BACKGROUND Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas. METHODS We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes. RESULTS Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma. CONCLUSIONS The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.)

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3, 9q31.1) and one for endometrioid EOC (5q12.3). We then meta-analysed the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified an additional three loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a novel susceptibility gene for low grade/borderline serous EOC.

Summary Granulocyte-monocyte progenitors (GMPs) and monocyte-dendritic cell progenitors (MDPs) produce monocytes during homeostasis and in response to increased demand during infection. Both progenitor populations are thought to derive from common myeloid progenitors (CMPs), and a hierarchical relationship (CMP-GMP-MDP-monocyte) is presumed to underlie monocyte differentiation. Here, however, we demonstrate that mouse MDPs arose from CMPs independently of GMPs, and that GMPs and MDPs produced monocytes via similar, but distinct, monocyte-committed progenitors. GMPs and MDPs yielded classical (Ly6Chi) monocytes with gene expression signatures that were defined by their origins and impacted their function. GMPs produced a subset of “neutrophil-like” monocytes, whereas MDPs gave rise to a subset of monocytes that yielded monocyte-derived dendritic cells. GMPs and MDPs were also independently mobilized to produce specific combinations of myeloid cell types following the injection of microbial components. Thus, the balance of GMP and MDP differentiation shapes the myeloid cell repertoire during homeostasis and following infection.

Treatment of prostate cancer (PC) by androgen suppression promotes the emergence of aggressive variants that are androgen receptor- (AR-) independent. Here we identify the transcription factor ONECUT2 (OC2) as a master regulator of AR networks in metastatic castration-resistant prostate cancer (mCRPC). OC2 acts as a survival factor in mCRPC models, suppresses the AR transcriptional program by direct regulation of AR target genes and the AR licensing factor FOXA1, and activates genes associated with neural differentiation and progression to lethal disease. OC2 appears active in a substantial subset of human prostate adenocarcinoma and neuroendocrine tumors. Inhibition of OC2 by a newly identified small molecule suppresses metastasis in mice. These findings suggest that OC2 displaces AR-dependent growth and survival mechanisms in many cases where AR remains expressed, but where its activity is bypassed. OC2 is also a potential drug target in the metastatic phase of aggressive PC.

Purpose To assess the clinical significance of the interaction between estrogen and Runx2 signaling, previously demonstrated in vitro. Experimental design MCF7/Rx2dox BCa cells were treated with estradiol and/or doxycycline to induce Runx2, and global gene expression was profiled to define genes regulated by estradiol, Runx2, or both. Anchorage-independent growth was assessed by soft agar colony formation assays. Expression of gene-sets defined using the MCF7/Rx2dox system was analyzed in pre- and on-treatment biopsies from hormone receptor-positive patients undergoing neo-adjuvant letrozole treatment in two independent studies, and short-term changes in gene expression were correlated with tumor size reduction or Ki67 index at surgery. Results Reflecting its oncogenic property, estradiol strongly promoted soft agar colony formation, whereas Runx2 blocked this process suggesting tumor suppressor property. Transcriptome analysis of MCF7/Rx2dox cells treated with estradiol and/or doxycycline demonstrated reciprocal attenuation of Runx2 and estrogen signaling. Correspondingly in BCa tumors, expression of estradiol- and Runx2-regulated genes was inversely correlated; and, letrozole increased expression of Runx2-stimulated genes as defined in the MCF7/Rx2dox model. Of particular interest was a gene-set upregulated by estradiol and downregulated by Runx2 in vitro; its short-term response to letrozole treatment associated with tumor size reduction and Ki67 index at surgery better than other estradiol-regulated gene-sets. Conclusion This work provides clinical evidence for the importance of antagonism between Runx2 and E2 signaling in BCa. Likely sensing the tension between them, letrozole responsiveness of a genomic node, positively regulated by estradiol and negatively regulated by Runx2 in vitro, best correlated with the clinical efficacy of letrozole treatment.

Recent studies indicate that DNA methylation can be used to identify changes at transcriptional enhancers and other cis-regulatory elements in primary human samples. A systematic approach to inferring gene regulatory networks has been provided by the R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships), which first identifies DNA methylation changes in distal regulatory elements and correlates these with the expression of nearby genes to identify direct transcriptional targets. Next, ELMER performs a transcription factor binding motif analysis and integrates with expression profiling of all human transcription factors, to identify master regulatory TFs and place each differentially methylated regulatory element into the context of an altered gene regulatory network (GRN).Here we present a completely updated version of the package (ELMER v. 2.0), which uses the latest Bioconductor data structures including the popular MultiAsssayExperiment, supports multiple reference genome assemblies as well as the DNA methylation platforms Infinium MethylationEPIC and Infinium HumanMethylation450, and provides a "Supervised" analysis mode for paired sample study designs (such as treated vs. untreated replicate samples). It also supports data import from the new NCI Genomic Data Commons (GDC) database. The new version is substantially re-written, improving stability, performance, and extensibility. It also uses improved databases for transcription factor binding domain families and binding motif specificities, and has newly designed output plots for publication-quality figures.Below, we describe the methods and new features of ELMER v. 2.0 and present two use case demonstrating how the tool can be used to analyze TCGA data in either Unsupervised or Supervised mode. ELMER (v2.0.0) is available as an R/Bioconductor package at

Genome annotation is critical to understand the function of disease variants, especially for clinical applications. To meet this need there are segmentations available from public consortia reflecting varying unsupervised approaches to functional annotation based on epigenetics data, but there remains a need for transparent, reproducible, and easily interpreted genomic maps of the functional biology of chromatin. We introduce a new methodological framework for defining a combinatorial epigenomic model of chromatin state on a web database, StateHub. In addition, we created an annotation tool for bioconductor, StatePaintR, which accesses these models and uses them to rapidly (on the order of seconds) produce chromatin state segmentations in standard genome browser formats. Annotations are fully documented with change history and versioning, authorship information, and original source files. StatePaintR calculates ranks for each state from next-gen sequencing peak statistics, facilitating variant prioritization, enrichment testing, and other types of quantitative analysis. StateHub hosts annotation tracks for major public consortia as a resource, and allows users to submit their own alternative models.

Genome annotation is critical to understand the function of disease variants, especially for clinical applications. To meet this need there are segmentations available from public consortia reflecting varying unsupervised approaches to functional annotation based on epigenetics data, but there remains a need for transparent, reproducible, and easily interpreted genomic maps of the functional biology of chromatin. We introduce a new methodological framework for defining a combinatorial epigenomic model of chromatin state on a web database, StateHub. In addition, we created an annotation tool for bioconductor, StatePaintR, which accesses these models and uses them to rapidly (on the order of seconds) produce chromatin state segmentations in standard genome browser formats. Annotations are fully documented with change history and versioning, authorship information, and original source files. StatePaintR calculates ranks for each state from next-gen sequencing peak statistics, facilitating variant prioritization, enrichment testing, and other types of quantitative analysis. StateHub hosts annotation tracks for major public consortia as a resource, and allows users to submit their own alternative models.