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Background: Pleural Mesothelioma (PM) is a highly aggressive cancer, for which effective early detection remains a challenge due to limited screening options and low sensitivity of biomarkers discovered so far. While extracellular vesicles (EVs) have emerged as promising candidates for blood-based biomarkers, their role in PM has not been studied yet. In this study, we characterized the transcriptomic profile of EVs secreted by PM primary cells and explored their potential as a biomarker source for PM detection., Methods: We collected cell culture supernatant from early-passage PM cell cultures derived from the pleural effusion of 4 PM patients. EVs were isolated from the supernatant using Qiagen exoEasy Maxi kit. RNA isolation from EVs was done using the mirVana PARIS kit. Finally, single-end RNA sequencing was done with Illumina Novaseq 6000., Results: We identified a range of RNA species expressed in EVs secreted by PM cells, including protein-coding RNA (80%), long non-coding RNA (13%), pseudogenes (4.5%), and short non-coding RNA (1.6%). We detected a subset of genes associated with the previously identified epithelioid (32 genes) and sarcomatoid molecular components (36 genes) in PM-EVs. To investigate whether these markers could serve as biomarkers for PM detection in blood, we compared the RNA content of PM-EVs with the cargo of EVs isolated from the plasma of healthy donors (publicly available data). Majority of upregulated genes in PM-EVs were protein-coding and long non-coding RNAs. Interestingly, 25 of them were the sarcomatoid and epithelioid marker genes. Finally, functional analysis revealed that the PM-EV RNA cargo was associated with Epithelial-Mesenchymal transition, glycolysis, and hypoxia., Conclusions: This is the first study to characterize the transcriptomic profile of EVs secreted by PM primary cell cultures, demonstrating their potential as biomarker source for early detection. Further investigation of the functional role of PM-EVs will provide new insights into disease biology and therapeutic avenues., Competing Interests: Declarations. Code availability: R code used to analyze the data have been provided in the Supplementary Files. Ethics approval and consent to participate: All procedures involving pleural mesothelioma primary cultures were approved by the Ethics Committee of the canton Zurich on 09/02/2021 with the approval number BASEC-Nr. 2020–02566. Consent for publication: Not applicable. Competing interests: IO has received institutional grants from Roche and Medtronic, consulting fees from Intuitive, and lecture honoraria from Roche and AstraZeneca. IO has also participated in advisory boards for AstraZeneca, MSD, BMS and Medtronic. The remaining authors declare no competing interests., (© 2025. The Author(s).)

Motivation: Sparse survival models are statistical models that select a subset of predictor variables while modeling the time until an event occurs, which can subsequently help interpretability and transportability. The subset of important features is often obtained with regularized models, such as the Cox Proportional Hazards model with Lasso regularization, which limit the number of non-zero coefficients. However, such models can be sensitive to the choice of regularization hyperparameter., Results: In this work, we develop a software package and demonstrate how knowledge distillation, a powerful technique in machine learning that aims to transfer knowledge from a complex teacher model to a simpler student model, can be leveraged to learn sparse survival models while mitigating this challenge. For this purpose, we present sparsesurv, a Python package that contains a set of teacher-student model pairs, including the semi-parametric accelerated failure time and the extended hazards models as teachers, which currently do not have Python implementations. It also contains in-house survival function estimators, removing the need for external packages. Sparsesurv is validated against R-based Elastic Net regularized linear Cox proportional hazards models as implemented in the commonly used glmnet package. Our results reveal that knowledge distillation-based approaches achieve competitive discriminative performance relative to glmnet across the regularization path while making the choice of the regularization hyperparameter significantly easier. All of these features, combined with a sklearn-like API, make sparsesurv an easy-to-use Python package that enables survival analysis for high-dimensional datasets through fitting sparse survival models via knowledge distillation., Availability and Implementation: sparsesurv is freely available under a BSD 3 license on GitHub (https://github.com/BoevaLab/sparsesurv) and The Python Package Index (PyPi) (https://pypi.org/project/sparsesurv/)., (© The Author(s) 2024. Published by Oxford University Press.)

Adrenocortical carcinoma (ACC) is a rare and highly heterogeneous disease with a notably poor prognosis due to significant challenges in diagnosis and treatment. Emphasizing on the importance of precision medicine, there is an increasing need for comprehensive genomic resources alongside well-developed experimental models to devise personalized therapeutic strategies. We present ACC_CellMinerCDB, a substantive genomic and drug sensitivity database (available at https://discover.nci.nih.gov/acc_cellminercdb) comprising ACC cell lines, patient-derived xenografts, surgical samples, and responses to more than 2,400 drugs examined by the NCI and National Center for Advancing Translational Sciences. This database exposes shared genomic pathways among ACC cell lines and surgical samples, thus authenticating the cell lines as research models. It also allows exploration of pertinent treatment markers such as MDR-1, SOAT1, MGMT, MMR, and SLFN11 and introduces the potential to repurpose agents like temozolomide for ACC therapy. ACC_CellMinerCDB provides the foundation for exploring larger preclinical ACC models., Significance: ACC_CellMinerCDB, a comprehensive database of cell lines, patient-derived xenografts, surgical samples, and drug responses, reveals shared genomic pathways and treatment-relevant markers in ACC. This resource offers insights into potential therapeutic targets and the opportunity to repurpose existing drugs for ACC therapy., (©2024 The Authors; Published by the American Association for Cancer Research.)

