The German Cancer Research Center (DKFZ) Integrative Bioinformatics and Systems Biology (iBioS)
Ontology highlight
ABSTRACT:
PROVIDER: EGAO00000000029 | EGA |
REPOSITORIES: EGA
Publications
Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations.
Rausch Tobias T Jones David T W DT Zapatka Marc M Stütz Adrian M AM Zichner Thomas T Weischenfeldt Joachim J Jäger Natalie N Remke Marc M Shih David D Northcott Paul A PA Pfaff Elke E Tica Jelena J Wang Qi Q Massimi Luca L Witt Hendrik H Bender Sebastian S Pleier Sabrina S Cin Huriye H Hawkins Cynthia C Beck Christian C von Deimling Andreas A Hans Volkmar V Brors Benedikt B Eils Roland R Scheurlen Wolfram W Blake Jonathon J Benes Vladimir V Kulozik Andreas E AE Witt Olaf O Martin Dianna D Zhang Cindy C Porat Rinnat R Merino Diana M DM Wasserman Jonathan J Jabado Nada N Fontebasso Adam A Bullinger Lars L Rücker Frank G FG Döhner Konstanze K Döhner Hartmut H Koster Jan J Molenaar Jan J JJ Versteeg Rogier R Kool Marcel M Tabori Uri U Malkin David D Korshunov Andrey A Taylor Michael D MD Lichter Peter P Pfister Stefan M SM Korbel Jan O JO
Cell 20120101 1-2
Genomic rearrangements are thought to occur progressively during tumor development. Recent findings, however, suggest an alternative mechanism, involving massive chromosome rearrangements in a one-step catastrophic event termed chromothripsis. We report the whole-genome sequencing-based analysis of a Sonic-Hedgehog medulloblastoma (SHH-MB) brain tumor from a patient with a germline TP53 mutation (Li-Fraumeni syndrome), uncovering massive, complex chromosome rearrangements. Integrating TP53 statu ...[more]
Similar Datasets
Project description:Organisation EGAO00000000799
| EGAO00000000799 | EGA
Project description:Organisation EGAO00000000298
| EGAO00000000298 | EGA
Project description:Delivering personalized therapeutic options to cancer patients based on the genetic and molecular aberrations of the tumor offers great promise to improve the outcomes of cancer therapy. Significant progress in biotechnology has allowed the measurement of tens of thousands of "omic" data points across multiple levels (DNA, RNA protein, metabolomics) from a single tumor biopsy sample in a reasonable time frame for making clinical decisions. With this data in hand, the challenge from the bioinformatics and systems biology point of view is how does one convert data into information and knowledge that can improve the delivery of personalized therapy to the patient.
| S-EPMC3345061 | biostudies-other
Project description:Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O'Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.
| S-EPMC9485002 | biostudies-literature
Project description:Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group "Personalized Radiotherapy in Head and Neck Cancer" of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology.
| S-EPMC3901372 | biostudies-literature
Project description:Whole-genome array comparative genomic hybridization (aCGH) of human ependymoma tumors. DOP-PCR products were spotted in triplicate onto NexterionTM Slide E epoxysilane-coated slides (PEQLAB, Erlangen, Germany) using a spotting robot (VersArray ChipWriterTM Pro system,BioRad, Munich, Germany) at 20C and 40% humidity. After spotting, slides were cross-linked,baked for 1 hr at 80C, and cross-linked again. Fresh frozen tumor material was collected during tumor resection. Copy number aberrations represent the status at diagnosis.
2011-08-14 | E-GEOD-27286 | biostudies-arrayexpress
Project description:Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a "top-down" method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders' complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software "omics"-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.
| S-EPMC3529307 | biostudies-other
Project description:Organisation EGAO00000000142
| EGAO00000000142 | EGA
Project description:Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal.
| S-EPMC4212281 | biostudies-other
Project description:Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.
| S-EPMC3972654 | biostudies-literature