Integrativer Ansatz zur Identifizierung neuer, prognostisch relevanter Metagene mittels Clusteranalyse

In Germany, breast cancer is the most common leading cause of cancer deaths in women. To gain insight into the processes related to the course of the disease, human genetic data can be used to identify associations between gene expression and prognosis. In the course of the several clinical studies and numerous microarray experiments, the enormous data volume is constantly generated. Its dimensionality reduction of thousands of genes to a smaller number is the aim of the so-called metagenes that aggregate the expression data of groups of genes with similar expression patterns and may be used for investigating complex diseases like breast cancer. Here, a cluster analytic approach for identification of potentially relevant metagenes is introduced. In a first step of the approach, gene expression patterns over time of receptor tyrosine kinase ErbB2 breast cancer MCF7 cell lines to obtain promising sets of genes for a metagene calculation were used. Three independent batches of MCF7/NeuT cells were exposed to doxycycline for periods of 0, 6, 12 and 24 hours as well as for 3 and 14 days in independent experiments, due to association of the oncogenic variant of ErbB2 overexpression in breast cancer with worse prognosis. With cluster analytic approaches DIB-C (difference-based clustering algorithm) and STEM (short time-series expression miner) as well as with the finite and infinite mixture models gene clusters with similar expression patterns were identified. Two non-model-based algorithms – k-means and PFP (penalized frame potential) – as well as the model-based procedure DIRECT were applied for the method comparisons. Potentially relevant gene groups were selected by promoter and Gene Ontology (GO) analysis. The verification of the applied methods was carried out with another short time-series data set. In the second step of the approach, this gene clusters were used to calculate metagenes of the gene expression data of 766 breast cancer patients from three breast cancer studies and Cox models were applied to determine the effect of the detected metagenes on the prognosis. Using this strategy, new metagenes associated with metastasis-free survival patients were identified.In Deutschland ist Brustkrebs die häufigste Krebserkrankung bei Frauen. Durch zahlreiche klinische Studien auf diesem Gebiet konnte festgestellt werden, dass die veränderten Gene zwar nicht zwangsläufig zum Ausbruch der Krankheit führen, deren Expressionen jedoch näher analysiert werden sollten, um das Karzinom rechtzeitig zu erkennen und dadurch bessere Therapien zu ermöglichen. Hierbei wird durch die Microarray-Experimente ein enormes Datenvolumen generiert, deren Dimensionsreduktion von mehreren Tausend Genen zu einer deutlich kleineren Anzahl angestrebt wird. Eine Möglichkeit bieten die sogenannten Metagene, zu denen Gene mit ähnlichen Expressionen zusammengefasst werden können und die sich als prognostische Faktoren für das Überleben der Patienten erwiesen haben. In der vorliegenden Arbeit wird ein neuer integrativer Ansatz zur Clusterung kurzer Expressionszeitreihen zur Identifizierung prognostisch relevanter Metagene vorgestellt. Der erste Teil des Ansatzes beruht auf der Analyse humaner Mammakarzinom-Zelllinien MCF7. Die onkogene Variante der Rezeptortyrosinkinase ErbB2, deren Überexpression mit einer schlechteren Prognose assoziiert ist, wurde in diesen MCF7-Zelllinien induziert und zu den Zeitpunkten 0, 6, 12 und 24 Stunden sowie und 3 und 14 Tagen nach der Induktion beobachtet. Mit den Clusteranalyseansätzen DIB-C (difference-based clustering algorithm) und STEM (short time-series expression miner) sowie mit den finiten und den infiniten Mischungsmodellen werden hier Gengruppen mit ähnlichen Expressionsverläufen identifiziert. Als Vergleichsmethoden werden die nicht-modellbasierten Algorithmen k-means und PFP (penalized frame potential) und das in R implementierte Tool DIRECT als modellbasierter Vergleich zur Analyse herangezogen. Mit der Gene Ontology (GO) - bzw. Promoteranalyse werden die biologisch interessantesten Cluster ermittelt. Zur Verifizierung der hier angewendeten Methoden wird ein weiterer Datensatz mit Expressionswerten kurzer Zeitreihen erfolgreich herangezogen. Im zweiten Teil des Ansatzes werden für diese Gruppen Metagene gebildet und auf ihre prognostische Relevanz in den Brustkrebsdaten von 766 Patientinnen mittels Überlebenszeitanalyse untersucht und so neue biologisch relevante Cluster aufgedeckt

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

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

International audienceEpithelial 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 ( = 0.001) and predicted in univariate analysis DFS ( = 0.001, HR = 2.4), MFS ( = 0.003, HR = 2.5), and OS ( = 0.002, HR = 3.1) accurately. The prognostic influence of Ep-CAM RNA was significant also in multivariate analysis for DFS ( = 0.017, HR = 2.0), MFS ( = 0.049, HR = 1.9), and OS ( = 0.042, HR = 2.3), respectively. The association with MFS was confirmed in an independent validation cohort in univariate ( = 0.006, HR = 1.9) and multivariate ( = 0.035, HR = 1.7) analysis. Ep-CAM RNA correlated with the proliferation metagene ( < 0.001, R=0.425) Nevertheless, in multivariate analysis, Ep-CAM was associated with MFS independent from the proliferation metagene ( = 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

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-xL 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. Clin Cancer Res; 16(2); 451-60. (C)2010 AACR