Copy number alterations: a catastrophic orchestration of the breast cancer genome.

Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact.

Coagulation factor V in breast cancer: a p53-regulated tumor suppressor and predictive marker for treatment response to chemotherapy.

BACKGROUND: Patients with cancer are at an increased risk of developing coagulation complications, and chemotherapy treatment increases the risk. Tumor progression is closely linked to the hemostatic system. Breast cancer tumors express coagulation factor V (FV), an essential factor in blood coagulation. The functional role of FV during treatment with chemotherapy is poorly understood and was explored in this study. OBJECTIVES: We aimed to investigate the role of FV in breast cancer progression by exploring associations with treatment response, gene regulation, and the functional effects of FV. METHODS: The receiver operating characteristic plotter was used to explore the predictive value of FV mRNA (F5) expression for treatment with FEC (5-fluorouracil, anthracycline, and cyclophosphamide). Breast cancer cohorts were analyzed to study treatment response to FEC. The effect of chemotherapy on F5 expression, the regulation of F5, and the functional effects of FV dependent and independent of chemotherapy were studied in breast cancer cell lines. RESULTS: F5 tumor expression was significantly higher in responders to FEC than in nonresponders. In vitro experiments revealed that anthracycline treatment increased the expression of F5. Inhibition and knockdown of p53 reduced the anthracycline-induced F5 expression. Mutation of a p53 half-site (c.158+1541/158+1564) in a luciferase plasmid reduced luciferase activity, suggesting that p53 plays a role in regulating F5. FV overexpression increased apoptosis and reduced proliferation slightly during anthracycline treatment. CONCLUSION: Our study identified F5 as a p53-regulated tumor suppressor candidate and a promising marker for response to chemotherapy. FV may have functional effects that are therapeutically relevant in breast cancer.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

OnTarget: in silico design of MiniPromoters for targeted delivery of expression.

MiniPromoters, or compact promoters, are short DNA sequences that can drive expression in specific cells and tissues. While broadly useful, they are of high relevance to gene therapy due to their role in enabling precise control of where a therapeutic gene will be expressed. Here, we present OnTarget (http://ontarget.cmmt.ubc.ca), a webserver that streamlines the MiniPromoter design process. Users only need to specify a gene of interest or custom genomic coordinates on which to focus the identification of promoters and enhancers, and can also provide relevant cell-type-specific genomic evidence (e.g. accessible chromatin regions, histone modifications, etc.). OnTarget combines the provided data with internal data to identify candidate promoters and enhancers and design MiniPromoters. To illustrate the utility of OnTarget, we designed and characterized two MiniPromoters targeting different cell populations relevant to Parkinson Disease.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers.

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

MoDLE: high-performance stochastic modeling of DNA loop extrusion interactions.

DNA loop extrusion emerges as a key process establishing genome structure and function. We introduce MoDLE, a computational tool for fast, stochastic modeling of molecular contacts from DNA loop extrusion capable of simulating realistic contact patterns genome wide in a few minutes. MoDLE accurately simulates contact maps in concordance with existing molecular dynamics approaches and with Micro-C data and does so orders of magnitude faster than existing approaches. MoDLE runs efficiently on machines ranging from laptops to high performance computing clusters and opens up for exploratory and predictive modeling of 3D genome structure in a wide range of settings.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers.

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesised that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression-methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

Expressed prognostic biomarkers for primary prostate cancer independent of multifocality and transcriptome heterogeneity.

The majority of prostate cancer patients are diagnosed with multiple primary malignant foci. The distinct foci are exceptionally heterogeneous with regard to DNA mutations, but whether this is recapitulated at the transcriptome level remains unknown. In this study, inter- and intrafocal heterogeneity has been assessed by whole-transcriptome sequencing of 87 tissue samples from 23 patients with localized prostate cancer treated with radical prostatectomy. From each patient, multiple samples were taken from one or more malignant foci, in addition to one sample from benign prostate tissue. Transcriptomic profiles of different malignant foci from the same patient showed a similar level of heterogeneity as tumors from different patients. This applies to expression of genes, fusion genes, and somatic mutations. Within-patient pair-wise analyses identified expression patterns linked to ETS status and extraprostatic extension. A set of 62 genes were found with low intrapatient heterogeneity and high interpatient heterogeneity, retaining stable expression profiles across foci within the same patient. Among these, 16 genes are associated with biochemical recurrence in a separately published study and are therefore nominated as biomarkers with prognostic value regardless of which malignant focus is sampled. In conclusion, an extensive heterogeneity in multifocal prostate cancer is confirmed at the gene expression level. Diagnostic biomarkers were identified for ETS positive samples and samples from extraprostatic extensions. Finally, prognostic biomarkers independent of multifocal heterogeneity were found.

JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

UniBind: maps of high-confidence direct TF-DNA interactions across nine species.

BACKGROUND: Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq). RESULTS: In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface ( https://unibind.uio.no/ ), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. CONCLUSIONS: UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer.

BACKGROUND: Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. METHODS: miRNA expression and DNA methylation data from two breast cancer cohorts, Oslo2 (n = 297) and The Cancer Genome Atlas (n = 439), were integrated through a correlation approach that we term miRNA-methylation Quantitative Trait Loci (mimQTL) analysis. Hierarchical clustering was used to identify clusters of miRNAs and CpGs that were further characterized through analysis of mRNA/protein expression, clinicopathological features, in silico deconvolution, chromatin state and accessibility, transcription factor binding, and long-range interaction data. RESULTS: Clustering of the significant mimQTLs identified distinct groups of miRNAs and CpGs that reflect important biological processes associated with breast cancer pathogenesis. Notably, two major miRNA clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding CpGs associated with the estrogen signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2, and ER-alpha. Further, at the hub of the estrogen cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already present in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER-positive breast cancer. CONCLUSIONS: We describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

Human White Adipose Tissue Displays Selective Insulin Resistance in the Obese State.

