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Blood, 15 April 2006, Vol. 107, No. 8, pp. 3189-3196.
Prepublished online as a Blood First Edition Paper on January 10, 2006; DOI 10.1182/blood-2005-07-2813.
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Submitted July 14, 2005
Accepted November 24, 2005
Quantitative PCR on 5 genes reliably identifies CTCL patients with 5-99% circulating tumor cells with 90% accuracy
Michael Nebozhyn, Andrey Loboda, Laszlo Kari, Alain H Rook, Eric C Vonderheid, Stuart Lessin, Carole Berger, Richard Edelson, Calen Nichols, Malik Yousef, Lalitha Gudipati, Meiling Shang, Michael K Showe, and Louise C Showe*
The Wistar Institute, Philadelphia, PA, USA
Department of Dermatology, The University of Pennsylvania School of Medicine, Philadelphia, PA, USA
Department of Dermatology, Johns Hopkins Medical Institutes, Baltimore, MD, USA
Fox Chase Cancer Center, Philadelphia, PA, USA
Department of Dermatology, Yale University, New Haven, CT, USA
* Corresponding author; email: lshowe{at}wistar.org.
We previously identified a small number of genes, using cDNA arrays that accurately diagnosed patients with Sezary Syndrome (SS), the erythrodermic and leukemic form of cutaneous T-cell lymphoma (CTCL). We now report the development of a quantitative real-time polymerase chain reaction (QRT-PCR) assay that uses expression values for just 5 of those genes, STAT4, GATA-3, PLS3, CD1d and TRAIL. QRT-PCR data from PBMC accurately classified 88% of 17 patients with high blood tumor burden and 100% of 12 healthy controls in the training set using Fisher Linear Discriminant Analysis (FLDA). The same 5 genes were then assayed on 56 new samples from 49 SS patients with blood tumor burdens of 5-99% and 69 samples from 65 new healthy controls. The average accuracy over 1000 re-samplings was 90% using FLDA and 88% using Support Vector Machine (SVM). We also tested the classifier on 14 samples from patients with CTCL with no detectable peripheral involvement and 3 patients with Atopic Dermatitis with severe erythroderma. The accuracy was 100% in identifying these samples as non-SS patients. These results are the first to demonstrate that gene expression profiling by quantitative PCR on a selected number of critical genes can be employed to molecularly diagnosis SS.
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