A Plasma Protein Biomarker Strategy for Detection of Small Intestinal Neuroendocrine Tumors.

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 have low sensitivity (SEN) and specificity (SPE). This is a first preplanned interim analysis (Nordic non-interventional, prospective, exploratory, EXPLAIN study [NCT02630654]). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy (ACC) in SI-NETs. METHODS: At the time of diagnosis, before 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 sex-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 SEN and 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 30 patients with normal CgA concentrations, the model provided a diagnostic SPE of 98%, SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake. CONCLUSION: This interim analysis demonstrates that a multi-biomarker/machine learning strategy improves diagnostic ACC 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.

New susceptibility loci for cutaneous melanoma risk and progression revealed using a porcine model.

Despite major advances, it is estimated that a large part of melanoma predisposing genes remains to be discovered. Animal models of spontaneous diseases are valuable tools and experimental crosses can be used to identify and fine-map new susceptibility loci associated with melanoma. We performed a Genome-Wide Association Study (GWAS) of melanoma occurrence and progression (clinical ulceration and presence of metastasis) in a porcine model of spontaneous melanoma, the MeLiM pig. Five loci on chromosomes 2, 5, 7, 8 and 16 showed genome-wide significant associations (p < 5 × 10-6) with either one of these phenotypes. Suggestive associations (p < 5 × 10-5) were also found at 16 additional loci. Moreover, comparison of the porcine results to those reported by human melanoma GWAS indicated shared association signals notably at CDKAL1 and TERT loci but also nearby CCND1, FTO, PLA2G6 and TMEM38B-RAD23B loci. Extensive search of the literature revealed a potential key role of genes at the identified porcine loci in tumor invasion (DST, PLEKHA5, CBY1, LIMK2 and ETV5) and immune response modulation (ETV5, HERC3 and DICER1) of the progression phenotypes. These biological processes are consistent with the clinico-pathological features of MeLiM tumors and can open new routes for future melanoma research in humans.