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ZFP36L1 is a tandem zinc-finger RNA-binding protein that recognizes conserved adenylate-uridylate–rich elements (ARE) located in 3′untranslated regions (UTR) to mediate mRNA decay. We hypothesized that ZFP36L1 is a negative regulator of a posttranscriptional hub involved in mRNA half-life regulation of cancer-related transcripts. Analysis of in silico data revealed that ZFP36L1 was significantly mutated, epigenetically silenced, and downregulated in a variety of cancers. Forced expression of ZFP36L1 in cancer cells markedly reduced cell proliferation in vitro and in vivo, whereas silencing of ZFP36L1 enhanced tumor cell growth. To identify direct downstream targets of ZFP36L1, systematic screening using RNA pull-down of wild-type and mutant ZFP36L1 as well as whole transcriptome sequencing of bladder cancer cells {plus minus} tet-on ZFP36L1 was performed. A network of 1,410 genes was identified as potential direct targets of ZFP36L1. These targets included a number of key oncogenic transcripts such as HIF1A, CCND1, and E2F1. ZFP36L1 specifically bound to the 3′UTRs of these targets for mRNA degradation, thus suppressing their expression. Dual luciferase reporter assays and RNA electrophoretic mobility shift assays showed that wild-type, but not zinc-finger mutant ZFP36L1, bound to HIF1A 3′UTR and mediated HIF1A mRNA degradation, leading to reduced expression of HIF1A and its downstream targets. Collectively, our findings reveal an indispensable role of ZFP36L1 as a posttranscriptional safeguard against aberrant hypoxic signaling and abnormal cell-cycle progression.Significance:RNA-binding protein ZFP36L1 functions as a tumor suppressor by regulating the mRNA stability of a number of mRNAs involved in hypoxia and cell-cycle signaling.

ZFP36L1 is significantly downregulated and methylated in breast and bladder cancer, and inhibits cell cycle progression and hypoxia/HIF1A signaling.

To reconstruct systematically hyperactive transcription factor (TF)-dependent transcription networks in squamous cell carcinomas (SCCs), a computational method (ELMER) was applied to 1293 pan-SCC patient samples, and 44 hyperactive SCC TFs were identified. As a top candidate, DLX5 exhibits a notable bifurcate re-configuration of its bivalent promoter in cancer. Specifically, DLX5 maintains a bivalent state in normal tissues; its promoter is hypermethylation, leading to DLX5 transcriptional silencing in esophageal adenocarcinoma (EAC). In stark contrast, DLX5 promoter gains active histone marks and becomes transcriptionally activated in ESCC, which is directly mediated by SOX2. Functionally, silencing of DLX5 substantially inhibits SCC viability both in vitro and in vivo. Mechanistically, DLX5 cooperates with TP63 in regulating ∼2000 enhancers and promoters, which converge on activating cancer-promoting pathways. Together, our data establish a novel and strong SCC-promoting factor and elucidate a new epigenomic mechanism - bifurcate chromatin re-configuration - during cancer development.

We performed a meta-analysis of two large independent blood-based Alzheimer’s disease (AD) epigenome-wide association studies, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that were significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four additional methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. Our integrative analysis revealed expressions levels of 13 genes and 10 pathways were significantly associated with the AD-associated methylation differences in both brain and blood, many are involved in the immune responses in AD, such as the CD79A, LY86, SP100, CD163, CD200, and MS4A1 genes and the neutrophil degranulation, antigen processing and presentation, interferon signaling pathways. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model included age, sex, cell types and methylation risk score based on prioritized CpGs from cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10−5). Our study provides a valuable resource for future mechanistic and biomarker studies in AD.

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs subtypes include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Through a comprehensive analysis of mutation, copy number, transcriptomic, epigenetic, proteomic, and phospho-proteomic patterns we describe the genomic landscape of TNBC subtypes. Mesenchymal subtype tumors display high mutation loads, genomic instability, absence of immune cells, low PD-L1 expression, decreased global DNA methylation, and transcriptional repression of antigen presentation genes. We demonstrate that major histocompatibility complex I (MHC-I) is transcriptionally suppressed by H3K27me3 modifications by the polycomb repressor complex 2 (PRC2). Pharmacological inhibition of PRC2 subunits EZH2 or EED restores MHC-I expression and enhances chemotherapy efficacy in murine tumor models, providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Subtype-specific differences in immune cell composition and differential genetic/pharmacological vulnerabilities suggest additional treatment strategies for TNBC., Triple negative breast cancer can be divided into additional subtypes. Here, using omics analyses, the authors show that in the mesenchymal subtype expression of MHC-1 is repressed and that this can be restored by using drugs that target subunits of the epigenetic modifier PRC2.

