TY - JOUR

T1 - Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumors and differentiation from small intestinal neuroendocrine tumors

AU - Thiis-Evensen, Espen

AU - Kjellman, Magnus

AU - Knigge, Ulrich

AU - Gronbaek, Henning

AU - Schalin-Jäntti, Camilla

AU - Welin, Staffan

AU - Sorbye, Halfdan

AU - del Pilar Schneider, Maria

AU - Belusa, Roger

AU - The Nordic NET Biomarker Group

N1 - Funding Information: This study was sponsored by Ipsen. We thank all of the patients who made this study possible through their participation in the study. We thank Torbjörn Ström at IPSEN Nordic and Karin Becker (both at IPSEN at the time the study was conducted) for study monitoring. We thank Daniel Seisdedos at Pharma Consulting Group Uppsala Sweden for data management. We thank Dr Nabil Al-Tawil at the Karolinska University Hospital Clinical Pharmacology Trial Unit, Stockholm Sweden for recruiting sex and age matched controls. We also thank all of the additional physicians and nurses at the 20 hospitals who have contributed to make this study possible. The Nordic NET Biomarker Group: E. Thiis-Evensen (Oslo University Hospital, Oslo, Norway). M. Kjellman (Karolinska Hospital, Stockholm, Sweden). U. Knigge (Rigshospitalet, Copenhagen, Denmark). S. Welin (Akademiska Hospital, Uppsala, Sweden). H. Gronbaek (Aarhus University Hospital, Aarhus, Denmark). H. Sorbye (Haukeland University Hospital, Bergen, Norway). M. T. Joergensen (Odense University Hospital, Odense, Denmark). A. K. Elf (Sahlgrenska Hospital, Gothenburg, Sweden). R. Belusa (Previously at Ipsen Nordic, Stockholm, Sweden). C. Schalin-Jäntti (Helsinki University Hospital, Helsinki, Finland). M. T. Jørgensen (Odense University Hospital, Odense, Denmark). H. Waldum (St Olav University Hospital, Trondheim, Norway). J. A. Søreide (Stavanger University Hospital. Stavanger, Norway). S. Metso (Tampere University Hospital, Tampere, Finland). T. Ebeling (Oulu University Hospital, Oulu, Finland). F. Lindberg (Norrland University Hospital, Umeå, Sweden). K. Landerholm (Ryhov County Hospital, Jönköping, Sweden). G. Wallin (Örebro University Hospital, Örebro, Sweden). F. Salem (Skåne University Hospital, Lund, Sweden). G. Purkalne (Pauls Stradiņš Clinical University Hospital, Riga, Latvia). I. Kudaba (Riga East University Hospital, Riga, Latvia). R. Janciauskiene (Lithuanian University of Health Sciences, Kaunas, Lithuania). T. Suuroja (North-Estonian Regional Hospital, Tallinn Estonia). Edita Baltruškevičienė (National Cancer Institute, Vilnius, Lithuania). Funding Information: ETE: Research grants from Novartis, consulting fees from Ipsen and speaker honoraria from Novartis, Ipsen and Pfizer. MK: Grants from Ipsen, consulting fees from Ipsen and Novartis Healthcare. UK: Grants from Ipsen and Novartis Healthcare, consulting fees from Ipsen. HG: Research grants from the NOVO Nordisk Foundation, Intercept, Abbvie, and consulting fees from Ipsen and Novartis. CSJ: Grants from Ipsen and Pfizer, consulting fees from Ipsen. HS: Research grant from Amgen. Consulting fees from Novartis, Pfizer, Keocyt, AstraZeneca, Hutchinson, Bayer and ITM. Honoraria from: Novartis, Roche, Ipsen, Merck, Bayer, SAM Nordic and Pierre Fabre. HW: No conflicts of interest. AKE: No conflicts of interest. MTJ: No conflicts of interest. SW: Consulting fees from Ipsen and Novartis Healthcare. JAS: Consulting fee from Novartis. SM: No conflicts of interest. TE: Speaker honoraria from Ipsen, AstraZeneca, Novo Nordisk Pharma, consulting fee from Amgen, educational grants by Novartis, Ipsen and MSD Finland to the employer institution for international symposia. FL: Speaker honorarium from Novartis. KL: No conflicts of interest. FS: No conflicts of interest. GW: No conflicts of interest. GP: speaker honoraria from Ipsen. RJ: speaker honoraria from Ipsen, Novartis, Merck Serono, Bristol Myers Squibb, MSD, Servier. TS: speaker honoraria from Ipsen. IK: No conflicts of interest. RS: No conflicts of interest. EB: speaker honoraria from Ipsen. MPS and RB: Ipsen employees. Publisher Copyright: © 2022 Ipsen. Journal of Neuroendocrinology published by John Wiley & Sons Ltd on behalf of British Society for Neuroendocrinology.

