Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations.

BACKGROUND: Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. OBJECTIVE: To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). METHODS: The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. RESULTS: The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. CONCLUSION: To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

Architecture of the Mass Spectrometry Data Management Pipeline in the SMART-CARE Project.

In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described.

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia.

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Towards Data Analysis Environment for Multi-Partner Clinical Research Project - SMART-CARE.

A Systems Medicine Approach to Stratification of Cancer Recurrence (SMART-CARE) establishes mass spectrometry-based systems medicine technologies and data analysis pipelines employing expertise of the multiple partners from Heidelberg biomedical campus. We have established a central linked data repository that links clinical, mass spectrometry, and data analysis teams to enable a full cycle of data management. Other questions of setting up the data analysis environment for the multi-partner clinical research project are addressed in this work, too.

Data Management for Systems Medicine: The SMART-CARE Joint Environment.

For a research project on mass spectrometry, a streamlined, harmonized and robust analytical pipeline is built to predict tumor recurrence. By means of standardization all steps from sample collection, analysis, proteome, and metabolome analysis are harmonized. Challenges like non-central identificators and distributed data are overcome with a centralized high-performant IT-platform in combination with a pseudonymization service and harmonization.

Implementing Systems Medicine: A Medical Informatics Perspective.

Systems medicine is a paradigm for translating in silico methods developed for modelling biological systems into the field of medicine. Such approaches rely on the integration of as many data sources as possible, both in the dimension of disease knowledge and patient data. This is a challenging task that can only be implemented in clinical routine with the help of suitable information technology from the field of Medical Informatics. For the research project "Clinically-applicable, omics-based assessment of survival, side effects, and targets in multiple myeloma" (CLIOMMICS) we developed a prototypical systems medicine application system. It is based on a three-level-architecture covering data representation, decision support, and user interface. The core decision support component is implemented as a case-based reasoning engine. However, the architecture follows a modular design that allows to replace individual components as needed.

Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research.

OBJECTIVES: In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically document the diagnosis and state of disease of myeloma patients. The first objective was to create a corpus consisting of free-text diagnosis paragraphs of patients with multiple myeloma from German diagnostic reports, and its manual annotation of relevant data elements by documentation specialists. The second objective was to construct and evaluate a framework using different NLP methods to enable automatic multiclass classification of relevant data elements from free-text diagnostic reports. METHODS: The main diagnoses paragraph was extracted from the clinical report of one third randomly selected patients of the multiple myeloma research database from Heidelberg University Hospital (in total 737 selected patients). An EDC system was setup and two data entry specialists performed independently a manual documentation of at least nine specific data elements for multiple myeloma characterization. Both data entries were compared and assessed by a third specialist and an annotated text corpus was created. A framework was constructed, consisting of a self-developed package to split multiple diagnosis sequences into several subsequences, four different preprocessing steps to normalize the input data and two classifiers: a maximum entropy classifier (MEC) and a support vector machine (SVM). In total 15 different pipelines were examined and assessed by a ten-fold cross-validation, reiterated 100 times. For quality indication the average error rate and the average F1-score were conducted. For significance testing the approximate randomization test was used. RESULTS: The created annotated corpus consists of 737 different diagnoses paragraphs with a total number of 865 coded diagnosis. The dataset is publicly available in the supplementary online files for training and testing of further NLP methods. Both classifiers showed low average error rates (MEC: 1.05; SVM: 0.84) and high F1-scores (MEC: 0.89; SVM: 0.92). However the results varied widely depending on the classified data element. Preprocessing methods increased this effect and had significant impact on the classification, both positive and negative. The automatic diagnosis splitter increased the average error rate significantly, even if the F1-score decreased only slightly. CONCLUSIONS: The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.

Systems Medicine for Multiple Myeloma: A Review on Decision Support Systems.

Systems medicine is a current approach trying to improve treatment for patients with complex diseases by analyzing as much phenotype and genotype data as possible for the disease in question. For individualized treatment decisions in clinical practice, this task has to be supported by an application system with decision support component. For a research project on systems medicine we reviewed methods for decision support. Criteria for selecting a method are derived from characteristics of the data and the diseases. They include, among others: dimensionality of data and existence of a priori models for diseases. As a result we decided to implement a prototype system with a case-based reasoning component for systems medicine on multiple myeloma.

An IT Architecture for Systems Medicine.

