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MicroRNAs accurately identify cancer tissue origin

Rosenfeld, Nitzan, Aharonov, Ranit, Meiri, Eti, Rosenwald, Shai, Spector, Yael, Zepeniuk, Merav, Benjamin, Hila, Shabes, Norberto, Tabak, Sarit, Levy, Asaf, Lebanony, Danit, Goren, Yaron, Silberschein, Erez, Targan, Nurit, Ben-Ari, Alex, Gilad, Shlomit, Sion-Vardy, Netta, Tobar, Ana, Feinmesser, Meora, Kharenko, Oleg, Nativ, Ofer, Nass, Dvora, Perelman, Marina, Yosepovich, Ady, Shalmon, Bruria, Polak-Charcon, Sylvie, Fridman, Eddie, Avniel, Amir, Bentwich, Isaac, Bentwich, Zvi, Cohen, Dalia, Chajut, Ayelet, Barshack, Iris·2008·Nature Biotechnology

Abstract

MicroRNAs (miRNAs) belong to a class of noncoding, regulatory RNAs that is involved in oncogenesis and shows remarkable tissue specificity. Their potential for tumor classification suggests they may be used in identifying the tissue in which cancers of unknown primary origin arose, a major clinical problem. We measured miRNA expression levels in 400 paraffin-embedded and fresh-frozen samples from 22 different tumor tissues and metastases. We used miRNA microarray data of 253 samples to construct a transparent classifier based on 48 miRNAs. Two-thirds of samples were classified with high confidence, with accuracy >90%. In an independent blinded test-set of 83 samples, overall high-confidence accuracy reached 89%. Classification accuracy reached 100% for most tissue classes, including 131 metastatic samples. We further validated the utility of the miRNA biomarkers by quantitative RT-PCR using 65 additional blinded test samples. Our findings demonstrate the effectiveness of miRNAs as biomarkers for tracing the tissue of origin of cancers of unknown primary origin.

