Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing

Nicole C. Panarelli, Kathrin Tyryshkin, Justin Jong Mun Wong, Adrianna Majewski, Xiaojing Yang, Theresa Scognamiglio, Michelle Kang Kim, Kimberly Bogardus, Thomas Tuschl, Yao Tseng Chen, Neil Renwick

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)47-57
Number of pages11
JournalEndocrine-Related Cancer
Volume26
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

MicroRNAs
RNA Sequence Analysis
Data Mining
Appendix
Gastro-enteropancreatic neuroendocrine tumor
Ileum
Rectum
Pancreas
Biomarkers
Immunohistochemistry
RNA
Genes

Keywords

  • Biomarkers
  • Classification
  • Gastroenteropancreatic neuroendocrine tumors
  • MicroRNA
  • Small RNA sequencing

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Oncology
  • Endocrinology
  • Cancer Research

Cite this

Panarelli, N. C., Tyryshkin, K., Mun Wong, J. J., Majewski, A., Yang, X., Scognamiglio, T., ... Renwick, N. (2019). Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing. Endocrine-Related Cancer, 26(1), 47-57. https://doi.org/10.1530/ERC-18-0244

Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing. / Panarelli, Nicole C.; Tyryshkin, Kathrin; Mun Wong, Justin Jong; Majewski, Adrianna; Yang, Xiaojing; Scognamiglio, Theresa; Kim, Michelle Kang; Bogardus, Kimberly; Tuschl, Thomas; Chen, Yao Tseng; Renwick, Neil.

In: Endocrine-Related Cancer, Vol. 26, No. 1, 01.01.2019, p. 47-57.

Research output: Contribution to journalArticle

Panarelli, NC, Tyryshkin, K, Mun Wong, JJ, Majewski, A, Yang, X, Scognamiglio, T, Kim, MK, Bogardus, K, Tuschl, T, Chen, YT & Renwick, N 2019, 'Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing', Endocrine-Related Cancer, vol. 26, no. 1, pp. 47-57. https://doi.org/10.1530/ERC-18-0244
Panarelli, Nicole C. ; Tyryshkin, Kathrin ; Mun Wong, Justin Jong ; Majewski, Adrianna ; Yang, Xiaojing ; Scognamiglio, Theresa ; Kim, Michelle Kang ; Bogardus, Kimberly ; Tuschl, Thomas ; Chen, Yao Tseng ; Renwick, Neil. / Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing. In: Endocrine-Related Cancer. 2019 ; Vol. 26, No. 1. pp. 47-57.
@article{5440f3ff0d02408b80620b49b16a6c92,
title = "Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing",
abstract = "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.",
keywords = "Biomarkers, Classification, Gastroenteropancreatic neuroendocrine tumors, MicroRNA, Small RNA sequencing",
author = "Panarelli, {Nicole C.} and Kathrin Tyryshkin and {Mun Wong}, {Justin Jong} and Adrianna Majewski and Xiaojing Yang and Theresa Scognamiglio and Kim, {Michelle Kang} and Kimberly Bogardus and Thomas Tuschl and Chen, {Yao Tseng} and Neil Renwick",
year = "2019",
month = "1",
day = "1",
doi = "10.1530/ERC-18-0244",
language = "English (US)",
volume = "26",
pages = "47--57",
journal = "Endocrine-Related Cancer",
issn = "1351-0088",
publisher = "Society for Endocrinology",
number = "1",

}

TY - JOUR

T1 - Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing

AU - Panarelli, Nicole C.

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

PY - 2019/1/1

Y1 - 2019/1/1

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

VL - 26

SP - 47

EP - 57

JO - Endocrine-Related Cancer

JF - Endocrine-Related Cancer

SN - 1351-0088

IS - 1

ER -