Neuroblastoma (NB) is one of the most frequently occurring neurogenic extracranial solid cancers in childhood and infancy. Over the years, many pieces of evidence suggested that NB development is controlled by gene expression dysregulation. These unleashed programs that outline NB cancer cells make them highly dependent on specific tuning of gene expression, which can act co-operatively to define the differentiation state, cell identity, and specialized functions. The peculiar regulation is mainly caused by genetic and epigenetic alterations, resulting in the dependency on a small set of key master transcriptional regulators as the convergence point of multiple signalling pathways. In this review, we provide a comprehensive blueprint of transcriptional regulation bearing NB initiation and progression, unveiling the complexity of novel oncogenic and tumour suppressive regulatory networks of this pathology. Furthermore, we underline the significance of multi-target therapies against these hallmarks, showing how novel approaches, together with chemotherapy, surgery, or radiotherapy, can have substantial antineoplastic effects, disrupting a wide variety of tumorigenic pathways through combinations of different treatments. Childhood and paediatric cancers are among the most relevant causes of death that affect children during the first years of life. Although researchers took into consideration several risk factors that can determine cancer development in children and adolescents, their causes are mostly unknown. Moreover, the absence of recommended screening tests able to define tumour formation and evolution was complicated by methodological difficulties related to the biological variety of these diseases, all contributing to the poor pathology outcome. Nevertheless, progresses of new high-throughput sequencing techniques and the application of genome-wide analyses made it possible to better understand the genetic backgrounds of many paediatric cancers, which influences their initiation and evolution. In this context, neuroblastoma (NB) is a clear example of how altered gene expression affects the dysregulation of crucial cellular mechanisms, such as proliferation, differentiation, chromosome stability, and self-renewal . RemarkaThe cellular origin of NB is not precisely identified yet; therefore, the observed clinical diversification of NB is ascribed to the disruption of the meticulously arranged process of neural crest maturation through the connection of many molecular components at various stages. The broad spectrum of NB clinical behaviour describes a challenging goal for diagnosis, prognosis, and selection of the most fruitful treatment strategy—moreover, its obstacles compare clinical trials between different studies. In 1988, an international congress was held to elaborate a system intended to help the clinical trial procedures. The result was the International NB Staging System (INSS), according to which NB can be classified into five distinct stages . Stage 1To date, no single genetic lesions are known to account for all NB patients. These data support the idea of NB as a spectrum of diseases rather than a single pathological condition. Nonetheless, some genetic alterations and oncogenic drivers have diagnostic and prognostic significance in specific stratification groups  4,5]. A,5. A4,5]Despite PHOX2B, the anaplastic lymphoma kinase (ALK) gene is considered the major susceptibility gene for familial NB and the most often mutated gene in sporadic cases. ALK encodes a transmembrane receptor tyrosine kinase involved in nervous system development. It acts as an oncogene for several human tumours , mainly activated by chromosomal rearrangements resulting in fusion genes. Differently, the most frequent ALK alterations in NB are both sequence mutations, involving the tyrosine kinase protein domain and causing constitutive ALK activation and kinase activity, and copy number alterations, given by trisomy 2p and gene amplification ,11. ALK https://ocg.cancer.gov/programs/target_phs000467 ) ..100].Although not yet approved by the American agency of Food and Drug Administration (FDA), the application of BETi seems to be one of the most promising approaches to treat NB patients with MYCN amplification. Further proof of this was provided by developing new drugs, such as birabresib  transcriptional activation is regulated by a specific set of cyclin-dependent kinases (CDKs), including CDK7 (cyclin-dependent kinase 7), a crucial component of the transcription initiation factor TFIIH that phosphorylates RNA Pol II to start transcription. In 2014, Chipumuro et al. reported that a covalent inhibitor of cyclin-dependent kinase 7 (CDK7), THZ1, was found to disrupt the transcription of MYCN-amplified NB cells selectively, leading to global repression of N-Myc-dependent transcriptional amplification and induction of tumour regression in mice models , Table 2These novel therapeutic approaches are gaining even greater importance considering the effect on the regulation on the CRC and global gene expression, confirming how particularly JQ1 and THZ1 injection can rapidly decrease the expression of CRC mRNA levels after just one hour of treatment in MYCN-amplified NB cells. The expression level of each of the CRC transcription factor genes was dramatically downregulated by the combination of JQ1 and THZ1, with more restricted consequences regarding either drug alone. These results underlined the impact of JQ1 and THZ1 combination treatment in MYCN-amplified NB, although the broad implication of combining transcriptional disruption is still not fully understood . In lineN-Myc is a nuclear intrinsically disordered protein that can exist in distinct complexes within the same cell by interacting with hundreds of components to keep the cell identity ,217,218.All the Myc proteins are basic helix–loop–helix/leucine zipper (bHLH/LZ) transcription factors capable of dimerising with the partner MAX (MYC-associated factor X) to regulate up to 10–15% of all genes. These data assume additional relevance within MYCN biology in NB models, considering how MAX can instruct transcriptional programmes that either reinforce or weaken the oncogenic process enacted by N-Myc . Thus, tAs for c-MYC, the N-Myc protein degradation is mainly induced by the ubiquitin–proteasome system . The disMYCN-amplified NBs show deregulation of several enzymes involved in polyamine metabolisms, such as ODC1, SRM, SMS, AMD1, OAZ2, and SMOX . PolyamiOn the other hand, N-Myc dysregulation can influence cell cycle progression by upregulating several genes, such as cyclin D2, E2F proteins, CDK4/6, and CDC2, resulting in the inactivation of genes involved in the G1 phase and DNA replication  235,236236. ThesOne of the principal pathways accountable for pushing the malignant transformation and drug resistance in solid tumours is the phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR pathway. PI3K is a class of molecules divided into three categories based on structure, regulation, and function . This paNowadays, mutated ALK is one of the few directly targetable main oncogenes in NB . As discFirst-generation ALK inhibitor crizotinib is a small-molecule adenosine triphosphate (ATP)—a competitive inhibitor of ALK kinase activity. Crizotinib showed satisfactory results in some paediatric cancers carrying gain-of-function ALK mutation. However, in a phase I clinical trial involving 11 NB patients with known ALK mutation, only one patient with Arg1275Gln mutation experienced complete regression and two patients with Arg1275Gln and Phe1174Leu mutation experienced stable disease . This isIn compliance with the low frequency of recurrent somatic mutations, NB is mainly characterised by the dysregulation of a multitude of genetic and epigenetic mechanisms, leading patients to poor outcomes . This caBased on the vast heterogeneity of the HDAC classes, many studies on different pathologies describe a full-blown uneven cellular response to both the specific and nonspecific HDAC inhibition ,250. ConFinally, recent studies suggested how HDAC inhibition can drive the enhancer remodelling and suppression of oncogenic super-enhancers by disrupting the three-dimensional structure of the chromatin looping and by depleting transcription factors on the same DNA sites ,263.Immuno-targeting MYCN-amplified NBs are challenging, since N-Myc is involved in downregulation of the major histocompatibility complex (MHC) class I antigen expression, leading to escape from cytotoxic T cells and interferon-mediated immune response ,265. TheGangliosides are modified sphingolipids highly expressed from cancer cells. Generally, gangliosides are not considered for target therapy because they are also found in healthy tissues. Exceptionally, the ganglioside GD2 is highly expressed on the surface of NB cells, and its expression in normal tissue is limited at a relatively low level to neurons, skin melanocytes, and peripheral nerve fibres  268]. M. M268]. NB is one of the topmost common neurogenic extracranial solid cancers occurring in childhood and infancy. The extreme heterogeneity of this pathology is still considered a significant challenge to overcome and assumes a relevant aspect for developing novel therapeutic strategies. Several new pieces of evidence show the critical level of plasticity of this neoplasia, unveiling the role of numerous layers of regulation that drastically influence patient outcomes. This review aims to provide a comprehensive blueprint of the transcriptional regulation bearing NB initiation and progression, unravelling the complexity of this pathology’s key oncogenic and tumour suppressive regulatory networks. Some interesting points emerged from our analysis.Firstly, while most familial NB cases are characterised by a “specific” subset of genetic alterations, the same cannot be said regarding the sporadic ones. Hence, the vast majority of NB patients are distinguished by multiple levels of epigenetic alterations. It is important to underline how this sort of dichotomy between these two subgroups of NBs is just apparent; indeed, the most severe NB cases are characterised by both genetic and epigenetic aberrations, resulting in widespread deregulation of gene expression profiles and disruption of these signalling networks that control proliferation and cellular response. These data lay the groundwork for evaluating the whole contribution of these intricate networks of regulation, which influence the destiny of the patients like a single far-reaching entity. Secondly, most NBs include tumour cells with diverging gene expression profiles: the two main subgroups are the undifferentiated mesenchymal (MES) cells and the committed adrenergic (ADRN) ones, which can interconvert and resemble from different lineage differentiation stages. Once again, NB’s identities are not governed by small groups of macromolecules, but by a set of core regulatory circuitries of lineage transcription factors associated with super-enhancers and several protein complexes that drastically influence the fate of the cell. This new evidence demonstrates that cancer cells’ diverging transcriptional states match with the normal lineage development stages. In particular, lineage transcription factors can lead to transdifferentiation via remodelling the epigenetic and transcriptional landscapes, mimicking the natural interconversion. All this information assumes additional relevance considering the different response of ADRN and MES cells to therapeutics, giving more strength to studying these complex grids of regulation. Third, the more profound investigation of these new vulnerabilities paves the way for developing novel therapeutic approaches, bypassing the difficulties in obtaining compounds against molecules complex to target, such as several master TFs like the same N-Myc protein. The significance of a network-based targeted therapy in neuroblastoma could be of great importance considering the standard strategies’ weakening approach in treating high-risk patients , distinguished by metastatic and aggressive tumours. Although the interplay between regulatory networks and oncogenesis has been unequivocally described, more in-depth research into their role in modulating cancerous-unrelated signalling pathways still needs to be carried out. According to the novel findings summarised above, discovering this intricate reticulum of interactions represents invaluable predictive factors for early-onset disease detection.


CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages and quantify the impact of all possible NCVs. To demonstrate applicability to NCV screens, we profile TF-COF binding to ∼1,700 single-nucleotide polymorphism quantitative trait loci (SNP-QTLs) in human macrophages and identify perturbed ETS domain-containing complexes. CASCADE will facilitate high-throughput testing of molecular mechanisms of NCVs for diverse biological applications.Non-coding DNA variants (NCVs) impact gene expression by altering binding sites for regulatory complexes. New high-throughput methods are needed to characterize the impact of NCVs on regulatory complexes. We developed CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements), an array-based high-throughput method to profile cofactor (COF) recruitment. CASCADE identifies DNA-bound transcription factor-cofactor (TF-COF) complexes in nuclear extracts and quantifies the impact of NCVs on their binding. We demonstrate CASCADE sensitivity in characterizing condition-specific recruitment of COFs p300 and RBBP5 (MLL subunit) to the   •CASCADE profiles binding of TF-COF complexes to DNA sequences•CASCADE identifies cell-state-specific binding of complexes at CREs•CASCADE can profile complexes affected by single-nucleotide polymorphisms CXCL10 cytokine promoter and to ∼1,700 single-nucleotide polymorphisms (SNPs). They anticipate that CASCADE can be applied to diverse biological systems to examine regulatory complex binding to DNA.Bray et al. develop CASCADE, a method to profile transcription factor (TF)-cofactor (COF) complexes binding to DNA. They demonstrate the approach by profiling complex binding across the  To date, genome-wide association studies (GWASs) have identified tens of thousands of NCV-trait associations, but the causal variants and their mechanism of action remain largely unknown.CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages. This demonstrates the accuracy of CASCADE for quantifying the impact of DNA variants on TF-COF binding. To demonstrate the utility of CASCADE in identifying TF-COF binding and the sequence specificity of COF recruitment, we profile TF-COF complex binding to CREs and non-coding SNP quantitative-trait loci (SNP-QTLs) in a stimulus-specific manner using human macrophages stimulated with LPS and interferon gamma (IFN-γ). CASCADE represents a methodological advance in the functional annotation of NCVs, establishing a direct link between NCVs and COFs that mediate diverse effects on gene regulation. We anticipate that CASCADE can be applied to a wide range of cellular systems for which antibody labeling reagents are available and sufficient high-concentration nuclear extracts can be generated, such as any cell line or primary cell or tissue system in which ∼100–200 million cell nuclei can be acquired.Here we report the development of CASCADE (customizable approach to survey complex assembly at DNA elements), an array-based method used to identify DNA-bound TF-COF (cofactor) complexes and measure the impact of NCVs on their binding. CASCADE addresses the practical limitations of current methods, enabling high-throughput characterization of the impact of NCVs on gene regulatory complexes in a cell-specific manner. TFs primarily function by recruiting COFs to DNA, which subsequently alters gene expression through diverse mechanisms including interactions with the transcriptional machinery , histone modification , DNA modification , chromatin remodeling , or bridging to other COFs  A.18 CASCin vitro approach to identify TF-COF complexes binding to CREs and to quantify the impact of NCVs on their binding.To identify cell-specific TF-COF complexes impacted by NCVs, we have developed the CASCADE method in which we use protein-binding microarrays (PBMs) incubated with nuclear extracts to profile the “recruitment” of COFs  to thousands of DNA probes A and 1B.CXCL10 in resting and LPS-stimulated human THP-1-derived macrophages. p300 is a broadly acting acetyltransferase that is recruited by diverse TFs, including both NF-κB and IRF3, which function at the CXCL10 promoter.,,CXCL10 promoter was chosen because it has been well studied and previously shown that the TFs that bind to this promoter are stimulus dependent. In LPS-induced activation of CXCL10 in macrophages, three separate TF binding sites in the promoter are required for full activation: two NF-κB binding sites and an interferon-sensitive response element (ISRE),To demonstrate the utility of CASCADE for profiling TF-COF binding and determining the DNA-sequence specificity of COF recruitment, we sought to examine a well-studied CRE with established TF binding sites bound by stimulus-dependent TF-COF complexes. We profiled the recruitment of the COF EP300, hereafter p300, to a promoter segment of the chemokine gene CXCL10 promoter segment (166 bp), we assayed recruitment to 29 tiling probes (each 26 bp long) generated at 5 bp intervals across the target promoter region  associated with gene expression and chromatin state in human immune cells. To increase the number of SNPs that we could screen, we developed a hierarchical two-step approach A. In ste,,,,We used this two-step approach to profile COF recruitment to 1,712 SNPs that have been previously associated with gene expression  and chromatin accessibility (caQTLs) changes in myeloid cellsOur step-one screen identified 164 total SNP-QTLs that reproducibly altered the recruitment of at least one of the tested COFs B and 3C,Z score) and non-reference alleles that led to a significant loss relative to the reference allele (Z score). In total, across all COFs and TFs screened, we observed differential recruitment and/or binding at 243 of the 1,712 non-reference (14.2%) alleles with 134 gains, 108 losses, and one locus demonstrating both. Of note, for each SNP-QTL that exhibited significant differential recruitment of more than one COF , the direction of the effect, either gain or loss, was consistent across each COF.By examining the direction of the differential recruitment, we identified SNP-QTLs that caused gain or loss of TF-COF binding B and S3.in vivo relevance. Furthermore, the ability to use customizable DNA probes to profile any possible NCV represents a key advantage over ChIP-based approaches that are constrained to heterozygous variants available in cell lines.To compare the results of our SNP-QTL screen with existing approaches to characterize COF recruitment at NCVs, we performed allelic imbalance analysis on 436 publicly available ChIP-seq experiments for the THP-1 cell line  and S4. For step two of our SNP-QTL analysis, we used CASCADE to determine COF recruitment motifs at select loci. These motifs allow us to infer the identity of the TFs mediating differential COF recruitment at each locus A, step 2in vivo, we examined available ENCODE ChIP-seq datasets where four of the COFs in our study  were profiled in K562 cells.in vivo.Our motif analyses also identified that a number of COFs share similar motif profiles, suggesting that at these loci they are recruited by a common TF or by related TF-family members. We first examined whether this is a result of the particular QTLs analyzed or a more general feature of these COFs. To examine COF specificity in our CASCADE assay, we designed a new microarray to profile COF recruitment to a panel of ∼350 consensus TF sites (and single-nucleotide variants) derived from motifs in the JASPAR database see . ProfiliZ score: 2.36), SMARCA4 (Z score: 2.99), and TBL1XR1 (Z score: 2.61) are recruited to the non-reference allele but are either not recruited or are below our detection threshold for the reference allele  (Z score: 5.99), though it could also be detected at the reference allele (Z score: 4.04). These results suggest a model where the non-reference allele enhances the DNA binding of an ETS-family TF, possibly PU.1, which leads to enhanced recruitment of these COFs , a basal SNP-eQTL in myeloid cells,e: 0.37) . The COFe: 0.37) A, right.ese COFs C. We notZ scores: REF = 9.71, non-REF = 28.49), GCN5 , and RBBP5  , in myeloid cells= 15.82) . The COF= 15.82) B, right,= 15.82) B, left. = 15.82) D, possibCXCL10 promoter  and then assayed a reduced set of significant loci at the single-nucleotide level to identify COF motifs . A similar two-stage approach could also be used to more efficiently profile TF-COF binding to CREs to achieve greater coverage. By first profiling binding using 5-bp overlapping tile sequences, we could screen 84,691 bp of CRE sequence from the genome and then assay 214 significantly bound loci at the single-nucleotide level.in vitro assays, our approach is meant to characterize the possible complexes that can assemble on each DNA loci when DNA is accessible. While these loci may be inaccessible in the native chromatin environment, one can integrate this approach with chromatin maps to bias the loci being analyzed. For example, one might first identify open chromatin regions using ATAC-seq or by profiling histone marks and then follow up with a CASCADE analysis to profile the TF-COF binding and impact of SNPs.Second, there is the question of how our binding results relate to genomic binding of regulatory complexes when chromatin factors  are not present on our DNA arrays. As with all CXCL10 promoter analysis. Finally, it remains unclear the extent to which multi-protein COF complexes are assembled on our DNA microarrays in their native conformation and whether we are assaying the recruitment of smaller sub-complexes or even single COFs . In either case, we demonstrate that profiling subunits of multi-protein COF complexes  can sensitively and accurately report on the impact of NCVs on the binding of associated TFs, validating the basic approach.The third is the technical requirement of CASCADE for high-concentration nuclear extracts and antibodies in order to probe the COFs of interest, which may be difficult to obtain when cellular material is limited and antibodies are not available. The fourth is the need for additional analyses to refine TF annotations beyond those attainable by motif comparison. In our approach, DNA-bound TFs are identified indirectly via motif comparison against COF recruitment motifs. Therefore, our approach is unable to distinguish between related TFs that have similar DNA binding motifs, which is often the case for members of the same TF family. Integrating CASCADE results with other data types, such as gene expression or protein abundance data, can provide a way to refine our TF annotations for follow-up experiments, as was done for the IRF and NF-κB-family proteins in our CXCL10 promoter, we identified binding of stimulus-specific TF-COF complexes and exhaustively mapped the impact of all NCVs on TF-COF binding across the promoter region. We propose that this approach can be used to map any CRE to uncover cell- or stimulus-specific TF-COF regulatory complexes and their DNA-sequence dependence. Using CASCADE to profile 1,712 SNP-QTLs previously associated with chromatin or gene expression changes, we identified altered binding for a number of ETS-family TF-COF complexes. We anticipate that CASCADE can be used to characterize the molecular impact of NCVs across diverse cell and stimulation conditions. Important next steps will be to apply the CASCADE approach to a range of disease-associated SNPs and NCVs using both cell lines and primary human cells to provide functional annotations and insights about the mechanisms of DNA variants.To demonstrate the utility of the CASCADE method, we applied it to the functional characterization of CREs and SNP-QTLs in resting and LPS-stimulated human macrophages. By profiling recruitment of activators (p300 and RBBP5) to the tsiggers@bu.edu).