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response., Competing Interests: E.M.V.: Advisory/Consulting: Enara Bio, Manifold Bio, Monte Rosa, Novartis Institute for Biomedical Research, Serinus Bio, TracerDx Research support: Novartis, BMS, Sanofi, NextPoint Equity: Tango Therapeutics, Genome Medical, Genomic Life, Enara Bio, Manifold Bio, Microsoft, Monte Rosa, Riva Therapeutics, Serinus Bio, Syapse, TracerDx Travel reimbursement: None Patents: Institutional patents filed on chromatin mutations and immunotherapy response, and methods for clinical interpretation; intermittent legal consulting on patents for Foaley & Hoag Editorial Boards: Science Advances A.J.A. has consulted for Anji Pharmaceuticals, Affini-T Therapeutics, Arrakis Therapeutics, AstraZeneca, Boehringer Ingelheim, Kestrel Therapeutics, Merck & Co., Inc., Mirati Therapeutics, Nimbus Therapeutics, Oncorus, Inc., Plexium, Quanta Therapeutics, Revolution Medicines, Reactive Biosciences, Riva Therapeutics, Servier Pharmaceuticals, Syros Pharmaceuticals, T-knife Therapeutics, Third Rock Ventures, and Ventus Therapeutics. A.J.A. holds equity in Riva Therapeutics and Kestrel Therapeutics. A.J.A. has research funding from Amgen, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Deerfield, Inc., Eli Lilly, Mirati Therapeutics, Nimbus Therapeutics, Novartis, Novo Ventures, Revolution Medicines, and Syros Pharmaceuticals.

Background: The phenomenon of field cancerization reflects the transition of normal cells into those predisposed to cancer. Assessing the scope and intensity of this process in the colon may support risk prediction and colorectal cancer prevention., Methods: The Swiss Epigenetic Colorectal Cancer Study (SWEPIC) study, encompassing 1111 participants for DNA methylation analysis and a subset of 84 for RNA sequencing, was employed to detect field cancerization in individuals with adenomatous polyps (AP). Methylation variations were evaluated for their discriminative capability, including in external cohorts, genomic localization, clinical correlations, and associated RNA expression patterns., Results: Normal cecal tissue of individuals harboring an AP in the proximal colon manifested dysregulated DNA methylation compared to tissue from healthy individuals at 558 unique loci. Leveraging these adenoma-related differentially variable and methylated CpGs (aDVMCs), our classifier discerned between healthy and AP-adjacent tissues across SWEPIC datasets (cross-validated area under the receiver operating characteristic curve [ROC AUC] = 0.63-0.81), including within age-stratified cohorts. This discriminative capacity was validated in 3 external sets, differentiating healthy from cancer-adjacent tissue (ROC AUC = 0.82-0.88). Notably, aDVMC dysregulation correlated with polyp multiplicity. More than 50% of aDVMCs were significantly associated with age. These aDVMCs were enriched in active regions of the genome (P < .001), and associated genes exhibited altered expression in AP-adjacent tissues., Conclusions: Our findings underscore the early onset of field cancerization in the right colon during the neoplastic transformation process. A more extensive validation of aDVMC dysregulation as a stratification tool could pave the way for enhanced surveillance approaches, especially given its linkage to adenoma emergence., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Adrenocortical carcinoma is rare and aggressive endocrine cancer of the adrenal gland. Within adrenocortical carcinoma, a recently described subtype characterized by a CpG island methylator phenotype (CIMP) has been associated with an especially poor prognosis. However, the drivers of CIMP remain unknown. Furthermore, the functional relation between CIMP and poor clinical outcomes of patients with adrenocortical carcinoma stays elusive., Results: Here, we show that CIMP in adrenocortical carcinoma is linked to the increased expression of DNA methyltransferases DNMT1 and DNMT3A driven by a gain of gene copy number and cell hyperproliferation. Importantly, we demonstrate that CIMP contributes to tumor aggressiveness by favoring tumor immune escape. This effect could be at least partially reversed by treatment with the demethylating agent 5-azacytidine., Conclusions: In sum, our findings suggest that co-treatment with demethylating agents might enhance the efficacy of immunotherapy and could represent a novel therapeutic approach for patients with high CIMP adrenocortical carcinoma., (© 2023. The Author(s).)

Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Noradrenergic and mesenchymal identities have been characterized in neuroblastoma cell lines according to their epigenetic landscapes and core regulatory circuitries. However, their relationship and relative contribution in patient tumors remain poorly defined. We now document spontaneous and reversible plasticity between the two identities, associated with epigenetic reprogramming, in several neuroblastoma models. Interestingly, xenografts with cells from each identity eventually harbor a noradrenergic phenotype suggesting that the microenvironment provides a powerful pressure towards this phenotype. Accordingly, such a noradrenergic cell identity is systematically observed in single-cell RNA-seq of 18 tumor biopsies and 15 PDX models. Yet, a subpopulation of these noradrenergic tumor cells presents with mesenchymal features that are shared with plasticity models, indicating that the plasticity described in these models has relevance in neuroblastoma patients. This work therefore emphasizes that intrinsic plasticity properties of neuroblastoma cells are dependent upon external cues of the environment to drive cell identity., (© 2023. The Author(s).)

As observed in several previous studies, integrating more molecular modalities in multi-omics cancer survival models may not always improve model accuracy. In this study, we compared eight deep learning and four statistical integration techniques for survival prediction on 17 multi-omics datasets, examining model performance in terms of overall accuracy and noise resistance. We found that one deep learning method, mean late fusion, and two statistical methods, PriorityLasso and BlockForest , performed best in terms of both noise resistance and overall discriminative and calibration performance. Nevertheless, all methods struggled to adequately handle noise when too many modalities were added. In summary, we confirmed that current multi-omics survival methods are not sufficiently noise resistant. We recommend relying on only modalities for which there is known predictive value for a particular cancer type until models that have stronger noise-resistance properties are developed., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

Although originally described as transcriptional activator, SPI1/PU.1, a major player in haematopoiesis whose alterations are associated with haematological malignancies, has the ability to repress transcription. Here, we investigated the mechanisms underlying gene repression in the erythroid lineage, in which SPI1 exerts an oncogenic function by blocking differentiation. We show that SPI1 represses genes by binding active enhancers that are located in intergenic or gene body regions. HDAC1 acts as a cooperative mediator of SPI1-induced transcriptional repression by deacetylating SPI1-bound enhancers in a subset of genes, including those involved in erythroid differentiation. Enhancer deacetylation impacts on promoter acetylation, chromatin accessibility and RNA pol II occupancy. In addition to the activities of HDAC1, polycomb repressive complex 2 (PRC2) reinforces gene repression by depositing H3K27me3 at promoter sequences when SPI1 is located at enhancer sequences. Moreover, our study identified a synergistic relationship between PRC2 and HDAC1 complexes in mediating the transcriptional repression activity of SPI1, ultimately inducing synergistic adverse effects on leukaemic cell survival. Our results highlight the importance of the mechanism underlying transcriptional repression in leukemic cells, involving complex functional connections between SPI1 and the epigenetic regulators PRC2 and HDAC1., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Numerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms., (© The Author(s) 2022. Published by Oxford University Press.)

