Gene expression profiles among murine strains segregate with distinct differences in the progression of radiation-induced lung disease.

Molecular mechanisms underlying development of acute pneumonitis and/or late fibrosis following thoracic irradiation remain poorly understood. Here, we hypothesize that heterogeneity in disease progression and phenotypic expression of radiation-induced lung disease (RILD) across murine strains presents an opportunity to better elucidate mechanisms driving tissue response toward pneumonitis and/or fibrosis. Distinct differences in disease progression were observed in age- and sex-matched CBA/J, C57L/J and C57BL/6J mice over 1 year after graded doses of whole-thorax lung irradiation (WTLI). Separately, comparison of gene expression profiles in lung tissue 24 h post-exposure demonstrated \u3e5000 genes to be differentially expressed (P\u3c0.01; \u3etwofold change) between strains with early versus late onset of disease. An immediate divergence in early tissue response between radiation-sensitive and -resistant strains was observed. In pneumonitis-prone C57L/J mice, differentially expressed genes were enriched in proinflammatory pathways, whereas in fibrosis-prone C57BL/6J mice, genes were enriched in pathways involved in purine and pyrimidine synthesis, DNA replication and cell division. At 24 h post-WTLI, different patterns of cellular damage were observed at the ultrastructural level among strains but microscopic damage was not yet evident under light microscopy. These data point toward a fundamental difference in patterns of early pulmonary tissue response to WTLI, consistent with the macroscopic expression of injury manifesting weeks to months after exposure. Understanding the mechanisms underlying development of RILD might lead to more rational selection of therapeutic interventions to mitigate healthy tissue damage

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

PROGTar: A database of Prognostically Inversely Correlated miRNAs and Genes (PICs) in multiple cancers

ABSTRACT PROGTar is a database of Prognostically Inversely Correlated miRNA-mRNA pairs (PIC’s) in 23 cancer types. Partner miRNA and mRNA in a PIC show inverse correlation of expression and opposite hazards. We analyzed miRNA and mRNA expression data downloaded from The Cancer Genome Atlas (TCGA) in a 3 step approach to identify PICs in different cancer types. In first step we performed correlation analysis between miRNAs and mRNAs for each cancer type. This was followed by performing hazard analysis separately for miRNAs and mRNAs using expression data and survival related clinical variables. In the third step we merged the correlation and hazard result sets. Resultant miRNA and mRNA pairs were filtered to retain only pairs that had negative correlation between miRNA and mRNA expression and opposite hazards for miRNA and mRNA, at a statistically significant level (p Results from our pan cancer analysis are available on the web based application PROGTar. Users can search for miRNA/mRNA of interest on the database to find inversely correlated partners. Users can also create prognostic plots for the PICs of interest. Prognostic plots created with PROGTar show arms for high and low expression of target molecule and its corresponding partner in the PIC, bifurcated at median of expression. The plots also show arms for a combined prognostic signature calculated using expression levels of both partners in the PIC. The application is available freely for non-commercial use at www.xvm145.jefferson.edu/progtarhttps://jdc.jefferson.edu/pacbposters/1000/thumbnail.jp

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

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

In-silico identification of Prognostically Inversely Correlated miRNAs and mRNAs (PIC’s) in multiple cancers

Despite numerous methods available to identify potential mRNA targets for miRNAs, prognostic relationship of these molecules in diseases like cancers where deregulation of gene expression is a major pathogenic factor, has not yet been emphasized. We performed in-silico identification of prognostically inversely correlated miRNA - mRNA pairs (PIC’s) in multiple cancers using expression data from The Cancer Genome Atlas. Partners in a PIC show inverse correlation of expression and opposite hazard implication. Using a three step approach, we identified a total of 1,253,443 PIC’s from 23 cancer types, several of which have previously been shown to have a predicted or experimentally validated relationship. A maximum 375,621 PICs were identified in Lower Grade Gliomas, while a minimum 300 PICs were identified in Prostate adenocarcinoma. Four miRNA-mRNA pairs were identified as PICs in 7 different cancer types. Two miRNA-mRNA pairs were identified as PICs in 5 different cancer types where the mRNA is also a validated target of miRNA. Organ specific analysis was performed to identify PICs common to cancers from same or related tissue of origin. We have also developed a database PROGTar for hosting our analysis results. PROGTar is available freely for non-commercial use at www.xvm145.jefferson.edu/progtar. We believe our method and analysis results will provide a novel prognostically relevant, pan-cancer perspective to study of miRNA-mRNA interactions and miRNA target validation.http://jdc.jefferson.edu/pacbposters/1001/thumbnail.jp

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