PY - 2022

Y1 - 2022

N2 - There is an unmet need for novel biomarkers to diagnose and monitor patients with neuroendocrine neoplasms. The EXPLAIN study explores a multi-plasma protein and supervised machine learning strategy to improve the diagnosis of pancreatic neuroendocrine tumors (PanNET) and differentiate them from small intestinal neuroendocrine tumors (SI-NET). At time of diagnosis, blood samples were collected and analyzed from 39 patients with PanNET, 135 with SI-NET (World Health Organization Grade 1–2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Prosed Oncology-II (i.e., OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A was analyzed separately. Median age in all groups was 65–67 years and with a similar sex distribution (females: PanNET, 51%; SI-NET, 42%; controls, 42%). Tumor grade (G1/G2): PanNET, 39/61%; SI-NET, 46/54%. Patients with liver metastases: PanNET, 78%; SI-NET, 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.84, specificity (SPE) 0.98, positive predictive value (PPV) of 0.92 and negative predictive value (NPV) of 0.95, and area under the receiver operating characteristic curve (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.61, SPE 0.96, PPV 0.83, NPV 0.90 and AUROC 0.98. These results suggest that a multi-plasma protein strategy can significantly improve diagnostic accuracy of PanNET and SI-NET.

AB - There is an unmet need for novel biomarkers to diagnose and monitor patients with neuroendocrine neoplasms. The EXPLAIN study explores a multi-plasma protein and supervised machine learning strategy to improve the diagnosis of pancreatic neuroendocrine tumors (PanNET) and differentiate them from small intestinal neuroendocrine tumors (SI-NET). At time of diagnosis, blood samples were collected and analyzed from 39 patients with PanNET, 135 with SI-NET (World Health Organization Grade 1–2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Prosed Oncology-II (i.e., OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A was analyzed separately. Median age in all groups was 65–67 years and with a similar sex distribution (females: PanNET, 51%; SI-NET, 42%; controls, 42%). Tumor grade (G1/G2): PanNET, 39/61%; SI-NET, 46/54%. Patients with liver metastases: PanNET, 78%; SI-NET, 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.84, specificity (SPE) 0.98, positive predictive value (PPV) of 0.92 and negative predictive value (NPV) of 0.95, and area under the receiver operating characteristic curve (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.61, SPE 0.96, PPV 0.83, NPV 0.90 and AUROC 0.98. These results suggest that a multi-plasma protein strategy can significantly improve diagnostic accuracy of PanNET and SI-NET.

KW - biomarker

KW - diagnosis

KW - machine learning

KW - NET

KW - Plasma proteins

U2 - 10.1111/jne.13176

DO - 10.1111/jne.13176

M3 - Journal article

C2 - 35829662

AN - SCOPUS:85133895840

VL - 34

JO - Journal of Neuroendocrinology

JF - Journal of Neuroendocrinology

SN - 0953-8194

IS - 7

M1 - e13176

ER -