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N6-Methyladenosine (m6A) is the most abundant modification of mammalian mRNAs. RNA methylation fine tunes RNA stability and translation, altering cell fate. The fat mass- and obesity-associated protein (FTO) is an m6A demethylase with oncogenic properties in leukemia. Here, we show that FTO expression is suppressed in ovarian tumors and cancer stem cells (CSC). FTO inhibited the self-renewal of ovarian CSC and suppressed tumorigenesis in vivo, both of which required FTO demethylase activity. Integrative RNA sequencing and m6A mapping analysis revealed significant transcriptomic changes associated with FTO overexpression and m6A loss involving stem cell signaling, RNA transcription, and mRNA splicing pathways. By reducing m6A levels at the 3′UTR and the mRNA stability of two phosphodiesterase genes (PDE1C and PDE4B), FTO augmented second messenger 3′, 5′-cyclic adenosine monophosphate (cAMP) signaling and suppressed stemness features of ovarian cancer cells. Our results reveal a previously unappreciated tumor suppressor function of FTO in ovarian CSC mediated through inhibition of cAMP signaling. Significance: A new tumor suppressor function of the RNA demethylase FTO implicates m6A RNA modifications in the regulation of cyclic AMP signaling involved in stemness and tumor initiation.

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

PURPOSE Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression–based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC). METHODS Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets. RESULTS Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant ( P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms. CONCLUSION Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

How mammalian neuronal identity is progressively acquired and reinforced during development is not understood. We have previously shown that loss of RP58 (ZNF238 or ZBTB18), a BTB/POZ-zinc finger-containing transcription factor, in the mouse brain leads to microcephaly, corpus callosum agenesis, and cerebellum hypoplasia and that it is required for normal neuronal differentiation. The transcriptional programs regulated by RP58 during this process are not known. Here, we report for the first time that in embryonic mouse neocortical neurons a complex set of genes normally expressed in other cell types, such as those from mesoderm derivatives, must be actively repressed in vivo and that RP58 is a critical regulator of these repressed transcriptional programs. Importantly, gene set enrichment analysis (GSEA) analyses of these transcriptional programs indicate that repressed genes include distinct sets of genes significantly associated with glioma progression and/or pluripotency. We also demonstrate that reintroducing RP58 in glioma stem cells leads not only to aspects of neuronal differentiation but also to loss of stem cell characteristics, including loss of stem cell markers and decrease in stem cell self-renewal capacities. Thus, RP58 acts as an in vivo master guardian of the neuronal identity transcriptome, and its function may be required to prevent brain disease development, including glioma progression.

Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.

Recent work has highlighted the tumor microenvironment as a central player in cancer. In particular, interactions between tumor and immune cells may help drive the development of brain tumors such as glioblastoma multiforme (GBM). Despite significant research into the molecular classification of glioblastoma, few studies have characterized in a comprehensive manner the immune infiltrate in situ and within different GBM subtypes.In this study, we use an unbiased, automated immunohistochemistry-based approach to determine the immune phenotype of the four GBM subtypes (classical, mesenchymal, neural and proneural) in a cohort of 98 patients. Tissue Micro Arrays (TMA) were stained for CD20 (B lymphocytes), CD5, CD3, CD4, CD8 (T lymphocytes), CD68 (microglia), and CD163 (bone marrow derived macrophages) antibodies. Using automated image analysis, the percentage of each immune population was calculated with respect to the total tumor cells. Mesenchymal GBMs displayed the highest percentage of microglia, macrophage, and lymphocyte infiltration. CD68+ and CD163+ cells were the most abundant cell populations in all four GBM subtypes, and a higher percentage of CD163+ cells was associated with a worse prognosis. We also compared our results to the relative composition of immune cell type infiltration (using RNA-seq data) across TCGA GBM tumors and validated our results obtained with immunohistochemistry with an external cohort and a different method. The results of this study offer a comprehensive analysis of the distribution and the infiltration of the immune components across the four commonly described GBM subgroups, setting the basis for a more detailed patient classification and new insights that may be used to better apply or design immunotherapies for GBM.

Patients with metastatic cancer experience a severe loss of skeletal muscle mass and function known as cachexia. Cachexia is associated with poor prognosis and accelerated death in patients with cancer, yet its underlying mechanisms remain poorly understood. Here, we identify the metal-ion transporter ZRT- and IRT-like protein 14 (ZIP14) as a critical mediator of cancer-induced cachexia. ZIP14 is upregulated in cachectic muscles of mice and in patients with metastatic cancer and can be induced by TNF-α and TGF-β cytokines. Strikingly, germline ablation or muscle-specific depletion of Zip14 markedly reduces muscle atrophy in metastatic cancer models. We find that ZIP14-mediated zinc uptake in muscle progenitor cells represses the expression of MyoD and Mef2c and blocks muscle-cell differentiation. Importantly, ZIP14-mediated zinc accumulation in differentiated muscle cells induces myosin heavy chain loss. These results highlight a previously unrecognized role for altered zinc homeostasis in metastatic cancer-induced muscle wasting and implicate ZIP14 as a therapeutic target for its treatment.

Purpose: The oncogenic miR-155 is upregulated in many human cancers, and its expression is increased in more aggressive and therapy-resistant tumors, but the molecular mechanisms underlying miR-155-induced therapy resistance are not fully understood. The main objectives of this study were to determine the role of miR-155 in resistance to chemotherapy and to evaluate anti-miR-155 treatment to chemosensitize tumors. Experimental Design: We performed in vitro studies on cell lines to investigate the role of miR-155 in therapy resistance. To assess the effects of miR-155 inhibition on chemoresistance, we used an in vivo orthotopic lung cancer model of athymic nude mice, which we treated with anti-miR-155 alone or in combination with chemotherapy. To analyze the association of miR-155 expression and the combination of miR-155 and TP53 expression with cancer survival, we studied 956 patients with lung cancer, chronic lymphocytic leukemia, and acute lymphoblastic leukemia. Results: We demonstrate that miR-155 induces resistance to multiple chemotherapeutic agents in vitro, and that downregulation of miR-155 successfully resensitizes tumors to chemotherapy in vivo. We show that anti-miR-155-DOPC can be considered non-toxic in vivo. We further demonstrate that miR-155 and TP53 are linked in a negative feedback mechanism and that a combination of high expression of miR-155 and low expression of TP53 is significantly associated with shorter survival in lung cancer. Conclusions: Our findings support the existence of an miR-155/TP53 feedback loop, which is involved in resistance to chemotherapy and which can be specifically targeted to overcome drug resistance, an important cause of cancer-related death. Clin Cancer Res; 23(11); 2891–904. ©2016 AACR.

