HOXB7 Is an ERα Cofactor in the Activation of HER2 and Multiple ER Target Genes Leading to Endocrine Resistance.

UNLABELLED: Why breast cancers become resistant to tamoxifen despite continued expression of the estrogen receptor-α (ERα) and what factors are responsible for high HER2 expression in these tumors remains an enigma. HOXB7 chromatin immunoprecipitation analysis followed by validation showed that HOXB7 physically interacts with ERα, and that the HOXB7-ERα complex enhances transcription of many ERα target genes, including HER2. Investigating strategies for controlling HOXB7, our studies revealed that MYC, stabilized via phosphorylation mediated by EGFR-HER2 signaling, inhibits transcription of miR-196a, a HOXB7 repressor. This leads to increased expression of HOXB7, ER target genes, and HER2. Repressing MYC using small-molecule inhibitors reverses these events and causes regression of breast cancer xenografts. The MYC-HOXB7-HER2 signaling pathway is eminently targetable in endocrine-resistant breast cancer. SIGNIFICANCE: HOXB7 acts as an ERα cofactor regulating a myriad of ER target genes, including HER2, in tamoxifen-resistant breast cancer. HOXB7 expression is controlled by MYC via transcriptional regulation of the HOXB7 repressor miR-196a; consequently, antagonists of MYC cause reversal of selective ER modulator resistance both in vitro and in vivo.

Predicting early brain metastases based on clinicopathological factors and gene expression analysis in advanced HER2-positive breast cancer patients.

The overexpression or amplification of the human epidermal growth factor receptor 2 gene (HER2/neu) is associated with high risk of brain metastasis (BM). The identification of patients at highest immediate risk of BM could optimize screening and facilitate interventional trials. We performed gene expression analysis using complementary deoxyribonucleic acid-mediated annealing, selection, extension and ligation and real-time quantitative reverse transcription PCR (qRT-PCR) in primary tumor samples from two independent cohorts of advanced HER2 positive breast cancer patients. Additionally, we analyzed predictive relevance of clinicopathological factors in this series. Study group included discovery Cohort A (84 patients) and validation Cohort B (75 patients). The only independent variables associated with the development of early BM in both cohorts were the visceral location of first distant relapse [Cohort A: hazard ratio (HR) 7.4, 95 % CI 2.4-22.3; p < 0.001; Cohort B: HR 6.1, 95 % CI 1.5-25.6; p = 0.01] and the lack of trastuzumab administration in the metastatic setting (Cohort A: HR 5.0, 95 % CI 1.4-10.0; p = 0.009; Cohort B: HR 10.0, 95 % CI 2.0-100.0; p = 0.008). A profile including 13 genes was associated with early (≤36 months) symptomatic BM in the discovery cohort. This was refined by qRT-PCR to a 3-gene classifier (RAD51, HDGF, TPR) highly predictive of early BM (HR 5.3, 95 % CI 1.6-16.7; p = 0.005; multivariate analysis). However, predictive value of the classifier was not confirmed in the independent validation Cohort B. The presence of visceral metastases and the lack of trastuzumab administration in the metastatic setting apparently increase the likelihood of early BM in advanced HER2-positive breast cancer.

PROGgeneV2: enhancements on the existing database.

BACKGROUND: We recently published PROGgene, a tool that can be used to study prognostic implications of genes in various cancers. The first version of the tool had several areas for improvement. In this paper we present some major enhancements we have made on the existing tool in the new version, PROGgeneV2. RESULTS: In PROGgeneV2, we have made several modifications to enhance survival analysis capability of the tool. First, we have increased the repository of public studies catalogued in our tool by almost two folds. We have also added additional functionalities to perform survival analysis in a variety of new ways. Survival analysis can now be performed on a) single genes b) multiple genes as a signature, c) ratio of expression of two genes, and d) curated/published gene signatures in new version. Users can now also adjust the survival analysis models for available covariates. Users can study prognostic implications of entire gene signatures in different cancer types, which are searchable by keywords. Also, unique to our tool, in the new version, users will be able to upload and use their own datasets to perform survival analysis on genes of interest. CONCLUSIONS: We believe, like its predecessor, PROGGeneV2 will continue to be useful for the scientific community for formulating research hypotheses and designing mechanistic studies. With added datasets PROGgeneV2 is the most comprehensive survival analysis tool available. PROGgeneV2 is available at http://www.compbio.iupui.edu/proggene.

PROGgene: gene expression based survival analysis web application for multiple cancers.

BACKGROUND: Identification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer. DESCRIPTION: We have created a web application that can be used for studying prognostic implications of mRNA biomarkers in a variety of cancers. We have compiled data from public repositories such as GEO, EBI Array Express and The Cancer Genome Atlas for creating this tool. With 64 patient series from 18 cancer types in our database, this tool provides the most comprehensive resource available for survival analysis to date. The tool is called PROGgene and it is available at http://www.compbio.iupui.edu/proggene. CONCLUSIONS: We present this tool as a hypothesis generation tool for researchers to identify potential prognostic mRNA biomarkers to follow up with further research. For this reason, we have kept the web application very simple and straightforward. We believe this tool will be useful in accelerating biomarker discovery in cancer and quickly providing results that may indicate disease-specific prognostic value of specific biomarkers.

