DKFZ-HIPO Data Access Committee of Heidelberg Center for Personalized Oncology
Ontology highlight
ABSTRACT:
PROVIDER: EGAC00001000452 | EGA |
REPOSITORIES: EGA
Publications
Integrative genomic and transcriptomic analysis of leiomyosarcoma.
Chudasama Priya P Mughal Sadaf S SS Sanders Mathijs A MA Hübschmann Daniel D Chung Inn I Deeg Katharina I KI Wong Siao-Han SH Rabe Sophie S Hlevnjak Mario M Zapatka Marc M Ernst Aurélie A Kleinheinz Kortine K Schlesner Matthias M Sieverling Lina L Klink Barbara B Schröck Evelin E Hoogenboezem Remco M RM Kasper Bernd B Heilig Christoph E CE Egerer Gerlinde G Wolf Stephan S von Kalle Christof C Eils Roland R Stenzinger Albrecht A Weichert Wilko W Glimm Hanno H Gröschel Stefan S Kopp Hans-Georg HG Omlor Georg G Lehner Burkhard B Bauer Sebastian S Schimmack Simon S Ulrich Alexis A Mechtersheimer Gunhild G Rippe Karsten K Brors Benedikt B Hutter Barbara B Renner Marcus M Hohenberger Peter P Scholl Claudia C Fröhling Stefan S
Nature communications 20180110 1
Leiomyosarcoma (LMS) is an aggressive mesenchymal malignancy with few therapeutic options. The mechanisms underlying LMS development, including clinically actionable genetic vulnerabilities, are largely unknown. Here we show, using whole-exome and transcriptome sequencing, that LMS tumors are characterized by substantial mutational heterogeneity, near-universal inactivation of TP53 and RB1, widespread DNA copy number alterations including chromothripsis, and frequent whole-genome duplication. Fu ...[more]
Similar Datasets
Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.
| S-EPMC3617375 | biostudies-literature
Project description:Cancer immunotherapy, which aims to control the immune system to eradicate cancer cells and prevent their spread, needs to be personalized because anticancer immune responses can be inhibited in several ways that vary from patient to patient. Cancer immunotherapy includes pharmaceuticals such as immune checkpoint inhibitors and monoclonal antibodies (MAbs) as well as cell therapy, immunogene therapy, and vaccines. Combination of programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) drugs with other immunotherapy drugs, for example, antibody-drug conjugates, as well as combination of PD-1/PD-L1 drugs with other therapies, for example, chemotherapy and radiation therapy, are being explored. Biomarkers are important for predicting the response to immunotherapy. Molecular diagnostics and sequencing are important technologies for guiding treatment in immuno-oncology. Genomic profiling of tumor mutational burden may enhance the predictive utility of PD-L1 expression and facilitate personalized combination immunotherapy. Optimization of personalized immuno-oncology requires integration of several technologies and selection of those best suited for an individual patient. Advances in immuno-oncology are also attributed to technologies for targeted delivery of anticancer therapeutics such as antigen-capturing nanoparticles for precision targeting and selective delivery. A breakthrough in cell therapy of cancer is a chimeric antigen receptors-T cell, which combines the antigen-binding site of a MAb with the signal activating machinery of a T cell, freeing antigen recognition from major histocompatibility complex restriction. Gene-editing tools such as clustered regularly interspaced short palindromic repeats have a promising application for removing alloreactivity and decreasing immunogenicity of third-party T cells. In conclusion, personalized immuno-oncology is one of the most promising approaches to management of cancer.
| S-EPMC7923844 | biostudies-literature
Project description:BackgroundCancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets.ResultsWe have developed a bioinformatics platform - Personalized Oncology Suite (POS) - that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. It features a genome browser as well as access to several instances of the bioimage management application Bisque. POS provides different visualization techniques and offers sophisticated upload and download possibilities. The modular architecture of POS allows the community to easily modify and extend the application.ConclusionsThe web-based integration of clinical, NGS, and imaging data represents a valuable resource for clinical researchers and future application in medical oncology. POS can be used not only in the context of cancer immunology but also in other studies in which NGS data and images of tissue sections are generated. The application is open-source and can be downloaded at http://www.icbi.at/POS.
