Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer

Abstract

Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in part because of the lack of effective early detection methods. Although alterations of several genes, such as c-erb-B2, c-myc, and p53, have been identified in a significant fraction of ovarian cancers, none of these mutations are diagnostic of malignancy or predictive of tumor behavior over time. Here, we used oligonucleotide microarrays with probe sets complementary to >6,000 human genes to identify genes whose expression correlated with epithelial ovarian cancer. We extended current microarray technology by simultaneously hybridizing ovarian RNA samples in a highly parallel manner to a single glass wafer containing 49 individual oligonucleotide arrays separated by gaskets within a custom-built chamber (termed "array-of-arrays"). Hierarchical clustering of the expression data revealed distinct groups of samples. Normal tissues were readily distinguished from tumor tissues, and tumors could be further subdivided into major groupings that correlated both to histological and clinical observations, as well as cell type-specific gene expression. A metric was devised to identify genes whose expression could be considered ideal for molecular determination of epithelial ovarian malignancies. The list of genes generated by this method was highly enriched for known markers of several epithelial malignancies, including ovarian cancer. This study demonstrates the rapidity with which large amounts of expression data can be generated. The results highlight important molecular features of human ovarian cancer and identify new genes as candidate molecular markers.

Figure 1

Expression patterns of 1,243 genes in 38 experimental samples. Rows represent individual genes; columns represent individual samples. Each cell in the matrix represents the expression level of a single transcript in a single sample, with red and green indicating transcript levels above and below the median for that gene across all samples, respectively. Color saturation is proportional to magnitude of the difference from the mean. Gray squares, data not available. (a) Dendrogram of samples showing overall similarity in gene expression profiles across the samples. Three main subdivisions, colored blue, black, and red, are discussed in the text. (b) Demonstration of overall groupings of genes and samples. Colored bars to the right indicate gene clusters of special interest. (c) Enlarged view of selected clusters of genes with common tissue distribution (Tumor, Stromal, Normal) or biochemical role (Proliferative, Ribosomal).

Figure 2

Expression levels of selected genes in normal and malignant ovarian tissues. Shown are the hybridization signals of 30 transcripts, which were ranked highest by the “diagnostic” metric (see Meterials and Methods). Each gene is represented by two mean values derived from the expression level in 24 malignant (red) and four normal (blue) samples. Error bars = 99% confidence intervals. Expression in a pool of six normal tissues is shown for comparison as horizontal green bars. Gene names correspond to Human Genome Organization (HUGO) Gene Nomenclature Committee recommendations.

Figure 3

Validation of ovarian tumor-specific gene expression. RT-PCR was used to amplify fragments of CD24 (a), HE4 (b), and LU (c) from RNAs of the normal, stromal-enriched fraction of sample 278 (lane 1), the three normal epithelial-enriched ovarian samples (lanes 2–4), nine randomly selected tumor tissues (lanes 5–13), the ovarian cancer cell line CAOV-3 (lane 14), and water as a control (lane 15). 18s rRNA amplified from the same samples shows similar loading in each lane (d).