Introduction: Chronic rhinosinusitis (CRS) is the most common immunological ENT disease, which has several phenotypes. The heterogeneity of CRS is due to the peculiarities of their pathogenetic mechanisms - the system of cytokines plays the crucial significance. They are biologically active substances and present regulatory peptides that demonstrate an immunomodulatory and regulatory effects not only in the local level but the system level as well., Objective: To determine specific features of the cytokine profile in blood serum among patients with CRS without polyps (CRSwP)., Material and Methods: Serum cytokines (IL-1α, IL-1RA, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12p40, IL-12p70, IL-13, IL-15, IL-17, IFN-α2, IFN-γ, GM-CSF) were defined in 75 patients: 32 patients with CRSwP were operated (main group) - 17 with cyst of maxillary sinus, 15 with edema of the maxillary sinuses. The control group - 43 patients were under surgery for a deviated nasal septum (septoplasty). The groups were comparable to gender and age., Results: The cytokines detection rate was different in all groups. IL-4 (detection rate 93.3-95.3%) and IFN-γ (79.1-86.7%) were measured nearly the all groups. IL-8 (73.3-76.5%) and IL-17 (76.5-80.0%) were often measured in the group with CRSwP; in contrast to the control group - these indicators were lower: 60.5% and 65.5%, respectively. IL-1α (82.4%) and IFN-α2 (76.5%) were often detected in CRS with cystic formations. IL-1β, IL-5, IL-7, IL-9, IL-15 were measured in all groups in less than 30% of patients; IL-2 and IL-6 - in the group of CRS with cystic formation; IL-10, IL-12p40, IL-13 in the group with edema of maxillary sinuses. In a quantitative comparison of the concentration of cytokines. Significant differences in concentration of cytokines between the groups were not obtained ( p> 0.05) in terms of quantity., Conclusion: CRSwP with cystic formation is characterized by the development of T2 type immune response and a higher inflammation-related tissue damage.

Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially solved by applying regularization techniques such as lasso, the models still suffer from unsatisfactory predictive power and low stability. Methods: Here, we investigated two methods to improve survival models. Firstly, we leveraged the biological knowledge that groups of genes act together in pathways and regularized both at the group and gene level using latent group lasso penalty term. Secondly, we designed and applied a multi-task learning penalty that allowed us leveraging the relationship between survival models for different cancers. Results: We observed modest improvements over the simple lasso model with the inclusion of latent group lasso penalty for six of the 16 cancer types tested. The addition of a multi-task penalty, which penalized coefficients in pairs of cancers from diverging too greatly, significantly improved accuracy for a single cancer, lung squamous cell carcinoma, while having minimal effect on other cancer types. Conclusion: While the use of pathway information and multi-tasking shows some promise, these methods do not provide a substantial improvement when compared with standard methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Malenová, Rowson and Boeva.)

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems' sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems' behavior and how it can aid domain experts in the systems' maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.

Background: Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization of ChIP-seq binding intensity signals across conditions, e.g., the normalization to the same number of reads, either assume a constant signal-to-noise ratio across conditions or base the estimates of correction factors on genomic regions with intrinsically different signals between conditions. Inaccurate normalization of ChIP-seq signal may, in turn, lead to erroneous biological conclusions., Results: We developed a new R package, CHIPIN, that allows normalizing ChIP-seq signals across different conditions/samples when spike-in information is not available, but gene expression data are at hand. Our normalization technique is based on the assumption that, on average, no differences in ChIP-seq signals should be observed in the regulatory regions of genes whose expression levels are constant across samples/conditions. In addition to normalizing ChIP-seq signals, CHIPIN provides as output a number of graphs and calculates statistics allowing the user to assess the efficiency of the normalization and qualify the specificity of the antibody used. In addition to ChIP-seq, CHIPIN can be used without restriction on open chromatin ATAC-seq or DNase hypersensitivity data. We validated the CHIPIN method on several ChIP-seq data sets and documented its superior performance in comparison to several commonly used normalization techniques., Conclusions: The CHIPIN method provides a new way for ChIP-seq signal normalization across conditions when spike-in experiments are not available. The method is implemented in a user-friendly R package available on GitHub: https://github.com/BoevaLab/CHIPIN., (© 2021. The Author(s).)