Selective hepatic insulin resistance is a feature of obesity and type 2 diabetes. Whether similar mechanisms operate in white adipose tissue (WAT) of those with obesity and to what extent these are normalized by weight loss are unknown. We determined insulin sensitivity by hyperinsulinemic euglycemic clamp and insulin response in subcutaneous WAT by RNA sequencing in 23 women with obesity before and 2 years after bariatric surgery. To control for effects of surgery, women postsurgery were matched to never-obese women. Multidimensional analyses of 138 samples allowed us to classify the effects of insulin into three distinct expression responses: a common set was present in all three groups and included genes encoding several lipid/cholesterol biosynthesis enzymes; a set of obesity-attenuated genes linked to tissue remodeling and protein translation was selectively regulated in the two nonobese states; and several postobesity-enriched genes encoding proteins involved in, for example, one-carbon metabolism were only responsive to insulin in the women who had lost weight. Altogether, human WAT displays a selective insulin response in the obese state, where most genes are normalized by weight loss. This comprehensive atlas provides insights into the transcriptional effects of insulin in WAT and may identify targets to improve insulin action.

Human MiniPromoters for ocular-rAAV expression in ON bipolar, cone, corneal, endothelial, Müller glial, and PAX6 cells.

Small and cell-type restricted promoters are important tools for basic and preclinical research, and clinical delivery of gene therapies. In clinical gene therapy, ophthalmic trials have been leading the field, with over 50% of ocular clinical trials using promoters that restrict expression based on cell type. Here, 19 human DNA MiniPromoters were bioinformatically designed for rAAV, tested by neonatal intravenous delivery in mouse, and successful MiniPromoters went on to be tested by intravitreal, subretinal, intrastromal, and/or intravenous delivery in adult mouse. We present promoter development as an overview for each cell type, but only show results in detail for the recommended MiniPromoters: Ple265 and Ple341 (PCP2) ON bipolar, Ple349 (PDE6H) cone, Ple253 (PITX3) corneal stroma, Ple32 (CLDN5) endothelial cells of the blood-retina barrier, Ple316 (NR2E1) Müller glia, and Ple331 (PAX6) PAX6 positive. Overall, we present a resource of new, redesigned, and improved MiniPromoters for ocular gene therapy that range in size from 784 to 2484 bp, and from weaker, equal, or stronger in strength relative to the ubiquitous control promoter smCBA. All MiniPromoters will be useful for therapies involving small regulatory RNA and DNA, and proteins ranging from 517 to 1084 amino acids, representing 62.9-90.2% of human proteins.

BiasAway: command-line and web server to generate nucleotide composition-matched DNA background sequences.

MOTIVATION: Accurate motif enrichment analyses depend on the choice of background DNA sequences used, which should ideally match the sequence composition of the foreground sequences. It is important to avoid false positive enrichment due to sequence biases in the genome, such as GC-bias. Therefore, relying on an appropriate set of background sequences is crucial for enrichment analysis. RESULTS: We developed BiasAway, a command line tool and its dedicated easy-to-use web server to generate synthetic sequences matching any k-mer nucleotide composition or select genomic DNA sequences matching the mononucleotide composition of the foreground sequences through four different models. For genomic sequences, we provide precomputed partitions of genomes from nine species with five different bin sizes to generate appropriate genomic background sequences. AVAILABILITY AND IMPLEMENTATION: BiasAway source code is freely available from Bitbucket (https://bitbucket.org/CBGR/biasaway) and can be easily installed using bioconda or pip. The web server is available at https://biasaway.uio.no and a detailed documentation is available at https://biasaway.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DNA copy number motifs are strong and independent predictors of survival in breast cancer.

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

MirGeneDB 2.0: the metazoan microRNA complement.

Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are special because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this distinction is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database - MirGeneDB - to 45 organisms, encompassing a wide phylogenetic swath of animal evolution. By consistently annotating and naming 10,899 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked >2000 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct ancestral miRNA repertoires. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs as well as biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.

JASPAR 2020: update of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.

Beware the Jaccard: the choice of similarity measure is important and non-trivial in genomic colocalisation analysis.

The generation and systematic collection of genome-wide data is ever-increasing. This vast amount of data has enabled researchers to study relations between a variety of genomic and epigenomic features, including genetic variation, gene regulation and phenotypic traits. Such relations are typically investigated by comparatively assessing genomic co-occurrence. Technically, this corresponds to assessing the similarity of pairs of genome-wide binary vectors. A variety of similarity measures have been proposed for this problem in other fields like ecology. However, while several of these measures have been employed for assessing genomic co-occurrence, their appropriateness for the genomic setting has never been investigated. We show that the choice of similarity measure may strongly influence results and propose two alternative modelling assumptions that can be used to guide this choice. On both simulated and real genomic data, the Jaccard index is strongly altered by dataset size and should be used with caution. The Forbes coefficient (fold change) and tetrachoric correlation are less influenced by dataset size, but one should be aware of increased variance for small datasets. All results on simulated and real data can be inspected and reproduced at https://hyperbrowser.uio.no/sim-measure.