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Trevor Siggers  was established from a make who had acture monocytic leukemia.The CASCADE method is used to profile the impact of non-coding DNA variants on TF-COF complex binding and specifically refers to PBM experiments performed using cell nuclear extracts in which COFs, as opposed to TFs, are fluorescently labeled in the experiment. Experimental details are included below in the section titled ‘THP-1 cells, a human monocyte cell line, were obtained from ATCC (TIB-202). The cells were grown in suspension in RPMI 1640 Glutamax media  with 10% heat-inactivated fetal bovine serum  and 1mM sodium pyruvate . T175  non-treated flasks were used when culturing THP-1 cells for experiments. Cells were grown in 50mL of media when being cultured in T175 flasks.5 cells/mL and treated with 25ng/mL Phorbol 12-Myristate 13-Acetate (PMA)  for 4 days. Following the 4 days of PMA treatment, the media was replaced with fresh RPMI media with 10% heat-inactivated fetal bovine serum and 1mM sodium pyruvate. The cells rested for two days in the fresh media before being stimulated with various reagents.To differentiate THP-1 cells into adherent macrophages, cells were grown to a density of 8.0 × 10THP-1 cells differentiated with PMA were treated with either Lipopolysaccharide (LPS)  or Interferon gamma (IFN-γ)  in combination with LPS. PMA treated THP-1 cells were treated with 1μg/mL of LPS for 45 min or with 100ng/mL IFN-γ for 2 h followed by 1μg/mL LPS for 1 h. For each condition, nuclear lysates were harvested. For all nuclear lysates assayed using PBM experiments, the expression levels of COFs and TFs profiled with CASCADE were confirmed by western blotting .The nuclear extract protocols are as previously described,,,Microarray DNA double stranding and PBM protocols are as previously described.CXCL10 promoter  was used in our array design.CXCL10 promoter segment. “Target” sequences corresponding to the genomic locus were obtained from the hg38 genome fasta file from Illumina iGenomes using the “fastaFromBed” function from bedtools v2.26.0.CXCL10 promoter segment (29 tiles + 29 × 3 variant probes x 26 positions). 500 additional 26-base target regions were randomly selected from the hg38 using the bedtools “shuffleBed” function and included in the array design to build a background distribution. Each 26-base target region in the array design was embedded in a larger 60-base PBM probe as follows:A known LPS-responsive segment of the “GCCTAG” 5′ flank – 26-base target region – “CTAG” 3′ flank – “GTCTTGATTCGCTTGACGCTGCTG” double-stranding primerEach target region was included in its reference (+) orientation as well as the reverse complement (-) orientation. 5 replicate spots of each probe (in each orientation) were included in the final array design. PBM microarray probes, relevant annotation for each, and the experimental results are provided . The micTo design the screen for differential COF recruitment at SNPs, the lead SNPs uncovered in previous studies were included in our high-throughput screen as follows: 1,446 basal eQTLsEach REF/non-REF pair was screened for differential recruitment of p300, SMARCA4, TBL1XR1, RBBP5, and GCN5 as well as differential binding of representative ETS factor PU.1. NextPBM experimental results were preprocessed as above. Z-scores were obtained for each probe as previously describedCXCL10 promoter segment used previously. In addition, only 291 background probes were included due to probe number limitations. PBM microarray probes, relevant annotation for each probe, and the experimental results are provided  of a given COF (or TF) between corresponding REF and non-REF allele probes must have obtained an adjusted p value  < 0.05 independently in both technical replicates with a concordant direction of effect. Single variant probes for the 26-base target regions (centered at the SNP position – as described above) were generated using the same procedure as above but without the tiling needed to span larger genomic loci such as the provided . The mic,,CXCL10 promoter segment, a weighted mean approach was applied as follows to overlapping positions in order to integrate results across sequential tiles: all variant probes corresponding to a given nucleotide at a given position within the promoter segment were averaged using each probe’s corresponding seed (reference genomic) z-score as a weight. Further, if a given SV probe’s z-score was above 1.645  and the SV probe’s corresponding reference probe z-score was less than or equal to 1.645, the SV probe’s z-score was reset to the reference seed value. This procedure ensured that the SV probe modeling approach was used to characterize true genomic recruitment sites and reduce the influence of COF recruitment sites gained specifically via a non-REF variant. Sequence logo plots for the COF recruitment and TF binding motifs were generated using the ggseqlogo R packageMotif modeling using single variant (SV) probes was performed as previously describedPBM experiments were performed as previously described see . PrimaryGC cap + 34 base target region (TF site or SNV within backbone) + 24 base primerMultiple background target DNA probes were also included in the design in order to be able to estimate background fluorescence intensities in the experiments. These regions were selected at 34-base genomic segments from the human genome (hg38). For COF recruitment motifs obtained at each TFBS, a minimal seed z-score of 1.5 was enforced. Recruitment energy matrices obtained from profiling COF recruitment of the array were converted to a probability-based matrix using the Botlzmann distribution as described below to be directly compared to published TF binding models see .CXCL10 locus, to simplify the analysis and reduce the number of comparisons, the promoter segment was first separated into 3 motifs broadly corresponding to each previously characterized TF site . For CASCADE profiling of the SNP-QTLs, a minimal seed z-score of 1.5 was enforced for motif analysis. Recruitment energy matrices obtained from CASCADE cofactor profiling (fluorescence intensity z-scores) were converted to a probability-based matrix using the Boltzmann distribution as previously describedFor CASCADE recruitment motifs obtained at the zik is the z-score for nucleotide variant k at position i within the motif window. β transformation parameters for the Boltzmann equation were scaled using the maximum z-score obtained in a given experiment using the following equation in order to account for differences in antibody efficiencies across cofactors:Resulting position-weight matrices were compared against the complete HOCOMOCOv11 databasehttps://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg19-blacklist.v2.bed.gz. For each dataset, a final set of peaks is established by taking the union of the four peaks sets from the four parameter settings. 33 datasets failed at the download, alignment, or peak calling steps, yielding a total of 403 ChIP-seq peak sets in .BED format.436 THP-1 ChIP-seq datasets were obtained from the Gene Expression Omnibus (GEO)We genotyped the THP-1 monocyte cell line on Illumina OMNI-5 arrays, as previously described.Cell line annotations were double checked by examining read counts at variants identified as heterozygous by the genotyping arrays. Specifically, for each dataset, we examined all heterozygotes with at least five sequencing reads. We then calculated the fraction of these variants with exactly zero reads on the weak allele . Zero weak allele reads is a hallmark of a mis-annotated cell line, since a variant that is thought to be a heterozygote, but in reality is a homozygote, will always exhibit zero weak allele reads. Any dataset with 45% or more of its heterozygous variants displaying at least 5 strong allele reads and exactly zero weak allele reads was flagged as a likely mis-annotation by the original dataset producers and removed from downstream analyses (59 of the 297 datasets were removed). This cutoff was chosen based on comparisons between purposely matched and purposely mis-matched genotyping array/ChIP-seq experiment pairs. As a result, 238 datasets were used for allelic analyses for the THP-1 cell line.To identify possible mechanisms underlying the allelic behavior observed in the PBM experiments, we applied our MARIOhttps://www.encodeproject.org/). To analyze binding of our COFs in the same cell type, Bed data files were downloaded for TBL1XR1 (ENCFF868SWL), RBBP5 (ENCFF666PCE), EP300 (ENCFF755HCK), and SMARCA4 (ENCFF267OGF), for binding data in K562 cells. ChIP-seq regions were considered overlapping if a single nucleotide of two regions overlapped. The UpSet plot to illustrate the overlaps of different regions was generated using the R software “upset” function.ChIP-seq datasets were downloaded from the ENCODE consortium site  for multiple hypothesis testing. The fluorescence intensity z-score difference for a given SNP pair (REF and non-REF probes) (termed Δz-score) was computed by subtracting the mean REF z-score from the mean non-REF z-score such that a positive Δz-score represents a gain-of-recruitment introduced by the non-REF allele and a negative Δz-score represents a loss , and 5.REF and non-REF allele pairs exhibiting reproducible significant differential COF recruitment and/or TF binding were selected for this CASCADE array design in order to infer regulators responsible for the differential activity observed. Inclusion criteria was as follows: the difference in recruitment (or binding) of a given COF (or TF) between corresponding REF and non-REF allele probes must have obtained an adjusted p value  < 0.05 independently in both technical replicates with a concordant direction of effect .To determine the identity of the underlying motif, recruitment energy matrices were first converted to a probability-based matrix (PWM) using the Boltzmann distribution as previously described.


