scholarly journals Intrinsic breast cancer subtypes defined by estrogen receptor signalling—prognostic relevance of progesterone receptor loss

2013 ◽  
Vol 26 (9) ◽  
pp. 1161-1171 ◽  
Author(s):  
Lisa Braun ◽  
Friederike Mietzsch ◽  
Petra Seibold ◽  
Andreas Schneeweiss ◽  
Peter Schirmacher ◽  
...  
2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dadi Jiang ◽  
Brandon Turner ◽  
Jie Song ◽  
Ruijiang Li ◽  
Maximilian Diehn ◽  
...  

Purpose Triple-negative breast cancers (TNBCs) are associated with a worse prognosis and patients with TNBC have fewer therapeutic options than patients with non-TNBC. Recently, the IRE1α-XBP1 branch of the unfolded protein response (UPR) was implicated in TNBC prognosis on the basis of a relatively small patient population, suggesting the diagnostic and therapeutic value of this pathway in TNBCs. In addition, the IRE1α-XBP1 and hypoxia-induced factor 1 α (HIF1α) pathways have been identified as interacting partners in TNBC, suggesting a novel mechanism of regulation. To comprehensively evaluate and validate these findings, we investigated the relative activities and relevance to patient survival of the UPR and HIF1α pathways in different breast cancer subtypes in large populations of patients. Materials and Methods We performed a comprehensive analysis of gene expression and survival data from large cohorts of patients with breast cancer. The patients were stratified based on the average expression of the UPR or HIF1α gene signatures. Results We identified a strong positive association between the XBP1 gene signature and estrogen receptor–positive status or the HIF1α gene signature, as well as the predictive value of the XBP1 gene signature for survival of patients who are estrogen receptor negative, or have TNBC or HER2+. In contrast, another important UPR branch, the ATF4/CHOP pathway, lacks prognostic value in breast cancer in general. Activity of the HIF1α pathway is correlated with patient survival in all the subtypes evaluated. Conclusion These findings clarify the relevance of the UPR pathways in different breast cancer subtypes and underscore the potential therapeutic importance of the IRE1α-XBP1 branch in breast cancer treatment.


2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ashish Saini ◽  
Jingyu Hou ◽  
Wanlei Zhou

Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposedRRHGEalgorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples.Results. The evaluation on real breast cancer samples showed that ourRRHGEalgorithm achieved higher classification accuracy than the existing approaches.


2013 ◽  
Vol 27 (4) ◽  
pp. 554-561 ◽  
Author(s):  
Linda P Feeley ◽  
Anna M Mulligan ◽  
Dushanthi Pinnaduwage ◽  
Shelley B Bull ◽  
Irene L Andrulis

2014 ◽  
Vol 16 (1) ◽  
Author(s):  
Melissa Rotunno ◽  
Xuezheng Sun ◽  
Jonine Figueroa ◽  
Mark E Sherman ◽  
Montserrat Garcia-Closas ◽  
...  

2010 ◽  
Vol 128 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Marcel Smid ◽  
Marlous Hoes ◽  
Anieta M. Sieuwerts ◽  
Stefan Sleijfer ◽  
Yi Zhang ◽  
...  

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