Relationship between differential hepatic microRNA expression and decreased hepatic cytochrome P450 3A activity in cirrhosis.

BACKGROUND AND AIM: Liver cirrhosis is associated with decreased hepatic cytochrome P4503A (CYP3A) activity but the pathogenesis of this phenomenon is not well elucidated. In this study, we examined if certain microRNAs (miRNA) are associated with decreased hepatic CYP3A activity in cirrhosis. METHODS: Hepatic CYP3A activity and miRNA microarray expression profiles were measured in cirrhotic (n=28) and normal (n=12) liver tissue. Hepatic CYP3A activity was measured via midazolam hydroxylation in human liver microsomes. Additionally, hepatic CYP3A4 protein concentration and the expression of CYP3A4 mRNA were measured. Analyses were conducted to identify miRNAs which were differentially expressed between two groups but also were significantly associated with lower hepatic CYP3A activity. RESULTS: Hepatic CYP3A activity in cirrhotic livers was 1.7-fold lower than in the normal livers (0.28 ± 0.06 vs. 0.47 ± 0.07mL* min(-1)*mg protein(-1) (mean ± SEM), P=0.02). Six microRNAs (miR-155, miR-454, miR-582-5p, let-7f-1*, miR-181d, and miR-500) had >1.2-fold increase in cirrhotic livers and also had significant negative correlation with hepatic CYP3A activity (range of r = -0.44 to -0.52, P <0.05). Notably, miR-155, a known regulator of liver inflammation, had the highest fold increase in cirrhotic livers (2.2-fold, P=4.16E-08) and significantly correlated with hepatic CYP3A activity (r=-0.50, P=0.017). The relative expression (2(-ΔΔCt) mean ± SEM) of hepatic CYP3A4 mRNA was significantly higher in cirrhotic livers (21.76 ± 2.65 vs. 5.91 ± 1.29, P=2.04E-07) but their levels did not significantly correlate with hepatic CYP3A activity (r=-0.43, P=0.08). CONCLUSION: The strong association between certain miRNAs, notably miR-155, and lower hepatic CYP3A activity suggest that altered miRNA expression may regulate hepatic CYP3A activity.

HOXB13 mediates tamoxifen resistance and invasiveness in human breast cancer by suppressing ERα and inducing IL-6 expression.

Most breast cancers expressing the estrogen receptor α (ERα) are treated successfully with the receptor antagonist tamoxifen (TAM), but many of these tumors recur. Elevated expression of the homeodomain transcription factor HOXB13 correlates with TAM-resistance in ERα-positive (ER+) breast cancer, but little is known regarding the underlying mechanism. Our comprehensive evaluation of HOX gene expression using tiling microarrays, with validation, showed that distant metastases from TAM-resistant patients also displayed high HOXB13 expression, suggesting a role for HOXB13 in tumor dissemination and survival. Here we show that HOXB13 confers TAM resistance by directly downregulating ERα transcription and protein expression. HOXB13 elevation promoted cell proliferation in vitro and growth of tumor xenografts in vivo. Mechanistic investigations showed that HOXB13 transcriptionally upregulated interleukin (IL)-6, activating the mTOR pathway via STAT3 phosphorylation to promote cell proliferation and fibroblast recruitment. Accordingly, mTOR inhibition suppressed fibroblast recruitment and proliferation of HOXB13-expressing ER+ breast cancer cells and tumor xenografts, alone or in combination with TAM. Taken together, our results establish a function for HOXB13 in TAM resistance through direct suppression of ERα and they identify the IL-6 pathways as mediator of disease progression and recurrence.

A gene signature to determine metastatic behavior in thymomas.

PURPOSE: Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management. METHODS: qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75). RESULTS: A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036). CONCLUSION: A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.

PROGmiR: a tool for identifying prognostic miRNA biomarkers in multiple cancers using publicly available data.

BACKGROUND: Identification of prognostic biomarkers is hallmark of cancer genomics. Since miRNAs regulate expression of multiple genes, they act as potent biomarkers in several cancers. Identification of miRNAs that are prognostically important has been done sporadically, but no resource is available till date that allows users to study prognostics of miRNAs of interest, utilizing the wealth of available data, in major cancer types. DESCRIPTION: In this paper, we present a web based tool that allows users to study prognostic properties of miRNAs in several cancer types, using publicly available data. We have compiled data from Gene Expression Omnibus (GEO), and recently developed "The Cancer Genome Atlas (TCGA)", to create this tool. The tool is called "PROGmiR" and it is available at http://www.compbio.iupui.edu/progmir. Currently, our tool can be used to study overall survival implications for approximately 1050 human miRNAs in 16 major cancer types. CONCLUSIONS: We believe this resource, as a hypothesis generation tool, will be helpful for researchers to link miRNA expression with cancer outcome and to design mechanistic studies. We studied performance of our tool using identified miRNA biomarkers from published studies. The prognostic plots created using our tool for specific miRNAs in specific cancer types corroborated with the findings in the studies.