Protein ubiquitination is mediated sequentially by ubiquitin activating enzyme E1, ubiquitin conjugating enzyme E2 and ubiquitin ligase E3. Uba1 was thought to be the only E1 until the recent identification of Uba6. To differentiate the biological functions of Uba1 and Uba6, we applied an orthogonal ubiquitin transfer (OUT) technology to profile their ubiquitination targets in mammalian cells. By expressing pairs of an engineered ubiquitin and engineered Uba1 or Uba6 that were generated for exclusive interactions, we identified 697 potential Uba6 targets and 527 potential Uba1 targets with 258 overlaps. Bioinformatics analysis reveals substantial differences in pathways involving Uba1- and Uba6-specific targets. We demonstrate that polyubiquitination and proteasomal degradation of ezrin and CUGBP1 require Uba6, but not Uba1, and that Uba6 is involved in the control of ezrin localization and epithelial morphogenesis. These data suggest that distinctive substrate pools exist for Uba1 and Uba6 that reflect non-redundant biological roles for Uba6., The transfer of ubiquitin (UB) to cellular targets is mediated sequentially by three groups of enzymes, UB activating enzyme (E1), UB conjugating enzyme (E2) and UB ligase (E3). Here the authors provide evidence that the two mammalian E1 enzymes, Uba1 and Uba6, exert biologically distinct functions.

Background In cystic fibrosis (CF), impaired immune cell responses, driven by the dysfunctional CF transmembrane conductance regulator (CFTR) gene, may determine the disease severity but clinical heterogeneity remains a major therapeutic challenge. The characterization of molecular mechanisms underlying impaired immune responses in CF may reveal novel targets with therapeutic potential. Therefore, we utilized simultaneous RNA sequencing targeted at identifying differentially expressed genes, transcripts, and miRNAs that characterize impaired immune responses triggered by CF and its phenotypes. Methods Peripheral blood mononuclear cells (PBMCs) extracted from a healthy donor were stimulated with plasma from CF patients (n = 9) and healthy controls (n = 3). The PBMCs were cultured (1 × 105 cells/well) for 9 h at 37 ° C in 5% CO2. After culture, total RNA was extracted from each sample and used for simultaneous total RNA and miRNA sequencing. Results Analysis of expression signatures from peripheral blood mononuclear cells induced by plasma of CF patients and healthy controls identified 151 genes, 154 individual transcripts, and 41 miRNAs differentially expressed in CF compared to HC while the expression signatures of 285 genes, 241 individual transcripts, and seven miRNAs differed due to CF phenotypes. Top immune pathways influenced by CF included agranulocyte adhesion, diapedesis signaling, and IL17 signaling, while those influenced by CF phenotypes included natural killer cell signaling and PI3K signaling in B lymphocytes. Upstream regulator analysis indicated dysregulation of CCL5, NF-κB and IL1A due to CF while dysregulation of TREM1 and TP53 regulators were associated with CF phenotype. Five miRNAs showed inverse expression patterns with three target genes relevant in CF-associated impaired immune pathways while two miRNAs showed inverse expression patterns with two target genes relevant to a dysregulated immune pathway associated with CF phenotypes. Conclusions Our results indicate that miRNAs and individual transcript variants are relevant molecular targets contributing to impaired immune cell responses in CF. Electronic supplementary material The online version of this article (10.1186/s12920-019-0529-0) contains supplementary material, which is available to authorized users.

IDH3α promotes glioblastoma progression and links mitochondrial metabolism to cSHMT-controlled one-carbon metabolism., Mutation or transcriptional up-regulation of isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) promotes cancer progression through metabolic reprogramming and epigenetic deregulation of gene expression. Here, we demonstrate that IDH3α, a subunit of the IDH3 heterotetramer, is elevated in glioblastoma (GBM) patient samples compared to normal brain tissue and promotes GBM progression in orthotopic glioma mouse models. IDH3α loss of function reduces tricarboxylic acid (TCA) cycle turnover and inhibits oxidative phosphorylation. In addition to its impact on mitochondrial energy metabolism, IDH3α binds to cytosolic serine hydroxymethyltransferase (cSHMT). This interaction enhances nucleotide availability during DNA replication, while the absence of IDH3α promotes methionine cycle activity, S-adenosyl methionine generation, and DNA methylation. Thus, the regulation of one-carbon metabolism via an IDH3α-cSHMT signaling axis represents a novel mechanism of metabolic adaptation in GBM.

Glioblastoma multiforme remains the deadliest malignant brain tumor, with glioma stem cells (GSC) contributing to treatment resistance and tumor recurrence. We have identified MAPK-interacting kinases (MNK) as potential targets for the GSC population in glioblastoma multiforme. Isoform-level subtyping using The Cancer Genome Atlas revealed that both MNK genes (MKNK1 and MKNK2) are upregulated in mesenchymal glioblastoma multiforme as compared with other subtypes. Expression of MKNK1 is associated with increased glioma grade and correlated with the mesenchymal GSC marker, CD44, and coexpression of MKNK1 and CD44 predicts poor survival in glioblastoma multiforme. In established and patient-derived cell lines, pharmacologic MNK inhibition using LY2801653 (merestinib) inhibited phosphorylation of the eukaryotic translation initiation factor 4E, a crucial effector for MNK-induced mRNA translation in cancer cells and a marker of transformation. Importantly, merestinib inhibited growth of GSCs grown as neurospheres as determined by extreme limiting dilution analysis. When the effects of merestinib were assessed in vivo using an intracranial xenograft mouse model, improved overall survival was observed in merestinib-treated mice. Taken together, these data provide strong preclinical evidence that pharmacologic MNK inhibition targets mesenchymal glioblastoma multiforme and its GSC population. Implications: These findings raise the possibility of MNK inhibition as a viable therapeutic approach to target the mesenchymal subtype of glioblastoma multiforme. Mol Cancer Res; 14(10); 984–93. ©2016 AACR.

// Hong-Jian Jin 1 , Segun Jung 1 , Auditi R. DebRoy 1 , Ramana V. Davuluri 1 1 Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA Correspondence to: Ramana V. Davuluri, email: ramana.davuluri@northwestern.edu Keywords: SNP, prostate cancer, transcription factor, CRISPR/Cas9, eQTL Received: March 23, 2016 Accepted: May 23, 2016 Published: July 09, 2016 ABSTRACT Prostate cancer (PCa) is the second most common solid tumor for cancer related deaths in American men. Genome wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with the increased risk of PCa. Because most of the susceptibility SNPs are located in noncoding regions, little is known about their functional mechanisms. We hypothesize that functional SNPs reside in cell type-specific regulatory elements that mediate the binding of critical transcription factors (TFs), which in turn result in changes in target gene expression. Using PCa-specific functional genomics data, here we identify 38 regulatory candidate SNPs and their target genes in PCa. Through risk analysis by incorporating gene expression and clinical data, we identify 6 target genes (ZG16B, ANKRD5, RERE, FAM96B, NAALADL2 and GTPBP10) as significant predictors of PCa biochemical recurrence. In addition, 5 SNPs (rs2659051, rs10936845, rs9925556, rs6057110 and rs2742624) are selected for experimental validation using Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay in LNCaP cells, showing allele-specific enhancer activity. Furthermore, we delete the rs2742624-containing region using CRISPR/Cas9 genome editing and observe the drastic downregulation of its target gene UPK3A. Taken together, our results illustrate that this new methodology can be applied to identify regulatory SNPs and their target genes that likely impact PCa risk. We suggest that similar studies can be performed to characterize regulatory variants in other diseases.

