Efficient Identification of miRNAs for Classification of Tumor Origin

TY - JOUR

T1 - Efficient Identification of miRNAs for Classification of Tumor Origin

AU - Søkilde, Rolf

AU - Vincent, Martin

AU - Møller, Anne K

AU - Hansen, Alastair

AU - Høiby, Poul E

AU - Blondal, Thorarinn

AU - Nielsen, Boye S

AU - Daugaard, Gedske

AU - Møller, Søren

AU - Litman, Thomas

N1 - Copyright © 2014 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

PY - 2014/1

Y1 - 2014/1

N2 - Carcinomas of unknown primary origin constitute 3% to 5% of all newly diagnosed metastatic cancers, with the primary source difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor; patients with metastases of unknown origin have poor prognosis and short survival. Because miRNA expression is highly tissue specific, the miRNA profile of a metastasis may be used to identify its origin. We therefore evaluated the potential of miRNA profiling to identify the primary tumor of known metastases. Two hundred eight formalin-fixed, paraffin-embedded samples, representing 15 different histologies, were profiled on a locked nucleic acid-enhanced microarray platform, which allows for highly sensitive and specific detection of miRNA. On the basis of these data, we developed and cross-validated a novel classification algorithm, least absolute shrinkage and selection operator, which had an overall accuracy of 85% (CI, 79%-89%). When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy; CI, 75%-94%). Our findings suggest that miRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors and, eventually, enable tailored therapy.

AB - Carcinomas of unknown primary origin constitute 3% to 5% of all newly diagnosed metastatic cancers, with the primary source difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor; patients with metastases of unknown origin have poor prognosis and short survival. Because miRNA expression is highly tissue specific, the miRNA profile of a metastasis may be used to identify its origin. We therefore evaluated the potential of miRNA profiling to identify the primary tumor of known metastases. Two hundred eight formalin-fixed, paraffin-embedded samples, representing 15 different histologies, were profiled on a locked nucleic acid-enhanced microarray platform, which allows for highly sensitive and specific detection of miRNA. On the basis of these data, we developed and cross-validated a novel classification algorithm, least absolute shrinkage and selection operator, which had an overall accuracy of 85% (CI, 79%-89%). When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy; CI, 75%-94%). Our findings suggest that miRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors and, eventually, enable tailored therapy.

U2 - 10.1016/j.jmoldx.2013.10.001

DO - 10.1016/j.jmoldx.2013.10.001

M3 - Journal article

C2 - 24211363

SN - 1525-1578

VL - 16

SP - 106

EP - 115

JO - The Journal of molecular diagnostics : JMD

JF - The Journal of molecular diagnostics : JMD

IS - 1

ER -