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Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease. Although the pathogenesis is poorly understood, evidence suggests that genetic and epigenetic alterations, such as DNA methylation, may play a key role. Bone morphogenetic proteins (BMPs) are members of the transforming growth factor-β (TGF-β) superfamily and are important regulators in IPF. Here we identified BMP endothelial cell precursor-derived regulator (BMPER) as a key regulator of fibroblast activation. BMPER is a secreted glycoprotein that binds directly to BMPs and may regulate TGF-β/BMP signaling, but its role in lung fibrosis is not clear. BMPER is highly expressed in human IPF lung fibroblasts compared to normal lung fibroblasts. Demethylation agent 5′-azacytidine decreased BMPER expression in fibroblasts, and attenuated the invasion and migration of IPF lung fibroblasts. Furthermore, siRNA-mediated reduction of BMPER in the human lung fibroblasts impaired cell migration and invasion. 5′-azacytidine treatment additionally regulated BMPER expression and reduced lung fibrosis in mice in vivo. These findings demonstrate that methylation of specific genes in fibroblasts may offer a new therapeutic strategy for IPF by modulating fibroblast activation.

BackgroundAlterations in epigenetic marks, including methylation or acetylation, are common in human cancers. For many epigenetic pathways, however, direct measures of activity are unknown, making their role in various cancers difficult to assess. Gene expression signatures facilitate the examination of patterns of epigenetic pathway activation across and within human cancer types allowing better understanding of the relationships between these pathways.MethodsWe used Bayesian regression to generate gene expression signatures from normal epithelial cells before and after epigenetic pathway activation. Signatures were applied to datasets from TCGA, GEO, CaArray, ArrayExpress, and the cancer cell line encyclopedia. For TCGA data, signature results were correlated with copy number variation and DNA methylation changes. GSEA was used to identify biologic pathways related to the signatures.ResultsWe developed and validated signatures reflecting downstream effects of enhancer of zeste homolog 2(EZH2), histone deacetylase(HDAC) 1, HDAC4, sirtuin 1(SIRT1), and DNA methyltransferase 2(DNMT2). By applying these signatures to data from cancer cell lines and tumors in large public repositories, we identify those cancers that have the highest and lowest activation of each of these pathways. Highest EZH2 activation is seen in neuroblastoma, hepatocellular carcinoma, small cell lung cancer, and melanoma, while highest HDAC activity is seen in pharyngeal cancer, kidney cancer, and pancreatic cancer. Across all datasets studied, activation of both EZH2 and HDAC4 is significantly underrepresented. Using breast cancer and glioblastoma as examples to examine intrinsic subtypes of particular cancers, EZH2 activation was highest in luminal breast cancers and proneural glioblastomas, while HDAC4 activation was highest in basal breast cancer and mesenchymal glioblastoma. EZH2 and HDAC4 activation are associated with particular chromosome abnormalities: EZH2 activation with aberrations in genes from the TGF and phosphatidylinositol pathways and HDAC4 activation with aberrations in inflammatory and chemokine related genes.ConclusionGene expression patterns can reveal the activation level of epigenetic pathways. Epigenetic pathways define biologically relevant subsets of human cancers. EZH2 activation and HDAC4 activation correlate with growth factor signaling and inflammation, respectively, and represent two distinct states for cancer cells. This understanding may allow us to identify targetable drivers in these cancer subsets.

Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

The Library of Integrated Cellular Signatures (LINCS) project provides comprehensive transcriptome profiling of human cell lines before and after chemical and genetic perturbations. Its L1000 platform utilizes 978 landmark genes to infer the transcript levels of 14,292 genes computationally. Here we conducted the L1000 data quality control analysis by using MCF7, PC3, and A375 cell lines as representative examples. Before perturbations, a promising 80% correlation in transcriptome was observed between L1000‐ and Affymetrix HU133A‐platforms. After library‐based shRNA perturbations, a moderate 30% of differentially expressed genes overlapped between any two selected controls viral vectors using the L1000 platform. The mitogen‐activated protein kinase, vascular endothelial growth factor, and T‐cell receptor pathways were identified as the most significantly shared pathways between chemical and genetic perturbations in cancer cells. In conclusion, L1000 platform is reliable in assessing transcriptome before perturbation. Its response to perturbagens needs to be interpreted with caution. A quality control analysis pipeline of L1000 is recommended before addressing biological questions.

