Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

FOXD2 regulations IQGAP3 mediated Ca2+ signaling pathway to facilitate gastric adenocarcinoma cell promotion

As a transcriptional factor, the Forkhead box (FOX) gene family is closely connected with apoptosis, proliferation, and other cellular processes. FOXD2, as one descendant of the FOX gene family, has been mentioned in many articles to show a high expression in several cancers. However, whether FOXD2 has a connection with gastric adenocarcinoma remains an unanswered question. Expression of FOXD2 and IQGAP3 in gastric adenocarcinoma was evaluated by bioinformatics analysis, which was further detected by real-time quantitative PCR (qRT-PCR) and western blot. The downstream target genes of FOXD2 were also mined by bioinformatics analysis. Pathway enrichment analysis was then performed on the target genes. Chromatin immunoprecipitation assay (ChIP) and dual-luciferase reporter assay were conducted to validate the regulatory relationship between FOXD2 and its downstream target gene IQGAP3. Methyl thiazolyl tetrazolium assay (MTT), combined with cell colony formation assay, was employed to assess the effect of FOXD2 and IQGAP3 on the proliferation of gastric adenocarcinoma cells. Intracytoplasmic Ca2+ concentration was measured by Fluo-3 fluorescence staining. FOXD2 showed a high expression in gastric adenocarcinoma tissues and cells, and FOXD2 silencing considerably attenuated gastric adenocarcinoma cell proliferation. IQGAP3, a downstream target gene of FOXD2, had a positive connection with the expression of FOXD2. The binding relationship between FOXD2 and the promoter region of IQGAP3 was further verified by ChIP and dual-luciferase reporter assays. The results of cell function experiments indicated that FOXD2 could promote gastric adenocarcinoma cell proliferation by transcriptionally activating IQGAP3 to induce an increase in intracellular Ca2+ level. This study confirmed that FOXD2 increased intracellular Ca2+ level through transcriptional activation of IQGAP3, which in turn propelled the proliferation of gastric adenocarcinoma cells, revealing the considerable significance of FOXD2 in the development of gastric adenocarcinoma.

MYCN Amplifications and Metabolic Rewiring in Neuroblastoma

Cancer is a disease caused by (epi)genomic and gene expression abnormalities and characterized by metabolic phenotypes that are substantially different from the normal phenotypes of the tissues of origin. Metabolic reprogramming is one of the key features of tumors, including those established in the human nervous system. In this work, we emphasize a well-known cancerous genomic alteration: the amplification of MYCN and its downstream effects in neuroblastoma phenotype evolution. Herein, we extend our previous computational biology investigations by conducting an integrative workflow applied to published genomics datasets and comprehensively assess the impact of MYCN amplification in the upregulation of metabolism-related transcription factor (TF)-encoding genes in neuroblastoma cells. The results obtained first emphasized overexpressed TFs, and subsequently those committed in metabolic cellular processes, as validated by gene ontology analyses (GOs) and literature curation. Several genes encoding for those TFs were investigated at the mechanistic and regulatory levels by conducting further omics-based computational biology assessments applied on published ChIP-seq datasets retrieved from MYCN-amplified- and MYCN-enforced-overexpression within in vivo systems of study. Hence, we approached the mechanistic interrelationship between amplified MYCN and overexpression of metabolism-related TFs in neuroblastoma and showed that many are direct targets of MYCN in an amplification-inducible fashion. These results illuminate how MYCN executes its regulatory underpinnings on metabolic processes in neuroblastoma.

