Serum mass profile signature as a biomarker of early lung cancer. (September 2016) - Widlak, Piotr; Pietrowska, Monika; Polanska, Joanna; Marczyk, Michal; Ros-Mazurczyk, Malgorzata; Dziadziuszko, Rafał; Jassem, Jacek; Rzyman, Witold
Highlights: The value of MALDI-based profiling of serum peptidome as a tool for the early detection of lung cancer and enhancement of LD-CT lung cancer screening was investigated. The analysis revealed several components with abundances discriminating patients with early lung cancer from healthy high-risk smokers, allowing for the development of an effective cancer discriminator. The mass profile-based signature has insufficient value as an independent marker (NPV of 88% and PPV of 30%, in a validation stage), yet its clinical usefulness in evaluation of indeterminate pulmonary nodules detected incidentally or within CT screening deserves further investigation. Abstract: Objectives: Circulating molecular biomarkers of lung cancer may allow the pre-selection of candidates for computed tomography screening or increase its efficacy. We aimed to identify features of serum mass profile distinguishing individuals with early lung cancer from healthy participants of the lung cancer screening program. Methods: Blood samples were collected during a low-dose computed tomography (LD-CT) screening program performed by one institution (Medical University of Gdansk, Poland). MALDI-ToF mass spectrometry was used to characterize the low-molecular-weight (1000–14, 000 Da) serum fraction. The analysis comprised 95 patients with early stage lung cancer (including 30 screen-detected cases) and a matched group of 285 healthy controls. The cases were split into two independent cohorts (discovery and validation), analyzed separately 6 months apart. Results: Several molecular components of serum (putatively components of endogenous peptidome) discriminating patients with early lung cancer from controls were identified in a discovery cohort. This allowed building an effective cancer classifier as a model tuned to maximize negative predictive value, with an area under the curve (AUC) of 0.88, a negative predictive value of 100%, and a positive predictive value of 48%. However, the classifier performed worse in a validation cohort including independent sample sets (AUC 0.73, NPV 88% and PPV 30%). Conclusions: We developed a serum mass profile-based signature identifying patients with early lung cancer. Although this marker has insufficient value as a stand-alone preselecting tool for LD-CT screening, its potential clinical usefulness in evaluation of indeterminate pulmonary nodules deserves further investigation.