Optimal strategies for sequential validation of significant features from high-dimensional genomic data.

High-dimensional genomic studies play a key role in identifying critical features that are significantly associated with a phenotypic outcome. The two most important examples are the detection of (1) differentially expressed genes from genome-wide gene expression studies and (2) single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Such experiments are often associated with high noise levels, and the validity of statistical conclusions suffers from low sample size compared to large number of features. The corresponding multiple testing problem calls for the identification of optimal strategies for controlling the numbers of false discoveries and false nondiscoveries. In addition, a frequent validation problem is that features identified as important in one study are often less so in another study. Adjustment for multiple testing in both studies separately increases the risk of missing the crucial features even further. These problems can be addressed by sequential validation strategies, where only significant features identified in one study enter as candidates in the next study. The quality associated with different studies, for example, in terms of noise levels, may vary considerably. By performing simulation studies it is possible to demonstrate that the optimal order for this stepwise procedure is to sort experimental studies according to their quality in descending order. The impact of the method for multiple testing adjustment (Bonferroni-Holm, FDR) was also analyzed. Finally, the sequential validation strategy was applied to three large breast cancer studies with gene expression measurements, confirming the crucial impact of the order of the validation steps in a real-world application.

Ep-CAM RNA expression predicts metastasis-free survival in three cohorts of untreated node-negative breast cancer.

Epithelial cell adhesion molecule (Ep-CAM) recently received increased attention as a prognostic factor in breast cancer. We aimed to validate the influence of Ep-CAM RNA expression in untreated node-negative breast cancer. Ep-CAM RNA expression was evaluated utilizing microarray-based gene-expression profiling in 194 consecutive node-negative breast cancer patients with long-term follow-up not treated in the adjuvant setting. The prognostic significance of Ep-CAM RNA expression for disease-free survival (DFS), metastasis-free survival (MFS), and breast cancer-specific overall survival (OS) was evaluated in univariate and multivariate analysis adjusted for age, grading, pTstage, ER as well as PR receptor and HER-2 status. Additionally, Ep-CAM RNA expression was compared with immunohistochemistry (IHC) for Ep-CAM in 194 patients. The prognostic impact of Ep-CAM gene expression was validated in further 588 node-negative breast cancer patients. Levels of Ep-CAM RNA expression showed a significant correlation with IHC (P = 0.001) and predicted in univariate analysis DFS (P = 0.001, HR = 2.4), MFS (P = 0.003, HR = 2.5), and OS (P = 0.002, HR = 3.1) accurately. The prognostic influence of Ep-CAM RNA was significant also in multivariate analysis for DFS (P = 0.017, HR = 2.0), MFS (P = 0.049, HR = 1.9), and OS (P = 0.042, HR = 2.3), respectively. The association with MFS was confirmed in an independent validation cohort in univariate (P = 0.006, HR = 1.9) and multivariate (P = 0.035, HR = 1.7) analysis. Ep-CAM RNA correlated with the proliferation metagene (P < 0.001, R=0.425) Nevertheless, in multivariate analysis, Ep-CAM was associated with MFS independent from the proliferation metagene (P = 0.030, HR = 1.8). In conclusion, Ep-CAM RNA expression is associated with poor MFS in three cohorts of untreated node-negative breast cancer.

Role of thioredoxin reductase 1 and thioredoxin interacting protein in prognosis of breast cancer.

INTRODUCTION: The purpose of this work was to study the prognostic influence in breast cancer of thioredoxin reductase 1 (TXNRD1) and thioredoxin interacting protein (TXNIP), key players in oxidative stress control that are currently evaluated as possible therapeutic targets. METHODS: Analysis of the association of TXNRD1 and TXNIP RNA expression with the metastasis-free interval (MFI) was performed in 788 patients with node-negative breast cancer, consisting of three individual cohorts (Mainz, Rotterdam and Transbig). Correlation with metagenes and conventional clinical parameters (age, pT stage, grading, hormone and ERBB2 status) was explored. MCF-7 cells with a doxycycline-inducible expression of an oncogenic ERBB2 were used to investigate the influence of ERBB2 on TXNRD1 and TXNIP transcription. RESULTS: TXNRD1 was associated with worse MFI in the combined cohort (hazard ratio = 1.955; P < 0.001) as well as in all three individual cohorts. In contrast, TXNIP was associated with better prognosis (hazard ratio = 0.642; P < 0.001) and similar results were obtained in all three subcohorts. Interestingly, patients with ERBB2-status-positive tumors expressed higher levels of TXNRD1. Induction of ERBB2 in MCF-7 cells caused not only an immediate increase in TXNRD1 but also a strong decrease in TXNIP. A subsequent upregulation of TXNIP as cells undergo senescence was accompanied by a strong increase in levels of reactive oxygen species. CONCLUSIONS: TXNRD1 and TXNIP are associated with prognosis in breast cancer, and ERBB2 seems to be one of the factors shifting balances of both factors of the redox control system in a prognostic unfavorable manner.

ERBB2 induces an antiapoptotic expression pattern of Bcl-2 family members in node-negative breast cancer.

PURPOSE: Members of the Bcl-2 family act as master regulators of mitochondrial homeostasis and apoptosis. We analyzed whether ERBB2 influences the prognosis of breast cancer by influencing the proapoptotic versus antiapoptotic balance of Bcl-2 family members. EXPERIMENTAL DESIGN: ERBB2-regulated Bcl-2 family members were identified by inducible expression of ERBB2 in MCF-7 breast cancer cells and by correlation analysis with ERBB2 expression in breast carcinomas. The prognostic relevance of ERBB2-regulated and all additional Bcl-2 family members was determined in 782 patients with untreated node-negative breast cancer. The biological relevance of ERBB2-induced inhibition of apoptosis was validated in a murine tumor model allowing conditional ERBB2 expression. RESULTS: ERBB2 caused an antiapoptotic phenotype by upregulation of MCL-1, TEGT, BAG1, BNIP1, and BECN1 as well as downregulation of BAX, BMF, BNIPL, CLU, and BCL2L13. Upregulation of the antiapoptotic MCL-1 [P = 0.001, hazard ratio (HR) 1.5] and BNIP3 (P = 0.024; HR, 1.4) was associated with worse prognosis considering metastasis-free interval, whereas clusterin (P = 0.008; HR, 0.88) and the proapoptotic BCL2L13 (P = 0.019; HR, 0.45) were associated with better prognosis. This indicates that ERBB2 alters the expression of Bcl-2 family members in a way that leads to adverse prognosis. Analysis of apoptosis and tumor remission in a murine tumor model confirmed that the prototypic Bcl-2 family member Bcl-x(L) could partially substitute for ERBB2 to antagonize tumor remission. CONCLUSIONS: Our results support the concept that ERBB2 influences the expression of Bcl-2 family members to induce an antiapoptotic phenotype. Antagonization of antiapoptotic Bcl-2 family members might improve breast cancer therapy, whereby MCL-1 and BNIP3 represent promising targets.