Telomerase activation by genomic rearrangements in high-risk neuroblastoma.

Neuroblastoma is a malignant paediatric tumour of the sympathetic nervous system. Roughly half of these tumours regress spontaneously or are cured by limited therapy. By contrast, high-risk neuroblastomas have an unfavourable clinical course despite intensive multimodal treatment, and their molecular basis has remained largely elusive. Here we have performed whole-genome sequencing of 56 neuroblastomas (high-risk, n = 39; low-risk, n = 17) and discovered recurrent genomic rearrangements affecting a chromosomal region at 5p15.33 proximal of the telomerase reverse transcriptase gene (TERT). These rearrangements occurred only in high-risk neuroblastomas (12/39, 31%) in a mutually exclusive fashion with MYCN amplifications and ATRX mutations, which are known genetic events in this tumour type. In an extended case series (n = 217), TERT rearrangements defined a subgroup of high-risk tumours with particularly poor outcome. Despite a large structural diversity of these rearrangements, they all induced massive transcriptional upregulation of TERT. In the remaining high-risk tumours, TERT expression was also elevated in MYCN-amplified tumours, whereas alternative lengthening of telomeres was present in neuroblastomas without TERT or MYCN alterations, suggesting that telomere lengthening represents a central mechanism defining this subtype. The 5p15.33 rearrangements juxtapose the TERT coding sequence to strong enhancer elements, resulting in massive chromatin remodelling and DNA methylation of the affected region. Supporting a functional role of TERT, neuroblastoma cell lines bearing rearrangements or amplified MYCN exhibited both upregulated TERT expression and enzymatic telomerase activity. In summary, our findings show that remodelling of the genomic context abrogates transcriptional silencing of TERT in high-risk neuroblastoma and places telomerase activation in the centre of transformation in a large fraction of these tumours.

iRODS metadata management for a cancer genome analysis workflow.

BACKGROUND: The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date. RESULTS: We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information. CONCLUSIONS: Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits. RESULTS: We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network. CONCLUSIONS: Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.

A multiserological line assay to potentially discriminate current from past Helicobacter pylori infection.

OBJECTIVES: Early diagnosis is important in controlling Helicobacter pylori-induced gastritis and progression to gastric malignancy. Serological testing is an efficient non-invasive diagnostic method, but currently does not allow differentiation between active and past infections. To fill this diagnostic gap we investigated the diagnostic value of a panel of ten H. pylori-specific antibodies in individuals with different H. pylori infection status within a German population. METHODS: We used the recomLine Helicobacter IgG 2.0 immunoblotting assay to analyse ten H. pylori-specific antibodies in serum samples collected from 1108 volunteers. From these, 788 samples were used to build exposure and infection status models and 320 samples for model validation. H. pylori infection status was verified by histological examination. We applied logistic regression to select antibodies correlated to infection status and developed, with independent validation, discriminating models and risk scores. Receiving operating characteristic analysis was performed to assess the accuracy of the discriminating models. RESULTS: Antibody reactivity against cytotoxin-associated gene A (CagA), H. pylori chaperone (GroEL), and hook-associated protein 2 homologue (FliD) was independently associated with the risk of H. pylori exposure with ORs and 95% CIs of 99.24 (46.50-211.80), 46.17 (17.45-122.17), and 22.16 (8.46-55.04), respectively. A risk score comprising these three selected antibodies differentiated currently H. pylori infected or eradicated participants from negatives with an area under the curve of 0.976 (95% CI: 0.965-0.987) (Model 1). Seropositivity for vacuolating cytotoxin A (VacA), GroEL, FliD, H. pylori adhesin A (HpaA), and γ-glutamyl transpeptidase (gGT) was associated with a current infection with an area under the curve of 0.870 (95% CI: 0.837-0.903), which may help discriminate currently infected patients from eradicated ones (Model 2). DISCUSSION: The recomLine assay is sensitive and specific in determining H. pylori infection and eradication status and thus represents a valuable tool in the management of H. pylori infection.

Binned k-d Tree Construction for Sparse Volume Data on Multi-Core and GPU Systems.

While k-d trees are known to be effective for spatial indexing of sparse 3-d volume data, full reconstruction, e.g. due to changes to the alpha transfer function during rendering, is usually a costly operation with this hierarchical data structure. In a recent publication we showed how to port a clever state of the art k-d tree construction algorithm to a multi-core CPU architecture and by means of thorough optimization we were able to obtain interactive reconstruction rates for moderately sized to large data sets. The construction scheme is based on maintaining partial summed-volume tables that fit in the L1 cache of the multi-core CPU and that allow for fast occupancy queries. In this work we propose a GPU implementation of the parallel k-d tree construction algorithm and compare it with the original multi-core CPU implementation. We conduct a thorough comparative study that outlines performance and scalability of our implementation.

Ray Tracing Structured AMR Data Using ExaBricks.

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations.

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust.

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia.

Deciphering the evolution of cancer cells under therapeutic pressure is a crucial step to understand the mechanisms that lead to treatment resistance. To this end, we analyzed whole-exome sequencing data of eight chronic lymphocytic leukemia (CLL) patients that developed resistance upon BCL2-inhibition by venetoclax. Here, we report recurrent mutations in BTG1 (2 patients) and homozygous deletions affecting CDKN2A/B (3 patients) that developed during treatment, as well as a mutation in BRAF and a high-level focal amplification of CD274 (PD-L1) that might pinpoint molecular aberrations offering structures for further therapeutic interventions.