MYCN expression or stability as well as cell cycle control and apoptosis. Finally, we summarize alternative approaches to identify and prioritize candidate copy-number driven dependency genes for neuroblastoma offering novel therapeutic opportunities.Neuroblastoma is a pediatric tumor arising from the sympatho-adrenal lineage and a worldwide leading cause of childhood cancer-related deaths. About half of high-risk patients die from the disease while survivors suffer from multiple therapy-related side-effects. While neuroblastomas present with a low mutational burden, focal and large segmental DNA copy number aberrations are highly recurrent and associated with poor survival. It can be assumed that the affected chromosomal regions contain critical genes implicated in neuroblastoma biology and behavior. More specifically, evidence has emerged that several of these genes are implicated in tumor dependencies thus potentially providing novel therapeutic entry points. In this review, we briefly review the current status of recurrent DNA copy number aberrations in neuroblastoma and provide an overview of the genes affected by these genomic variants for which a direct role in neuroblastoma has been established. Several of these genes are implicated in networks that positively regulate  Neuroblastoma (NB) is a pediatric tumor arising from the sympathetic neuronal lineage with a remarkably variable clinical presentation. The majority of children diagnosed with NB below the age of 18 months present with low or intermediate risk disease and have an excellent prognosis  . Older cMYCN oncogene was one of the first discovered genetic alterations in NB  and associated with a 10-year overall survival probability of only 5.8% [Segmental chromosomal gains in general, and chromosome 17q gain in particular, were found to be associated with poor survival ,35,39. Tnly 5.8% .DHFR amplification under methotrexate exposure revealed that chromothripsis not only drives ecDNA formation but is also implicated in further ecDNA evolution [DNA arrays with increased resolution and ultimately whole genome sequencing allowed reliable detection of smaller focal copy number alterations or indels and paved the way for the identification of several novel genes critically involved in NB. Whole genome sequencing unveiled chromothripsis as a mechanism driving complex multi-locus rearrangements causing combined amplification, deletion, gain and structural variants affecting particular loci . Circle-volution . In NB, volution .MYCN regulation or activity [MYCN activity in these cells. Here below we describe in more detail the key copy number driven genes with an established role in regulating MYCN activity  RAS/MAPK/ERK signaling activated the ETV5 transcription factor which controlled migration, invasion and colony formation in vitro and proliferation in a murine xenograft model  was also identified as a major ALK downstream target further controlling the PI3K/AKT/FOXO3a axis [ALK upregulates RET and RET-driven sympathetic neuronal markers of the cholinergic lineage [Following knockdown or pharmacological inhibition of mutant pathways . For eact model  arisk NB . O3a axis . Finally lineage  which is lineage .ALK activation through mutation or amplification is the most accessible target for precision medicine in NB [ALK inhibitor Lorlatinib [ALK ligand ALKAL2 is located on the distal 2p segment (2p25.3). In view of the highly recurrent distal 2p gains in high-risk NBs this suggests that, in addition to increased expression levels of MYCN in cases of MYCN non-amplified tumors, an extra copy of the ALK gene and the gene for its ligand could impact tumor formation. The Palmer team recently investigated whether the ALKAL2 ligand could potentiate NB progression in the absence of ALK mutation and showed that ALKAL2 overexpression in mice drives ALK TKI-sensitive NB in the absence of ALK mutation. Consequently, both ALK mutated/amplified as well as 2p-gain high-risk NBs with elevated ALK/ALKAL2 protein, may benefit from ALK tyrosine kinase inhibitor-based therapeutic intervention [At present, constitutive ne in NB ,9,10,11 rlatinib ,52,53. Rrvention .LIN28B involvement in NB was triggered through the finding of a new amplified region on chromosome 6q21 in three high-risk NB tumors in a dataset of 263 tumors [LIN28B. LIN28B overexpression was identified in three independent NB tumor series compared to normal reference tissues and a series of tissues from other malignancies. Most importantly, analysis of LIN28B expression showed pronounced overexpression in the tumors with LIN28B amplification while Kaplan-Meier analyses showed that high expression of LIN28B was significantly associated with poor overall survival in two cohorts of 88 and 283 patients with NB [LIN28B is a key regulator of developmental processes by modulating microRNAs (miRNAs) of the let-7 family. LIN28B represses the let-7 miRNAs which consequently results in elevated MYCN protein expression in NB cells. Molenaar et al. further showed that LIN28B–let-7–MYCN signaling blocked differentiation of normal neuroblasts and NB cells. They recapitulated these findings in a mouse model in which LIN28B expression in the sympathetic adrenergic lineage induced development of NBs marked by low let-7 miRNA levels and high MYCN protein expression [LIN28B in NB, with a specific focus on the contribution of miRNAs and showed that MYCN regulates LIN28B expression in NB tumors via two parallel mechanisms. First, using an unbiased LIN28B-3′UTR reporter screen, we found that miR-26a-5p and miR-26b-5p regulate LIN28B expression. We provided evidence that MYCN indirectly regulates the expression of miR-26a-5p, which on its turn reduces LIN28B levels, therefore establishing an MYCN-miR-26a-5p-LIN28B regulatory axis [MYCN regulates LIN28B expression via interaction with the LIN28B promoter, establishing a direct MYCN-LIN28B regulatory axis [LIN28B target with implication in NB, repressed by let-7 and positively regulated by MYCN [LIN28B in NB was provided by the Diskin team who identified the oncogene RAN, located on chromosome 12, as a LIN28B direct RNA target. They further showed that regional gains of chromosome 12q24 as an additional somatic alteration resulted in increased RAN expression. LIN28B influences RAN expression by promoting ‘RAN Binding Protein 2′ (RANBP2) expression in a let-7-dependent manner and by directly binding RAN mRNA. Aurora kinase A (AURKA) was found to be activated by both RAN and LIN28B through phosphorylation and let-7-mediated repression respectively [LIN28B in NB development through the analysis of overexpression of wild-type (WT) LIN28B and a LIN28B mutant that is unable to inhibit let-7 processing. Unexpectedly, both increase the penetrance of MYCN-induced NB, potentiate the invasion and migration of transformed sympathetic neuroblasts, and drive distant metastases in vivo in zebrafish. Genome-wide chromatin immunoprecipitation coupled with DNA sequencing and co-immunoprecipitation experiments showed that LIN28B is recruited to active gene promoters in NB cells through protein-protein interaction with the sequence-specific zinc-finger transcription factor ZNF143 [The discovery of 3 tumors  as well 3 tumors ,55. The  with NB . LIN28B pression . In a suory axis . Second ory axis . In addi by MYCN . Furtherectively . Remarkar ZNF143 . The actr ZNF143 ,135,136 r ZNF143 .MYCN [MYCN induced, in addition to the miR-17~92 cluster, a broader core set of miRNAs for cooperative repression of common transcriptional programs related to disease aggressiveness [The oncogenic miR-17~92 cluster consists of six individual miRNAs  located within a polycistronic transcript on human chromosome 13 and is widely implicated in cancer ,61,62. OMYCN ,64,65. TMYCN . The VenMYCN . InvestiMYCN . Using csiveness .MYCN-amplified patients (2) in the same cohort. Further correlation with gene copy number (3) and MYCN expression (4) as well as association with poor NB patient outcome (5) were taken into account. Using this approach, ALYREF was the only gene to pass all five criteria among the 1044 17q21-ter genes, and suggests that MYCN and ALYREF co-operate as tumorigenic factors in NB. Further functional studies revealed that MYCN increases NB cell viability in an ALYREF-dependent manner and directly regulates ALYREF transcription. The known role of ALYREF as a regulator of DNA binding guided further analyses that uncovered ALYREF-MYCN interaction in a nuclear coactivator complex which stimulates transcription of the ‘ubiquitin specific peptidase 3′ (USP3), consequently reducing MYCN ubiquitination and degradation. This finding therefore opens novel opportunities for targeting this deubiquitinase activity through the development of USP3 inhibitors [The Marshall team used a bioinformatic approach to identify critical genes on 17q in high-risk NBs by evaluating frequency of gains distal to 17q21.31 using whole genome sequencing data from NB tumors from the US TARGET database . A totalhibitors .Given that MYC also drives expression of the miR-17~92 locus it would not be unexpected that MYC overexpression could also cause NB and that MYC could be a target for amplification in a subset of NB. We first reported a bona fide NB cell line with evidence for a chromothripsis event involving amongst others multiple chromosome 8q loci including MYC . More reSAS, MDM2, CDK4, GLI, CHOP and CDK2 [Cdk2 abundance and loss of CDK4 activity was observed in a mouse NB cell line upon neuronal differentiation in response to DMSO [CDK4 regulatory subunit ‘cyclin D1′ (CCND1) were detected in early DNA copy number profiling efforts of high-risk NBs. In most cases the CCND1 gains were accompanying large 11q deletions in MYCN non-amplified tumors [CCND1 expression [Already in 1995, we found evidence for amplification of a region encompassing several putative oncogenes on the long arm of chromosome 12 including and CDK2 . Decreas to DMSO . Gains ad tumors . In a repression .CCND1 and CDK4 for NB survival and proliferation was demonstrated and a functional dependency on overexpression of G1-regulating genes to maintain the undifferentiated phenotype was shown [CCND1 and CDK4 which were amplified or gained and the chromosomal regions containing the CDKN2 (INK4) group of CDKIs which were frequently deleted. Cluster analysis showed that tumors with genomic aberrations in G1 regulating genes over-expressed E2F target genes, which regulate S and G2/M phase progression [CDK4/cyclin D-pRB axis in MYCN-amplified NB cells allowed to evade a G1/S arrest after doxorubicin-induced DNA damage. Additional chromosomal aberrations affecting the p53-p21 and CDK4-pRB axes further enhance the effects of MYCN on the G1 checkpoint and reduce