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01.12.2014 | Research Paper | Ausgabe 8/2014

Clinical & Experimental Metastasis 8/2014

Gene expression accurately distinguishes liver metastases of small bowel and pancreas neuroendocrine tumors

Zeitschrift:
Clinical & Experimental Metastasis > Ausgabe 8/2014
Autoren:
Scott K. Sherman, Jessica E. Maxwell, Jennifer C. Carr, Donghong Wang, Andrew M. Bellizzi, M. Sue O’Dorisio, Thomas M. O’Dorisio, James R. Howe
Wichtige Hinweise
An earlier version of this research was presented as an abstract at the Surgical Forum Session of the American College of Surgeons Clinical Congress in Washington, DC, October 7, 2013.

Abstract

Small bowel (SBNETs) and pancreatic neuroendocrine tumors (PNETs) often present with liver metastases. Although liver biopsy establishes a neuroendocrine diagnosis, the primary tumor site is frequently unknown without exploratory surgery. Gene expression differences in metastases may distinguish primary SBNETs and PNETs. This study sought to determine expression differences of four genes in neuroendocrine metastases and to create a gene expression algorithm to distinguish the primary site. Nodal and liver metastases from SBNETs and PNETs (n = 136) were collected at surgery under an Institutional Review Board-approved protocol. Quantitative PCR measured expression of bombesin-like receptor-3, opioid receptor kappa-1, oxytocin receptor, and secretin receptor in metastases. Logistic regression models defined an algorithm predicting the primary tumor site. Models were developed on a training set of 21 nodal metastases and performance was validated on an independent set of nodal and liver metastases. Expression of all four genes was significantly different in SBNET compared to PNET metastases. The optimal model employed expression of bombesin-like receptor-3 and opioid receptor kappa-1. When these genes did not amplify, the algorithm used oxytocin receptor and secretin receptor expression, which allowed classification of all 136 metastases with 94.1 % accuracy. In the independent liver metastasis validation set, 52/56 (92.9 %) were correctly classified. Positive predictive values were 92.5 % for SBNETs and 93.8 % for PNETs. This validated algorithm accurately distinguishes SBNET and PNET metastases based on their expression of four genes. High accuracy in liver metastases demonstrates applicability to the clinical setting. Studies assessing this algorithm’s utility in prospective clinical decision-making are warranted.

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