Patterns of Gene Expression That Characterize Long-term Survival in Advanced Stage Serous Ovarian Cancers

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.

Epigenetic regulation of CD133 and tumorigenicity of CD133+ ovarian cancer cells

“…This collective gene profile (Supplementary Table S6) accurately classified 37 of 42 advanced stage (III/IV) serous epithelial ovarian cancers from our previously published microarray data set (Berchuck et al, 2005) as CD133 þ or CD133À using leave-one-out cross-validation (88% accuracy; Supplementary Figures S6b). The probability of the ovarian cancers possessing this CD133 gene signature was also positively correlated with the expression of CD133 (P ¼ 0.04; data not shown).…”

“…Two additional specimens were stained from standard 5 mm sections prepared from paraffin-embedded tissues. Primary ovarian cancer specimens used in this study were derived from a previously described data set (Berchuck et al, 2005). Before tissue microarray preparation, hematoxylin and eosin sections from CD133 þ cells in ovarian cancer T Baba et al each donor block were examined by a board-certified pathologist for tumor content and marked for core selection.…”

Bayesian Weibull tree models for survival analysis of clinico-genomic data

“…, n z , and y zi ∼ Exp(μ z,τ ). The data density is A careful examination of data from earlier studies of survival and cancer [2,15] revealed that the survival distribution could be more accurately represented by a Weibull distribution. The Weibull may be not only the most widely used parametric survival model but with its shape parameter it can be viewed as a generalization of the Exponential [30].…”

“…When making ovarian cancer diagnoses and prognoses clinicians rely on subjective interpretations of both clinical and histopathological information, which can be incomplete or unreliable [62]. Recent studies in ovarian cancer have demonstrated the potential of genomic data to improve our ability to predict patient survival and treatment response [62,2].…”

“…Recent studies have generated such signatures related to disease recurrence and survival in ovarian cancer [62,2] as well as in numerous other disease contexts [72,58,48]. Analyses involving gene expression signatures have focused on clustering or classification to associate such signatures or patterns with 'low-risk' versus 'high-risk' survival prognoses.…”