ZFP36L1 is a tandem zinc-finger RNA-binding protein that recognizes conserved adenylate-uridylate–rich elements (ARE) located in 3′untranslated regions (UTR) to mediate mRNA decay. We hypothesized that ZFP36L1 is a negative regulator of a posttranscriptional hub involved in mRNA half-life regulation of cancer-related transcripts. Analysis of in silico data revealed that ZFP36L1 was significantly mutated, epigenetically silenced, and downregulated in a variety of cancers. Forced expression of ZFP36L1 in cancer cells markedly reduced cell proliferation in vitro and in vivo, whereas silencing of ZFP36L1 enhanced tumor cell growth. To identify direct downstream targets of ZFP36L1, systematic screening using RNA pull-down of wild-type and mutant ZFP36L1 as well as whole transcriptome sequencing of bladder cancer cells {plus minus} tet-on ZFP36L1 was performed. A network of 1,410 genes was identified as potential direct targets of ZFP36L1. These targets included a number of key oncogenic transcripts such as HIF1A, CCND1, and E2F1. ZFP36L1 specifically bound to the 3′UTRs of these targets for mRNA degradation, thus suppressing their expression. Dual luciferase reporter assays and RNA electrophoretic mobility shift assays showed that wild-type, but not zinc-finger mutant ZFP36L1, bound to HIF1A 3′UTR and mediated HIF1A mRNA degradation, leading to reduced expression of HIF1A and its downstream targets. Collectively, our findings reveal an indispensable role of ZFP36L1 as a posttranscriptional safeguard against aberrant hypoxic signaling and abnormal cell-cycle progression. Significance: RNA-binding protein ZFP36L1 functions as a tumor suppressor by regulating the mRNA stability of a number of mRNAs involved in hypoxia and cell-cycle signaling.

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology and immune composition. TNBCs display transcriptional diversity with at least four tumor-intrinsic subtypes that include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Using integrative analyses of transcriptomic, epigenetic, proteomic and phospho-proteomic patterns we have identified subtype-specific vulnerabilities, which advanced our understanding of the cellular origins of TNBC subtypes. In mesenchymal subtype tumors we observed high mutation load and genomic instability, absence of immune cells, low PD-L1 expression, loss of global DNA methylation and transcriptional repression of antigen presentation genes through the polycomb repressor complex 2 (PRC2). Using cell line models, we demonstrate that MHC-I expression is epigenetically silenced by the PRC2 in mesenchymal TNBC cell lines and inhibition of EZH2 restores expression. Pharmacological inhibition of EZH2 enhances chemotherapy efficacy in syngeneic murine tumor models providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Citation Format: Brian D Lehmann, Antonio Colaprico, Tiago C Silva, Jianjiao Chen, Hanbing An, Yuguang Ban, Lily Wang, Jamaal L James, Justin Balko, Paula I Gonzalez-Ericsson, Melinda E Sanders, Bing Zhang, Jennifer A Pietenpol, Xi S Chen. Multi-omics characterization of triple-negative breast cancer identifies therapeutic vulnerabilities and epigenetic immune suppression in the mesenchymal subtype [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD3-04.

To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI) become DNA hypermethylated and silenced in cancer. Here, we perform a systematic analysis of CGI genes across TCGA cancer types, finding that PRC2+-CGI genes are frequently prone to transcriptional upregulation as well. These upregulated PRC2+-CGI genes control important pathways such as Epithelial-Mesenchymal Transition (EMT) and TNFα-associated inflammatory response, and have greater cancer-type specificity than other CGI genes. Using publicly available chromatin datasets and genetic perturbations, we show that transcription factor binding sites (TFBSs) within distal enhancers underlie transcriptional activation of PRC2+-CGI genes, coinciding with loss of the PRC2-associated mark H3K27me3 at the linked promoter. In contrast, PRC2-free CGI genes are predominantly regulated by promoter TFBSs which are common to most cancer types. Surprisingly, a large subset of PRC2+-CGI genes that are upregulated in one cancer type are also hypermethylated/silenced in at least one other cancer type, underscoring the high degree of regulatory plasticity of these genes, likely derived from their complex regulatory control during normal development., A subset of CpG Island promoter genes are regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI), which become DNA hypermethylated and silenced in cancer. Here, the authors investigate the transcriptomic and epigenomic characteristics of PRC2-occupied CGI and free CGI across pan-cancer types.