sensitivity to cell death after doxorubicin treatment. CDK4 inhibition partly restores G1/S arrest and sensitizes cells to doxorubicin-mediated cell death in MYCN-amplified cells with an intact p53 pathway [CCND1 and PLK1 as top hits and further investigated the effects of a small molecule compound palbociclib which is a potent and highly selective inhibitor of CDK4 and CDK6. NB cell lines IMR-32, SH-SY5Y, and NGP responded in a time- and dose-dependent manner with reduced proliferation while SK-N-SH and CLB-GA cells were relatively resistant to the treatment and other cell lines such as SH-EP responded at relatively high concentrations [CDK4/CDK6 inhibition on NB viability using the highly specific CDK4/6 inhibitor ribociclib  showing the expected reduction in proliferation in 12 of 17 human NB-derived cell lines through cell-cycle arrest and cellular senescence [ALK inhibitor ceritinib were tested showing a higher cytotoxicity and synergy scores in cell lines with ALK mutations as compared with cell lines lacking mutations or alterations in ALK. Combination therapy achieved complete sustained regressions in NB xenografts with ALK-F1174L and F1245C de novo mutations and prevented the emergence of resistance. Murine ribociclib and ceritinib plasma concentrations were unaltered by the use of combination therapy [CDK4/6 and MDM2 activity did not show noticeable synergistic effects [Dependency on as shown . In linegression . This wa pathway . Taken t pathway . Using atrations . The Marnescence . More renescence . Next, t therapy . Synergi therapy . In cont effects .TRIM37, the centrosomal ubiquitin ligase on chromosome 17q, was identified as a top hit in a genome-wide screen for genes whose inactivation enables sustained proliferation of PLK4 inhibitor (centrinone) treated retinal pigment epithelium (RPE1) cells. Loss of TRIM37 rescued the delayed spindle assembly and chromosome-segregation failure seen in cells that lack centrosomes treated with centrinone. Further analyses revealed that TRIM37 is an essential determinant of mitotic vulnerability to PLK4 inhibition: low TRIM37 levels accelerate acentrosomal spindle assembly and improve proliferation following PLK4 inhibition, whereas high TRIM37 levels inhibit acentrosomal spindle assembly, leading to mitotic failure and cessation of proliferation. NB and breast cancer cells with gain of chr17q were found to be highly sensitive to PLK4 inhibition [hibition .MDM2 amplification in NB was reported by Waber et al. [MDM2, and while TP53 mutations are rather rare in NB, its occurrence is associated with relapse cases [TP53/MDM2/p14 signaling axis in wild-type p53 NB and the functional consequences have been extensively investigated [MDM2 ubiquitin ligase also controls MYCN protein stability which itself drives MDM2 expression through promotor binding and transcriptional activation [MDM2 overexpression and dependency offers a target for precision drugging given the potential to disrupt MDM2-p53 protein-protein interaction in TP53 wild type NB cells. Using a low-molecular-weight compound that competes with p53 for binding into a hydrophobic cleft on the surface of MDM2, termed nutlin-3 [MDM2 inhibition by nutlin-3 also dramatically sensitizes NB cells to chemotherapy-induced apoptotic cell death [TP53 mutation status [The first evidence for r et al.  in 1993 r et al. ,83,84,85se cases . Mutatiose cases . Multiplstigated ,88,89,90tivation ,93,94,95nutlin-3 , we havenutlin-3 . MDM2 inll death , and nutn status ,101.BIRC5 locus is an essential protein for cell division and can inhibit cell death in most, if not all cancers including high-risk NBs. Of further interest, the BIRC5 gene maps to the commonly gained 17q segment and thus could represent a bona fide target affect by extra 17q copies [BIRC5 in Tg(dβh:eGFP-MYCN) transgenic zebrafish promotes the development and growth of MYCN-driven NB  in keeping with data from Hipp et al. showing that survivin enhanced the tumorigenic functions of MYCN in Rat1 fibroblasts [MYCN non-amplified and MYCN-amplified in vitro NB model systems [Survivin, encoded by the q copies ,103. Givq copies  and its q copies . A recenroblasts . Recentl systems . In vivo systems . Further systems , or adja systems , antibod systems , and sho systems .TBX2), a gene with unknown function in NB, was prioritized as transcription factor with top-ranked super-enhancer score in NB cell lines and with expression levels highly correlated with survival outcome in NB tumors [TBX2 genome-wide DNA binding pattern and transcriptome analysis upon knockdown, we identified TBX2 as novel member of the core regulatory circuitry that marks high-risk NBs. Similar findings were obtained using an unbiased genome-scale CRISPR screen identifying 147 candidate essential genes in MYCN-amplified NB cell lines, pinpointing TBX2 as member of the core regulatory circuitry in MYCN-amplified NB [TBX2 in positive control of E2F-FOXM1 signaling and proliferation. We propose that TBX2, as member of the core regulatory circuit, further enhances MYCN driven proliferation. Combined pharmacological targeting of transcriptional addiction using a BET (JQ1) and CDK7 (THZ1) inhibitor, yielded synergistic effects on TBX2 downregulation leading to massive apoptosis.Given the established important role of super-enhancer marked transcription factor encoding loci in several cancer entities we set out to identify such candidates residing on chromosome 17q. The ‘T-box 2 transcription factor’ (B tumors . Based oified NB . Our datJMJD6) gene which encodes a dual arginine demethylase and lysyl hydroxylase of histone and non-histone proteins. As a histone arginine demethylase, JMJD6 modulates RNA polymerase II release from promoter-proximal pause regions at target gene anti-pause enhancers by forming a protein complex with BRD4 and demethylating histone H4 at arginine 3 (H4R3) which consequently results in transcriptional activation. Whereas as a lysyl hydroxylase, JMJD6 also forms a protein complex with p53 and catalyzes p53 protein hydroxylation, which results in p53 inactivation. JMJD6 was shown to form protein complexes with MYCN and BRD4, controlling E2F2 and MYCN transcription, supporting proliferation and survival of NB cells. Combination therapy with the CDK7 (super-enhancer) inhibitor THZ1 and the histone deacetylase inhibitor panobinostat synergistically reduces JMJD6, E2F2 and MYCN expression, inducing apoptosis in vitro and causing NB tumor regression in mice [To further identify transcriptional regulators affected by 17q gains, the Tao team looked for candidates and identified the ‘Jumonji domain-containing 6′  development. Re-analysis of NB DNA copy number profiling data revealed the sympatho-adrenal lineage specific SOX11 gene in recurrent chromosome 2p focal gains and amplifications [SOX11 is specifically expressed in adrenergic NBs and absent in mesenchymal NBs [SOX11 is regulated by multiple adrenergic specific cis-acting (super-) enhancers, along with adrenergic NBs being strongly dependent on high SOX11 expression levels for growth and proliferation [SOX11 was also shown to be involved in nucleokinesis in adrenergic NB cells [SOX11 target genes through genome-wide DNA-binding and transcriptome analysis and identified several genes which are implicated in chromatin remodeling and epigenetic modification. Given the broad control of SOX11 on multiple epigenetic regulatory complexes and its presumed pioneer factor function [SOX11 as a crucial regulator of chromatin accessibility and cell identity [Our team identified ications . SOX11 iymal NBs ,112, twoymal NBs ,136. Indferation . SOX11 wfunction , we hypoidentity .CASC15 and NBAT1 lncRNAs acting as tumor suppressors with reduced expression in high-risk NBs. These lncRNAs were shown to repress SOX9 expression levels through CHD7  analysis to identify NB predisposing loci. One such locus on 6p22.3 was further investigated and shown to encode gh CHD7 , gh CHD7 ) was delineated. Further functional experiments revealed reduced proliferation and survival upon PPM1D knockdown [PPM1D, RAD51C and BRIP1, and these chromosome 17q segmental gains were shown to further accumulate during clonal evolution. Furthermore, a gene fusion of PPM1D and BCAS3 was also found to be present in one NB tumor, subsequently resulting in accumulating PPM1D expression levels [PPM1D is a negative regulator of p53 and positively regulates MDM2 thus further enhancing p53 inhibition  through intronic polyadenylation [CDK12 inhibitors have been developed and are currently under evaluation for clinical trials for other cancers, herapies .BRCA1 gene. MYCN was found to recruit BRCA1 to promoter-proximal regions preventing MYCN-dependent accumulation of stalled RNAPII and enhancing transcriptional activation by MYCN. BRCA1 enables MYCN to suppress R-loop formation in promoter-proximal regions by stabilizing mRNA decapping complexes. The critical signal in MYCN that enables recruitment of BRCA1 is the dephosphorylation of Thr58, which allows binding of the ubiquitin-specific protease USP11 and stabilizes MYCN and BRCA1 on chromatin, preventing proteasomal turnover of MYCN  maps in close proximity distal to PPM1D and TBX2 on 17q23.2 and has been implicated in unwinding of stable G-quadruplex DNA structures and acting through multiple mechanisms to the replication fork [BRIP1 in further details including its effects in MYCN driven NB formation in a MYCN- driven zebrafish model.Interestingly, the Eilers team attributed a novel function to the repertoire of the  of MYCN , loop 3 eraction . Becauseion fork . Our teaRRM2 gene as target for focal gains and amplifications in high-risk NBs, amongst others resulting from chromothripsis [RRM2 encodes for the ‘ribonucleotide reductase regulatory subunit 2′ (RRM2) and together with RRM1 forms the RNR ribonucleotide reductase enzyme that ensures dNTP production essential for DNA replication and repair. RRM2 has been proposed as driver oncogene in prostate cancer [MYCN driven NB in mice and inverse correlation with survival in NB patients, our team further investigated the role of RRM2 through in vitro and in vivo experiments [RRM2 for survival. Furthermore, a zebrafish MYCN/RRM2 co-overexpression model revealed a strong increase in tumor penetrance compared to MYCN only overexpressing animals. Finally, we demonstrated synergism for combined RRM2 and CHK1 inhibition thus pointing towards this drug vulnerability as an important future angle for therapeutic exploration [We recently identified the thripsis . RRM2 ene cancer  and has e cancer  and Ewine cancer . Based oeriments . NB cellloration .ALK, LIN28B, CCND1, PPM1D, TBX2, SOX11, MYCN and BRIP1 are strongly selective for NB as compared to other tumor entities, further