Recurrent rearrangements of FOS and FOSB define osteoblastoma.

The transcription factor FOS has long been implicated in the pathogenesis of bone tumours, following the discovery that the viral homologue, v-fos, caused osteosarcoma in laboratory mice. However, mutations of FOS have not been found in human bone-forming tumours. Here, we report recurrent rearrangement of FOS and its paralogue, FOSB, in the most common benign tumours of bone, osteoblastoma and osteoid osteoma. Combining whole-genome DNA and RNA sequences, we find rearrangement of FOS in five tumours and of FOSB in one tumour. Extending our findings into a cohort of 55 cases, using FISH and immunohistochemistry, provide evidence of ubiquitous mutation of FOS or FOSB in osteoblastoma and osteoid osteoma. Overall, our findings reveal a human bone tumour defined by mutations of FOS and FOSB

Inference and validation of cancer gene regulatory networks

While cancer is unmistakably a genetic disease, it is becoming increasingly apparent that it shares many of its essential characteristics with those typical for developmental processes. Indeed, cancer cells can be considered a potential cell type arising by acquiring specific mutations that alter its regulatory state. Underlying such a cancerous phenotype is a specific gene regulatory network (GRN). In this light, gaining mechanistic insight into the structure and dynamics of GRNs is essential when trying to understand oncogenesis and cancer progression. Decoding these networks will be key in the rational development of new cancer therapies. To this end, an integrated approach is needed, combining high-throughput technologies such as next generation sequencing with computational methods and advanced experimental validation tools to explore the GRNs underlying cancer. In this thesis, we have used several integrated approaches to identify and decode regulatory networks underlying two different cancer types. On the one hand we have combined transcriptomics, epigenomics and extensive computational methods with experimental validation to determine the regulatory differences that make up the heterogeneity underlying melanoma. Particularly, we identified two distinct cellular states, each with their own regulatory network and pinpointed essential master regulators for these networks. Additionally, using experimental validation techniques we have shown the relevance of these networks to the biology and potential treatment of melanoma. At the same time we have worked on the development of new tools and methods that allow better prediction and validation of GRNs and the components thereof. With the development of iRegulon, we provided a new computational method that proposes potential master regulators of a set of co-expressed genes. By integrating motif and track discovery, this tool brings motif prediction and network engineering to a new level, and allows biologist to gain a better insight into their data. During my PhD I have been closely involved in the testing and validation of this tool and have exploited it to map p53 related networks. For instance, by using iRegulon on a set of co-expressed genes after p53 activation, we managed to expand the network of p53 even further, identifying a range of novel target genes. Additionally, we generated several hypotheses to answer some crucial questions still much debated today in the field of p53. Building on this work, we have also developed a new experimental method called CHEQ-seq that allows researchers to functionally validate predicted enhancers. Using CHEQ-seq on our previously proposed expanded p53 network, we validated a number of regulatory regions as functional enhancers. The tool also allowed us to work towards a model to better understand and predict p53 binding on a global scale and answer some of the questions previously formulated with iRegulon. All together, this work shows that an integrated approach, combining both high-throughput computational and experimental methods can create a more complete view on genomic regulatory control and provide us with a deeper understanding of the molecular pathology of cancer.nrpages: 202status: publishe

iRegulon and i-cisTarget: Reconstructing Regulatory Networks Using Motif and Track Enrichment

Gene expression profiling is often used to identify genes that are co-expressed in a biological process or disease. Downstream analyses of co-expressed gene sets using bioinformatics methods can reveal candidate transcription factors (TF) that co-regulate these genes, based on the presence of shared TF binding sites. Drawing gene regulatory networks that connect TFs to their predicted target genes can uncover gene modules that implement a particular function. Here, we describe several protocols to analyze any set of co-expressed genes using iRegulon and i-cisTarget. These tools perform regulatory sequence analysis (motif discovery) and integrate and mine large collections of existing regulatory data, such as ChIP-Seq, DHS-seq, and FAIRE-seq (track discovery). While iRegulon focuses on sets of co-expressed genes, i-cisTarget also analyses genomic regions as input. The following protocols describe how to install and use these tools, how to interpret the obtained results, and will thus help to create meaningful regulatory networks. © 2015 by John Wiley & Sons, Inc.status: publishe