Purpose: TERT gene rearrangement with transcriptional superenhancers leads to TERT overexpression and neuroblastoma. No targeted therapy is available for clinical trials in patients with TERT -rearranged neuroblastoma., Experimental Design: Anticancer agents exerting the best synergistic anticancer effects with BET bromodomain inhibitors were identified by screening an FDA-approved oncology drug library. The synergistic effects of the BET bromodomain inhibitor OTX015 and the proteasome inhibitor carfilzomib were examined by immunoblot and flow cytometry analysis. The anticancer efficacy of OTX015 and carfilzomib combination therapy was investigated in mice xenografted with TERT -rearranged neuroblastoma cell lines or patient-derived xenograft (PDX) tumor cells, and the role of TERT reduction in the anticancer efficacy was examined through rescue experiments in mice., Results: The BET bromodomain protein BRD4 promoted TERT -rearranged neuroblastoma cell proliferation through upregulating TERT expression. Screening of an approved oncology drug library identified the proteasome inhibitor carfilzomib as the agent exerting the best synergistic anticancer effects with BET bromodomain inhibitors including OTX015. OTX015 and carfilzomib synergistically reduced TERT protein expression, induced endoplasmic reticulum stress, and induced TERT -rearranged neuroblastoma cell apoptosis which was blocked by TERT overexpression and endoplasmic reticulum stress antagonists. In mice xenografted with TERT -rearranged neuroblastoma cell lines or PDX tumor cells, OTX015 and carfilzomib synergistically blocked TERT expression, induced tumor cell apoptosis, suppressed tumor progression, and improved mouse survival, which was largely reversed by forced TERT overexpression., Conclusions: OTX015 and carfilzomib combination therapy is likely to be translated into the first clinical trial of a targeted therapy in patients with TERT -rearranged neuroblastoma., (©2020 American Association for Cancer Research.)

ChIP-seq is the method of choice for profiling protein-DNA interactions, and notably for characterizing the landscape of transcription factor binding and histone modifications. This technique has been widely used to study numerous aspects of tumor biology and led to the development of several promising cancer therapies. In Ewing sarcoma research, ChIP-seq provided important insights into the mechanism of action of the major oncogenic fusion protein EWSR1-FLI1 and related epigenetic and transcriptional changes. In this chapter, we provide a detailed pipeline to analyze ChIP-seq experiments from the preprocessing of raw data to tertiary analysis of detected binding sites. We also advise on best practice to prepare tumor samples prior to sequencing.

EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation. However, it remains elusive how genetically homogeneous EwS cells can drive the heterogeneity of transcriptional programs. Here, we combine independent component analysis of single-cell RNA sequencing data from diverse cell types and model systems with time-resolved mapping of EWSR1-FLI1 binding sites and of open chromatin regions to characterize dynamic cellular processes associated with EWSR1-FLI1 activity. We thus define an exquisitely specific and direct enhancer-driven EWSR1-FLI1 program. In EwS tumors, cell proliferation and strong oxidative phosphorylation metabolism are associated with a well-defined range of EWSR1-FLI1 activity. In contrast, a subpopulation of cells from below and above the intermediary EWSR1-FLI1 activity is characterized by increased hypoxia. Overall, our study reveals sources of intratumoral heterogeneity within EwS tumors., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore unsuitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvolving Hi-C data and identifies significant many-body interactions. For a diverse set of highly active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions.

Adrenal cortex steroids are essential for body homeostasis, and adrenal insufficiency is a life-threatening condition. Adrenal endocrine activity is maintained through recruitment of subcapsular progenitor cells that follow a unidirectional differentiation path from zona glomerulosa to zona fasciculata (zF). Here, we show that this unidirectionality is ensured by the histone methyltransferase EZH2. Indeed, we demonstrate that EZH2 maintains adrenal steroidogenic cell differentiation by preventing expression of GATA4 and WT1 that cause abnormal dedifferentiation to a progenitor-like state in Ezh2 KO adrenals. EZH2 further ensures normal cortical differentiation by programming cells for optimal response to adrenocorticotrophic hormone (ACTH)/PKA signaling. This is achieved by repression of phosphodiesterases PDE1B, 3A, and 7A and of PRKAR1B. Consequently, EZH2 ablation results in blunted zF differentiation and primary glucocorticoid insufficiency. These data demonstrate an all-encompassing role for EZH2 in programming steroidogenic cells for optimal response to differentiation signals and in maintaining their differentiated state., Competing Interests: The authors declare no conflict of interest.

Chromosome 17q gains are almost invariably present in high-risk neuroblastoma cases. Here, we perform an integrative epigenomics search for dosage-sensitive transcription factors on 17q marked by H3K27ac defined super-enhancers and identify TBX2 as top candidate gene. We show that TBX2 is a constituent of the recently established core regulatory circuitry in neuroblastoma with features of a cell identity transcription factor, driving proliferation through activation of p21-DREAM repressed FOXM1 target genes. Combined MYCN/TBX2 knockdown enforces cell growth arrest suggesting that TBX2 enhances MYCN sustained activation of FOXM1 targets. Targeting transcriptional addiction by combined CDK7 and BET bromodomain inhibition shows synergistic effects on cell viability with strong repressive effects on CRC gene expression and p53 pathway response as well as several genes implicated in transcriptional regulation. In conclusion, we provide insight into the role of the TBX2 CRC gene in transcriptional dependency of neuroblastoma cells warranting clinical trials using BET and CDK7 inhibitors.

Neuroblastoma, a pediatric tumor of the sympathetic nervous system, is predominantly driven by copy number aberrations, which predict survival outcome in global neuroblastoma cohorts and in low-risk cases. For high-risk patients there is still a need for better prognostic biomarkers. Via an international collaboration, we collected copy number profiles of 556 high-risk neuroblastomas generated on different array platforms. This manuscript describes the composition of the dataset, the methods used to process the data, including segmentation and aberration calling, and data validation. t-SNE analysis shows that samples cluster according to MYCN status, and shows a difference between array platforms. 97.3% of samples are characterized by the presence of segmental aberrations, in regions frequently affected in neuroblastoma. Focal aberrations affect genes known to be involved in neuroblastoma, such as ALK and LIN28B. To conclude, we compiled a unique large copy number dataset of high-risk neuroblastoma tumors, available via R2 and a Shiny web application. The availability of patient survival data allows to further investigate the prognostic value of copy number aberrations.