highlighting these genes as potential drug targets  and BRIP1 . One particular advantage of zebrafish models is access to early developing lesions that can be isolated by FACs sorting [MYCN overexpression construct and injection into nude mice, it was shown that in most instances NB develop that recapitulate much of the molecular and cellular phenotype of mouse and human NBs, including CNAs syntenic as those observed in human e.g., 17q gain, 2p gain and 1p36 deletion. This model was also exploited to further study the role of ARID1A in the process of early tumor formation [MYCN and ALK expression in utero into developing mouse embryos. These mouse-human neural crest chimeras developed tumors resembling human NB upon activation of the oncogenes by adding doxycycline to the drinking water [MYCN-driven NB formation, where we induce MYCN overexpression during in vitro development to analyse the effect on development and malignant cell transformation. Using the 17q gained-stem cell line available in our lab, we are also in the process of studying the role of the 17q gain in MYCN driven NB. These state-of-the art experimental platforms are highly valuable to study NB tumor initiation, progression, manifestation, tumor-immune-system interaction and drug response in depth.Both mouse and zebrafish models have been established for ormation ,50,54,59ion [ALK , LMO1 [1ion [ALK , LIN28B B [RRM2 ,  sorting  and whic sorting . Subsequormation , a locusormation , by CRISormation . In contng water . So far,ng water . In vitrComputational approaches have been developed and applied to mine genome-wide CNAs and gene expression profiles in order to identify novel tumor dependencies. One of the first algorithms, ‘Copy Number and Expression In Cancer’ (CONEXIC), is a module networks-based approach that integrates matched CNA and gene expression data from tumor samples to identify and score the combination of CNAs that best explain the behavior of a gene co-expression module across tumor samples . CONEXICMYCN-amplified status and 1p36 deletion for cluster 1, 11q deletion for cluster 2 and a mesenchymal signature without any strong genomic aberration association or cluster 3. Subsequently, master regulators for each of the high-risk subtypes were inferred using the VIPER and ARACNe-AP algorithms based on the enrichment of their transcriptional target genes in the subtype specific gene expression signatures. As an example, a TEAD4-MYCN positive feedback loop emerged as a regulatory driver of the MYCN-amplified high-risk NB subtype [MYCN-driven NB tumor formation at different time points revealing MEIS2 as candidate NB tumor-initiating factor [MYCN amplification and 1p deletion, while modules responding to immune system process are associated with MYCN amplification only. BUB1B and CD53, which serve as hub genes in modules responding to cell cycle and immune system processes respectively, are put forward as potential novel drivers. Also from this analysis, CADM1 emerges as a top tumor suppressor gene candidate in 11q-deleted NB [The transcriptional network-based methods described above have already been successfully applied to NB. Consensus clustering of the TARGET NB patient cohort revealed three molecular subtypes of high-risk NB, consistent with genomic aberrations:  subtype . A similg factor . In anotleted NB . A deep leted NB . In the leted NB .Currently, the potential for adding multiple additional omics layers to copy number, gene expression and methylation is rapidly expanding such as chromatin accessibility (using ATAC-seq), epigenetic status (using ChIP or CUT&RUN/TAG for chromatin marks) and proteomics . MoreoveMYCN amplification. In this review, we have provided a summary of our current insights into copy number driven dependencies. In view of the limited search space for precision oncology drug targets due to the low mutation burden of NBs, further approaches to identify novel druggable vulnerabilities are critically important if we want to increase survival rates significantly. Together with current developments of novel approaches to target so-call undruggable targets such as protein degraders [Insights into NB biology and behavior have come a long way ever since the discovery of egraders , we expe


Genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are known to preferentially co-locate to active regulatory elements in tissues and cell types relevant to disease aetiology. Further characterisation of associated cell type-specific regulation can broaden our understanding of how GWAS signals may contribute to disease risk.https://forge2.altiusinstitute.org/), which is an updated version of the FORGE web tool. FORGE2 uses an expanded atlas of cell type-specific regulatory element annotations, including DNase I hotspots, five histone mark categories and 15 hidden Markov model (HMM) chromatin states, to identify tissue- and cell type-specific signals. An analysis of 3,604 GWAS from the NHGRI-EBI GWAS catalogue yielded at least one significant disease/trait-tissue association for 2,057 GWAS, including > 400 associations specific to epigenomic marks in immune tissues and cell types, > 30 associations specific to heart tissue, and > 60 associations specific to brain tissue, highlighting the key potential of tissue- and cell type-specific regulatory elements. Importantly, we demonstrate that FORGE2 analysis can separate previously observed accessible chromatin enrichments into different chromatin states, such as enhancers or active transcription start sites, providing a greater understanding of underlying regulatory mechanisms. Interestingly, tissue-specific enrichments for repressive chromatin states and histone marks were also detected, suggesting a role for tissue-specific repressed regions in GWAS-mediated disease aetiology.To gain insight into potential functional mechanisms underlying GWAS associations, we developed FORGE2 (In summary, we demonstrate that FORGE2 has the potential to uncover previously unreported disease-tissue associations and identify new candidate mechanisms. FORGE2 is a transparent, user-friendly web tool for the integrative analysis of loci discovered from GWAS.The online version contains supplementary material available at 10.1186/s13059-021-02560-3. Integrative analysis method spanning multiple epigenomic datasets can link GWAS data to key cell types and tissuesAnalysis reveals novel tissue-specific regulatory associations for 2,057 phenotypes from the GWAS catalogue.Redesigned FORGE2 tool can separate previous associations into specific regulatory element classes, such as promoters and enhancersResults for H3K27me3 and H3K9me3 suggest novel role for repressed regions in GWAS-mediated disease mechanismsOver the past two decades, Genome-Wide Association Studies (GWAS) have emerged as one of the most important frameworks to improve our understanding of the contribution of genetics to a wide variety of complex diseases and traits , 2. As oOne way to aid the interpretation of GWAS results is by integrating these with large-scale epigenomic mapping data generated by projects like ENCODE, Roadmap Epigenomics, and BLUEPRINT –7. In 20Although integrative analysis can offer an approach for pinpointing both systems-level and locus-specific disease-associated regulation, implementations of these types of analyses are not without their pitfalls, as appropriate statistical analysis and background correction methods are needed to accurately prioritise and categorise GWAS variants. To address this problem and provide a robust methodology for scientists conducting GWAS, we developed the Functional element Overlap analysis of the Results of GWAS Experiments (FORGE) tool in 2015 . This toOver the past few years, there has been an explosion in epigenomic data tracks, including ChIP-seq, DNase-seq and hidden Markov model (HMM) chromatin state tracks. When integrated with GWAS results, this new generation of data can offer further insight into the functional mechanisms of genetic loci prioritised by GWAS and help discriminate causal SNPs from other nearby genetic variants. However, these new epigenomic data can be difficult to access and integrate into GWAS analyses. There is a need to provide an updated, user-friendly and comprehensive framework for the categorisation, analysis and prioritisation of GWAS loci based on regulatory elements, in order to facilitate the identification of associated regulatory pathways, drug targets, cis-regulatory elements and cell types/tissues. To address this need, we developed FORGE2, a new statistical framework and analysis tool, which expands upon FORGE and incorporates new epigenomic data tracks, including DNase I hotspots, histone mark broadPeaks and HMM chromatin state tracks. The expanded atlas of cell type-specific regulatory element annotations in FORGE2 provides additional information that can be used to further refine and understand loci from GWAS. To demonstrate the breadth of information that can be gained from FORGE2, we analysed GWAS results from 3,604 phenotypes (GWAS phenotypes), representing a broad range of diseases and traits, downloaded from the NHGRI-EBI GWAS catalogue. We present results across DNase I hotspots, five histone mark categories and 15 HMM chromatin states and examine the relationship between associations among these different marks.q value < 0.01) , which are regions of chromatin sensitive to cleavage by the DNase I enzyme . DHSs arThus, we demonstrated that FORGE2 can systematically link a wide variety of GWAS associations to DNase I hotspots (or accessible chromatin elements) specific to particular tissues and cell types, providing extensive analytical confirmation of previous findings , and alsDNase I hotspot enrichments may be driven by variants located in different classes of CREs, such as promoters and enhancers, all of which are in accessible chromatin. Particular histone modifications (e.g. H3K4me3) more closely associated with specific CRE classes (such as promoters). These posttranslational modifications of histone proteins are known to influence processes as diverse as gene regulation, DNA repair and chromosome condensation . Among tAs a proof of principle, we first sought to verify if we could detect cell type-specific associations using histone mark data from immune cells, as immune cells are some of the best characterised cell types of any organ or system. We focused on H3K4me1 for this analysis, because it is the most tissue- and cell type-specific of the 5 main histone marks assayed by the Roadmap Epigenomics consortium . UnsuperFor certain phenotypes, analyses integrating histone mark data could reveal new cell type-specific enrichments which are not found by analysing DNase I hotspots, as a different