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation differences in AD neuropathology, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort, we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with the AD Braak stage in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated genes in AD neuropathology, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation differences were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Our study implicated multiple new loci and biological processes that affected AD neuropathology in a sex-specific manner.

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation changes in AD, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with AD in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated AD genes, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation changes were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Importantly, a number of drugs commonly prescribed for AD patients also targeted these genes with sex-specific DNA methylation changes. Our study implicated multiple new loci and biological processes that affected AD in a sex-specific manner and highlighted the importance of sex-specific treatment regimens for AD patients.

Epigenome-wide association studies (EWAS) often detect a large number of differentially methylated sites or regions, many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here we present MethReg, an R/Bioconductor package that analyzes matched DNA-methylation and gene-expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize, and annotate CpG sites with high regulatory potential. By simultaneous modeling three key elements that contribute to gene transcription (CpG methylation, target gene expression and TF activity), MethReg identifies TF-target gene associations that are present only in a subset of samples with high (or low) methylation levels at the CpG that influences TF activities, which can be missed in analyses that use all samples. Using real colorectal cancer and Alzheimer’s disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

SUMMARY RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(–) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(–) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(–) cases are required to understand this important LUAD subset., In Brief Carrot-Zhang et al. perform whole-genome characterization of lung adenocarcinomas (LUADs) lacking RTK/RAS/RAF pathway alterations (RPAs) and identify mutations or structural variants in both coding and non-coding spaces that define a unique entity of RPA(–) LUADs and potentially explain the underlying biology of this disease., Graphical Abstract

DNA methylation differences in Alzheimer’s disease (AD) have been reported. Here, we conducted a meta-analysis of more than 1000 prefrontal cortex brain samples to prioritize the most consistent methylation differences in multiple cohorts. Using a uniform analysis pipeline, we identified 3751 CpGs and 119 differentially methylated regions (DMRs) significantly associated with Braak stage. Our analysis identified differentially methylated genes such as MAMSTR, AGAP2, and AZU1. The most significant DMR identified is located on the MAMSTR gene, which encodes a cofactor that stimulates MEF2C. Notably, MEF2C cooperates with another transcription factor, PU.1, a central hub in the AD gene network. Our enrichment analysis highlighted the potential roles of the immune system and polycomb repressive complex 2 in pathological AD. These results may help facilitate future mechanistic and biomarker discovery studies in AD., Previous studies have identified DNA methylation changes in Alzheimer’s disease. Here the authors perform a meta-analysis of DNA methylation datasets to identify epigenetic correlates of Alzheimer’s disease in the prefrontal cortex.

BackgroundDistinct genome-wide methylation patterns have consistently clustered pituitary neuroendocrine tumors (PT) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize and validate the methylation signatures that objectively classify PT into those molecular groups.MethodsCombining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumor and 20 non-tumor specimens from the pituitary gland. We also retrieved methylome data from an independent pituitary tumor (PT) cohort (N=86) to validate our findings.ResultsWe identified three methylation clusters associated with functional status and adenohypophyseal cell lineages using an unsupervised approach. We also identified signatures based on differentially methylated CpG probes (DMP), some of which overlapped with pituitary-specific transcription factors genes (SF1 and Tpit), that significantly distinguished pairs of clusters related to functional status and adenohypophyseal cell lineage. These findings were reproduced in an independent cohort, validating these methylation signatures. The DMPs were mainly annotated in enhancer regions associated with pathways and genes involved in cell identity and tumorigenesis.ConclusionsWe identified and validated methylation signatures that distinguished PT by distinct functional status and adenohypophyseal cell lineages. These signatures, annotated in enhancer regions, indicate the importance of these elements in pituitary tumorigenesis. They also provide an unbiased approach to classify pituitary tumors according to the most recent classification recommended by the WHO 2017 using methylation profiling.Key-pointsDistinct methylation landscapes define PT groups with specific functional status/subtypes and adenohypophyseal lineages subtypes.Methylation abnormalities in each cluster mainly occur in CpG annotated in distal regions overlapping predicted enhancers regions associated with pathways and genes involved in cell identity and tumorigenesis.DNA methylation signatures provide an unbiased approach to classify PT.Importance of the studyThis study harnessed the largest methylome data to date from a comprehensive cohort of pituitary specimens obtained from four different institutions. We identified and validated methylation signatures that distinguished pituitary tumors into molecular groups that reflect the functionality and adenohypophyseal cell lineages of these tumors. These signatures, mainly located in enhancers, are associated with pathways and genes involved in cell identity and tumorigenesis. Our results show that methylome profiling provides an objective approach to classify PT according to the most recent classification of PT recommended by the 2017 WHO.