Background: Neuroblastoma is characterized by substantial clinical heterogeneity. Despite intensive treatment, the survival rates of high-risk neuroblastoma patients are still disappointingly low. Somatic chromosomal copy number aberrations have been shown to be associated with patient outcome, particularly in low- and intermediate-risk neuroblastoma patients. To improve outcome prediction in high-risk neuroblastoma, we aimed to design a prognostic classification method based on copy number aberrations., Methods: In an international collaboration, normalized high-resolution DNA copy number data (arrayCGH and SNP arrays) from 556 high-risk neuroblastomas obtained at diagnosis were collected from nine collaborative groups and segmented using the same method. We applied logistic and Cox proportional hazard regression to identify genomic aberrations associated with poor outcome., Results: In this study, we identified two types of copy number aberrations that are associated with extremely poor outcome. Distal 6q losses were detected in 5.9% of patients and were associated with a 10-year survival probability of only 3.4% (95% confidence interval [CI] = 0.5% to 23.3%, two-sided P = .002). Amplifications of regions not encompassing the MYCN locus were detected in 18.1% of patients and were associated with a 10-year survival probability of only 5.8% (95% CI = 1.5% to 22.2%, two-sided P < .001)., Conclusions: Using a unique large copy number data set of high-risk neuroblastoma cases, we identified a small subset of high-risk neuroblastoma patients with extremely low survival probability that might be eligible for inclusion in clinical trials of new therapeutics. The amplicons may also nominate alternative treatments that target the amplified genes.

Motivation: In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants., Results: We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution., Availability and Implementation: Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone., Contact: gudrun.schleiermacher@curie.fr or valentina.boeva@inserm.fr., Supplementary Information: Supplementary data are available at Bioinformatics online.

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources)., (Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Activating mutations of the ALK receptor occur in a subset of neuroblastoma tumors. We previously demonstrated that Alk mutations cooperate with MYCN overexpression to induce neuroblastoma in mice and identified Ret as being strongly upregulated in MYCN/Alk mut tumors. By a genetic approach in vivo, we now document an oncogenic cooperation between activated Ret and MYCN overexpression in neuroblastoma formation. We show that MYCN/Ret M919T tumors exhibit histological features and expression profiles close to MYCN/Alk mut tumors. We show that RET transcript levels decrease precedes RET protein levels decrease upon ALK inhibition in neuroblastoma cell lines. Etv5 was identified as a candidate transcription factor regulating Ret expression from murine MYCN/Alk mut tumor transcriptomic data. We demonstrate that ETV5 is regulated both at the protein and mRNA levels upon ALK activation or inhibition in neuroblastoma cell lines and that this regulation precedes RET modulation. We document that ALK activation induces ETV5 protein upregulation through stabilization in a MEK/ERK-dependent manner. We show that RNAi-mediated inhibition of ETV5 decreases RET expression. Reporter assays indicate that ETV5 is able to drive RET gene transcription. ChIP-seq analysis confirmed ETV5 binding on the RET promoter and identified an enhancer upstream of the promoter. Finally, we demonstrate that combining RET and ALK inhibitors reduces tumor growth more efficiently than each single agent in MYCN and Alk F1178L -driven murine neuroblastoma. Altogether, these results define the ERK-ETV5-RET pathway as a critical axis driving neuroblastoma oncogenesis downstream of activated ALK.

The objective of the present study was to elucidate the possible correlations between the vitamin D 3 level in the blood serum and lactase gene polymorphism (LCT-13910 T>C) in the patients presenting with chronic polypous rhinosinusitis (CPRS). The study included 50 patients with this condition and 14 subjects comprising the control group. The variants of lactase gene polymorphism (LCT-13910 T>C) were identified with the use of polymerase chain reaction (PCR) in real time. The total level of serum vitamin D 3 (VD 3 ) was determined by means of the immunochemical analysis (the electrochemiluminescence technique). In the group of patients presenting with chronic polypous rhinosinusitis, the level of VD 3 in the blood serum ranged from 48 nm/l to 85 nm/l (mean 60 nm/l) compared with that in the patients of the control group (from 78 nm/l to 112 nm/l; mean 97 nm/l) . The level of vitamin D 3 'below the normal values' was documented in 71% of the patients with CPRS in comparison with 7% in the control subjects. Lactase gene polymorphism (LCT-13910 CC, CT) suggesting pronounced and latent hypolactasia was identified in 94% of the patients with CPRS compared with 78.6% in the control group. The occurrence of the CC genotype in the patients of both study groups was virtually identical: 52% in the patients presenting with chronic polypous rhinosinusitis and 57% in the control group. CT polymorphism was identified in 42% of the patients with CPRS and in 21% of the control subjects. The significant difference between the patients of the two groups was documented for the occurrence of TT polymorphism: 6% among the patients with CPRS and 21% in the controls (i.e. much higher in the healthy subjects). There was no significant difference between the serum levels of vitamin D 3 either among the patients with CPRS having LCT-13910 gene polymorphisms (CC, CT, TT) or among the control subjects. It is concluded that the study revealed the higher levels of vitamin D 3 in the blood sera from the control subjects in comparison with that in the patients with chronic polypous rhinosinusitis. Moreover, the patients of the latter group more frequently exhibited the variant of the LCT CT-13910 gene polymorphism suggesting latent hypolactasia whereas the subjects comprising the control group more frequently had the variant of the LCT CT-13910 gene polymorphism indicative of the normal tolerance of lactose.

Neuroblastoma is a tumor of the peripheral sympathetic nervous system, derived from multipotent neural crest cells (NCCs). To define core regulatory circuitries (CRCs) controlling the gene expression program of neuroblastoma, we established and analyzed the neuroblastoma super-enhancer landscape. We discovered three types of identity in neuroblastoma cell lines: a sympathetic noradrenergic identity, defined by a CRC module including the PHOX2B, HAND2 and GATA3 transcription factors (TFs); an NCC-like identity, driven by a CRC module containing AP-1 TFs; and a mixed type, further deconvoluted at the single-cell level. Treatment of the mixed type with chemotherapeutic agents resulted in enrichment of NCC-like cells. The noradrenergic module was validated by ChIP-seq. Functional studies demonstrated dependency of neuroblastoma with noradrenergic identity on PHOX2B, evocative of lineage addiction. Most neuroblastoma primary tumors express TFs from the noradrenergic and NCC-like modules. Our data demonstrate a previously unknown aspect of tumor heterogeneity relevant for neuroblastoma treatment strategies.