set of genomic regions are used. We evaluated the extent to which tissue- or cell type-specific enrichments for the 5 core histone marks assayed by the Roadmap Epigenomics consortium  were shared with enrichments observed for DNase I hotspots across the GWAS catalogue. We uncovered tissue- or cell type-specific enrichment for at least one histone mark for 496 traits/diseases. Of these 496 phenotypes, 231 also showed enrichment in DNase I hotspot analysis of the GWAS catalogue  in a tissue-specific context, hinting towards a potential systems effect involving common underlying biological processes or pathways. Some phenotypes individually point to a disproportionate effect of GWAS variants on one of the CRE classes. For example, PR interval, an electrocardiographic trait, shows a heart-specific enrichment solely for H3K4me1, indicating an association with heart-specific enhancer elements. Other phenotypes show tissue-specific enrichment across multiple histone marks, suggesting that the underlying variants exert their effects on gene regulation through several different classes of active CRE, such as enhancers and promoters . We also observed a number of tissue-specific associations for histone marks associated with repressive regions, such as a brain-specific H3K27me3 enrichment for “autism spectrum disorder (or schizophrenia)”, or a thymus-specific H3K9me3 enrichment for primary sclerosing cholangitis. These formerly unreported tissue-specific enrichments for repressive histone marks hint towards the possibility that GWAS SNPs may also have tissue-specific effects in repressive regions.Although examination of histone marks can yield information about GWAS findings, additional information can be gained by evaluating the combination of multiple marks, which can inform segmentations of chromatin known as chromatin states. These chromatin states can further refine classes of genomic or regulatory elements, such as promoters, enhancers, or repressed regions, as well novel classes or subclasses of elements . We evalAnalysing the 3,604 GWAS from GWAS catalogue revealed enrichment in chromatin states for 254 phenotypes, of which 225 also showed enrichment in DNase I hotspot or histone marks; 29 phenotypes did not show enrichment in DNase I hotspot or histone mark broadPeak analysis of the GWAS catalogue and thus were specific to chromatin states  was the enhancer (Enh) state. To understand the phenotypes associated with this class further, we clustered enhancer-associated phenotypes according to their tissue-specific signal. Figure The chromatin state for which we observed the highest number of enriched phenotypes n=19 was thA large proportion of the enriched phenotypes that we observed for the enhancer chromatin state were shared with other chromatin states (100 out of 119). In order to get a general view of shared vs non-shared enriched phenotypes across chromatin state classes we categorised chromatin state results into two categories: unique for a particular state, or shared with other states Fig. B. This rTo further categorise shared and unique chromatin state-associated phenotypes, we conducted set intersection analyses via the R package UpSetR . We uncoIn this study, we have analysed GWAS data across multiple epigenomic datasets. However, it is important to consider that these datasets are interrelated. For instance, each one of these datasets presents a specific relationship with CREs. Chromatin regions accessible to the DNase I enzyme are representative of all main classes of CREs, while certain histone marks are enriched for different categories of CREs (e.g. H3K4me1 is enriched for enhancer regions). HMM chromatin state tracks constitute a highly refined dataset providing further granularity with classes and subclasses of genomic regions. Given the interrelationship between DNase I hypersensitive regions, histone mark broadPeaks and HMM chromatin state regions, we sought to compare the FORGE2 results across these different data types. Clustering of the FORGE2 results revealed large-scale substructure with segregation into 5 main groups, ranked on the number of associated GWAS phenotypes identified by each group , as well as DNase I hotspots from the ENCODE and Roadmap Epigenomics projects, H3K36me3 (a transcription-associated mark), and H3K4me3 (a promoter-associated mark). This group had the highest number of associated phenotypes (n=479), reinforcing the concept that active regions of chromatin associated with promoters, enhancers and transcribed elements represent the main category of regions associated with tissue-specific GWAS mechanisms.Group 1 included the most tissue-specific mark, H3K4me1, which had the highest number of associated phenotypes of any single mark (Group 2 contained a lower number of unique associated phenotypes (n=254) and comprised two histone marks, H3K27me3 (a polycomb-repressed region associated mark), and H3K9me3 (a heterochromatin associated mark). These histone mark regions, which are known to be associated with either constitutively repressed regions forming heterochromatin, or regions subject to binding by the polycomb repressive complex 2 (PRC2), displayed enrichments for a markedly different set of phenotypes when compared to regions of active chromatin, suggesting the potential for additional understanding of GWAS SNP disease-associated mechanisms in these regions.n=230) and included active chromatin states, such as enhancer (Enh), flanking active TSS (TssAFlnk), and DNase I hotspots from BLUEPRINT. Although many of these chromatin state categories showed enrichment for fewer GWAS phenotypes on average, a number of a number of these categories included smaller regions, and were more closely associated with specific classes of regulatory elements. The BLUEPRINT project focused on immune-associated cell types, and the resulting data constitute a reduced part of the overall human tissue accessible chromatin register. These datasets may be particularly valuable when evaluating the functional genomics for GWAS of immune-related traits.Group 3 had the third highest number of associated phenotypes (n=36), consistent with the fact that GWAS SNPs are less likely to co-localise with repressed regions of chromatin than with active regions, especially if these are well segmented. However, it is important to highlight that analysis of repressed and heterochromatic regions suggests that these regions are not completely exempt from mechanisms mediated through disease-associated GWAS variants.Group 4 included constitutively repressed chromatin states, such as heterochromatin (Het), quiescent (Quies), and ZNF genes and repeats (ZNF-Rpts). This group represents the lowest number of associated phenotypes (n=173) and included polycomb-repressed chromatin states , and bivalent chromatin states , and transcribed regions . These results suggest that bivalent and polycomb-repressed chromatin states play a role in some GWAS variant-associated mechanisms and that their role may be more prominent than that of heterochromatic regions. Some classes of repressed regions may be more informative than others. Interestingly, among the phenotypes associated with polycomb-repressed regions, we observed GWAS for neurodegenerative diseases, such as Alzheimer’s disease, as having enrichment for repressed polycomb (ReprPC) in brain as the top associated tissue. This finding highlights this regulatory class for GWAS loci of Alzheimer’s disease, which involves a loss of neurons and synapses across the cerebral cortex and specific subcortical regions [Group 5 had the second lowest number of associated phenotypes  [To demonstrate the utility of FORGE2 for individual GWAS, we used data from a recent GWAS on myeloproliferative neoplasms or MPNs , not yet < 10−6) . FORGE2 By applying FORGE2 to thousands of GWAS, we demonstrate the potential of FORGE2 to uncover tissue- and cell type-specific signal and shed light on candidate functional mechanisms underlying GWAS loci. Of the 3604 GWAS evaluated, we identified at least one tissue- or cell-specific association for 2057 GWAS, detecting more associations than FORGE, which can only detect 413 associations for the same data, and highlighting the importance of tissue-specific regulatory elements. Using FORGE2, we profiled associated regulatory element classes such as promoters and enhancers, both for study-level results and for specific variants at the cell type and tissue-specific level. This multidimensional analysis provides a valuable resource for scientists conducting follow up research on GWAS findings.Among the findings in this study, we discovered tissue-specific enrichments for repressive histone marks and chromatin states, such as H3K27me3, H3K9me3, polycomb-repressed, and heterochromatin chromatin states. This finding is particularly remarkable, as it indicates a link between tissue-specific repressed genomic regions and disease-associated GWAS variants, a finding previously not reported in epigenomic analyses of GWAS data.In addition, it was thought that previously reported FORGE accessible chromatin results for different phenotypes might be driven by variants located in different CRE classes . Our analysis shows that previously known open chromatin enrichments can be further refined and separated into associations with promoter-, enhancer-, and transcription-associated histone marks  and chromatin states . This provides further knowledge of the underlying classes of regulatory elements involved, yielding insights into potential mechanisms underlying GWAS disease-associated variants.The observation that GWAS-associated variants for certain phenotypes, such as cognitive performance or electrocardiographic traits, are predominantly enriched for tissue-specific enhancer-associated marks, but not for marks associated with other CREs is noteworthy. Considering the evolution of the phenotype in question might be important to understand the mechanistic causes underlying these findings. Indeed, research on the evolution of complex traits suggests that selective pressure can work to favour or discriminate against regulatory elements linked to specific pathways, which then may become over- or underrepresented for certain phenotypes . In addiEvidence relating GWAS findings to underlying disease mechanisms has led to a surge in drug company investment in GWAS-related research, with the aim of identifying more reliable drug targets , 20. HowWe also uncovered novel associations between certain phenotypes and DNase I hotspots for particular tissues and cell types, such as the association between waist circumference and mesenchymal stem cells, or between hair colour and melanocytes (quantifying and confirming previously reported high overlap  with a