Gastrointestinal adenocarcinomas (GIAC) of the tubular gastrointestinal (GI) tract including esophagus, stomach, colon, and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIAC are poorly characterized. Using 907 GIAC samples from The Cancer Genome Atlas, we applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks and identify a list of functionally hyperactive master regulator (MR) TF shared across different GIAC. The top candidate HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. A complex interplay between the HNF4A promoter and three distal enhancer elements was coordinated by GIAC-specific MRTF including ELF3, GATA4, GATA6, and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptional activation of many downstream targets, including HNF1A and factors of interleukin signaling, in a lineage-specific manner. Overall, our study provides new insights into the GIAC-specific gene regulatory networks and identifies potential therapeutic strategies against these common cancers. Significance: These findings show that GIAC-specific master regulatory transcription factors control HNF4A via three distal enhancers to promote GIAC cell proliferation and survival.

Introduction/Aim Inflammatory bowel diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), have a complex immunopathogenesis involving chronic, dysregulated inflammation of the gut in response to commensal microbes. Current therapies, such as Anti-TNF drugs, target this immune dysregulation, but many patients exhibit recurrent inflammation despite treatment (refractoriness). Research shows that IBD patient phagocytic immune cells have altered cytokine production and bacterial clearing defects. We thus set out to analyze the transcriptome of phagocytic cells (CD11b+) isolated from patient gut biopsies. To shed light on potential causes of refractoriness, we examined cells from both not inflamed (successfully treated) and inflamed (refractory) patients on Anti-TNF therapy. Methods We used magnetic sorting to isolate CD11b+ cells from the lamina propria of a diverse set of IBD patient biopsies and performed RNA sequencing (n = 61). We used differential expression analysis to compare combinations of four patient biopsy variables: location (ileum or colon), condition (inflamed or uninflamed), IBD type (CD or UC), and patient treatment (Anti-TNF therapy or other). We also performed pathway analysis on genes significantly upregulated or downregulated (p < 0.05) between these variables. Results The highest number of differentially expressed genes was found in the colon (5578 genes upregulated) vs ileum (5582). Genes upregulated in colon mapped to innate immune pathways, while ileum pathways were mainly metabolic. Only 29 genes were differentially expressed in UC vs CD, regardless of inflammation status or location. 26 genes were differentially expressed between inflamed and uninflamed biopsies on Anti-TNF therapy (n = 16). Genes upregulated in inflamed biopsies were associated with neutrophils, while genes in uninflamed biopsies were associated with adaptive immunity, short chain fatty acid synthesis, transcription and translation, and protein secretion. Conclusion CD11b+ cells of the colon are more immunologically active, likely due to their interaction with the dense microbiome of the colon. In contrast, the metabolic pathways upregulated in ileum may point to a role for phagocytes in digestion and metabolism. These results suggest that intestinal CD11b+ gene expression is highly influenced by regional microenvironment. Fewer differences were seen comparing CD11b+ cells from CD vs UC, suggesting that this cell population is not solely responsible for the clinical differences between these diseases. The small number of differentially expressed genes in Anti-TNF refractoriness shows a clear difference between innate and adaptive immune pathways, as well as increased general cell activity in Anti-TNF responders. These genes should be further investigated to determine their precise role in refractoriness.

