BACKGROUND: Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose in the early stage of disease. Current blood biomarkers such as chromogranin A (CgA) and 5-hydroxyindolacetic acid (5-HIAA) have low sensitivity and specificity. This is a first pre-planned interim analysis (NORDIC non-interventional, exploratory, EXPLAIN study (NCT02630654)). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy in SI-NETs.
METHODS: At time of diagnosis, prior any disease specific treatment was initiated, blood was collected from patients with advanced SI-NETs and 92 putative cancer-related plasma proteins from 135 patients were analyzed and compared with the results of age and gender matched controls (n=143), using multiplex proximity extension assay and machine learning techniques.
RESULTS: Using a random forest model including 12 top ranked plasma proteins in patients with SI-NETs, the multi-biomarker strategy showed sensitivity (SEN) and specificity (SPE) of 89% and 91%, respectively, with negative predictive value (NPV) and positive predictive value (PPV) of 90% and 91%, respectively, to identify patients with regional or metastatic disease with an area under the receiver operator characteristic curve (AUROC) of 99%. In thirty patients with normal CgA concentrations the model provided diagnostic SPE of 98%, a SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake.
CONCLUSION: This interim analysis demonstrate that a multi-biomarker/machine learning strategy improve diagnostic accuracy of patients with SI-NET at the time of diagnosis, especially in patients with normal CgA levels. The results indicate that this multi-biomarker strategy can be useful for early detection of SI-NETs at presentation and conceivably detect recurrence after radical primary resection.
- Cancer och onkologi