Pangenomic studies identified distinct molecular classes for many cancers, with major clinical applications. However, routine use requires cost-effective assays. We assessed whether targeted next-generation sequencing (NGS) could call chromosomal alterations and DNA methylation status. A training set of 77 tumors and a validation set of 449 (43 tumor types) were analyzed by targeted NGS and single-nucleotide polymorphism (SNP) arrays. Thirty-two tumors were analyzed by NGS after bisulfite conversion, and compared to methylation array or methylation-specific multiplex ligation-dependent probe amplification. Considering allelic ratios, correlation was strong between targeted NGS and SNP arrays (r = 0.88). In contrast, considering DNA copy number, for variations of one DNA copy, correlation was weaker between read counts and SNP array (r = 0.49). Thus, we generated TARGOMICs, optimized for detecting chromosome alterations by combining allelic ratios and read counts generated by targeted NGS. Sensitivity for calling normal, lost, and gained chromosomes was 89%, 72%, and 31%, respectively. Specificity was 81%, 93%, and 98%, respectively. These results were confirmed in the validation set. Finally, TARGOMICs could efficiently align and compute proportions of methylated cytosines from bisulfite-converted DNA from targeted NGS. In conclusion, beyond calling mutations, targeted NGS efficiently calls chromosome alterations and methylation status in tumors. A single run and minor design/protocol adaptations are sufficient. Optimizing targeted NGS should expand translation of genomics to clinical routine., (Copyright © 2017 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)

Comparing histone modification profiles between cancer and normal states, or across different tumor samples, can provide insights into understanding cancer initiation, progression and response to therapy. ChIP-seq histone modification data of cancer samples are distorted by copy number variation innate to any cancer cell. We present HMCan-diff, the first method designed to analyze ChIP-seq data to detect changes in histone modifications between two cancer samples of different genetic backgrounds, or between a cancer sample and a normal control. HMCan-diff explicitly corrects for copy number bias, and for other biases in the ChIP-seq data, which significantly improves prediction accuracy compared to methods that do not consider such corrections. On in silico simulated ChIP-seq data generated using genomes with differences in copy number profiles, HMCan-diff shows a much better performance compared to other methods that have no correction for copy number bias. Additionally, we benchmarked HMCan-diff on four experimental datasets, characterizing two histone marks in two different scenarios. We correlated changes in histone modifications between a cancer and a normal control sample with changes in gene expression. On all experimental datasets, HMCan-diff demonstrated better performance compared to the other methods., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Super-enhancers (SEs) are key transcriptional drivers of cellular, developmental, and disease states in mammals, yet the conservational and regulatory features of these enhancer elements in nonmammalian vertebrates are unknown. To define SEs in zebrafish and enable sequence and functional comparisons to mouse and human SEs, we used genome-wide histone H3 lysine 27 acetylation (H3K27ac) occupancy as a primary SE delineator. Our study determined the set of SEs in pluripotent state cells and adult zebrafish tissues and revealed both similarities and differences between zebrafish and mammalian SEs. Although the total number of SEs was proportional to the genome size, the genomic distribution of zebrafish SEs differed from that of the mammalian SEs. Despite the evolutionary distance separating zebrafish and mammals and the low overall SE sequence conservation, ∼42% of zebrafish SEs were located in close proximity to orthologs that also were associated with SEs in mouse and human. Compared to their nonassociated counterparts, higher sequence conservation was revealed for those SEs that have maintained orthologous gene associations. Functional dissection of two of these SEs identified conserved sequence elements and tissue-specific expression patterns, while chromatin accessibility analyses predicted transcription factors governing the function of pluripotent state zebrafish SEs. Our zebrafish annotations and comparative studies show the extent of SE usage and their conservation across vertebrates, permitting future gene regulatory studies in several tissues., (© 2017 Pérez-Rico et al.; Published by Cold Spring Harbor Laboratory Press.)

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA&#95;Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Motivation: Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to create a computational method that could use both types of information, i.e. normal and abnormal reads, and demonstrate that by doing so we can highly improve both sensitivity and specificity rates of structural variant prediction., Results: We developed a computational method, SV-Bay, to detect structural variants from whole genome sequencing mate-pair or paired-end data using a probabilistic Bayesian approach. This approach takes into account depth of coverage by normal reads and abnormalities in read pair mappings. To estimate the model likelihood, SV-Bay considers GC-content and read mappability of the genome, thus making important corrections to the expected read count. For the detection of somatic variants, SV-Bay makes use of a matched normal sample when it is available. We validated SV-Bay on simulated datasets and an experimental mate-pair dataset for the CLB-GA neuroblastoma cell line. The comparison of SV-Bay with several other methods for structural variant detection demonstrated that SV-Bay has better prediction accuracy both in terms of sensitivity and false-positive detection rate., Availability and Implementation: https://github.com/InstitutCurie/SV-Bay, Contact: valentina.boeva@inserm.fr, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

CDK12 is a recurrently mutated gene in serous ovarian carcinoma, whose downregulation is associated with impaired expression of DNA damage repair genes and subsequent hypersensitivity to DNA-damaging agents and PARP1/2 inhibitors. In this study, we investigated the genomic landscape associated with CDK12 inactivation in patients with serous ovarian carcinoma. We show that CDK12 loss was consistently associated with a particular genomic instability pattern characterized by hundreds of tandem duplications of up to 10 megabases (Mb) in size. Tandem duplications were characterized by a bimodal (∼0.3 and ∼3 Mb) size distribution and overlapping microhomology at the breakpoints. This genomic instability, denoted as the CDK12 TD-plus phenotype, is remarkably distinct from other alteration patterns described in breast and ovarian cancers. The CDK12 TD-plus phenotype was associated with a greater than 10% gain in genomic content and occurred at a 3% to 4% rate in The Cancer Genome Atlas-derived and in-house cohorts of patients with serous ovarian carcinoma. Moreover, CDK12-inactivating mutations together with the TD-plus phenotype were also observed in prostate cancers. Our finding provides new insight toward deciphering the function of CDK12 in genome maintenance and oncogenesis. Cancer Res; 76(7); 1882-91. ©2016 AACR., (©2016 American Association for Cancer Research.)

Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites, and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators. The usefulness of motif analysis is further exemplified in this review by how motif discovery improves peak calling in ChIP-seq and ChIP-exo experiments and, when coupled with information on gene expression, allows insights into physical mechanisms of transcriptional modulation.

Motivation: Read simulators combined with alignment evaluation tools provide the most straightforward way to evaluate and compare mappers. Simulation of reads is accompanied by information about their positions in the source genome. This information is then used to evaluate alignments produced by the mapper. Finally, reports containing statistics of successful read alignments are created.In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads., Results: To solve this obstacle, we have created a generic format Read Naming Format (Rnf) for assigning read names with encoded information about original positions. Futhermore, we have developed an associated software package RnfTools containing two principal components. MIShmash applies one of popular read simulating tools (among DwgSim, Art, Mason, CuReSim, etc.) and transforms the generated reads into Rnf format. LAVEnder evaluates then a given read mapper using simulated reads in Rnf format. A special attention is payed to mapping qualities that serve for parametrization of Roc curves, and to evaluation of the effect of read sample contamination., Availability and Implementation: RnfTools: http://karel-brinda.github.io/rnftools Spec. of Rnf: http://karel-brinda.github.io/rnf-spec, Contact: karel.brinda@univ-mlv.fr., (© The Author 2015. Published by Oxford University Press.)

Background: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation. Aberrant promoter methylation is considered a hallmark of cancer involved in silencing of tumor suppressor genes and activation of oncogenes. However, recent studies have also challenged the simple model of gene expression control by promoter methylation in cancer, and the precise mechanism of and role played by changes in DNA methylation in carcinogenesis remains elusive., Results: Using a large dataset of 672 matched cancerous and healthy methylomes, gene expression, and copy number profiles accross 3 types of tissues from The Cancer Genome Atlas (TCGA), we perform a detailed meta-analysis to clarify the interplay between promoter methylation and gene expression in normal and cancer samples. On the one hand, we recover the existence of a CpG island methylator phenotype (CIMP) with prognostic value in a subset of breast, colon and lung cancer samples, where a common subset of promoter CGIs hypomethylated in normal samples become hypermethylated. However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes. On the other hand, we identify tissue-specific sets of genes, different between normal and cancer samples, whose inter-individual variation in expression is significantly correlated with the variation in methylation of the 3' flanking regions of the promoter CGIs. These subsets of genes are not the same in the different tissues, nor between normal and cancerous samples, but transcription factors are over-represented in all subsets., Conclusion: Our results suggest that epigenetic reprogramming in cancer does not contribute to cancer development via direct inhibition of gene expression through promoter hypermethylation. It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

Deciphering the ways in which somatic mutations and germline susceptibility variants cooperate to promote cancer is challenging. Ewing sarcoma is characterized by fusions between EWSR1 and members of the ETS gene family, usually EWSR1-FLI1, leading to the generation of oncogenic transcription factors that bind DNA at GGAA motifs. A recent genome-wide association study identified susceptibility variants near EGR2. Here we found that EGR2 knockdown inhibited proliferation, clonogenicity and spheroidal growth in vitro and induced regression of Ewing sarcoma xenografts. Targeted germline deep sequencing of the EGR2 locus in affected subjects and controls identified 291 Ewing-associated SNPs. At rs79965208, the A risk allele connected adjacent GGAA repeats by converting an interspaced GGAT motif into a GGAA motif, thereby increasing the number of consecutive GGAA motifs and thus the EWSR1-FLI1-dependent enhancer activity of this sequence, with epigenetic characteristics of an active regulatory element. EWSR1-FLI1 preferentially bound to the A risk allele, which increased global and allele-specific EGR2 expression. Collectively, our findings establish cooperation between a dominant oncogene and a susceptibility variant that regulates a major driver of Ewing sarcomagenesis.

Motivation: Because of its low cost, amplicon sequencing, also known as ultra-deep targeted sequencing, is now becoming widely used in oncology for detection of actionable mutations, i.e. mutations influencing cell sensitivity to targeted therapies. Amplicon sequencing is based on the polymerase chain reaction amplification of the regions of interest, a process that considerably distorts the information on copy numbers initially present in the tumor DNA. Therefore, additional experiments such as single nucleotide polymorphism (SNP) or comparative genomic hybridization (CGH) arrays often complement amplicon sequencing in clinics to identify copy number status of genes whose amplification or deletion has direct consequences on the efficacy of a particular cancer treatment. So far, there has been no proven method to extract the information on gene copy number aberrations based solely on amplicon sequencing., Results: Here we present ONCOCNV, a method that includes a multifactor normalization and annotation technique enabling the detection of large copy number changes from amplicon sequencing data. We validated our approach on high and low amplicon density datasets and demonstrated that ONCOCNV can achieve a precision comparable with that of array CGH techniques in detecting copy number aberrations. Thus, ONCOCNV applied on amplicon sequencing data would make the use of additional array CGH or SNP array experiments unnecessary., (© The Author 2014. Published by Oxford University Press.)

Motivation: DNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile., Results: We propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes., Availability and Implementation: The breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr., (© The Author 2014. Published by Oxford University Press.)

Background: Presently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them., Results: We propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group.These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals., Conclusions: The proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices.

