TY - JOUR
T1 - Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing
AU - Panarelli, Nicole
AU - Tyryshkin, Kathrin
AU - Mun Wong, Justin Jong
AU - Majewski, Adrianna
AU - Yang, Xiaojing
AU - Scognamiglio, Theresa
AU - Kim, Michelle Kang
AU - Bogardus, Kimberly
AU - Tuschl, Thomas
AU - Chen, Yao Tseng
AU - Renwick, Neil
N1 - Funding Information:
This work was supported through the Southeastern Ontario Academic Medical Organization Innovation Fund, the Canada Foundation for Innovation John R Evans Leaders Fund and the Ontario Research Fund-Research Infrastructure.
Publisher Copyright:
© 2019 Society for Endocrinology Published by Bioscientifica Ltd.
PY - 2019/1
Y1 - 2019/1
N2 - Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.
AB - Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.
KW - Biomarkers
KW - Classification
KW - Gastroenteropancreatic neuroendocrine tumors
KW - MicroRNA
KW - Small RNA sequencing
UR - http://www.scopus.com/inward/record.url?scp=85056984695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056984695&partnerID=8YFLogxK
U2 - 10.1530/ERC-18-0244
DO - 10.1530/ERC-18-0244
M3 - Article
C2 - 30021866
AN - SCOPUS:85056984695
SN - 1351-0088
VL - 26
SP - 47
EP - 57
JO - Endocrine-Related Cancer
JF - Endocrine-Related Cancer
IS - 1
ER -