6025 Background: Platinum resistant ovarian cancer (PROC) remains a disease of high need. Immune checkpoint inhibitors (ICI) have modest activity. We hypothesized that priming with a hypomethylating agent (HMA) guadecitabine (G) will improve the anti-tumor activity of ICI in PROC by enhancing tumor cell recognition by CD8+ T cells. Methods: This open-label phase II study used a Simon’s two-stage design. Eligible patients (pts) had recurrent PROC; ECOG PS of 0-1; normal end organ function; and measurable disease. Up to 5 prior cytotoxic regimens were allowed. Treatment consisted of G 30mg/m2 sq D1-4 and pembrolizumab (P) 200mg iv D5. Each cycle was 21 days. The primary endpoint was response rate (RR). Secondary endpoints were progression-free survival (PFS), clinical benefit rate (CBR), and toxicity assessment. Translational endpoints were LINE1 methylation in PBMCs, global tumor methylation, and immune endpoints. Tumor biopsies were obtained at baseline and after 2 cycles. If 2 patients experienced clinical benefit in stage I [n = 16], enrollment proceeded to stage II. The null hypothesis was rejected for ≥ 6 responses in 35 evaluable patients. Results: 48 pts were enrolled, 43 were treated, and 33 were evaluable for response. Histology was serous (35), endometrioid (2), clear cell (3) and other (3). Median age was 63 (range 40-88) and median number of prior regimens was 4 [range 1-8]. Two PRs were recorded in the first stage, allowing second stage of enrollment. Overall, there were 2 PRs (RR = 6.6%) and 16 pts had stable disease (SD) [48%]. The clinical benefit rate (PR + SD > 3 months) was 27%. One patient continued treatment for > 2 yrs. Grade 3-4 related toxicities were neutropenia [20], lymphopenia, (9), anemia (2), neutropenic fever (1), rash (1), and others (8). There were 13 grade 3-4 SAEs and 4 grade 5 SAEs, assessed as being unrelated to treatment. LINE1 was hypomethylated in PBMCs D5 vs. D1 (n = 21, p = 0.001). Epic arrays measured global tumor methylation, with 39579 CpG sites (0.05%) being differentially methylated (C2D5 vs. C1D1, n = 11, paired t-test; p < 0.01). Main pathways affected included endosomal transport, K+ transport, cathecolamine secretion, etc. PDL1 staining in archival tissue showed tumor staining > 0 in 16 of 35 and tumor/stroma interface staining > 0 in 20 of 35 specimens. Antigen-specific cytotoxic T cell activity was increased in CD8+ cells from ascites (C2D5 vs. C1D1). Conclusions: G+P has modest anti-tumor activity in patients with PROC, but some patients experienced prolonged disease stabilization. Biomarkers of response are being investigated. Clinical trial information: NCT02901899.

The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O6-methylguanine–DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment.

Motivation Access to large-scale genomics and transcriptomics data from various tissues and cell lines allowed the discovery of wide-spread alternative splicing events and alternative promoter usage in mammalians. Between human and mouse, gene-level orthology is currently present for nearly 16k protein-coding genes spanning a diverse repertoire of over 200k total transcript isoforms. Results Here, we describe a novel method, ExTraMapper, which leverages sequence conservation between exons of a pair of organisms and identifies a fine-scale orthology mapping at the exon and then transcript level. ExTraMapper identifies more than 350k exon mappings, as well as 30k transcript mappings between human and mouse using only sequence and gene annotation information. We demonstrate that ExTraMapper identifies a larger number of exon and transcript mappings compared to previous methods. Further, it identifies exon fusions, splits and losses due to splice site mutations, and finds mappings between microexons that are previously missed. By reanalysis of RNA-seq data from 13 matched human and mouse tissues, we show that ExTraMapper improves the correlation of transcript-specific expression levels suggesting a more accurate mapping of human and mouse transcripts. We also applied the method to detect conserved exon and transcript pairs between human and rhesus macaque genomes to highlight the point that ExTraMapper is applicable to any pair of organisms that have orthologous gene pairs. Availability and implementation The source code and the results are available at https://github.com/ay-lab/ExTraMapper and http://ay-lab-tools.lji.org/extramapper. Supplementary information Supplementary data are available at Bioinformatics online.

The transcribed ultraconserved regions (T-UCRs) encode long non-coding RNAs implicated in human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. Here we show that high expression of uc.339 correlates with lower survival in 210 non-small cell lung cancer (NSCLC) patients. We provide evidence from cell lines and primary samples that TP53 directly regulates uc.339. We find that transcribed uc.339 is upregulated in archival NSCLC samples, functioning as a decoy RNA for miR-339-3p, -663b-3p, and -95-5p. As a result, Cyclin E2, a direct target of all these microRNAs is upregulated, promoting cancer growth and migration. Finally, we find that modulation of uc.339 affects microRNA expression. However, overexpression or downregulation of these microRNAs causes no significant variations in uc.339 levels, suggesting a type of interaction for uc.339 that we call “entrapping”. Our results support a key role for uc.339 in lung cancer., T-UCRs encode long non-coding RNAs implicated in human carcinogenesis, but the underlying mechanisms are poorly understood. Here, the authors identify uc.339 as an oncogene in lung cancer that is upregulated through the loss of TP53 and promotes Cyclin E activation by entrapping regulatory miRNAs.

The transcribed ultraconserved regions (T-UCRs) encode long non-coding RNAs implicated in human carcinogenesis. Their mechanisms of action and the factors regulating their expression in cancers are poorly understood. Here we show that high expression of uc.339 correlates with lower survival in 210 non-small cell lung cancer (NSCLC) patients. We provide evidence from cell lines and primary samples that TP53 directly regulates uc.339. We find that transcribed uc.339 is upregulated in archival NSCLC samples, functioning as a decoy RNA for miR-339-3p, -663b-3p, and -95-5p. As a result, Cyclin E2, a direct target of all these microRNAs is upregulated, promoting cancer growth and migration. Finally, we find that modulation of uc.339 affects microRNA expression. However, overexpression or downregulation of these microRNAs causes no significant variations in uc.339 levels, suggesting a type of interaction for uc.339 that we call “entrapping”. Our results support a key role for uc.339 in lung cancer.