eOne important step forward in FORGE2 is the detection of cell type-specific enrichments (as opposed to tissue-specific enrichments). Since FORGE provided a tissue-level multiple testing correction method, it could only detect tissue-specific enrichments. Using FORGE2 H3K4me1 analysis, we not only categorise enrichments at a tissue-specific level, but confirm that specificity analysis can also be performed in a systematic way at the cell type level across several immune cell type categories, including CD3+ T cells, CD19+ B cells, CD14+ monocytes and CD34+ haematopoietic stem cells. This refinement of enrichment accuracy to the level of cell type-specificity is an important step forward.We also evaluated the relationship between DNase I hotspot, histone mark broadPeak and HMM chromatin state datasets in the context of GWAS analysis, charting shared and specific GWAS phenotypes across these epigenomic marks. Our results show high variability in enrichment across histone marks and chromatin states for GWAS results, with a high number of phenotypes displaying enrichment for certain features, such as H3K4me1, and a low number of phenotypes having enrichment for other features, such as the quiescent (Quies) chromatin state. We also discovered that for some phenotypes, tissue-specific enrichments for marks indicating general regulatory activity (e.g. accessible chromatin) were also present in the same tissue for two or more different marks indicating specific regulatory activity (e.g. active promoters and enhancer chromatin states), with different sets of SNPs underlying each one of these two categories. By understanding which regulatory elements were underlying the previous “accessible chromatin” enrichment and separating these enrichments into different classes of regulatory elements, we can gain a greater understanding of the mechanisms underlying the GWAS associations. We also observed that some phenotypes tend to be associated with specific regulatory regions (e.g. enhancers), and others seem to be associated with multiple types of regulatory marks. Although we may not understand why GWAS SNPs tend to localise to specific types of regulatory regions for some phenotypes, these results are a step forward and provide additional information relating to the underlying biological architecture of genetic susceptibility to these diseases and traits.Regarding limitations and future analyses, it should be noted that analysis of tissue-specific regulatory regions cannot capture all potential biological mechanisms, but instead provide a tool by which many loci may be understood. In-depth analysis of tissue-invariant GWAS-associated regions, which are also catalogued by FORGE2 and annotated in our results (which cover the full spectrum of regulatory regions across tissues and cell types), may also help understand the biology behind some GWAS associations, but this is outside the scope of this manuscript. In addition, we noted differences in the number of enriched phenotypes between related analysis categories, for example, histone marks characteristic of enhancers, and enhancer chromatin states. This is likely driven in part by the distance between causal variants and GWAS-mapped variants. Broad epigenomic peaks may identify signals precisely because, due to their larger size, they overlap both causal variants and tagged variants which are in linkage disequilibrium. However, finer genomic segmentations, such as HMM chromatin states, which are smaller, cannot do this nearly as well. Advances in fine mapping of GWAS regions may improve our ability to further refine data to help identify functional variants across different phenotypes.FORGE2 has a specific and unique role in the field of GWAS analysis methods. It performs multidimensional epigenomic data analysis across hundreds of samples and chromatin data types , utilising background SNP sets comprising thousands of selected variants adjusted for multiple genomic biases to robustly test for enrichment, and optimised in a fast and easy to use web tool. Unlike LD score regression , 30 FORGIn the context of computational epigenomic methods for integrative analysis of GWAS data, the utility of a common and easily-accessible framework is noteworthy. While several command-line methods have been developed in this area , 32  [http://www.genboree.org/EdaccData/Current-Release/experiment-sample/Chromatin_Accessibility/ (GEO accession number GSE18927), and processed these files using the Hotspot method with default settings (http://www.uwencode.org/proj/hotspot/) [http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/; (5) BroadPeak Histone mark files (39 biosamples) from http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/broadPeak/; and (6) HMM Chromatin State files (39 biosamples) from https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/ [https://genome.ucsc.edu/encode/cellTypes.html) or information from sample group SAMEG31306 from the BioSamples database (http://www.ebi.ac.uk/biosamples/).FORGE2 utilises epigenomic data files that were downloaded from several sites: (1) ENCODE DNase I hotspot files (125 biosamples) from otspots/ ; (2) BLU1002713) , 36; (3)otspot/) ; (4) ConBedops  was usedThe addition of new epigenomic track data to the standalone FORGE2 database can be performed using bedtools/bedops and similar openly available code to previous methods .ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase1/analysis_results/integrated_call_sets/). To avoid including multiple SNPs that are in linkage disequilibrium (LD), a filter may be applied. The LD filter included in the FORGE2 web tool is based on an international sample of 14 populations from the 1000 Genomes Project . When used, it retains the first SNP from the SNP list and excludes all other SNPs that are in LD with this SNP. The user may also apply their own population-specific LD filter to the input SNP list prior to FORGE2 analysis or utilise other filtering metrics.FORGE2 takes a set of input SNPs (e.g. a list of rsIDs) and computes their overlap (based on base pair position) with epigenomic track data. For a SNP to be analysed, it must be present in the 1000 genomes dataset  method [q values as a result. BH is applied at a sample level, including all samples and p values. Enrichments at a q value< 0.01 are considered significant. Alongside q values, corresponding uncorrected p values and Z-scores are also provided by FORGE2.To test for enrichment (higher overlap than expected), FORGE2 first computes an overlap score for a SNP set  against epigenomic track data across a range of cell types and tissues. For each epigenomic track sample, the overlap score of the input set is then compared to the overlap scores of 1000 background SNP sets that FORGE2 generates. SNPs from these 1000 background sets are selected for similarity to the input SNP set, via matching decile bins for minor allele frequency (MAF), distance to transcription start site (TSS) and GC content. A binomial test is used to compare the overlap score of the input SNP set with the overlap scores of the 1000 background sets. This is repeated across multiple cell/tissue samples, until all samples have been analysed. The ) method , generatp value < 0.01). False Discovery Rate (FDR) correction was implemented in a similar way to the eFORGE tool, which previously implemented FDR for the detection of cell type-specific and tissue-specific enrichment signal in epigenome-wide association studies (EWAS) [To evaluate the false positive rate, we ran > 27 million random input SNP tests with FORGE2, including 1000 sets of 5, 10, 15, 20, 30, 40, 50, and 100 SNPs. This analysis showed a low level of false positives  , 36, 40.http://dimplejs.org) d3 interactive graphic using rCharts, a tab-separated values (TSV) file, a PDF file, R code source files for generating the charts and the table, as well as a compressed file with the input data. FORGE2 provides output similar to FORGE [FORGE2 returns several different outputs, including charts, tables, original raw text files and R code for plotting. Tables and charts are also generated in interactive format to allow for an intuitive exploration of cell type- and tissue-specific enrichment results. Specifically, FORGE2 outputs: a web-based interactive table, a web-based dimple . For this analysis, we required each phenotype (or GWAS) to have a minimum of 5 SNPs. To account for SNP density, we used the default LD filter of r2 ≥ 0.8 for this analysis. We ran FORGE2 with default settings (i.e. 1000 background repetitions) and a significance threshold of q value< 0.01, across the following datasets: DNase I hotspot data from ENCODE, BLUEPRINT, and consolidated and unconsolidated Roadmap Epigenomics, 5 histone mark categories from consolidated Roadmap Epigenomics data and 15 HMM chromatin state datasets from consolidated Roadmap Epigenomics data.We downloaded the NHGRI-EBI GWAS catalogue  from https://www.easyzoom.com) and are available in the following easyzoom folder:The clustering analysis was performed using R statistical software. Clustered heatmaps were generated using the pheatmap R package . Upset phttps://www.easyzoom.com/albumaccess/3de0ae35c00b41189f63d13635bdb8d9.Additional file 1: Supplementary figures S1-S26. and supplementary figure and table legends.Additional file 2: Table S1. Consolidated Epigenomics Roadmap DNase I hotspot FORGE2 GWAS catalogue analysis results .Additional file 3: Table S2. Unconsolidated (2012) Epigenomics Roadmap DNase I hotspot FORGE2 GWAS catalogue analysis results .Additional file 4: Table S3. ENCODE DNase I hotspot FORGE2 GWAS catalogue analysis results .Additional file 5: Table S4. BLUEPRINT DNase I hotspot FORGE2 GWAS catalogue analysis results .Additional file 6: Table S5. Consolidated Epigenomics Roadmap H3K4me1 FORGE2 GWAS catalogue analysis results .Additional file 7: Table S6. Consolidated Epigenomics Roadmap H3K4me3 FORGE2 GWAS catalogue analysis results .Additional file 8: Table S7. Consolidated Epigenomics Roadmap H3K36me3 FORGE2 GWAS catalogue analysis results .Additional file 9: Table S8. Consolidated Epigenomics Roadmap H3K27me3 FORGE2 GWAS catalogue analysis results .Additional file 10: Table S9. Consolidated Epigenomics Roadmap H3K9me3 FORGE2 GWAS catalogue analysis results .Additional file 11: Table S10. Consolidated Epigenomics Roadmap HMM Chromatin State (15-state model) FORGE2 GWAS catalogue analysis results .Additional file 12: Table S11. FORGE2 histone mark broadPeak analysis results  for a set of 25 variants from a GWAS on myeloproliferative neoplasms.Additional file 13: Table S12. FORGE2 DNase I hotspot analysis results  for a set of 25 variants from a GWAS on myeloproliferative neoplasms.Additional file 14: Review history.