A novel High-throughput Enhancer reporter assay reveals unsophisticated p53 enhancer logic

Deciphering the cis-regulatory logic encoded in enhancer sequences requires large-scale reporter assays to experimentally validate candidate enhancers predicted by genomic approaches such as chromatin accessibility and ChIP-seq. Here, we propose a novel high-throughput enhancer-reporter assay called CHEQ-Seq (Captured High-throughput Enhancer testing by Quantitative Sequencing). A set of candidate enhancers are pre-selected as regions of 0.5-1 kb and enriched from genomic, sheared DNA using custom-designed capturing baits. They are subsequently cloned into a reporter library and randomly combined with unique barcodes, before being tested under various conditions in cell culture. The relationship between each enhancer and its reporter-barcode is determined by PacBio long-read sequencing of the entire library; while the barcode expression level is determined by Illumina short-read cDNA sequencing. We have applied Cheq-seq to test the enhancer activity of 1526 p53 ChIP-seq peaks under p53 knock-down and p53 over-activating conditions. We obtained reproducible reporter expression for 1060 captured enhancers, of which 397 showed a significant p53-dependent activation. Strikingly, the large majority (99%) of p53 target enhancers can be characterized and distinguished from negative sequences by the occurrence of a single p53 binding site. Thus, the p53 enhancer logic represents a new ancestral class of enhancers, distinct from developmental enhancers that adhere to the billboard and enhanceosome models. The p53 enhancers do not contain obvious combinatorial complexity of binding sites for multiple transcription factors. This suggests that p53 acts alone on its target enhancers, and that context-dependent regulation of target genes is not encoded in the p53 enhancer sequences, but at different upstream or downstream layers of the cell’s gene regulatory network.status: accepte

EXPLORING THE P53 TRANSCRIPTIONAL NETWORK THROUGH INTEGRATIVE GENOMICS REVEALS NEW CANDIDATE TARGET GENES AND CO-FACTORS

As tumor suppressor many roles have been ascribed to p53 like cell cycle arrest and apoptosis but also metabolism and developmental processes. p53 functions as a transcription factor (TF) by interacting with a variety of target genes of which many have been reported but p53’s full targetome is likely incomplete. In addition, many other aspects of p53’s activity require further investigation. To address these questions we performed RNA-seq on MCF7-cells, revealing a list of differentially expressed genes. On this set we applied an in-house developed motif discovery tool called iRegulon, generating subsets of direct and indirect target genes. It also enabled us to retrieve possible master regulators like p53 itself but also possible new co-factors like AP-1. We observed E2F as regulator of the downregulated targets with a pronounced absence of the p53 motifs amongst these genes supporting the possibility of a p21-Rb-E2F approach for p53-repression. Next, we performed both ChIP-seq and FAIRE-seq in order to get a comprehensive view on the genomic landscape of p53 binding. While the p53 ChIP peaks improved our predicted set of p53 targets, the FAIRE profile established a correlation between open chromatin regions and upregulated genes. Finally we selected four enhancers from our direct targets for in vitro validation. Three of four enhancers showed the ability to functionally drive gene expression. In conclusion, by using NGS experiments, motif discovery and experimental validation we were able to address key questions about p53’s transcriptional mechanism and identify several new candidate target genes.status: accepte

EXPLORING THE p53 TRANSCRIPTIONAL NETWORK THROUGH INTEGRATIVE GENOMICS

status: accepte

A pan-cancer landscape of somatic mutations in non-unique regions of the human genome