ObjectivesOesophageal squamous cell carcinoma (OSCC) and adenocarcinoma (OAC) are distinct cancers in terms of a number of clinical and epidemiological characteristics, complicating the design of clinical trials and biomarker developments. We analysed 1048 oesophageal tumour-germline pairs from both subtypes, to characterise their genomic features, and biological and clinical significance.DesignPreviously exome-sequenced samples were re-analysed to identify significantly mutated genes (SMGs) and mutational signatures. The biological functions of novel SMGs were investigated using cell line and xenograft models. We further performed whole-genome bisulfite sequencing and chromatin immunoprecipitation (ChIP)-seq to characterise epigenetic alterations.ResultsOSCC and OAC displayed nearly mutually exclusive sets of driver genes, indicating that they follow independent developmental paths. The combined sample size allowed the statistical identification of a number of novel subtype-specific SMGs, mutational signatures and prognostic biomarkers. Particularly, we identified a novel mutational signature similar to Catalogue Of Somatic Mutations In Cancer (COSMIC)signature 16, which has prognostic value in OSCC. Two newly discovered SMGs, CUL3 and ZFP36L2, were validated as important tumour-suppressors specific to the OSCC subtype. We further identified their additional loss-of-function mechanisms. CUL3 was homozygously deleted specifically in OSCC and other squamous cell cancers (SCCs). Notably, ZFP36L2 is associated with super-enhancer in healthy oesophageal mucosa; DNA hypermethylation in its super-enhancer reduced active histone markers in squamous cancer cells, suggesting an epigenetic inactivation of a super-enhancer-associated SCC suppressor.ConclusionsThese data comprehensively contrast differences between OSCC and OAC at both genomic and epigenomic levels, and reveal novel molecular features for further delineating the pathophysiological mechanisms and treatment strategies for these cancers.

ObjectiveWhile oesophageal squamous cell carcinoma remains infrequent in Western populations, the incidence of oesophageal adenocarcinoma (EAC) has increased sixfold to eightfold over the past four decades. We aimed to characterise oesophageal cancer-specific and subtypes-specific gene regulation patterns and their upstream transcription factors (TFs). DesignTo identify regulatory elements, we profiled fresh-frozen oesophageal normal samples, tumours and cell lines with chromatin immunoprecipitation sequencing (ChIP-Seq). Mathematical modelling was performed to establish (super)-enhancers landscapes and interconnected transcriptional circuitry formed by master TFs. Coregulation and cooperation between master TFs were investigated by ChIP-Seq, circularised chromosome conformation capture sequencing and luciferase assay. Biological functions of candidate factors were evaluated both in vitro and in vivo.ResultsWe found widespread and pervasive alterations of the (super)-enhancer reservoir in both subtypes of oesophageal cancer, leading to transcriptional activation of a myriad of novel oncogenes and signalling pathways, some of which may be exploited pharmacologically (eg, leukemia inhibitory factor (LIF) pathway). Focusing on EAC, we bioinformatically reconstructed and functionally validated an interconnected circuitry formed by four master TFs—ELF3, KLF5, GATA6 and EHF—which promoted each other’s expression by interacting with each super-enhancer. Downstream, these master TFs occupied almost all EAC super-enhancers and cooperatively orchestrated EAC transcriptome. Each TF within the transcriptional circuitry was highly and specifically expressed in EAC and functionally promoted EAC cell proliferation and survival.ConclusionsBy establishing cancer-specific and subtype-specific features of the EAC epigenome, our findings promise to transform understanding of the transcriptional dysregulation and addiction of EAC, while providing molecular clues to develop novel therapeutic modalities against this malignancy.

BackgroundsGastrointestinal adenocarcinomas (GIACs) of the tubular GI tract including esophagus, stomach, colon and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIACs are less well-characterized.We applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks using 907 GIAC samples from The Cancer Genome Atlas (TCGA). Complementary epigenomic technologies were performed to investigate HNF4A activation, including Circularized Chromosome Conformation Capture (4C), Chromatin immunoprecipitation (ChIP) sequencing, Whole Genome Bisulfite Sequencing (WGBS), and Assay for Transposase-Accessible Chromatin (ATAC) sequencing. In vitro and in vivo cellular phenotypical assays were conducted to study HNF4A functions.ResultsWe identified a list of functionally hyperactive master regulator (MR)TFs shared across different GIACs. As the top candidate, HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. We further characterized a complex interplay between HNF4A promoter and three distal enhancer elements, which was coordinated by GIAC-specific MRTFs including ELF3, GATA4, GATA6 and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptionally activating many downstream targets including HNF1A and factors of Interleukin signaling in a lineage-specific manner.ConclusionWe use a large cohort of patient samples and an unbiased mathematical approach to highlight lineage-specific oncogenic MRTFs, which provide new insights into the GIAC-specific gene regulatory networks, and identify potential therapeutic strategies against these common cancers.

