4.7 Article

Novel markers of subclinical disease for Ewing family tumors from gene expression profiling

Journal

CLINICAL CANCER RESEARCH
Volume 13, Issue 23, Pages 6978-6983

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-07-1417

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Funding

  1. NCI NIH HHS [CA118845, CA106450] Funding Source: Medline

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Purpose: Targeting subclinical disease in the bone marrow is particularly relevant in metastatic Ewing family tumors (EFT) where cure is difficult. Genome-wide expression arrays can uncover novel genes differentially expressed in tumors over normal marrow/blood, which may have potentials as markers of subclinical disease. Experimental Design: Gene expression array data were obtained on 28 EFT tumors using the Affymetrix U133 gene chip and compared with 10 normal blood samples. Ten genes with high tumor to blood ratios were identified. Quantitative reverse transcription-PCR was done to study (a) the dynamic range of detection of rare tumor cells, (b) the gene expression in normal blood/ marrow samples, (c) the gene expression among EFT tumors, and (d) the detection and prognostic impact of marker positivity in histology-negative diagnostic marrows of EFT patients. Results: Five of 10 genes (i.e., six-transmembrane epithelial antigen of the prostate 1 [STEAP1], cyclin D1 [CCND1], NKX2-2 transcription factor [NKX2-2], plakophilin 1 [PKP1], and transmembrane protein 47 [TMEM47]) were chosen for further analyses based on their steep linear dynamic range in detecting tumor cells seeded in normal mononuclear cells and on their homogeneous expression among EFT tumors. Prognostic effect was evaluated in 35 histology-negative diagnostic marrows. Marker negativity of STEAP1, CCND1, or NKX2-2, as well as three markers in combination, was strongly correlated with patient survival as well as survival without new metastases. Conclusions: This gene expression array-based approach identified novel markers that may be informative at diagnosis for risk group assessment. Their clinical utility needs to be tested in large patient cohorts.

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