Article

Prognostically relevant gene signatures of high-grade serous ovarian carcinoma.

The Journal of clinical investigation (Impact Factor: 13.77). 12/2012; DOI: 10.1172/JCI65833
Source: PubMed

ABSTRACT Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named "Classification of Ovarian Cancer" (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens.

Download full-text

Full-text

Available from: Ari B Kahn, Jun 19, 2015
2 Followers
 · 
172 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Motivation: The successful translation of genomic signatures into clinical settings relies on good discrimination between patient sub-groups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated. Results: We give a precise definition of a popular simple method we refer to as mas-o-menos, which calculates prognostic scores for discrimination by summing standardized predictors, weighted by the signs of their marginal associations with the outcome. We study its behavior theoretically, in simulations and in an extensive analysis of 27 independent gene expression studies of bladder, breast and ovarian cancer, altogether totaling 3833 patients with survival outcomes. We find that despite its simplicity, mas-o-menos can achieve good discrimination performance. It performs no worse, and sometimes better, than popular and much more CPU-intensive methods for discrimination, including lasso and ridge regression.
    Bioinformatics 07/2014; 30(21). DOI:10.1093/bioinformatics/btu488 · 4.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Ovarian Cancer (OC) is the most lethal gynecological malignancy among women. Over 70% of women with OC are diagnosed in advanced stages and most of these cases are incurable. Although most patients respond well to primary chemotherapy, tumors become resistant to treatment. Mechanisms of chemoresistance in cancer cells may be associated with mutational events and/or alterations of gene expression through epigenetic events. Although focusing on known genes has already yielded new information, previously unknown non-coding RNAs, such as microRNAs (miRNAs), also lead insight into the biology of chemoresistance. In this review we summarize the current evidence examining the role of miRNAs as biomarkers of response and survival to therapy in OC. Beside their clinical implications, we also discuss important differences between studies that may have limited their use as clinical biomarkers and suggest new approaches.
    Molecular and Cellular Endocrinology 04/2014; DOI:10.1016/j.mce.2014.03.006 · 4.24 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this study, we examined the association between RKIP expression and the molecular subtypes of breast cancer. Microarray gene expression data of 2,333 human breast cancer from 26 different cohorts performed on Affymetrix U133A or U133Plus2 platforms were downloaded from Array Express and Gene Expression Omnibus (GEO) and the molecular subtype of breast cancer for the samples was determined by Single sample Gene Set Enrichment Analysis (ssGSEA). Differences in Recurrence-free survival (RFS) were tested using the Log-rank test in univariate analysis and displayed using Kaplan-Meier curves. Cox proportional-hazards model was used to calculate the hazard ratio using univariate and multivariate analysis. Loss or reduced RKIP expression was associated with reduced RFS in breast cancer using univariate and multivariate analyses, which was independent of lymph node (LN) metastasis status. Basal-like, Claudin-low and Her-2-enriched tumors had significantly lower RKIP levels compared to other subclasses (p < 0.0001). Conversely, the Luminal subclass exhibited the highest expression levels of RKIP (p < 0.0001 for Luminal A and p = 0.0005 for Luminal B subtype), while in Normal-like breast cancer subtype, RKIP expression was not informative. RKIP expression was prognostic in ER+ and ER- subgroups. RKIP expression had no significant prognostic power within Basal-like, Claudine-low, Luminal B, or Her-2-enriched breast cancer subtypes. However, its expression pinpointed excellent from intermediate-poor Luminal A survivors, in both ER+ (p = 0.035) and ER- (p = 0.012) subgroups, especially in lymph node negative breast cancers. In conclusion, RKIP expression adds significant value to the molecular subclassification of breast cancer especially for the Luminal A subtype. J. Cell. Biochem. © 2013 Wiley Periodicals, Inc.
    Journal of Cellular Biochemistry 03/2014; 115(3). DOI:10.1002/jcb.24682 · 3.37 Impact Factor