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.