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A plasma protein biomarker strategy for detection of small intestinal neuroendocrine tumors

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  • Magnus Kjellman
  • Ulrich Knigge
  • Staffan Welin
  • Espen Thiis-Evensen
  • Henning Gronbæk
  • Camilla Schalin-Jäntti
  • Halfdan Sorbye
  • Maiken Thyregod Joergensen
  • Viktor Johanson
  • Saara Metso
  • Helge Waldum
  • Jon Arne Søreide
  • Tapani Ebeling
  • Fredrik Lindberg
  • Kalle Landerholm
  • Goran Wallin
  • Farhad Salem
  • Maria Del Pilar Schneider
  • Roger Belusa
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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.

Original languageEnglish
Issue number9
Pages (from-to)840-849
Number of pages10
Publication statusPublished - Aug 2021

    Research areas

  • Biomarker, Diagnosis, Machine learning, Neuroendocrine tumor

ID: 61433405