CNS Primitive Neuroectodermal tumors (CNS-PNETs) are members of the embryonal family of malignant childhood brain tumors, which remain refractory to current therapeutic treatments. Current paradigm of brain tumorigenesis implicates brain tumor-initiating cells (BTIC) in the onset of tumorigenesis and tumor maintenance. However, despite their significance, there is currently no comprehensive characterization of CNS-PNETs BTICs. Recently, we described an animal model of CNS-PNET generated by orthotopic transplantation of human Radial Glial (RG) cells - the progenitor cells for adult neural stem cells (NSC) - into NOD-SCID mice brain and proposed that BTICs may play a role in the maintenance of these tumors. Here we report the characterization of BTIC lines derived from this CNS-PNET animal model. BTIC's orthotopic transplantation generated highly aggressive tumors also characterized as CNS-PNETs. The BTICs have the hallmarks of NSCs as they demonstrate self-renewing capacity and have the ability to differentiate into astrocytes and early migrating neurons. Moreover, the cells demonstrate aberrant accumulation of wild type tumor-suppressor protein p53, indicating its functional inactivation, highly up-regulated levels of onco-protein cMYC and the BTIC marker OCT3/4, along with metabolic switch to glycolysis - suggesting that these changes occurred in the early stages of tumorigenesis. Furthermore, based on RNA- and DNA-seq data, the BTICs did not acquire any transcriptome-changing genomic alterations indicating that the onset of tumorigenesis may be epigenetically driven. The study of these BTIC self-renewing cells in our model may enable uncovering the molecular alterations that are responsible for the onset and maintenance of the malignant PNET phenotype.

Oncogenic mutations in two isocitrate dehydrogenase (IDH)-encoding genes (IDH1 and IDH2) have been identified in acute myelogenous leukemia, low-grade glioma, and secondary glioblastoma (GBM). Our in silico and wet-bench analyses indicate that non-mutated IDH1 mRNA and protein are commonly overexpressed in primary GBMs. We show that genetic and pharmacologic inactivation of IDH1 decreases GBM cell growth, promotes a more differentiated tumor cell state, increases apoptosis in response to targeted therapies, and prolongs the survival of animal subjects bearing patient-derived xenografts (PDXs). On a molecular level, diminished IDH1 activity results in reduced α-ketoglutarate (αKG) and NADPH production, paralleled by deficient carbon flux from glucose or acetate into lipids, exhaustion of reduced glutathione, increased levels of reactive oxygen species (ROS), and enhanced histone methylation and differentiation marker expression. These findings suggest that IDH1 upregulation represents a common metabolic adaptation by GBMs to support macromolecular synthesis, aggressive growth, and therapy resistance.

Recently, we described a new animal model of CNS primitive neuroectodermal tumors (CNS-PNET), which was generated by orthotopic transplantation of human Radial Glial (RG) cells into NOD-SCID mice’s brain sub-ventricular zone. In the current study we conducted comprehensive RNA-Seq analyses to gain insights on the mechanisms underlying tumorigenesis in this mouse model of CNS-PNET. Here we show that the RNA-Seq profiles derived from these tumors cluster with those reported for patients’ PNETs. Moreover, we found that (i) stabilization of HIF-1α and HIF-2α, which are involved in mediation of the hypoxic responses in the majority of cell types, (ii) up-regulation of MYCC, a key onco-protein whose dysregulation occurs in ~70% of human tumors, and (iii) accumulation of stabilized p53, which is commonly altered in human cancers, constitute hallmarks of our tumor model, and might represent the basis for CNS-PNET tumorigenesis in this model. We discuss the possibility that these three events might be interconnected. These results indicate that our model may prove invaluable to uncover the molecular events leading to MYCC and TP53 alterations, which would be of broader interest considering their relevance to many human malignancies. Lastly, this mouse model might prove useful for drug screening targeting MYCC and related members of its protein interaction network.

IntroductionBreast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).Materialsand MethodsWe developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.ResultsThe comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.ConclusionsThus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.

The functional roles of SNPs within the 8q24 gene desert in the cancer phenotype are not yet well understood. Here, we report that CCAT2, a novel long noncoding RNA transcript (lncRNA) encompassing the rs6983267 SNP, is highly overexpressed in microsatellite-stable colorectal cancer and promotes tumor growth, metastasis, and chromosomal instability. We demonstrate that MYC, miR–17–5p, and miR–20a are up-regulated by CCAT2 through TCF7L2-mediated transcriptional regulation. We further identify the physical interaction between CCAT2 and TCF7L2 resulting in an enhancement of WNT signaling activity. We show that CCAT2 is itself a WNT downstream target, which suggests the existence of a feedback loop. Finally, we demonstrate that the SNP status affects CCAT2 expression and the risk allele G produces more CCAT2 transcript. Our results support a new mechanism of MYC and WNT regulation by the novel lncRNA CCAT2 in colorectal cancer pathogenesis, and provide an alternative explanation of the SNP-conferred cancer risk.

Author(s): Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L; Bilello, Michel; O'Rourke, Donald M; Davatzikos, Christos | Abstract: BackgroundMRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).MethodsOne hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients.ResultsSurvival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy.ConclusionsBy employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

Background Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. Results We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. Conclusions The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.

MPromDb (Mammalian Promoter Database) is a curated database that strives to annotate gene promoters identified from ChIP-seq results with the goal of providing an integrated resource for mammalian transcriptional regulation and epigenetics. We analyzed 507 million uniquely aligned RNAP-II ChIP-seq reads from 26 different data sets that include six human cell-types and 10 distinct mouse cell/tissues. The updated MPromDb version consists of computationally predicted (novel) and known active RNAP-II promoters (42 893 human and 48 366 mouse promoters) from various data sets freely available at NCBI GEO database. We found that 36% and 40% of protein-coding genes have alternative promoters in human and mouse genomes and ∼40% of promoters are tissue/cell specific. The identified RNAP-II promoters were annotated using various known and novel gene models. Additionally, for novel promoters we looked into other evidences—GenBank mRNAs, spliced ESTs, CAGE promoter tags and mRNA-seq reads. Users can search the database based on gene id/symbol, or by specific tissue/cell type and filter results based on any combination of tissue/cell specificity, Known/Novel, CpG/NonCpG, and protein-coding/non-coding gene promoters. We have also integrated GBrowse genome browser with MPromDb for visualization of ChIP-seq profiles and to display the annotations. The current release of MPromDb can be accessed at http://bioinformatics.wistar.upenn.edu/MPromDb/.

We present the implementation of an application using caGrid, which is the service-oriented Grid software infrastructure of the NCI cancer Biomedical Informatics Grid (caBIG™), to support design and analysis of custom microarray experiments in the study of epigenetic alterations in cancer. The design and execution of these experiments requires synthesis of information from multiple data types and datasets. In our implementation, each data source is implemented as a caGrid Data Service, and analytical resources are wrapped as caGrid Analytical Services. This service-based implementation has several advantages. A backend resource can be modified or upgraded, without needing to change other components in the application. A remote resource can be added easily, since resources are not required to be collected in a centralized infrastructure.

