Alison Rodriguez
Current screening criteria for lung cancer risk assessments often miss a large proportion of cases. Research suggests that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.
Current screening criteria for lung cancer risk assessments often miss a large proportion of cases. Research suggests that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.

A recent study in JAMA Oncology investigated whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria.

The study involved prediagnostic samples from 108 ever-smoking patients with lung cancer, diagnosed within 1 year after blood collection, and samples from 216 smoking-matched controls from the Carotene Retinol Efficacy Trial cohort. The samples were used to develop a biomarker risk score based on 4 proteins—cancer antigen 125, carcinoembryonic antigen, cytokeratin-19 fragment, and the precursor form of surfactant protein B.

While previous studies have shown these markers are useful for the workup and diagnosis of lung cancer, "there are limited data regarding the performance of these markers in discriminating between future lung cancer cases and controls," the authors wrote.

The researchers blindly validated the biomarker score by using absolute risk estimates among 63 of the 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 and the Northern Sweden Health and Disease Study.

Using the 63 ever-smoking patients with lung cancer and 90 matched controls, the researchers were able to establish an integrated risk prediction model that combined smoking exposure with the biomarker score, yielding an AUC (area under the receiver-operating characteristics curve) of 0.83 compared to 0.73 for a model based on smoking exposure alone.

With an overall specificity of 0.83, based on the US Preventive Services Task Force (USPSTF) screening criteria, the sensitivity of the integrated risk model was 0.63 compared to 0.43 for the smoking model. Additionally, at an overall sensitivity of 0.41 the integrated risk model yielded a specificity of 0.95 compared with 0.86 for the smoking model, based on the USPSTF screening criteria.

“These improvements in sensitivity and specificity were consistently observed across each evaluated stratum. Our findings also indicated that the improvement in discrimination afforded by the biomarker score is more modest beyond the initial year after blood draw, which suggests that an annual biomarker test may be necessary in a screening program,” concluded the authors.

 
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