O câncer configura uma das maiores causas de mortalidade no mundo, caracterizando-se como uma doença complexa orquestrada por alterações genômicas e epigenômicas capazes de alterar a expressão gênica e a identidade celular. Nova evidência obtida por meio de um estudo genômico em larga escala e cujos dados encontram-se disponíveis no banco público do TCGA sugere que um em cada dez pacientes portadores de câncer pode ser classificado com maior eficácia tendo como base a taxonomia molecular quando comparada à histologia. Dessa maneira, nós hipotetizamos que o estabelecimento de mapas genômicos exibindo a localização de sítios de ligação de fatores de transcrição combinada à identificação de regiões diferencialmente metiladas e perfis alterados de expressão gênica possa nos auxiliar a caracterizar e explorar, ao nível molecular, fenótipos associados ao câncer. Avanços tecnológicos e bancos de dados públicos a exemplo do The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE) e o NIH Roadmap Epigenomics Mapping Consortium (Roadmap) têm proporcionado um recurso inestimável para interrogar o (epi)genoma de linhagens de células tumorais em cultura, bem como de tecidos normais e tumorais em alta resolução. Todavia, a informação biológica encontra-se armazenada em diferentes formatos e não há ferramentas computacionais para integrar esses dados, evidenciando um cenário atual que requer, com urgência, o desenvolvimento de ferramentas de bioinformática e softwares capazes de direcionar a solução deste obstáculo. Nesse contexto, o objetivo principal deste estudo consiste em implementar o desenvolvimento de ferramentas de bioinformática, na linguagem de programação R que, ao final do estudo, será submetido à comunidade científica do projeto Bioconductor sob a licença de código aberto GNU GPL versão 3. Além disso, ajudaremos nossos colaboradores com o aperfeiçoamento do ELMER, um pacote R/Bioconductor que identifica elementos reguladores usando dados de expressão gênica, de metilação do DNA e análise de motivo. Nossa expectativa é que essas ferramentas possam automatizar com acurácia a pesquisa, o download e a análise dos dados (epi)genômicos que se encontram atualmente disponíveis nas bases de dados públicas dos consórcios internacionais TCGA, ENCODE e Roadmap, além de integrá-los facilmente aos dados genômicos e epigenômicos gerados por pesquisadores por meio de experimentos em larga escala. Além disso, realizaremos também o processamento e a análise manual dos dados que serão automatizados pelas ferramentas, visando validar sua capacidade em descobrir assinaturas epigenômicas que possam redefinir subtipos de câncer. Por xi fim, as usaremos para investigar as diferenças moleculares entre dois subgrupos de gliomas recentemente descobertos por nosso laboratório. Cancer, which is one of the major causes of mortality worldwide, is a complex disease orchestrated by aberrant genomic and epigenomic changes that can modify gene regulatory circuits and cellular identity. Emerging evidence obtained through high-throughput genomic data deposited within the public TCGA international consortium suggests that one in ten cancer patients would be more accurately classified by molecular taxonomy versus histology. Therefore, we have hypothesized that the establishment of genome-wide maps of the de novo DNA binding motifs localization coupled with differentially methylated regions and gene expression changes might help to characterize and exploit cancer phenotypes at the molecular level. Technological advances and public databases like The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (roadmap) have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high resolution. Markedly however, biological information is stored in different formats and there is no current tool to integrate the data, highlighting an urgent need to develop bioinformatic tools and/or computational softwares to overcome this challenge. In this context, the main purpose of this study is the development of bioinformatics tools in R programming language that will be submitted to the larger open-source Bioconductor community project under the GNU GPL3 (General Public License version 3). Also, we will help our collaborators improve of the R/Bioconductor ELMER package that identifies regulatory enhancers using gene expression, DNA methylation data and motif analysis. Our expectation is that these tools can effectively automate search, retrieve, and analyze the vast (epi)genomic data currently available from TCGA, ENCODE, and Roadmap, and integrate genomics and epigenomics features with researchers own high-throughput data. Furthermore, we will also navigate through these data manually in order to validate the capacity of these tools in discovering epigenomic signatures able to redefine subtypes of cancer. Finally, we will use them to investigate the molecular differences between two subgroups of gliomas, one of the most aggressive primary brain cancer, recently discovered by our laboratory.

