BCAR4 induces antioestrogen resistance but sensitises breast cancer to lapatinib

BACKGROUND: High BCAR4 and ERBB2 mRNA levels in primary breast cancer associate with tamoxifen resistance and poor patient outcome. We determined whether BCAR4 expression sensitises breast cancer cells to lapatinib, and identifies a subgroup of patients who possibly may benefit from ERBB2-targeted therapies despite having tumours with low ERBB2 expression. METHODS: Proliferation assays were applied to determine the effect of BCAR4 expression on lapatinib treatment. Changes in cell signalling were quantified with reverse-phase protein microarrays. Quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) of ERBB2 and BCAR4 was performed in 1418 primary breast cancers. Combined BCAR4 and ERBB2 mRNA levels were evaluated for association with progression-free survival (PFS) in 293 oestrogen receptor-alpha (ER)-positive patients receiving t RESULTS: BCAR4 expression strongly sensitised ZR-75-1 and MCF7 breast cancer cells to the combination of lapatinib and antioestrogens. Lapatinib interfered with phosphorylation of ERBB2 and its downstream mediators AKT, FAK, SHC, STAT5, and STAT6. Reverse transcriptase-PCR analysis showed that 27.6% of the breast cancers were positive for BCAR4 and 22% expressed also low levels of ERBB2. The clinical significance of combining BCAR4 and ERBB2 mRNA status was underscored by the finding that the gr CONCLUSION: This study shows that BCAR4 expression identifies a subgroup of ER-positive breast cancer patients without overexpression of ERBB2 who have a poor outcome and might benefit from combined ERBB2-targeted and antioestrogen therapy. British Journal of Cancer (2012) 107, 947-955. doi:10.1038/bjc.2012.351 www.bjcancer.com Published online 14 August 2012 (C) 2012 Cancer Research U

Systems Analysis of the NCI-60 Cancer Cell Lines by Alignment of Protein Pathway Activation Modules with "-OMIC" Data Fields and Therapeutic Response Signatures

The NCI-60 cell line set is likely the most molecularly profiled set of human tumor cell lines in the world. However, a critical missing component of previous analyses has been the inability to place the massive amounts of "-omic" data in the context of functional protein signaling networks, which often contain many of the drug targets for new targeted therapeutics. We used reverse-phase protein array (RPPA) analysis to measure the activation/ phosphorylation state of 135 proteins, with a total analysis of nearly 200 key protein isoforms involved in cell proliferation, survival, migration, adhesion, etc., in all 60 cell lines. We aggregated the signaling data into biochemical modules of interconnected kinase substrates for 6 key cancer signaling pathways: AKT, mTOR, EGF receptor (EGFR), insulin-like growth factor-1 receptor (IGF-1R), integrin, and apoptosis signaling. The net activation state of these protein network modules was correlated to available individual protein, phosphoprotein, mutational, metabolomic, miRNA, transcriptional, and drug sensitivity data. Pathway activation mapping identified reproducible and distinct signaling cohorts that transcended organ-type distinctions. Direct correlations with the protein network modules involved largely protein phosphorylation data but we also identified direct correlations of signaling networks with metabolites, miRNA, and DNA data. The integration of protein activation measurements into biochemically interconnected modules provided a novel means to align the functional protein architecture with multiple "-omic" data sets and therapeutic response correlations. This approach may provide a deeper understanding of how cellular biochemistry defines therapeutic response. Such "-omic" portraits could inform rational anticancer agent screenings and drive personalized therapeutic approache

Do serum biomarkers really measure breast cancer?

Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.United States. Dept. of Defense. Breast Cancer Research Program (Grant No. W81XWH-05-1-0292)National Institutes of Health (U.S.) (R01 CA-112437-01)National Institutes of Health (U.S.) (NIH CA 84955

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples

S100A7 (psoriasin) expression is associated with aggressive features and alteration of Jab1 in ductal carcinoma in situ of the breast

INTRODUCTION: The S100A7 (psoriasin) gene is highly expressed in ductal carcinoma in situ (DCIS) of the breast and can be downregulated in invasive carcinoma. Persistent S100A7 expression in invasive carcinoma is associated with a worse prognosis, and this effect may be mediated in part through interaction with the multifunctional cell signaling protein Jab1. METHODS: In order to investigate the relationship between S100A7 and progression from DCIS to invasive carcinoma, we studied S100A7 expression in 136 patients with DCIS (including 46 patients with associated invasive carcinoma) by immunohistochemistry. RESULTS: S100A7 expression was present in 63 out of 136 (46%) of DCIS lesions and was associated with estrogen receptor negative status (P = 0.0002), higher nuclear grade (P < 0.0001), necrosis (P < 0.0001) and inflammation (P < 0.0001). S100A7 status was no different between DCIS with and DCIS without an invasive component, but higher levels of S100A7 were present in DCIS associated with invasive carcinoma (P < 0.004). Analysis of a subset of cases showed that S100A7 expression was also associated with an increase in nuclear Jab1 (n = 43; P = 0.0019) and reduced p27(kip1 )(n = 47; P = 0.0168). In cases of DCIS associated with invasive carcinoma, there was also a significant reduction in S100A7 between in situ and invasive components (n = 46; P < 0.0001). In pure DCIS cases treated by local excision, there was no difference in frequency of S100A7 expression between patients with recurrence of DCIS (n = 9) and those without (n = 36). CONCLUSION: The findings reported here suggest that, although S100A7 may not be a marker for recurrence of DCIS, it is associated with poor prognostic markers in DCIS and may influence progression of breast carcinoma through its interaction with and influence on Jab1

Discovery of New Molecular Subtypes in Oesophageal Adenocarcinoma

A large number of patients suffering from oesophageal adenocarcinomas do not respond to conventional chemotherapy; therefore, it is necessary to identify new predictive biomarkers and patient signatures to improve patient outcomes and therapy selections. We analysed 87 formalin-fixed and paraffin-embedded (FFPE) oesophageal adenocarcinoma tissue samples with a reverse phase protein array (RPPA) to examine the expression of 17 cancer-related signalling molecules. Protein expression levels were analysed by unsupervised hierarchical clustering and correlated with clinicopathological parameters and overall patient survival. Proteomic analyses revealed a new, very promising molecular subtype of oesophageal adenocarcinoma patients characterised by low levels of the HSP27 family proteins and high expression of those of the HER family with positive lymph nodes, distant metastases and short overall survival. After confirmation in other independent studies, our results could be the foundation for the development of a Her2-targeted treatment option for this new patient subgroup of oesophageal adenocarcinoma

Prognostic molecular markers in early breast cancer

A multitude of molecules involved in breast cancer biology have been studied as potential prognostic markers. In the present review we discuss the role of established molecular markers, as well as potential applications of emerging new technologies. Those molecules used routinely to make treatment decisions in patients with early-stage breast cancer include markers of proliferation (e.g. Ki-67), hormone receptors, and the human epidermal growth factor receptor 2. Tumor markers shown to have prognostic value but not used routinely include cyclin D(1 )and cyclin E, urokinase-like plasminogen activator/plasminogen activator inhibitor, and cathepsin D. The level of evidence for other molecular markers is lower, in part because most studies were retrospective and not adequately powered, making their findings unsuitable for choosing treatments for individual patients. Gene microarrays have been successfuly used to classify breast cancers into subtypes with specific gene expression profiles and to evaluate prognosis. RT-PCR has also been used to evaluate expression of multiple genes in archival tissue. Proteomics technologies are in development