Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins

Abstract

Importance: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.

Objective: To investigate 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.

Design, setting, and participants: 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 and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly 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).

Main outcomes 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 the receiver-operating characteristics curve [AUC], sensitivity, and specificity).

Results: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model.

Conclusions and relevance: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.

Figure 1.. Predicted Probability of Lung Cancer Within 1 Year for a Male From the Northern Sweden Health and Disease Study (NSHDS) According to Smoking History

A, Predicted probability of lung cancer according to the smoking risk prediction model based on age in years and smoking history. The rug plot shows the observed distribution of age in the validation study (European Prospective Investigation into Cancer and Nutrition [EPIC] and NSHDS, ever smokers). B, Predicted probability of lung cancer according to the integrated risk prediction model based on the biomarker score and the smoking history. The rug plot shows the observed distribution of the biomarker score in the validation study (EPIC and NSHDS, ever smokers). The vertical lines correspond to the quartiles threshold for biomarker score among controls (Q1, Q2, Q3, and Q4).

Figure 2.. Predicted Probabilities of Lung Cancer Within 1 Year Based on the Smoking and Integrated Risk Prediction Models in the Validation Study (European Prospective Investigation Into Cancer and Nutrition [EPIC] and Northern Sweden Health and Disease Study [NSHDS], Ever Smokers)

The validation samples consist of EPIC and NSHDS ever-smoking participants who received a diagnosis of lung cancer within 1 year after blood collection. For the controls, the size of the points is proportional to the number of eligible participants represented (corresponding to the inverse of the sampling probability). The right panel represents a magnified excerpt of the full figure.

Figure 3.. Receiver Operating Characteristic (ROC) Curve Analysis in the Validation Study (European Prospective Investigation Into Cancer and Nutrition [EPIC] and Northern Sweden Health and Disease Study [NSHDS], Ever Smokers)

A, ROC curve analysis in the validation study (EPIC and NSHDS ever-smoker participants who received a diagnosis of lung cancer within 1 year after blood collection) for 2 risk prediction models: a model that used smoking variables only (smoking) and an integrated model with the smoking variables and the biomarker score combined (smoking + biomarkers). AUC indicates area under the curve; USPSTF, US Preventive Services Task Force. The horizontal dashed line indicates sensitivity and the vertical dashed line, specificity. B, Sensitivity and specificity in relation to the probability of lung cancer within 1 year predicted by the integrated model.