Hypermethylation of CpG islands is a common epigenetic alteration associated with cancer. Global patterns of hypermethylation are tumor-type specific and nonrandom. The biological significance and the underlying mechanisms of tumor-specific aberrant promoter methylation remain unclear, but some evidence suggests that this specificity involves differential sequence susceptibilities, the targeting of DNA methylation activity to specific promoter sequences, or the selection of rare DNA methylation events during disease progression. Using restriction landmark genomic scanning on samples derived from tissue culture and in vivo models of T cell lymphomas, we found that MYC overexpression gave rise to a specific signature of CpG island hypermethylation. This signature reflected gene transcription profiles and was detected only in advanced stages of disease. The further inactivation of the Pten, p53, and E2f2 tumor suppressors in MYC-induced lymphomas resulted in distinct and diagnostic CpG island methylation signatures. Our data suggest that tumor-specific DNA methylation in lymphomas arises as a result of the selection of rare DNA methylation events during the course of tumor development. This selection appears to be driven by the genetic configuration of tumor cells, providing experimental evidence for a causal role of DNA hypermethylation in tumor progression and an explanation for the tremendous epigenetic heterogeneity observed in the evolution of human cancers. The ability to predict genome-wide epigenetic silencing based on relatively few genetic alterations will allow for a more complete classification of tumors and understanding of tumor cell biology., Author Summary Genetic and epigenetic alterations of the genome are common features of cancers. The relationship between these two types of alterations, however, remains unclear. One type of epigenetic modification—DNA methylation in promoter sequences of genes—is of particular interest, since tumor cells have different patterns of promoter methylation than normal cells. Previous studies on human tumor samples have suggested a link between genetic alterations and the induction of aberrant DNA methylation; however, this link has been difficult to rigorously assess because of the incredible genetic heterogeneity found in human cancer. In this study, a mouse model of T cell lymphoma was used to explore the relationship between genetic and epigenetic modifications experienced by tumor cells. By introducing defined genetic changes into preneoplastic T cells of mice, such as the overexpression of the MYC oncogene and the ablation of tumor suppressor genes, we could carefully evaluate how these genetic changes impacted promoter methylation profiles during development of lymphomas in vivo. We found that the introduction of different genetic insults resulted in unique and diagnostic profiles of promoter methylation. Understanding how these methylation signatures contribute to tumor progression could eventually have diagnostic, prognostic, and therapeutic value for human cancers.

Given that the majority of multi-exon genes generate diverse functional products, it is important to evaluate expression at the isoform level. Previous studies have demonstrated strong gene-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied their concordance at the isoform level. We performed transcript abundance estimation on raw RNA-seq and exon-array expression profiles available for common glioblastoma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared both the isoform- and gene-level expression estimates between programs and platforms. The results showed better concordance between RNA-seq/exon-array and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) platforms for fold change estimates than for raw abundance estimates, suggesting that fold change normalization against a control is an important step for integrating expression data across platforms. Based on RT-qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the best performance for RNA-seq and exon-array platforms, respectively, for deriving the isoform-level fold change values. While eXpress achieved the highest correlation with the RT-qPCR and exon-array (MMBGX) results overall, RSEM was more highly correlated with MMBGX for the subset of transcripts that are highly variable across the samples. eXpress appears to be most successful in discriminating lowly expressed transcripts, but IsoformEx and RSEM correlate more strongly with MMBGX for highly expressed transcripts. The results also reinforce how potentially important isoform-level expression changes can be masked by gene-level estimates, and demonstrate that exon arrays yield comparable results to RNA-seq for evaluating isoform-level expression changes.

In breast cancer and normal estrogen target tissues, estrogen receptor-alpha (ERalpha) signaling results in the establishment of spatiotemporal patterns of gene expression. Whereas primary target gene regulation by ERalpha involves recruitment of coregulatory proteins, coactivators, or corepressors, activation of these downstream promoters by receptor signaling may also involve partnership of ERalpha with other transcription factors. By using an integrated, genome-wide approach that involves ChIP-chip and computational modeling, we uncovered 13 ERalpha-responsive promoters containing both ERalpha and c-MYC binding elements located within close proximity (13-214 bp) to each other. Estrogen stimulation enhanced the c-MYC-ERalpha interaction and facilitated the association of ERalpha, c-MYC, and the coactivator TRRAP with these estrogen-responsive promoters, resulting in chromatin remodeling and increased transcription. These results suggest that ERalpha and c-MYC physically interact to stabilize the ERalpha-coactivator complex, thereby permitting other signal transduction pathways to fine-tune estrogen-mediated signaling networks.

We have developed Mammalian Promoter Database (MPromDb), a novel database that integrates gene promoters with experimentally supported annotation of transcription start sites, cis-regulatory elements, CpG islands and chromatin immunoprecipitation microarray (ChIP-chip) experimental results with intuitively designed presentation. Release 1.0 of MPromDb currently contains 36,407 promoters and first exons (19,170 from human, 15,953 from mouse and 1284 from rat), 3739 transcription factor (TF)-binding sites (2027 from human, 1181 mouse and 531 rat) and 224 TFs with links to PubMed and GenBank references. Target promoters of TFs that have been identified by ChIP-chip assay are integrated into the database. MPromDb serves as a portal for genome-wide promoter analysis of data generated by ChIP-chip experimental studies. MPromDb can be accessed from http://bioinformatics.med.ohio-state.edu/MPromDb.

Hirschsprung disease (HSCR) is a common congenital disorder characterized by aganglionosis of the gut. The seemingly unrelated multiple endocrine neoplasia type 2 (MEN 2) is an autosomal dominant disorder characterized by medullary thyroid carcinoma (MTC), pheochromocytoma, and hyperparathyroidism. Yet, germline mutations in the RET proto-oncogene are associated with both MEN 2 and HSCR. In the former, gain-of-function mutations in a limited set of codons is found, whereas, in the latter, loss-of-function mutations are found. However, germline RET mutation is associated with only 3% of a population-based series of isolated HSCR, and little is known about susceptibility to sporadic MTC. We have found previously that specific haplotypes comprising RET coding single-nucleotide polymorphisms (SNPs) comprising exon 2 SNP A45A were strongly associated with HSCR, whereas haplotypes associated with exon 14 SNP S836S were associated with MTC. In this study, we describe three novel intron 1 SNPs, and, together with the coding SNP haplotypes, the data suggest the presence of distinct ancestral haplotypes for HSCR and sporadic MTC in linkage disequilibrium with a putative founding susceptibility locus/loci. The data are consistent with the presence of a very ancient, low-penetrance founder locus approximately 20-30 kb upstream of SNP A45A, but the failure of the SNPs to span the locus presents challenges in modeling mode of transmission or ancestry. We postulate that this founding locus is germane to both isolated HSCR and MTC but also that different mutations in this locus would predispose to one or the other.

