Biology subjects

Silva, T. C.

Publications and source records attributed to Silva, T. C..

8 recordsLinked to original sources

bioRxiv2020

A pan-cancer analysis of CpG Island gene regulation reveals extensive plasticity within Polycomb targets

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 methylated and silenced in cancer. Here, we perform a systematic regulatory 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 and TNF-associated inflammatory response, and have the greatest cancer-type specificity among any class of 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, in contrast to PRC2-free CGI genes which are predominantly regulated by promoter TFBSs. 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, highlighting the high degree of regulatory plasticity likely derived from their complex regulatory patterns during normal development.

cancer biology↗

bioRxiv2020

DNA Methylation-based Signatures Classify Sporadic Pituitary Tumors According to Clinicopathological Features.

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-pointsO_LIDistinct methylation landscapes define PT groups with specific functional status/subtypes and adenohypophyseal lineages subtypes. C_LIO_LIMethylation 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. C_LIO_LIDNA methylation signatures provide an unbiased approach to classify PT. C_LI 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.

cancer biology↗

bioRxiv2019

Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

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.\n\nResultsWe 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.\n\nConclusionWe 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.

cancer biology↗

bioRxiv2018

Analyses of cancer data in the Genomic Data Commons Data Portal with new functionalities in the TCGAbiolinks R/Bioconductor package

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.\n\nThe 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.\n\nEvidence 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.

bioinformatics↗

bioRxiv2018

HiCAGE: an R package for large-scale annotation and visualization of 3C-based genomic data

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.\n\nAvailability 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).

bioinformatics↗

bioRxiv2018

Moonlight: a tool for biological interpretation and driver genes discovery

Cancer is a complex and heterogeneous disease. It is crucial to identify the key driver genes and their role in cancer mechanisms with attention to different cancer stages, types or subtypes. Cancer driver genes are elusive and their discovery is complicated by the fact that the same gene can play a diverse role in different contexts. Key biological processes, such as cell proliferation and cell death, have been linked to cancer progression. Thus, in principle, they can be exploited to classify the cancer genes and unveil their role. Here, we present a new method, Moonlight, that exploit expression data to classify cancer genes. Moonlight relies on the integration of functional enrichment analysis, gene regulatory networks and upstream regulator analysis from expression data to score the importance of biological cancer-related processes taking into account either the inter- or intra-tumor heterogeneity. We then employed these scores to predict if each gene acts as a tumor suppressor gene (TSG) or as an oncogene (OCG). Our methodology also allow to predict genes with dual role, i.e. the moonlight genes (TSG in one cancer type or stage and OCG in another), as well as to elucidate the underlying biological processes. Availability: https://bioconductor.org/packages/MoonlightR & https://github.com/ibsquare/MoonlightR/

bioinformatics↗

bioRxiv2017

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends the molecular diagnosis of the IDH mutational status in gliomas. IDH-mutant gliomas manifest the CpG Island Methylator Phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provide a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.\n\nConflict of interestThe authors declare no conflict of interest

cancer biology↗

bioRxiv2017

TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data

BackgroundThe GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users.\n\nResultsTo overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed TCGAbiolinksGUI that uses shiny graphical user interface (GUI) available through the R/Bioconductor package.\n\nConclusionThe TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available at http://bit.do/TCGAbiolinksDocs.

bioinformatics↗