Prognostically relevant gene signatures of high-grade serous ovarian carcinoma

The Journal of clinical investigation (Impact Factor: 13.22). 12/2012; 123(1). DOI: 10.1172/JCI65833
Source: PubMed


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.

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Available from: Ari B Kahn
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    • "Ranking methods include the t-statistic for difference in expression of good versus bad prognosis genes (Bell et al., 2011; Verhaak et al., 2013) and signed averaging of discretized or continuous expression values (Colman et al., 2010; Dave et al., 2004; Hallett et al., 2010; Kang et al., 2012;Rè me et al., 2013). Replacing lasso coefficients by their signs has been proposed for summarizing gene pathway activity (Eng et al., 2013). "
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    ABSTRACT: Motivation: The successful translation of genomic signatures into clinical settings relies on good discrimination between patient subgroups. 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 más-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, más-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. Availability and implementation: Más-o-menos is implemented for survival analysis as an option in the survHD package, available from and submitted to Bioconductor.
    Full-text · Article · Jul 2014 · Bioinformatics
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    • "This comparison revealed that age at diagnosis was significantly negatively correlated with PC1 and positively correlated PC2 (Pearson correlation p = 1.29−3 and p = 3.56−4 respectively), suggesting that differentiated and immunoreactive patients tend to present at younger age. The percentage of stromal cells is positively correlated with PC2 (Pearson correlation p = 1.79−3), which is in consensus with the mesenchymal subtype having greater percentage of stromal cells [56]. Other clinical factors, such as somatic mutation, drug treatment and tumor stages did not significantly correlate with either axis. "
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    ABSTRACT: Background To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.
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    • "Yang et al. (2013) took protein data from the TCGA study and generated a model called PROtein-derived index of OVARian cancer (PROVAR) to predict progression and time to tumor recurrence. Verhaak et al. (2013) developed a subtype and survival gene expression signatures from TCGA data that, when combined, provide a prognostic model, named CLassification of OVARian cancer (CLOVAR), which help to outcome classification. Yoshihara et al. (2012) used gene expression data to establish 126-gene signature risk classification system for predicting clinical outcome. "
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