The Protein Kinase A (PKA) and Wnt signaling cascades are fundamental pathways involved in cellular development and maintenance. In the osteoblast lineage, these pathways have been demonstrated functionally to be essential for the production of mineralized bone. Evidence for PKA-Wnt crosstalk has been reported both during tumorigenesis and during organogenesis, and the nature of the interaction is thought to rely on tissue and cell context. In this manuscript, we analyzed bone tumors arising from mice with activated PKA caused by mutation of the PKA regulatory subunit Prkar1a. In primary cells from these tumors, we observed relocalization of β-catenin to intranuclear punctuate structures, which were identified as PML bodies. Cellular redistribution of β-catenin could be recapitulated by pharmacologic activation of PKA. Using 3T3-E1 pre-osteoblasts as a model system, we found that PKA phosphorylation sites on β-catenin were required for nuclear re-localization. Further, β-catenin's transport to the nucleus was accompanied by an increase in canonical Wnt-dependent transcription, which also required the PKA sites. PKA-Wnt crosstalk in the cells was bi-directional, including enhanced interactions between β-catenin and the cAMP-responsive element binding protein (CREB) and transcriptional crosstalk between the Wnt and PKA signaling pathways. Increases in canonical Wnt/β-catenin signaling were associated with a decrease in the activity of the non-canonical Wnt/Ror2 pathway, which has been shown to antagonize canonical Wnt signaling. Taken together, this study provides a new understanding of the complex regulation of the subcellular distribution of β-catenin and its differential protein-protein interaction that can be modulated by PKA signaling.

Item does not contain fulltext Adenosine deaminases acting on RNA (ADARs) are involved in RNA editing that converts adenosine residues to inosine specifically in double-stranded RNAs. In this study, we investigated the interaction of the RNA editing mechanism with the RNA interference (RNAi) machinery and found that ADAR1 forms a complex with Dicer through direct protein-protein interaction. Most importantly, ADAR1 increases the maximum rate (Vmax) of pre-microRNA (miRNA) cleavage by Dicer and facilitates loading of miRNA onto RNA-induced silencing complexes, identifying a new role of ADAR1 in miRNA processing and RNAi mechanisms. ADAR1 differentiates its functions in RNA editing and RNAi by the formation of either ADAR1/ADAR1 homodimer or Dicer/ADAR1 heterodimer complexes, respectively. As expected, the expression of miRNAs is globally inhibited in ADAR1(-/-) mouse embryos, which, in turn, alters the expression of their target genes and might contribute to their embryonic lethal phenotype.

The ecotropic virus integration site 1 (EVI1) transcription factor is associated with human myeloid malignancy of poor prognosis and is overexpressed in 8-10% of adult AML and strikingly up to 27% of pediatric MLL-rearranged leukemias. For the first time, we report comprehensive genomewide EVI1 binding and whole transcriptome gene deregulation in leukemic cells using a combination of ChIP-Seq and RNA-Seq expression profiling. We found disruption of terminal myeloid differentiation and cell cycle regulation to be prominent in EVI-induced leukemogenesis. Specifically, we identified EVI1 directly binds to and downregulates the master myeloid differentiation gene Cebpe and several of its downstream gene targets critical for terminal myeloid differentiation. We also found EVI1 binds to and downregulates Serpinb2 as well as numerous genes involved in the Jak-Stat signaling pathway. Finally, we identified decreased expression of several ATP-dependent P2X purinoreceptors genes involved in apoptosis mechanisms. These findings provide a foundation for future study of potential therapeutic gene targets for EVI1-induced leukemia.

mRNA-Seq is a precise and highly reproducible technique for measurement of transcripts levels and yields sequence information of a transcriptome at a single nucleotide base-level thus enabling us to determine splice junctions and alternative splicing events with high confidence. Often analysis of mRNA-Seq data does not attempt to quantify the expressions at isoform level. In this paper our objective would be use the mRNA-Seq data to infer expression at isoform level, where splicing patterns of a gene is assumed to be known. A Bayesian latent variable based modeling framework is proposed here, where the parameterization enables us to infer at various levels. For example, expression variability of an isoform across different conditions; the model parameterization also allows us to carry out two-sample comparisons, e.g., using a Bayesian t-test, in addition simple presence or absence of an isoform can also be estimated by the use of the latent variables present in the model. In this paper we would carry out inference on isoform expression under different normalization techniques, since it has been recently shown that one of the most prominent sources of variation in differential call using mRNA-Seq data is the normalization method used. The statistical framework is developed for multiple isoforms and easily extends to reads mapping to multiple genes. This could be achieved by slight conceptual modifications in definitions of what we consider as a gene and what as an exon. Additionally proposed framework can be extended by appropriate modeling of the design matrix to infer about yet unknown novel transcripts. However such attempts should be made judiciously since the input date used in the proposed model does not use reads from splice junctions.

Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.

Background Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context. Methods We trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters. Results We present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues (Brain, Kidney, Liver, Lung and Spleen). We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in the mouse genome. The resulting annotations contain 13,413 (4,747) protein-coding (non-coding) genes with single promoters and 9,929 (1,858) protein-coding (non-coding) genes with two or more alternative promoters, and a significant number of unassigned novel promoters. Conclusion Our new algorithm can successfully predict the promoters from the genome wide profile of Pol-II bound regions. In addition, our algorithm performs significantly better than existing promoter prediction methods and can be applied for genome-wide predictions of Pol-II promoters.

BackgroundIndependent lines of evidence suggested that a large fraction of human genes possess multiple promoters driving gene expression from distinct transcription start sites. Understanding which promoter is employed in which cellular context is required to unravel gene regulatory networks within the cell.ResultsWe have developed a custom microarray platform that tiles roughly 35,000 alternative putative promoters from nearly 7,000 genes in the human genome. To demonstrate the utility of this array platform, we have analyzed the patterns of promoter usage in 17β-estradiol (E2)-treated and untreated MCF7 cells and show widespread usage of alternative promoters. Most intriguingly, we show that the downstream promoter in E2-sensitive multiple promoter genes tends to be very close to the 3'-terminus of the gene, suggesting exotic mechanisms of expression regulation in these genes.ConclusionThe usage of alternative promoters greatly multiplies the transcriptional complexity available within the human genome. The fact that many of these promoters are incapable of driving the synthesis of a meaningful protein-encoding transcript further complicates the story.