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Super-enhancer hypermutation alters oncogene expression in B cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is the most common B cell non-Hodgkin lymphoma and remains incurable in around 40% of patients. Efforts to sequence the coding genome identified several genes and pathways that are altered in this disease, including potential therapeutic targets1-5. However, the non-coding genome of DLBCL remains largely unexplored. Here we show that active super-enhancers are highly and specifically hypermutated in 92% of samples from individuals with DLBCL, display signatures of activation-induced cytidine deaminase activity, and are linked to genes that encode B cell developmental regulators and oncogenes. As evidence of oncogenic relevance, we show that the hypermutated super-enhancers linked to the BCL6, BCL2 and CXCR4 proto-oncogenes prevent the binding and transcriptional downregulation of the corresponding target gene by transcriptional repressors, including BLIMP1 (targeting BCL6) and the steroid receptor NR3C1 (targeting BCL2 and CXCR4). Genetic correction of selected mutations restored repressor DNA binding, downregulated target gene expression and led to the counter-selection of cells containing corrected alleles, indicating an oncogenic dependency on the super-enhancer mutations. This pervasive super-enhancer mutational mechanism reveals a major set of genetic lesions deregulating gene expression, which expands the involvement of known oncogenes in DLBCL pathogenesis and identifies new deregulated gene targets of therapeutic relevance.

abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis

BACKGROUND

Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters.

RESULTS

A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach.

CONCLUSION

This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.

Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma

A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes

B-cell lymphoproliferative disorders exhibit a diverse spectrum of diagnostic entities with heterogeneous behaviour. Multiple efforts have focused on the determination of the genomic drivers of B-cell lymphoma subtypes. In the meantime, the aggregation of diverse tumors in pan-cancer genomic studies has become a useful tool to detect new driver genes, while enabling the comparison of mutational patterns across tumors. Here we present an integrated analysis of 354 B-cell lymphoid disorders. 112 recurrently mutated genes were discovered, of which KMT2D, CREBBP, IGLL5 and BCL2 were the most frequent, and 31 genes were putative new drivers. Mutations in CREBBP, TNFRSF14 and KMT2D predominated in follicular lymphoma, whereas those in BTG2, HTA-A and PIM1 were more frequent in diffuse large B-cell lymphoma. Additionally, we discovered 31 significantly mutated protein networks, reinforcing the role of genes such as CREBBP, EEF1A1, STAT6, GNA13 and TP53, but also pointing towards a myriad of infrequent players in lymphomagenesis. Finally, we report aberrant expression of oncogenes and tumor suppressors associated with novel noncoding mutations (DTX1 and S1PR2), and new recurrent copy number aberrations affecting immune check-point regulators (CD83, PVR) and B-cell specific genes (TNFRSF13C). Our analysis expands the number of mutational drivers of B-cell lymphoid neoplasms, and identifies several differential somatic events between disease subtypes.

Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers

Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients

Following a successful renal transplantation circulating markers of inflammation may remain elevated, and systemic inflammation is associated with worse clinical outcome in renal transplant recipients (RTRs). Vitamin D-receptor (VDR) activation is postulated to modulate inflammation and endothelial function. We aimed to explore if a synthetic vitamin D, paricalcitol, could influence systemic inflammation and immune activation in RTRs. Newly transplanted RTRs were included in an open-label randomized controlled trial on the effect of paricalcitol on top of standard care over the first post-transplant year. Fourteen pre-defined circulating biomarkers reflecting leukocyte activation, endothelial activation, fibrosis and general inflammatory burden were analyzed in 74 RTRs at 8 weeks (baseline) and 1 year post-engraftment. Mean changes in plasma biomarker concentrations were compared by t-test. The expression of genes coding for the same biomarkers were investigated in 1-year surveillance graft biopsies (n = 60). In patients treated with paricalcitol circulating osteoprotegerin levels increased by 0.19 ng/ml, compared with a 0.05 ng/ml increase in controls (p = 0.030). In graft tissue, a 21% higher median gene expression level of TNFRSF11B coding for osteoprotegerin was found in paricalcitol-treated patients compared with controls (p = 0.026). Paricalcitol treatment did not significantly affect the blood- or tissue levels of any other investigated inflammatory marker. In RTRs, paricalcitol treatment might increase both circulating and tissue levels of osteoprotegerin, a modulator of calcification, but potential anti-inflammatory treatment effects in RTRs are likely very modest. [NCT01694160 (2012/107D)]; [www.clinicaltrials.gov].

