No abstract

Purpose: A better understanding of the underlying biology of invasive serous ovarian cancer is critical for the development of early detection strategies and new therapeutics. The objective of this study was to define gene expression patterns associated with favorable survival. Experimental Design: RNA from 65 serous ovarian cancers was analyzed using Affymetrix U133A microarrays.This included 54 stage III/IV cases (30 short-term survivors who lived <3 years and 24 long-term survivors who lived >7 years) and11stage I/II cases. Genes were screened on the basis of their level of and variability in expression, leaving 7,821for use in developing a predictive model for survival. A composite predictive model was developed that combines Bayesian classification tree and multivariate discriminant models. Leave-one-out cross-validation was used to select and evaluate models. Results: Patterns of genes were identified that distinguish short-term and long-term ovarian cancer survivors. The expression model developed for advanced stage disease classified all 11early-stage ovarian cancers as long-term survivors. The MAL gene, which has been shown to confer resistance to cancer therapy, was most highly overexpressed in short-term survivors (3-fold compared with long-term survivors, and 29-fold compared with early-stage cases).These results suggest that gene expression patterns underlie differences in outcome, and an examination of the genes that provide this discrimination reveals that many are implicated in processes that define the malignant phenotype. Conclusions: Differences in survival of advanced ovarian cancers are reflected by distinct patterns of gene expression.This biological distinction is further emphasized by the finding that earlystage cancers share expression patterns with the advanced stage long-term survivors, suggesting a shared favorable biology.

High-density DNA microarrays measure expression of large numbers of genes in one assay. The ability to find underlying structure in complex gene expression data sets and rigorously test association of that structure with biological conditions is essential to developing multi-faceted views of the gene activity that defines cellular phenotype. We sought to connect features of gene expression data with biological hypotheses by integrating 'metagene' patterns from DNA microarray experiments in the characterization and prediction of oncogenic phenotypes. We applied these techniques to the analysis of regulatory pathways controlled by the genes HRAS (Harvey rat sarcoma viral oncogene homolog), MYC (myelocytomatosis viral oncogene homolog) and E2F1, E2F2 and E2F3 (encoding E2F transcription factors 1, 2 and 3, respectively). The phenotypic models accurately predict the activity of these pathways in the context of normal cell proliferation. Moreover, the metagene models trained with gene expression patterns evoked by ectopic production of Myc or Ras proteins in primary tissue culture cells properly predict the activity of in vivo tumor models that result from deregulation of the MYC or HRAS pathways. We conclude that these gene expression phenotypes have the potential to characterize the complex genetic alterations that typify the neoplastic state, whether in vitro or in vivo, in a way that truly reflects the complexity of the regulatory pathways that are affected.