Background Lung cancer is the leading cause of cancer-related death worldwide. Currently, tumor, node, metastasis (TNM) staging provides the most accurate prognostic parameter for patients with non-small cell lung cancer (NSCLC). However, the overall survival of patients with resectable tumors varies significantly, indicating the need for additional prognostic factors to better predict the outcome of the disease, particularly within a given TNM subset. Methods and Findings In this study, we investigated whether adenocarcinomas and squamous cell carcinomas could be differentiated based on their global aberrant DNA methylation patterns. We performed restriction landmark genomic scanning on 40 patient samples and identified 47 DNA methylation targets that together could distinguish the two lung cancer subgroups. The protein expression of one of those targets, oligodendrocyte transcription factor 1 (OLIG1), significantly correlated with survival in NSCLC patients, as shown by univariate and multivariate analyses. Furthermore, the hazard ratio for patients negative for OLIG1 protein was significantly higher than the one for those patients expressing the protein, even at low levels. Conclusions Multivariate analyses of our data confirmed that OLIG1 protein expression significantly correlates with overall survival in NSCLC patients, with a relative risk of 0.84 (95% confidence interval 0.77–0.91, p < 0.001) along with T and N stages, as indicated by a Cox proportional hazard model. Taken together, our results suggests that OLIG1 protein expression could be utilized as a novel prognostic factor, which could aid in deciding which NSCLC patients might benefit from more aggressive therapy. This is potentially of great significance, as the addition of postoperative adjuvant chemotherapy in T2N0 NSCLC patients is still controversial., Christopher Plass and colleagues find thatOLIG1 expression correlates with survival in lung cancer patients and suggest that it could be used in deciding which patients are likely to benefit from more aggressive therapy., Editors' Summary Background. Lung cancer is the commonest cause of cancer-related death worldwide. Most cases are of a type called non-small cell lung cancer (NSCLC). Like other cancers, treatment of NCSLC depends on the “TNM stage” at which the cancer is detected. Staging takes into account the size and local spread of the tumor (its T classification), whether nearby lymph nodes contain tumor cells (its N classification), and whether tumor cells have spread (metastasized) throughout the body (its M classification). Stage I tumors are confined to the lung and are removed surgically. Stage II tumors have spread to nearby lymph nodes and are treated with a combination of surgery and chemotherapy. Stage III tumors have spread throughout the chest, and stage IV tumors have metastasized around the body; patients with both of these stages are treated with chemotherapy alone. About 70% of patients with stage I or II lung cancer, but only 2% of patients with stage IV lung cancer, survive for five years after diagnosis. Why Was This Study Done? TNM staging is the best way to predict the likely outcome (prognosis) for patients with NSCLC, but survival times for patients with stage I and II tumors vary widely. Another prognostic marker—maybe a “molecular signature”—that could distinguish patients who are likely to respond to treatment from those whose cancer will inevitably progress would be very useful. Unlike normal cells, cancer cells divide uncontrollably and can move around the body. These behavioral changes are caused by alterations in the pattern of proteins expressed by the cells. But what causes these alterations? The answer in some cases is “epigenetic changes” or chemical modifications of genes. In cancer cells, methyl groups are aberrantly added to GC-rich gene regions. These so-called “CpG islands” lie near gene promoters (sequences that control the transcription of DNA into mRNA, the template for protein production), and their methylation stops the promoters working and silences the gene. In this study, the researchers have investigated whether aberrant methylation patterns vary between NSCLC subtypes and whether specific aberrant methylations are associated with survival and can, therefore, be used prognostically. What Did the Researchers Do and Find? The researchers used “restriction landmark genomic scanning” (RLGS) to catalog global aberrant DNA methylation patterns in human lung tumor samples. In RLGS, DNA is cut into fragments with a restriction enzyme (a protein that cuts at specific DNA sequences), end-labeled, and separated using two-dimensional gel electrophoresis to give a pattern of spots. Because methylation stops some restriction enzymes cutting their target sequence, normal lung tissue and lung tumor samples yield different patterns of spots. The researchers used these patterns to identify 47 DNA methylation targets (many in CpG islands) that together distinguished between adenocarcinomas and squamous cell carcinomas, two major types of NSCLCs. Next, they measured mRNA production from the genes with the greatest difference in methylation between adenocarcinomas and squamous cell carcinomas. OLIG1 (the gene that encodes a protein involved in nerve cell development) had one of the highest differences in mRNA production between these tumor types. Furthermore, three-quarters of NSCLCs had reduced or no expression of OLIG1 protein and, when the researchers analyzed the association between OLIG1 protein expression and overall survival in patients with NSCLC, reduced OLIG1 protein expression was associated with reduced survival. What Do These Findings Mean? These findings indicate that different types of NSCLC can be distinguished by examining their aberrant methylation patterns. This suggests that the establishment of different DNA methylation patterns might be related to the cell type from which the tumors developed. Alternatively, the different aberrant methylation patterns might reflect the different routes that these cells take to becoming tumor cells. This research identifies a potential new prognostic marker for NSCLC by showing that OLIG1 protein expression correlates with overall survival in patients with NSCLC. This correlation needs to be tested in a clinical setting to see if adding OLIG1 expression to the current prognostic parameters can lead to better treatment choices for early-stage lung cancer patients and ultimately improve these patients' overall survival. Additional Information. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040108. Patient and professional information on lung cancer, including staging (in English and Spanish), is available from the US National Cancer Institute The MedlinePlus encyclopedia has pages on non-small cell lung cancer (in English and Spanish) Cancerbackup provides patient information on lung cancer CancerQuest, provided by Emory University, has information about how cancer develops (in English, Spanish, Chinese and Russian) Wikipedia pages on epigenetics (note that Wikipedia is a free online encyclopedia that anyone can edit) The Epigenome Network of Excellence gives background information and the latest news about epigenetics (in several European languages)

Background The canonical core promoter elements consist of the TATA box, initiator (Inr), downstream core promoter element (DPE), TFIIB recognition element (BRE) and the newly-discovered motif 10 element (MTE). The motifs for these core promoter elements are highly degenerate, which tends to lead to a high false discovery rate when attempting to detect them in promoter sequences. Results In this study, we have performed the first analysis of these core promoter elements in orthologous mouse and human promoters with experimentally-supported transcription start sites. We have identified these various elements using a combination of positional weight matrices (PWMs) and the degree of conservation of orthologous mouse and human sequences – a procedure that significantly reduces the false positive rate of motif discovery. Our analysis of 9,010 orthologous mouse-human promoter pairs revealed two combinations of three-way synergistic effects, TATA-Inr-MTE and BRE-Inr-MTE. The former has previously been putatively identified in human, but the latter represents a novel synergistic relationship. Conclusion Our results demonstrate that DNA sequence conservation can greatly improve the identification of functional core promoter elements in the human genome. The data also underscores the importance of synergistic occurrence of two or more core promoter elements. Furthermore, the sequence data and results presented here can help build better computational models for predicting the transcription start sites in the promoter regions, which remains one of the most challenging problems.

Background Four genes designated as PTPRK (PTPκ), PTPRL/U (PCP-2), PTPRM (PTPμ) and PTPRT (PTPρ) code for a subfamily (type R2B) of receptor protein tyrosine phosphatases (RPTPs) uniquely characterized by the presence of an N-terminal MAM domain. These transmembrane molecules have been implicated in homophilic cell adhesion. In the human, the PTPRK gene is located on chromosome 6, PTPRL/U on 1, PTPRM on 18 and PTPRT on 20. In the mouse, the four genes ptprk, ptprl, ptprm and ptprt are located in syntenic regions of chromosomes 10, 4, 17 and 2, respectively. Results The genomic organization of murine R2B RPTP genes is described. The four genes varied greatly in size ranging from ~64 kb to ~1 Mb, primarily due to proportional differences in intron lengths. Although there were also minor variations in exon length, the number of exons and the phases of exon/intron junctions were highly conserved. In situ hybridization with digoxigenin-labeled cRNA probes was used to localize each of the four R2B transcripts to specific cell types within the murine central nervous system. Phylogenetic analysis of complete sequences indicated that PTPρ and PTPμ were most closely related, followed by PTPκ. The most distant family member was PCP-2. Alignment of RPTP polypeptide sequences predicted putative alternatively spliced exons. PCR experiments revealed that five of these exons were alternatively spliced, and that each of the four phosphatases incorporated them differently. The greatest variability in genomic organization and the majority of alternatively spliced exons were observed in the juxtamembrane domain, a region critical for the regulation of signal transduction. Conclusions Comparison of the four R2B RPTP genes revealed virtually identical principles of genomic organization, despite great disparities in gene size due to variations in intron length. Although subtle differences in exon length were also observed, it is likely that functional differences among these genes arise from the specific combinations of exons generated by alternative splicing.

