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Efficient identification of miRNAs for classification of tumor origin

Søkilde, Rolf LU ; Vincent, Martin; Møller, Anne K; Hansen, Alastair; Høiby, Poul E; Blondal, Thorarinn; Nielsen, Boye S.; Daugaard, Gedske; Møller, Søren and Litman, Thomas (2014) In Journal of Molecular Diagnostics 16(1). p.15-106
Abstract

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... (More)

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.

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author
publishing date
type
Contribution to journal
publication status
published
keywords
Algorithms, Base Sequence, Biomarkers, Tumor, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, MicroRNAs, Molecular Diagnostic Techniques, Neoplasms, Unknown Primary, Oligonucleotide Array Sequence Analysis, Organ Specificity, Paraffin Embedding, Sequence Analysis, RNA
in
Journal of Molecular Diagnostics
volume
16
issue
1
pages
10 pages
publisher
Elsevier Inc.
external identifiers
  • scopus:84890323096
ISSN
1525-1578
DOI
10.1016/j.jmoldx.2013.10.001
language
English
LU publication?
no
id
93c0e183-75d2-4ac2-8c96-608eb8a9b6cf
date added to LUP
2017-09-01 14:32:02
date last changed
2017-09-08 14:23:35
@article{93c0e183-75d2-4ac2-8c96-608eb8a9b6cf,
  abstract     = {<p>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.</p>},
  author       = {Søkilde, Rolf and Vincent, Martin and Møller, Anne K and Hansen, Alastair and Høiby, Poul E and Blondal, Thorarinn and Nielsen, Boye S. and Daugaard, Gedske and Møller, Søren and Litman, Thomas},
  issn         = {1525-1578},
  keyword      = {Algorithms,Base Sequence,Biomarkers, Tumor,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Humans,MicroRNAs,Molecular Diagnostic Techniques,Neoplasms, Unknown Primary,Oligonucleotide Array Sequence Analysis,Organ Specificity,Paraffin Embedding,Sequence Analysis, RNA},
  language     = {eng},
  number       = {1},
  pages        = {15--106},
  publisher    = {Elsevier Inc.},
  series       = {Journal of Molecular Diagnostics},
  title        = {Efficient identification of miRNAs for classification of tumor origin},
  url          = {http://dx.doi.org/10.1016/j.jmoldx.2013.10.001},
  volume       = {16},
  year         = {2014},
}