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Molecular Oncology, Markers, Clinical Correlates |
Department of Cell and Cancer Biology [K. K. Z., G. J. G., M. J. B.] and Laboratory of Biosystems and Cancer [A. A. J.], Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20850; Departments of Surgery [C. S. A.] and Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York 10021 [J. B.]; and Laboratory of Gynecologic Oncology, Dana Farber Harvard Cancer Center, Harvard Medical School, Boston, Massachusetts 02115 [S. C. M.]
Purpose: As with many cancers thought to be of epithelial origin, expression profiling studies of ovarian cancer have relied on a variety of sources of normal cells for comparison with tumors, including whole ovary samples (WO), ovarian surface epithelium (OSE) exposed to short-term culture, and immortalized OSE cell lines (IOSE). Our purpose was to assess the impact of the use of different types of normal controls on the determination of gene expression alterations in ovarian cancer studies.
Experimental Design: We compared the gene expression profiles generated on an 11,000-element cDNA microarray of OSE brushings, whole ovary samples, short-term cultures of normal OSE, SV40 large T antigen-immortalized OSE cell lines, and telomerase-immortalized OSE cell lines. The function of the groups as normal controls was then assessed by separate comparisons of each group to a set of 24 serous ovarian carcinoma samples.
Results: The normal groups formed robust, distinct clusters in hierarchical clustering and multidimensional scaling. The Pearson correlation coefficient for all combinations of any two of the groups ranged from 0.04 to 0.54, emphasizing the disparity of the groups. In the gene lists produced by comparing each normal group with the ovarian cancer samples, the majority of genes were unique to that normal-cancer comparison, with no gene appearing on all five lists.
Conclusions: These results suggest that the selection of a normal control to compare with epithelial ovarian cancer samples in microarray studies strongly influences the genes that are identified as differentially expressed and complicates comparison with studies using a different normal control.
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