A mechanistic classification of clinical phenotypes in neuroblastoma.

Neuroblastoma is a pediatric tumor of the sympathetic nervous system. Its clinical course ranges from spontaneous tumor regression to fatal progression. To investigate the molecular features of the divergent tumor subtypes, we performed genome sequencing on 416 pretreatment neuroblastomas and assessed telomere maintenance mechanisms in 208 of these tumors. We found that patients whose tumors lacked telomere maintenance mechanisms had an excellent prognosis, whereas the prognosis of patients whose tumors harbored telomere maintenance mechanisms was substantially worse. Survival rates were lowest for neuroblastoma patients whose tumors harbored telomere maintenance mechanisms in combination with RAS and/or p53 pathway mutations. Spontaneous tumor regression occurred both in the presence and absence of these mutations in patients with telomere maintenance-negative tumors. On the basis of these data, we propose a mechanistic classification of neuroblastoma that may benefit the clinical management of patients.

Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors.

Pulmonary large-cell neuroendocrine carcinomas (LCNECs) have similarities with other lung cancers, but their precise relationship has remained unclear. Here we perform a comprehensive genomic (n = 60) and transcriptomic (n = 69) analysis of 75 LCNECs and identify two molecular subgroups: "type I LCNECs" with bi-allelic TP53 and STK11/KEAP1 alterations (37%), and "type II LCNECs" enriched for bi-allelic inactivation of TP53 and RB1 (42%). Despite sharing genomic alterations with adenocarcinomas and squamous cell carcinomas, no transcriptional relationship was found; instead LCNECs form distinct transcriptional subgroups with closest similarity to SCLC. While type I LCNECs and SCLCs exhibit a neuroendocrine profile with ASCL1high/DLL3high/NOTCHlow, type II LCNECs bear TP53 and RB1 alterations and differ from most SCLC tumors with reduced neuroendocrine markers, a pattern of ASCL1low/DLL3low/NOTCHhigh, and an upregulation of immune-related pathways. In conclusion, LCNECs comprise two molecularly defined subgroups, and distinguishing them from SCLC may allow stratified targeted treatment of high-grade neuroendocrine lung tumors.

A groundwater management plan for Stuttgart.

In general, groundwater in urban areas is exposed to anthropogenic influence and suffers from concentrations of contaminants. Stuttgart, as a highly industrialized city, has more than 5000 contaminated sites which might influence the Stuttgart's mineral water quality. Despite tremendous efforts and intensive single site orientated remediation since 1984 in downtown, the mineral springs were still affected with chlorinated hydrocarbons at low concentrations. Therefore, the applied practices of environmental management and measures for mitigation of pollution sources were not sufficient and had to be adjusted. The main goal of this study is to define an integral remediation plan (a groundwater management plan), focusing on the key sources of chlorinated solvents which are relevant for the mineral springs. For the large-scale investigated area of 26.6km(2) and eight aquifers, an extensive investigation and characterization methods were used in order to delineate the contamination plumes. By means of a 3D numerical model, the prioritization of the contaminated sites could be performed. Five contaminated sites with high remediation priority and need for optimized or additional remediation efforts were determined. For those five contaminated sites feasibility studies were performed which resulted in recommendation of remediation measures with total costs of more than 12.5 million euros. The proposed strategy and approach are suitable for multiple sources of contamination. Only in this way, the contributions of single contaminated sites to the total groundwater contamination can be identified and local remediation measures with their spatial impact simulated. Due to very complex geological conditions, technically there is no alternative to this strategy in order to achieve the contamination reduction in groundwater.

Leveraging the power of high performance computing for next generation sequencing data analysis: tricks and twists from a high throughput exome workflow.

Next generation sequencing (NGS) has been a great success and is now a standard method of research in the life sciences. With this technology, dozens of whole genomes or hundreds of exomes can be sequenced in rather short time, producing huge amounts of data. Complex bioinformatics analyses are required to turn these data into scientific findings. In order to run these analyses fast, automated workflows implemented on high performance computers are state of the art. While providing sufficient compute power and storage to meet the NGS data challenge, high performance computing (HPC) systems require special care when utilized for high throughput processing. This is especially true if the HPC system is shared by different users. Here, stability, robustness and maintainability are as important for automated workflows as speed and throughput. To achieve all of these aims, dedicated solutions have to be developed. In this paper, we present the tricks and twists that we utilized in the implementation of our exome data processing workflow. It may serve as a guideline for other high throughput data analysis projects using a similar infrastructure. The code implementing our solutions is provided in the supporting information files.

Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids.

Pulmonary carcinoids are rare neuroendocrine tumours of the lung. The molecular alterations underlying the pathogenesis of these tumours have not been systematically studied so far. Here we perform gene copy number analysis (n=54), genome/exome (n=44) and transcriptome (n=69) sequencing of pulmonary carcinoids and observe frequent mutations in chromatin-remodelling genes. Covalent histone modifiers and subunits of the SWI/SNF complex are mutated in 40 and 22.2% of the cases, respectively, with MEN1, PSIP1 and ARID1A being recurrently affected. In contrast to small-cell lung cancer and large-cell neuroendocrine lung tumours, TP53 and RB1 mutations are rare events, suggesting that pulmonary carcinoids are not early progenitor lesions of the highly aggressive lung neuroendocrine tumours but arise through independent cellular mechanisms. These data also suggest that inactivation of chromatin-remodelling genes is sufficient to drive transformation in pulmonary carcinoids.