A substantial fraction of the human genome displays high sequence similarity with at least one other genomic sequence, posing a challenge for the identification of somatic mutations from short-read sequencing data. Here we annotate genomic variants in 2,658 cancers from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort with links to similar sites across the human genome. We train a machine learning model to use signals distributed over multiple genomic sites to call somatic events in non-unique regions and validate the data against linked-read sequencing in an independent dataset. Using this approach, we uncover previously hidden mutations in ~1,700 coding sequences and in thousands of regulatory elements, including in known cancer genes, immunoglobulins and highly mutated gene families. Mutations in non-unique regions are consistent with mutations in unique regions in terms of mutation burden and substitution profiles. The analysis provides a systematic summary of the mutation events in non-unique regions at a genome-wide scale across multiple human cancers.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Evidence for DNA-binding domain-ligand-binding domain communications in the androgen receptor

DNA binding as well as ligand binding by nuclear receptors has been studied extensively. Both binding functions are attributed to isolated domains of which the structure is known. The crystal structure of a complete receptor in complex with its ligand and DNA-response element, however, has been solved only for the peroxisome proliferator-activated receptor gamma (PPAR gamma)-retinoid X receptor alpha (RXR alpha) heterodimer. This structure provided the first indication of direct interactions between the DNA-binding domain (DBD) and ligand-binding domain (LBD). In this study, we investigated whether there is a similar interface between the DNA- and ligand-binding domains for the androgen receptor (AR). Despite the structural differences between the AR- and PPAR gamma-LBD, a combination of in silico modeling and docking pointed out a putative interface between AR-DBD and AR-LBD. The surfaces were subjected to a point mutation analysis, which was inspired by known AR mutations described in androgen insensitivity syndromes and prostate cancer. Surprisingly, AR-LBD mutations D695N, R710A, F754S, and P766A induced a decrease in DNA binding but left ligand binding unaffected, while the DBD-residing mutations K590A, K592A, and E621A lowered the ligand-binding but not the DNA-binding affinity. We therefore propose that these residues are involved in allosteric communications between the AR-DBD and AR-LBD

Expression and chromatin profiling of melanoma reveals novel candidate master regulators supporting the phenotype switching model

Melanoma is one of the most aggressive cancers to date and is marked by a therapy-resistant character reflecting its high heterogeneity and plasticity. Expression profiling of melanoma shows the presence of at least two distinct transcriptional programs within the tumor reflecting different cellular states: more proliferative versus invasive and migratory cells. These states are defined within a phenotype switching model, a process that is thought to be driven by the cells microenvironment rather then by mutations. Besides characterizing each state with a specifc expression program, this model attributes cells with the capacity to switch back and forth between each cellular state. However, the underlying molecular mechanisms and the origin of these states are poorly understood. Identifying the key regulatory networks behind them would mean a great advancement in comprehending melanoma. To investigate this, we combined publicly available data from TCGA, ENCODE and GEO with in-house datasets generated from patient-derived cultures. Besides gene expression data, we applied FAIRE-seq and ChIP-seq against both H3K27ac and H3K27me3 to investigate the chromatin activity. In addition, we used a large collection of transcription factor binding motifs allowing us to identify master regulators within each state. Together, this data enabled us to identify SOX10/MITF and AP1/STAT/TEAD as potential master regulators for the proliferative and invasive state. In addition, the chromatin data allowed us to pinpoint distal enhancers controlled by these factors. Finally, to support our findings, we performed a perturbation of TEAD to verify its role in the invasive state of melanoma tumor cells. All together, by collecting and mining through an extensive set of data we gained insight into the mechanisms supporting melanoma’s plasticity and heterogeneity. This itself is a step forward and can proof to be vital for the development of more effective therapies to battle melanoma.status: accepte

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although the binding preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, the genome is scattered with recognition sequences that remain unoccupied. Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif. By combining machine learning with a meta-analysis of TP53 ChIP-seq data sets, we identified a core set of more than 1000 responsive enhancers in the human genome. This TP53 cistrome is invariably used between cell types and experimental conditions, whereas differences among experiments can be attributed to indirect nonfunctional binding events. Our data suggest that TP53 enhancers represent a class of unsophisticated cell-autonomous enhancers containing a single TP53 binding site, distinct from complex developmental enhancers that integrate signals from multiple transcription factors.status: publishe