During X chromosome inactivation (XCI), the Polycomb Repressive Complex 2 (PRC2) is thought to participate in the early maintenance of the inactive state. Although Xist RNA is essential for the recruitment of PRC2 to the X chromosome, the precise mechanism remains unclear. Here, we demonstrate that the PRC2 cofactor Jarid2 is an important mediator of Xist-induced PRC2 targeting. The region containing the conserved B and F repeats of Xist is critical for Jarid2 recruitment via its unique N-terminal domain. Xist-induced Jarid2 recruitment occurs chromosome-wide independently of a functional PRC2 complex, unlike at other parts of the genome, such as CG-rich regions, where Jarid2 and PRC2 binding are interdependent. Conversely, we show that Jarid2 loss prevents efficient PRC2 and H3K27me3 enrichment to Xist-coated chromatin. Jarid2 thus represents an important intermediate between PRC2 and Xist RNA for the initial targeting of the PRC2 complex to the X chromosome during onset of XCI., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Motivation: Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain., Results: We present HMCan (Histone modifications in cancer), a tool specially designed to analyze histone modification ChIP-seq data produced from cancer genomes. HMCan corrects for the GC-content and copy number bias and then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations. HMCan also showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene, missed by other tools we tested.

Neuroblastoma is a pediatric cancer of the peripheral nervous system in which structural chromosome aberrations are emblematic of aggressive tumors. In this study, we performed an in-depth analysis of somatic rearrangements in two neuroblastoma cell lines and two primary tumors using paired-end sequencing of mate-pair libraries and RNA-seq. The cell lines presented with typical genetic alterations of neuroblastoma and the two tumors belong to the group of neuroblastoma exhibiting a profile of chromothripsis. Inter and intra-chromosomal rearrangements were identified in the four samples, allowing in particular characterization of unbalanced translocations at high resolution. Using complementary experiments, we further characterized 51 rearrangements at the base pair resolution that revealed 59 DNA junctions. In a subset of cases, complex rearrangements were observed with templated insertion of fragments of nearby sequences. Although we did not identify known particular motifs in the local environment of the breakpoints, we documented frequent microhomologies at the junctions in both chromothripsis and non-chromothripsis associated breakpoints. RNA-seq experiments confirmed expression of several predicted chimeric genes and genes with disrupted exon structure including ALK, NBAS, FHIT, PTPRD and ODZ4. Our study therefore indicates that both non-homologous end joining-mediated repair and replicative processes may account for genomic rearrangements in neuroblastoma. RNA-seq analysis allows the identification of the subset of abnormal transcripts expressed from genomic rearrangements that may be involved in neuroblastoma oncogenesis.

Background: Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chosen using various heuristics., Results: We present three contributions for copy number profile smoothing model selection. First, we propose to select the model and degree of smoothness that maximizes agreement with visual breakpoint region annotations. Second, we develop cross-validation procedures to estimate the error of the trained models. Third, we apply these methods to compare 17 smoothing models on a new database of 575 annotated neuroblastoma copy number profiles, which we make available as a public benchmark for testing new algorithms., Conclusions: Whereas previous studies have been qualitative or limited to simulated data, our annotation-guided approach is quantitative and suggests which algorithms are fastest and most accurate in practice on real data. In the neuroblastoma data, the equivalent pelt.n and cghseg.k methods were the best breakpoint detectors, and exhibited reasonable computation times.

Activating mutations of the ALK gene have been identified in sporadic and familial cases of neuroblastoma (NB), a cancer of the peripheral nervous system, and are thought to be the primary mechanism of oncogenic activation of this receptor in this pediatric neoplasm. To address the possibility that ALK activation may occur through genomic rearrangements as detected in other cancers, we first took advantage of high-resolution array-comparative genomic hybridization to search for ALK rearrangements in NB samples. Using complementary experiments by capture/paired-end sequencing and FISH experiments, various types of rearrangements were fully characterized, including partial gains or amplifications, in several NB cell lines and primary tumors. In the CLB-Bar cell line, we described a genomic rearrangement associated with an amplification of the ALK locus, leading to the expression of a 170 kDa protein lacking part of the extracellular domain encoded by exons 4 to 11, named ALK(Δ4-11). Analysis of genomic DNA from the tumor at diagnosis and relapse revealed that the ALK gene was amplified at diagnosis but that the rearranged ALK allele was observed at the relapse stage only, suggesting that it may be implicated in tumor aggressiveness. Consistently, oncogenic and tumorigenic properties of the ALK(Δ4-11) variant were shown after stable expression in NIH3T3 cells. Moreover, we documented an increased constitutive kinase activity of this variant, as well as an impaired maturation and retention into intracellular compartments. These results indicate that genomic rearrangements constitute an alternative mechanism to ALK point mutations resulting in receptor activation.

Motivation: ChIP-seq consists of chromatin immunoprecipitation and deep sequencing of the extracted DNA fragments. It is the technique of choice for accurate characterization of the binding sites of transcription factors and other DNA-associated proteins. We present a web service, Nebula, which allows inexperienced users to perform a complete bioinformatics analysis of ChIP-seq data., Results: Nebula was designed for both bioinformaticians and biologists. It is based on the Galaxy open source framework. Galaxy already includes a large number of functionalities for mapping reads and peak calling. We added the following to Galaxy: (i) peak calling with FindPeaks and a module for immunoprecipitation quality control, (ii) de novo motif discovery with ChIPMunk, (iii) calculation of the density and the cumulative distribution of peak locations relative to gene transcription start sites, (iv) annotation of peaks with genomic features and (v) annotation of genes with peak information. Nebula generates the graphs and the enrichment statistics at each step of the process. During Steps 3-5, Nebula optionally repeats the analysis on a control dataset and compares these results with those from the main dataset. Nebula can also incorporate gene expression (or gene modulation) data during these steps. In summary, Nebula is an innovative web service that provides an advanced ChIP-seq analysis pipeline providing ready-to-publish results., Availability: Nebula is available at http://nebula.curie.fr/, Supplementary Information: Supplementary data are available at Bioinformatics online.