Lung adenocarcinomas (LUAD) are typically characterized by genetic activation of the receptor tyrosine kinase (RTK)/RAS/RAF/MAP kinase (MAPK) pathway. A minority of LUAD cases (20-25%) lack apparent genetic alterations in this pathway, and thus are ineligible for most targeted therapies. These candidate “oncogene negative” LUADs may harbor novel classes of oncogenic drivers or represent a biologically distinct class of tumors. To characterize the genomic landscape of oncogene-negative LUADs, we nominated 98 cases that were found to lack an activating RTK/RAS/RAF/MAPK pathway alteration in a TCGA study utilizing whole exome sequencing, microarray, and transcriptome data. We profiled these tumors with high-depth whole genome sequencing (WGS), with the goal of identifying noncoding and structural variant driver DNA alterations in both known and novel loci. Of the 98 cases, 20 harbored somatic KRAS mutations that had been missed in the prior WES and transcriptome studies because of insufficient coverage, including 8 cases with the recently targetable p.G12C mutation. 16 samples harbored oncogenic or loss-of-function structural variants in FGFR1, MAPK1, EGFR, NF1, RASA1, ARAF, NTRK2 and NRG1. 5 other samples with SNV or indels in EGFR, ERBB2 and SOS1 were reclassified as oncogene positive. Thus via comprehensive genomic analysis, we confirmed that 57 of the 98 WGS cases did not harbor any detectable alterations in genes encoding any known RTK/RAS/RAF/MAPK members, representing 13% cases chosen as “lung adenocarcinomas” for the TCGA study. Among the 57 confirmed oncogene-negative LUADs, we identified focal deletions targeting the promoter and transcription start site of tumor suppressor genes STK11, KEAP1 and SMARCA4 in 10 samples. Expression and methylation profiling suggested an enrichment of the TP53-deficient phenotype, including cell cycle and FOXM1 deregulation, among the oncogene-negative samples. Moreover, novel promoter mutations associated with increased expression were identified in ILF2, which regulates DNA damage response pathways. Finally, a subset of confirmed oncogene-negative LUADs harbored increased expression of neuroendocrine markers, suggesting that these oncogene-negative samples may either be mis-diagnosed as LUAD or represent LUAD with mixed features of other subtypes of lung cancer; indeed, 14 of the 57 confirmed oncogene-negative cases show histological features of large cell neuroendocrine lung carcinoma. This would suggest that 10% of the cases in this study are both lung adenocarcinoma and “oncogene-negative” to date. Our results provide some of the first comprehensive genomic characterization of oncogene-negative LUADs, implicating TP53 and structural variants in the pathogenesis of this common and difficult to treat entity. Citation Format: Jian Carrot-Zhang, Siddhartha Devarakonda, Nicolas Robine, Xiaotong Yao, Tiago C. Silva, Jeff Damrauer, Aditya Deshpande, Ming-Sound Tsao, Christina Yao, Chris Wong, Lisui Bao, Hyo Young Choi, Ina Felau, Jean C. Zenklusen, Gordon Robertson, Tuan Trieua, Wei-Wei Liang, Meng Zhou, Esther Rheinbay, Neil Hayes, Ekta Khurana, Li Ding, Peter Laird, Olivier Elemento, John Weinstein, David Kwiatkowski, Chris Benz, Josh Stuart, Lixing Yang, Mauro Castro, William Travis, Katherine Hoadley, Ben Berman, TCGA Analysis Network, Matthew Meyerson, Ramaswamy Govindan, Marcin Imielinski. Whole-genome characterization of lung adenocarcinomas lacking alterations in RTK/RAS/RAF/MAPK pathway [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5895.

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

Motivation DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. Availability and implementation ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. Supplementary information Supplementary data are available at Bioinformatics online.

