Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes.

BACKGROUND: A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples. RESULTS: We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (p < 10-12). Many genes with heavy tails generate subgroups of patients with different prognosis. CONCLUSIONS: Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.

ERBB2 and TOP2A in breast cancer: a comprehensive analysis of gene amplification, RNA levels, and protein expression and their influence on prognosis and prediction.

PURPOSE: The prognostic and predictive relevance of epidermal growth factor receptor 2 (ERBB2) and topoisomerase II alpha (TOP2A) have long been a matter of debate. However, the correlation of DNA amplification, RNA levels, and protein expression and their prognostic role and association with anthracycline responses in node-negative breast cancer have not yet been evaluated. EXPERIMENTAL DESIGN: We first analyzed TOP2A and ERBB2 at the levels of gene amplification, and RNA and protein expression, and studied their correlations. Additionally, TOP2A and ERBB2 were analyzed in 782 node-negative breast carcinomas in patients who did not receive systemic therapy and in 80 patients treated with epirubicin and cyclophosphamide (EC) prior to surgery. RESULTS: TOP2A gene amplification did not correlate with protein expression (P = 0.283) and showed an association with gene expression with only borderline significance (P = 0.047). By contrast, TOP2A RNA levels correlated with protein expression (P < 0.001). TOP2A gene expression was significantly associated with the metastasis-free interval (MFI; P < 0.001) and was associated with complete remission in patients treated with EC (P = 0.002). In contrast to TOP2A, ERBB2 gene amplification correlated with RNA level (P < 0.001) and protein expression (P < 0.001). ERBB2 gene expression was associated with the MFI only in estrogen receptor-positive carcinomas, whereas ERBB2 protein expression (P = 0.032) was associated with MFI in the entire cohort. CONCLUSIONS: Overall, our study indicates that the TOP2A RNA level is a good prognostic marker and is also associated with a favorable response to anthracyclin-based therapy. By contrast, ESR1 was associated with poorer responses to anthracyclin-based therapy, whereas the association with ERBB2 RNA was not significant.

Coordinates in the universe of node-negative breast cancer revisited.

We present a global picture of the natural history of node-negative breast cancer in which two of three important biological processes have outstanding prognostic consequences. We propose that the transition from slow to fast proliferation of the tumor leads to the most dramatic aggravation of prognosis. Second, immune cell infiltration is of major importance to prevent disease progression in fast-proliferating breast carcinomas, regardless of estrogen receptor status. In the absence of endocrine treatment, steroid hormone receptor expression as a third axis is of limited prognostic importance. Dissecting tumors according to these three major biological axes will allow further understanding of biological processes relevant for tumor progression in patients with node-negative breast cancer.