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Crossref · 10.1038/nbt1392

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  1. Pimiento, J.M., Teso, D., Malkan, A., Dudrick, S.J. & Palesty, J.A. Cancer of unknown primary origin: a decade of experience in a community-based hospital. Am. J. Surg. 194, 833–7, discussion 837–8 (2007). · DOI
  2. Shaw, P.H., Adams, R., Jordan, C. & Crosby, T.D. A clinical review of the investigation and management of carcinoma of unknown primary in a single cancer network. Clin. Oncol. (R. Coll. Radiol.) 19, 87–95 (2007). · DOI
  3. Hainsworth, J.D. & Greco, F.A. Treatment of patients with cancer of an unknown primary site. N. Engl. J. Med. 329, 257–263 (1993). · DOI
  4. Blaszyk, H., Hartmann, A. & Bjornsson, J. Cancer of unknown primary: clinicopathologic correlations. APMIS 111, 1089–1094 (2003). · DOI
  5. Varadhachary, G.R., Abbruzzese, J.L. & Lenzi, R. Diagnostic strategies for unknown primary cancer. Cancer 100, 1776–1785 (2004). · DOI
  6. Ramaswamy, S. et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149–15154 (2001). · DOI
  7. Bloom, G. et al. Multi-platform, multi-site, microarray-based human tumor classification. Am. J. Pathol. 164, 9–16 (2004). · DOI
  8. Ma, X.J. et al. Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay. Arch. Pathol. Lab. Med. 130, 465–473 (2006). · DOI
  9. Talantov, D. et al. A quantitative reverse transcriptase-polymerase chain reaction assay to identify metastatic carcinoma tissue of origin. J. Mol. Diagn. 8, 320–329 (2006). · DOI
  10. Tothill, R.W. et al. An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Res. 65, 4031–4040 (2005). · DOI
  11. Shedden, K.A. et al. Accurate molecular classification of human cancers based on gene expression using a simple classifier with a pathological tree-based framework. Am. J. Pathol. 163, 1985–1995 (2003). · DOI
  12. Baskerville, S. & Bartel, D.P. Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA 11, 241–247 (2005). · DOI · Paper page
  13. He, L. et al. A microRNA polycistron as a potential human oncogene. Nature 435, 828–833 (2005). · DOI
  14. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005). · DOI
  15. Volinia, S. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. USA 103, 2257–2261 (2006). · DOI · Paper page
  16. Raver-Shapira, N. et al. Transcriptional activation of miR-34a contributes to p53-mediated apoptosis. Mol. Cell 26, 731–743 (2007). · DOI
  17. Bentwich, I. et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 37, 766–770 (2005). · DOI · Paper page
  18. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A. & Enright, A.J. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006). · DOI · Paper page
  19. Xiao, C. et al. MiR-150 controls B cell differentiation by targeting the transcription factor c-Myb. Cell 131, 146–159 (2007). · DOI
  20. Hornstein, E. et al. The microRNA miR-196 acts upstream of Hoxb8 and Shh in limb development. Nature 438, 671–674 (2005). · DOI
  21. Lee, Y.S., Kim, H.K., Chung, S., Kim, K.S. & Dutta, A. Depletion of human micro-RNA miR-125b reveals that it is critical for the proliferation of differentiated cells but not for the down-regulation of putative targets during differentiation. J. Biol. Chem. 280, 16635–16641 (2005). · DOI
  22. Ein-Dor, L., Kela, I., Getz, G., Givol, D. & Domany, E. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005). · DOI
  23. Paik, S. et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 3726–3734 (2006). · DOI
  24. van de Vijver, M.J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002). · DOI
  25. Li, J. et al. Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 7, 36 (2007). · DOI
  26. Parker, D.C. et al. Potential utility of uroplakin III, thrombomodulin, high molecular weight cytokeratin, and cytokeratin 20 in noninvasive, invasive, and metastatic urothelial (transitional cell) carcinomas. Am. J. Surg. Pathol. 27, 1–10 (2003). · DOI
  27. Thomson, J.M., Parker, J., Perou, C.M. & Hammond, S.M. A custom microarray platform for analysis of microRNA gene expression. Nat. Methods 1, 47–53 (2004). · DOI
  28. Shi, R. & Chiang, V.L. Facile means for quantifying microRNA expression by real-time PCR. Biotechniques 39, 519–525 (2005). · DOI
  29. Thomson, J.M. et al. Extensive post-transcriptional regulation of microRNAs and its implications for cancer. Genes Dev. 20, 2202–2207 (2006). · DOI · Paper page
  30. Hino, K., Fukao, T. & Watanabe, M. Regulatory interaction of HNF1α to microRNA194 gene during intestinal epithelial cell differentiation. Nucleic Acids Symp. Ser. (Oxf.), 415–416 (2007). · DOI
  31. van Duin, M. et al. High-resolution array comparative genomic hybridization of chromosome 8q: evaluation of putative progression markers for gastroesophageal junction adenocarcinomas. Cytogenet. Genome Res. 118, 130–137 (2007). · DOI

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Metadata

Authors
Rosenfeld, N., Aharonov, R., Meiri, E., Rosenwald, S., Spector, Y., Zepeniuk, M., Benjamin, H., Shabes, N., Tabak, S., Levy, A., Lebanony, D., Goren, Y., Silberschein, E., Targan, N., Ben-Ari, A., Gilad, S., Sion-Vardy, N., Tobar, A., Feinmesser, M., Kharenko, O., Nativ, O., Nass, D., Perelman, M., Yosepovich, A., Shalmon, B., Polak-Charcon, S., Fridman, E., Avniel, A., Bentwich, I., Bentwich, Z., Cohen, D., Chajut, A., & Barshack, I.
Venue
Nature Biotechnology
Published
2008-03-23
Volume
26
Issue
4
Pages
462-469
ISSN
  • 1087-0156
  • 1546-1696
Cited by
812
References
3
Language
en

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