CTCF (CCCTC-binding factor) is a highly conserved multifunctional DNA-binding protein with thousands of binding sites genome-wide. Our previous work suggested that differences in CTCF's binding site sequence may affect the regulation of CTCF recruitment and its function. To investigate this possibility, we characterized changes in genome-wide CTCF binding and gene expression during differentiation of mouse embryonic stem cells. After separating CTCF sites into three classes (LowOc, MedOc and HighOc) based on similarity to the consensus motif, we found that developmentally regulated CTCF binding occurs preferentially at LowOc sites, which have lower similarity to the consensus. By measuring the affinity of CTCF for selected sites, we show that sites lost during differentiation are enriched in motifs associated with weaker CTCF binding in vitro. Specifically, enrichment for T at the 18(th) position of the CTCF binding site is associated with regulated binding in the LowOc class and can predictably reduce CTCF affinity for binding sites. Finally, by comparing changes in CTCF binding with changes in gene expression during differentiation, we show that LowOc and HighOc sites are associated with distinct regulatory functions. Our results suggest that the regulatory control of CTCF is dependent in part on specific motifs within its binding site.

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.

Background RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions. Results We developed a novel nonparametric empirical Bayesian-based approach (NPEBseq) to model the RNA-seq data. The prior distribution of the Bayesian model is empirically estimated from the data without any parametric assumption, and hence the method is “nonparametric” in nature. Based on this model, we proposed a method for detecting differentially expressed genes across different conditions. We also extended this method to detect differential usage of exons from RNA-seq data. The evaluation of NPEBseq on both simulated and publicly available RNA-seq datasets and comparison with three popular methods showed improved results for experiments with or without biological replicates. Conclusions NPEBseq can successfully detect differential expression between different conditions not only at gene level but also at exon level from RNA-seq datasets. In addition, NPEBSeq performs significantly better than current methods and can be applied to genome-wide RNA-seq datasets. Sample datasets and R package are available at http://bioinformatics.wistar.upenn.edu/NPEBseq.

Background The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level. Methods We analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels. Results Hierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level. Conclusions In summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.

Background The TGF-β/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context. Results To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-β activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1). Conclusion Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

Transforming growth factor-beta (TGF-beta)/SMAD signaling is a key growth regulatory pathway often dysregulated in ovarian cancer and other malignancies. Although loss of TGF-beta-mediated growth inhibition has been shown to contribute to aberrant cell behavior, the epigenetic consequence(s) of impaired TGF-beta/SMAD signaling on target genes is not well established. In this study, we show that TGF-beta1 causes growth inhibition of normal ovarian surface epithelial cells, induction of nuclear translocation SMAD4, and up-regulation of ADAM19 (a disintegrin and metalloprotease domain 19), a newly identified TGF-beta1 target gene. Conversely, induction and nuclear translocation of SMAD4 were negligible in ovarian cancer cells refractory to TGF-beta1 stimulation, and ADAM19 expression was greatly reduced. Furthermore, in the TGF-beta1 refractory cells, an inactive chromatin environment, marked by repressive histone modifications (trimethyl-H3K27 and dimethyl-H3K9) and histone deacetylase, was associated with the ADAM19 promoter region. However, the CpG island found within the promoter and first exon of ADAM19 remained generally unmethylated. Although disrupted growth factor signaling has been linked to epigenetic gene silencing in cancer, this is the first evidence demonstrating that impaired TGF-beta1 signaling can result in the formation of a repressive chromatin state and epigenetic suppression of ADAM19. Given the emerging role of ADAMs family proteins in growth factor regulation in normal cells, we suggest that epigenetic dysregulation of ADAM19 may contribute to the neoplastic process in ovarian cancer.

Background mRNA-Seq technology has revolutionized the field of transcriptomics for identification and quantification of gene transcripts not only at gene level but also at isoform level. Estimating the expression levels of transcript isoforms from mRNA-Seq data is a challenging problem due to the presence of constitutive exons. Results We propose a novel algorithm (IsoformEx) that employs weighted non-negative least squares estimation method to estimate the expression levels of transcript isoforms. Validations based on in silico simulation of mRNA-Seq and qRT-PCR experiments with real mRNA-Seq data showed that IsoformEx could accurately estimate transcript expression levels. In comparisons with published methods, the transcript expression levels estimated by IsoformEx showed higher correlation with known transcript expression levels from simulated mRNA-Seq data, and higher agreement with qRT-PCR measurements of specific transcripts for real mRNA-Seq data. Conclusions IsoformEx is a fast and accurate algorithm to estimate transcript expression levels and gene expression levels, which takes into account short exons and alternative exons with a weighting scheme. The software is available at http://bioinformatics.wistar.upenn.edu/isoformex.

The Epstein-Barr Virus (EBV) Nuclear Antigen 1 (EBNA1) protein is required for the establishment of EBV latent infection in proliferating B-lymphocytes. EBNA1 is a multifunctional DNA-binding protein that stimulates DNA replication at the viral origin of plasmid replication (OriP), regulates transcription of viral and cellular genes, and tethers the viral episome to the cellular chromosome. EBNA1 also provides a survival function to B-lymphocytes, potentially through its ability to alter cellular gene expression. To better understand these various functions of EBNA1, we performed a genome-wide analysis of the viral and cellular DNA sites associated with EBNA1 protein in a latently infected Burkitt lymphoma B-cell line. Chromatin-immunoprecipitation (ChIP) combined with massively parallel deep-sequencing (ChIP-Seq) was used to identify cellular sites bound by EBNA1. Sites identified by ChIP-Seq were validated by conventional real-time PCR, and ChIP-Seq provided quantitative, high-resolution detection of the known EBNA1 binding sites on the EBV genome at OriP and Qp. We identified at least one cluster of unusually high-affinity EBNA1 binding sites on chromosome 11, between the divergent FAM55 D and FAM55B genes. A consensus for all cellular EBNA1 binding sites is distinct from those derived from the known viral binding sites, suggesting that some of these sites are indirectly bound by EBNA1. EBNA1 also bound close to the transcriptional start sites of a large number of cellular genes, including HDAC3, CDC7, and MAP3K1, which we show are positively regulated by EBNA1. EBNA1 binding sites were enriched in some repetitive elements, especially LINE 1 retrotransposons, and had weak correlations with histone modifications and ORC binding. We conclude that EBNA1 can interact with a large number of cellular genes and chromosomal loci in latently infected cells, but that these sites are likely to represent a complex ensemble of direct and indirect EBNA1 binding sites.