INTRODUCTION Cancer is one of the leading causes of death worldwide. Although the 2% of the human genome that encodes proteins has been extensively studied, much remains to be learned about the noncoding genome and gene regulation in cancer. Genes are turned on and off in the proper cell types and cell states by transcription factor (TF) proteins acting on DNA regulatory elements that are scattered over the vast noncoding genome and exert long-range influences. The Cancer Genome Atlas (TCGA) is a global consortium that aims to accelerate the understanding of the molecular basis of cancer. TCGA has systematically collected DNA mutation, methylation, RNA expression, and other comprehensive datasets from primary human cancer tissue. TCGA has served as an invaluable resource for the identification of genomic aberrations, altered transcriptional networks, and cancer subtypes. Nonetheless, the gene regulatory landscapes of these tumors have largely been inferred through indirect means. RATIONALE A hallmark of active DNA regulatory elements is chromatin accessibility. Eukaryotic genomes are compacted in chromatin, a complex of DNA and proteins, and only the active regulatory elements are accessible by the cell’s machinery such as TFs. The assay for transposase-accessible chromatin using sequencing (ATAC-seq) quantifies DNA accessibility through the use of transposase enzymes that insert sequencing adapters at these accessible chromatin sites. ATAC-seq enables the genome-wide profiling of TF binding events that orchestrate gene expression programs and give a cell its identity. RESULTS We generated high-quality ATAC-seq data in 410 tumor samples from TCGA, identifying diverse regulatory landscapes across 23 cancer types. These chromatin accessibility profiles identify cancer- and tissue-specific DNA regulatory elements that enable classification of tumor subtypes with newly recognized prognostic importance. We identify distinct TF activities in cancer based on differences in the inferred patterns of TF-DNA interaction and gene expression. Genome-wide correlation of gene expression and chromatin accessibility predicts tens of thousands of putative interactions between distal regulatory elements and gene promoters, including key oncogenes and targets in cancer immunotherapy, such as MYC , SRC , BCL2 , and PDL1 . Moreover, these regulatory interactions inform known genetic risk loci linked to cancer predisposition, nominating biochemical mechanisms and target genes for many cancer-linked genetic variants. Lastly, integration with mutation profiling by whole-genome sequencing identifies cancer-relevant noncoding mutations that are associated with altered gene expression. A single-base mutation located 12 kilobases upstream of the FGD4 gene, a regulator of the actin cytoskeleton, generates a putative de novo binding site for an NKX TF and is associated with an increase in chromatin accessibility and a concomitant increase in FGD4 gene expression. CONCLUSION The accessible genome of primary human cancers provides a wealth of information on the susceptibility, mechanisms, prognosis, and potential therapeutic strategies of diverse cancer types. Prediction of interactions between DNA regulatory elements and gene promoters sets the stage for future integrative gene regulatory network analyses. The discovery of hundreds of noncoding somatic mutations that exhibit allele-specific regulatory effects suggests a pervasive mechanism for cancer cells to manipulate gene expression and increase cellular fitness. These data may serve as a foundational resource for the cancer research community.

ZFP36L1 is a tandem zinc-finger RNA-binding protein that recognizes conserved adenylate-uridylate-rich elements (ARE) located in 3'untranslated regions (UTR) to mediate mRNA decay. We hypothesized that ZFP36L1 is a negative regulator of a posttranscriptional hub involved in mRNA half-life regulation of cancer-related transcripts. Analysis of

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7., SCOPUS: ar.j, info:eu-repo/semantics/published

The advent of Next Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data.The Genomic Data Commons (GDC) Data Portal is a large data collection platform that includes different genomic studies included the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data without renouncing a robust statistical and theoretical framework are needed. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from the Genotype-Tissue-Expression (GTEx) platform iv) support for other genomics datasets, here exemplified by the TARGET data.Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. Henceforth, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGABiolinks v 2.8 and higher released in Bioconductor version 3.7.

Chromatin interactions measured by the 3C-based family of next generation technologies are becoming increasingly important for measuring the physical basis for regulatory interactions between different classes of functional domains in the genome. Software is needed to streamline analyses of these data and integrate them with custom genome annotations, RNA-seq, and gene ontologies. We introduce a new R package compatible with Bioconductor—Hi-C Annotation and Graphics Ensemble (HiCAGE)—to perform these tasks with minimum effort. In addition, the package contains a shiny/R web app interface to provide ready access to its functions.Availability and ImplementationThe software is implemented in R and is freely available under GPLv3. HiCAGE runs in R (version 3.4) and is freely available through github (https://github.com/mworkman13/HiCAGE) or on the web (https://junkdnalab.shinyapps.io/hicage).