A plasma protein biomarker strategy for detection of small intestinal neuroendocrine tumors
(2021) In Neuroendocrinology 111(9). p.840-849- Abstract
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... (More)
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.
(Less)
- author
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Neuroendocrinology
- volume
- 111
- issue
- 9
- pages
- 840 - 849
- publisher
- Karger
- external identifiers
-
- pmid:32721955
- scopus:85113294444
- ISSN
- 0028-3835
- DOI
- 10.1159/000510483
- language
- English
- LU publication?
- no
- id
- 09c0ae54-8912-4394-95b0-cc9dbc9682c2
- date added to LUP
- 2021-02-18 13:30:08
- date last changed
- 2024-06-27 08:40:52
@article{09c0ae54-8912-4394-95b0-cc9dbc9682c2,
abstract = {{<p>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.</p><p>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.</p><p>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.</p><p>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.</p>}},
author = {{Kjellman, Magnus and Knigge, Ulrich and Welin, Staffan and Thiis-Evensen, Espen and Gronbæk, Henning and Schalin-Jäntti, Camilla and Sorbye, Halfdan and Joergensen, Maiken Thyregod and Johanson, Viktor and Metso, Saara and Waldum, Helge and Søreide, Jon Arne and Ebeling, Tapani and Lindberg, Fredrik and Landerholm, Kalle and Wallin, Goran and Salem, Farhad and Schneider, Maria Del Pilar and Belusa, Roger}},
issn = {{0028-3835}},
language = {{eng}},
number = {{9}},
pages = {{840--849}},
publisher = {{Karger}},
series = {{Neuroendocrinology}},
title = {{A plasma protein biomarker strategy for detection of small intestinal neuroendocrine tumors}},
url = {{http://dx.doi.org/10.1159/000510483}},
doi = {{10.1159/000510483}},
volume = {{111}},
year = {{2021}},
}