期刊
JOURNAL OF MOLECULAR DIAGNOSTICS
卷 16, 期 1, 页码 106-115出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmoldx.2013.10.001
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资金
- Danish National Advanced Technology Foundation [048-2008-1]
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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