We leverage genomic and biochemical data to identify synergistic drug regimens for breast cancer. In order to study the mechanism of the histone deacetylase (HDAC) inhibitors valproic acid (VPA) and suberoylanilide hydroxamic acid (SAHA) in breast cancer, we generated and validated genomic profiles of drug response using a series of breast cancer cell lines sensitive to each drug. These genomic profiles were then used to model drug response in human breast tumors and show significant correlation between VPA and SAHA response profiles in multiple breast tumor datasets, highlighting their similar mechanism of action. The genes deregulated by VPA and SAHA converge on the cell cycle pathway (Bayes Factor 5.21, and 5.94, respectively, p-value 10−8.6 and 10−9, respectively). In particular, VPA and SAHA upregulate key cyclin-dependent kinase (CDK) inhibitors. In two independent datasets, cancer cells treated with CDK inhibitors have similar gene expression profile changes to the cellular response to HDAC inhibitors. Together, these results led us to hypothesize that VPA and SAHA may interact synergistically with CDK inhibitors such as PD-033299. Experiments show that HDAC and CDK inhibitors have statistically significant synergy in both breast cancer cell lines and primary 3-dimensional cultures of cells from pleural effusions of patients. Therefore, synergistic relationships between HDAC and CDK inhibitors may provide an effective combinatorial regimen for breast cancer. Importantly, these studies provide an example of how genomic analysis of drug response profiles can be used to design rational drug combinations for cancer treatment.

Cigarette smoke produces a molecular “field of injury” in epithelial cells lining the respiratory tract. However, the specific signaling pathways that are altered in the airway of smokers and the signaling processes responsible for the transition from smoking-induced airway damage to lung cancer remain unknown. In this study, we use a genomic approach to study the signaling processes associated with tobacco smoke exposure and lung cancer. First, we developed and validated pathway-specific gene expression signatures in bronchial airway epithelium that reflect activation of signaling pathways relevant to tobacco-exposure including ATM, BCL2, GPX1, NOS2, IKBKB, and SIRT1. Using these profiles and four independent gene expression datasets, we found that SIRT1 activity is significantly up-regulated in cytologically normal bronchial airway epithelial cells from active smokers compared to non-smokers. In contrast, this activity is strikingly down-regulated in non-small cell lung cancer. This pattern of signaling modulation was unique to SIRT1, and down-regulation of SIRT1 activity is confined to tumors from smokers. Decreased activity of SIRT1 was validated using genomic analyses of mouse models of lung cancer and biochemical testing of SIRT1 activity in patient lung tumors. Together, our findings indicate a role of SIRT1 in response to smoke and a potential role in repressing lung cancer. Further, our findings suggest that the airway gene-expression signatures derived in this study can provide novel insights into signaling pathways altered in the “field of inury” induced by tobacco smoke and thus may impact strategies for prevention of tobacco-related lung cancer.

<p>PDF file, 38K, SIRT1 copy number changes in the Cancer Cell Line Encyclopedia (CCLE) dataset. The dataset consists of both gene expression (measured by Affymetrix HGU133 Plus 2.0 microarrays) and DNA copy number data (measured by Affymetrix SNP 6.0 microarrays) across 991 cancer cell lines. A. Across the 174 lung cancer cell lines, copy number values for SIRT1 in the CCLE dataset are in the insignificant range (dark gray, from -1 to 1), indicating that there is not a loss or amplification of the SIRT1 locus in lung tumors; in contrast, the values for MYC, range from -1 to 3 (light gray). B. SIRT1 and C. MYC pathway activation were predicted across the cancer cell lines using the SIRT1 signature and a MYC signature derived in human mammary epithelial cells (17). There is a significant Pearson correlation between MYC pathway activation and MYC copy number (r=0.51, p<<0.001) but not between SIRT1 pathway activation and SIRT1 copy number (r=0.14, p=0.07), suggesting that decreases in SIRT1 pathway activation are not due to copy loss at the locus. The results are derived by performing RMA as described in the manuscript using the ENTREZ Gene CDF file on each of the 14 batches of CCLE CEL files. Pathway predictions were made using either the SIRT1 or MYC signature across each batch of the CCLE dataset, the predictions from each batch were combined, and lung cancer cell lines were separated out for the analyses in B and C.</p>