Chronic lymphocytic leukemia patients with IGH translocations are characterized by a distinct genetic landscape with prognostic implications

Chromosome 14q32 rearrangements/translocations involving the immunoglobulin heavy chain (IGH) are rarely detected in chronic lymphocytic leukemia (CLL). The prognostic significance of the IGH translocation is controversial and its mutational profile remains unknown. Here, we present for the first time a comprehensive next-generation sequencing (NGS) analysis of 46 CLL patients with IGH rearrangement (IGHR-CLLs) and we demonstrate that IGHR-CLLs have a distinct mutational profile with recurrent mutations in NOTCH1, IGLL5, POT1, BCL2, FBXW7, ZMYM3, MGA, BRAF and HIST1H1E genes. Interestingly, BCL2 and FBXW7 mutations were significantly associated with this subgroup and almost half of BCL2, IGLL5 and HISTH1E mutations reported were previously identified in non-Hodgkin lymphomas. Notably, IGH/BCL2 rearrangements were associated with a lower mutation frequency and carried BCL2 and IGLL5 mutations, while the other IGHR-CLLs had mutations in genes related to poor prognosis (NOTCH1, SF3B1 and TP53) and shorter time to first treatment (TFT). Moreover, IGHR-CLLs patients showed a shorter TFT than CLL patients carrying 13q-, normal fluorescence in situ hybridization (FISH) and +12 CLL, being this prognosis particularly poor when NOTCH1, SF3B1, TP53, BIRC3 and BRAF were also mutated. The presence of these mutations not only was an independent risk factor within IGHR-CLLs, but also refined the prognosis of low-risk cytogenetic patients (13q-/normal FISH). Hence, our study demonstrates that IGHR-CLLs have a distinct mutational profile from the majority of CLLs and highlights the relevance of incorporating NGS and the status of IGH by FISH analysis to refine the risk-stratification CLL model.

IGAP-integrative genome analysis pipeline reveals new gene regulatory model associated with nonspecific TF-DNA binding affinity

The human genome is regulated in a multi-dimensional way. While biophysical factors like Non-specific Transcription factor Binding Affinity (nTBA) act at DNA sequence level, other factors act above sequence levels such as histone modifications and 3-D chromosomal interactions. This multidimensionality of regulation requires many of these factors for a proper understanding of the regulatory landscape of the human genome. Here, we propose a new biophysical model for estimating nTBA. Integration of nTBA with chromatin modifications and chromosomal interactions, using a new Integrative Genome Analysis Pipeline (IGAP), reveals additive effects of nTBA to regulatory DNA sequences and identifies three types of genomic zones in the human genome (Inactive Genomic Zones, Poised Genomic Zones, and Active Genomic Zones). It also unveils a novel long distance gene regulatory model: chromosomal interactions reduce the physical distance between the high occupancy target (HOT) regions that results in high nTBA to DNA in the area, which in turn attract TFs to such regions with higher binding potential. These findings will help to elucidate the three-dimensional diffusion process that TFs use during their search for the right targets.

Novel Mutation Hotspots within Non-Coding Regulatory Regions of the Chronic Lymphocytic Leukemia Genome

Mutations in non-coding DNA regions are increasingly recognized as cancer drivers. These mutations can modify gene expression in cis or by inducing high-order chormatin structure modifications with long-range effects. Previous analysis reported the detection of recurrent and functional non-coding DNA mutations in the chronic lymphocytic leukemia (CLL) genome, such as those in the 3′ untranslated region of NOTCH1 and in the PAX5 super-enhancer. In this report, we used whole genome sequencing data produced by the International Cancer Genome Consortium in order to analyze regions with previously reported regulatory activity. This approach enabled the identification of numerous recurrently mutated regions that were frequently positioned in the proximity of genes involved in immune and oncogenic pathways. By correlating these mutations with expression of their nearest genes, we detected significant transcriptional changes in genes such as PHF2 and S1PR2. More research is needed to clarify the function of these mutations in CLL, particularly those found in intergenic regions.

BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts

Most of somatic mutations in cancer occur outside of gene coding regions. These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at http://folk.uio.no/junbaiw/BayesPI-BAR2/.

Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model

DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor-DNA interaction, as well as for predicting cancer mutation effects in the future.