| S-EPMC4261581 | biostudies-literature
Project description:Data Access Committee EGAC00001001952
| EGAC00001001952 | EGA
Project description:A formal Mentorship Program within the Children's Oncology Group (COG) was established to pair young investigators (mentees) with established COG members (mentors). Despite the American Academy of Pediatrics policy statement promoting mentorship programs, there are no publications describing and evaluating national mentorship programs in pediatric subspecialties. In this study, a series of internal program evaluations were performed using surveys of both mentors and mentees. Responses were deidentified and analyzed to determine the utility of the program by both participant satisfaction and self-reported academic productivity. Results indicated that mentees were generally satisfied with the program. Mentor-mentee pairs that met at least quarterly demonstrated greater academic productivity than pairings that met less frequently. This formal mentorship program appeared to have subjective and objective utility for the development of academic pediatric subspecialists.
| S-EPMC3839621 | biostudies-literature
Project description:Data Access Committee EGAC00001001844
| EGAC00001001844 | EGA
Project description:(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41-235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61-0.88 range to 0.70-0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.
| S-EPMC7037338 | biostudies-literature
Project description:MotivationMetastasis prediction is a well-known problem in breast cancer research. As breast cancer is a complex and heterogeneous disease with many molecular subtypes, predictive models trained for one cohort often perform poorly on other cohorts, and a combined model may be suboptimal for individual patients. Furthermore, attempting to develop subtype-specific models is hindered by the ambiguity and stereotypical definitions of subtypes.ResultsHere, we propose a personalized approach by relaxing the definition of breast cancer subtypes. We assume that each patient belongs to a distinct subtype, defined implicitly by a set of patients with similar molecular characteristics, and construct a different predictive model for each patient, using as training data, only the patients defining the subtype. To increase robustness, we also develop a committee-based prediction method by pooling together multiple personalized models. Using both intra- and inter-dataset validations, we show that our approach can significantly improve the prediction accuracy of breast cancer metastasis compared with several popular approaches, especially on those hard-to-learn cases. Furthermore, we find that breast cancer patients belonging to different canonical subtypes tend to have different predictive models and gene signatures, suggesting that metastasis in different canonical subtypes are likely governed by different molecular mechanisms.Availability and implementationSource code implemented in MATLAB and Java available at www.cs.utsa.edu/∼jruan/PCC/.
| S-EPMC4071199 | biostudies-literature
Project description:Oncology indispensably leads us to personalized medicine, which allows an individual approach to be taken with each patient. Personalized oncology is based on pharmacogenomics and the effect of genetic differences in individuals (germline and somatic) on the way cancer patients respond to chemotherapeutics. Biomarkers detected using molecular biology tools allow the molecular characterization of cancer signatures and provide information relevant for personalized treatment. Biomarkers can be divided into two main subgroups: prognostic and predictive. The aim of the application of prognostic biomarkers, which provide information on the overall cancer outcome in patients, is to facilitate cancer diagnosis, usually with no need for putting invasive methods into use. Predictive biomarkers help to optimize therapy decisions, as they provide information on the likelihood of response to a given chemotherapeutic. Among the prognostic factors that identify patients with different outcome risks (e.g., recurrence of the disease), the following factors can be distinguished: somatic and germline mutations, changes in DNA methylation that lead to the enhancement or suppression of gene expression, the occurrence of elevated levels of microRNA (miRNA) capable of binding specific messenger RNA (mRNA) molecules, which affects gene expression, as well as the presence of circulating tumor cells (CTCs) in blood, which leads to a poor prognosis for the patient. Biomarkers for personalized oncology are used mainly in molecular diagnostics of chronic myeloid leukemia, colon, breast and lung cancer, and recently in melanoma. They are successfully used in the evaluation of the benefits that can be achieved through targeted therapy or in the evaluation of toxic effects of the chemotherapeutic used in the therapy.
| S-EPMC4031398 | biostudies-other