Systems medicine aims to support treatment of complex diseases like cancer by integrating all available data for the disease. To provide such a decision support in clinical practice, a suitable IT architecture is necessary. We suggest a generic architecture comprised of the following three layers: data representation, decision support, and user interface. For the systems medicine research project "Clinically-applicable, omics-based assessment of survival, side effects, and targets in multiple myeloma" (CLIOMMICS) we developed a concrete instance of the generic architecture. We use i2b2 for representing the harmonized data. Since no deterministic model exists for multiple myeloma we use case-based reasoning for decision support. For clinical practice, visualizations of the results must be intuitive and clear. At the same time, they must communicate the uncertainty immanent in stochastic processes. Thus, we develop a specific user interface for systems medicine based on the web portal software Liferay.

Requirements for data integration platforms in biomedical research networks: a reference model.

Biomedical research networks need to integrate research data among their members and with external partners. To support such data sharing activities, an adequate information technology infrastructure is necessary. To facilitate the establishment of such an infrastructure, we developed a reference model for the requirements. The reference model consists of five reference goals and 15 reference requirements. Using the Unified Modeling Language, the goals and requirements are set into relation to each other. In addition, all goals and requirements are described textually in tables. This reference model can be used by research networks as a basis for a resource efficient acquisition of their project specific requirements. Furthermore, a concrete instance of the reference model is described for a research network on liver cancer. The reference model is transferred into a requirements model of the specific network. Based on this concrete requirements model, a service-oriented information technology architecture is derived and also described in this paper.

Overexpression of far upstream element (FUSE) binding protein (FBP)-interacting repressor (FIR) supports growth of hepatocellular carcinoma.

UNLABELLED: The far upstream element binding protein (FBP) and the FBP-interacting repressor (FIR) represent molecular tools for transcriptional fine tuning of target genes. Strong overexpression of FBP in human hepatocellular carcinoma (HCC) supports tumor growth and correlates with poor patient prognosis. However, the role of the transcriptional repressor FIR in hepatocarcinogenesis remains poorly delineated. We show that overexpression of FIR correlates with tumor dedifferentiation and tumor cell proliferation in about 60% of primary HCCs. Elevated FIR levels are associated with genomic gains of the FIR gene locus at chromosome 8q24.3 in human HCC specimens. In vitro, nuclear enrichment of FIR supports HCC cell proliferation and migration. Expression profiling of HCC cells after small interfering RNA (siRNA)-mediated silencing of FIR identified the transcription factor DP-1 (TFDP1) as a transcriptional target of FIR. Surprisingly, FIR stimulates the expression of FBP in a TFDP1/E2F1-dependent manner. FIR splice variants lacking or containing exon 2 and/or exon 5 are expressed in the majority of HCCs but not in normal hepatocytes. Specific inhibition of FIR isoforms with and without exon 2 revealed that both groups of FIR splice variants facilitate tumor-supporting effects. This finding was confirmed in xenograft transplantation experiments with lentiviral-infected short hairpin RNA (shRNA) targeting all FIR variants as well as FIR with and without exon 2. CONCLUSION: High-level nuclear FIR does not facilitate repressor properties but supports tumor growth in HCC cells. Thus, the pharmacological inhibition of FIR might represent a promising therapeutic strategy for HCC patients with elevated FIR expression.

Effectiveness of eHealth interventions and information needs in palliative care: a systematic literature review.

BACKGROUND: One of the key components in palliative care is communication. eHealth technologies can be an effective way to support communications among participants in the process of palliative care. However, it is unclear to what extent information technology has been established in this field. OBJECTIVE: Our goal was to systematically identify studies and analyze the effectiveness of eHealth interventions in palliative care and the information needs of people involved in the palliative care process. METHODS: We conducted a systematic literature search using PubMed, Embase, and LILACS according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We collected and analyzed quantitative and qualitative data regarding effectiveness of eHealth interventions and users' information needs in palliative care. RESULTS: Our search returned a total of 240 articles, 17 of which met our inclusion criteria. We found no randomized controlled trial studying the effects of eHealth interventions in palliative care. Studies tended to be observational, non-controlled studies, and a few quasi-experimental studies. Overall there was great heterogeneity in the types of interventions and outcome assessments; some studies reported some improvement on quality of care, documentation effort, cost, and communications. The most frequently reported information need concerned pain management. CONCLUSIONS: There is limited evidence around the effectiveness of eHealth interventions for palliative care patients, caregivers, and health care professionals. Focused research on information needs and high-quality clinical trials to assess their effectiveness are needed.

Palliative care from a medical informatics perspective in Chile, Germany, and Peru.

