A model based on the quantification of complement C4c, CYFRA 21-1 and CRP exhibits high specificity for the early diagnosis of lung cancer

Abstract

Lung cancer screening detects early-stage cancers, but also a large number of benign nodules. Molecular markers can help in the lung cancer screening process by refining inclusion criteria or guiding the management of indeterminate pulmonary nodules. In this study, we developed a diagnostic model based on the quantification in plasma of complement-derived fragment C4c, cytokeratin fragment 21-1 (CYFRA 21-1) and C-reactive protein (CRP). The model was first validated in two independent cohorts, and showed a good diagnostic performance across a range of lung tumor types, emphasizing its high specificity and positive predictive value. We next tested its utility in two clinically relevant contexts: assessment of lung cancer risk and nodule malignancy. The scores derived from the model were associated with a significantly higher risk of having lung cancer in asymptomatic individuals enrolled in a computed tomography (CT)-screening program (OR = 1.89; 95% CI = 1.20–2.97). Our model also served to discriminate between benign and malignant pulmonary nodules (AUC: 0.86; 95% CI = 0.80–0.92) with very good specificity (92%). Moreover, the model performed better in combination with clinical factors, and may be used to reclassify patients with intermediate-risk indeterminate pulmonary nodules into patients who require a more aggressive work-up. In conclusion, we propose a new diagnostic biomarker panel that may dictate which incidental or screening-detected pulmonary nodules require a more active work-up. Lung cancer screening detects early-stage cancers, but also a large number of benign nodules. Molecular markers can help in the lung cancer screening process by refining inclusion criteria or guiding the management of indeterminate pulmonary nodules. In this study, we developed a diagnostic model based on the quantification in plasma of complement-derived fragment C4c, cytokeratin fragment 21-1 (CYFRA 21-1) and C-reactive protein (CRP). The model was first validated in two independent cohorts, and showed a good diagnostic performance across a range of lung tumor types, emphasizing its high specificity and positive predictive value. We next tested its utility in two clinically relevant contexts: assessment of lung cancer risk and nodule malignancy. The scores derived from the model were associated with a significantly higher risk of having lung cancer in asymptomatic individuals enrolled in a computed tomography (CT)-screening program (OR = 1.89; 95% CI = 1.20–2.97). Our model also served to discriminate between benign and malignant pulmonary nodules (AUC: 0.86; 95% CI = 0.80–0.92) with very good specificity (92%). Moreover, the model performed better in combination with clinical factors, and may be used to reclassify patients with intermediate-risk indeterminate pulmonary nodules into patients who require a more aggressive work-up. In conclusion, we propose a new diagnostic biomarker panel that may dictate which incidental or screening-detected pulmonary nodules require a more active work-up. AT A GLANCE COMMENTARYAjona, et alBackgroundRecent results from randomized controlled trials have rekindled the interest for the implementation of lung cancer screening. However, better diagnostic models are needed to reduce the numbers of unnecessarily screenings, and to optimize the management of patients with indeterminate nodules.Translational SignificanceWe developed and validated a molecular diagnostic model able to identify asymptomatic individuals enrolled in a CT-screening program at higher risk of having lung cancer. The model was also able to discriminate which pulmonary nodules may require a more active work-up. This model may improve inclusion criteria and management in the context of lung cancer screening.

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