This work explores a) the use of e-health systems in the context of palliative care and b) the information needs of patients, care givers and healthcare professionals in palliative care. To achieve this we conducted a systematic literature review and interviewed health professionals in Germany, Peru, and Chile. All countries have in common that specific e-health systems are rarely used in this context and the presence of a gradient of available care between rural and urban areas.

Semantic prerequisites for data sharing in a biomedical research network.

We investigated for a research network on liver cancer semantic prerequisites for successful data sharing. To support collaboration with information technology, it is important to annotate research data with metadata. Ideally, all data handled are described ontologically to allow for automated reasoning. However, a complete ontology is hard to define. As a preliminary step we acquired a project wide common vocabulary by interviewing project partners. The vocabulary contains terms for describing the projects' processes and related data. Where the vocabulary intersects with Unified Medical Language System (UMLS) terms, the terms will be replaced by UMLS-terms. Cell line data are a subclass of the data handled in our research network. For these data we reviewed existing ontologies and developed a new ontology for cell lines. The Cell Culture Ontology (CCONT) reuses existing ontologies and enhances those with more specific cell line related properties to achieve a comprehensive description of cell lines. The results of our work can be transferred to other research networks with a similarly limited biomedical scope.

On the ontology based representation of cell lines.

Cell lines are frequently used as highly standardized and reproducible in vitro models for biomedical analyses and assays. Cell lines are distributed by cell banks that operate databases describing their products. However, the description of the cell lines' properties are not standardized across different cell banks. Existing cell line-related ontologies mostly focus on the description of the cell lines' names, but do not cover aspects like the origin or optimal growth conditions. The objective of this work is to develop an ontology that allows for a more comprehensive description of cell lines and their metadata, which should cover the data elements provided by cell banks. This will provide the basis for the standardized annotation of cell lines and corresponding assays in biomedical research. In addition, the ontology will be the foundation for automated evaluation of such assays and their respective protocols in the future. To accomplish this, a broad range of cell bank databases as well as existing ontologies were analyzed in a comprehensive manner. We identified existing ontologies capable of covering different aspects of the cell line domain. However, not all data fields derived from the cell banks' databases could be mapped to existing ontologies. As a result, we created a new ontology called cell culture ontology (CCONT) integrating existing ontologies where possible. CCONT provides classes from the areas of cell line identification, origin, cell line properties, propagation and tests performed.

An encapsulated R application for the guided analysis of genomic data.

Microarrays are widely used in biomedical research. However, researchers conducting the biomedical assays are often not skilled to perform the necessary biostatistical preprocessing of the resulting data. As a result, researchers with different backgrounds contribute to the analysis, but often without documenting how the data were transformed. For a biomedical research network on liver cancer, we implemented a prototype that has two major aims: First, it should guide biomedical researchers through the analysis of microarray data by providing a limited amount of appropriate choices for the biostatistical procedures to be applied. Second, it should help to ensure data quality by documenting all transformations applied to the data set.

The intellectual property management for data sharing in a German liver cancer research network.

Sharing data in biomedical research networks has great potential benefits including efficient use of resources, avoiding duplicate experiments and promoting collaboration. However, concerns from data producers about difficulties of getting proper acknowledgement for their contributions are becoming obstacles for efficient and network wide data sharing in reality. Effective and convenient ways of intellectual property management and acknowledging contributions to the data producers are required. This paper analyzed the system requirements for intellectual property management in a German liver cancer research network and proposed solutions for facilitating acknowledgement of data contributors using informatics tools instead of pure policy level strategies.

Service oriented data integration for a biomedical research network.

In biomedical research, a variety of data like clinical, genetic, expression of coding or non-coding ribonucleic acid (RNA) transcripts, or proteomic data are processed to gain new insights into diseases and therapies. In transregional research networks, geographically distributed projects work on comparable research questions with data from different resources and in different formats. Providing an information platform that integrates the data of the projects can enable cross-project analysis and provides an overview of available data and resources (tissue, blood, etc.). For a German liver cancer research network consisting of 22 individual projects, we develop the integrated information platform pelican - platform enhancing liver cancer networked research. In our generic approach, data are made available to the research network by standardized data services based on technologies provided by the cancer Biomedical Informatics Grid (caBIG). It has shown that publishing service metadata in a corresponding repository is a major prerequisite for automated discovery, integration, and conversion of data records and data services. We identified data confidentiality and intellectual property considerations as major challenges while establishing such an integrated information platform. As a first result we implemented a working prototype to validate our approach.