Clonal hematopoiesis with DNMT3A and PPM1D mutations impairs regeneration in autologous stem cell transplant recipients.

Clonal hematopoiesis (CH) is an age-related condition driven by stem and progenitor cells harboring recurrent mutations linked to myeloid neoplasms. Currently, potential effects on hematopoiesis, stem cell function and regenerative potential under stress conditions are unknown. We performed targeted DNA sequencing of 457 hematopoietic stem cell grafts collected for autologous stem cell transplantation (ASCT) in myeloma patients and correlated our findings with high-dimensional longitudinal clinical and laboratory data (26,510 data points for blood cell counts/serum values in 25 days around transplantation). We detected CHrelated mutations in 152 patients (33.3%). Since many patients (n=54) harbored multiple CH mutations in one or more genes, we applied a non-negative matrix factorization (NMF) clustering algorithm to identify genes that are commonly co-mutated in an unbiased approach. Patients with CH were assigned to one of three clusters (C1-C3) and compared to patients without CH (C0) in a gene specific manner. To study the dynamics of blood cell regeneration following ASCT, we developed a time-dependent linear mixed effect model to validate differences in blood cell count trajectories amongst different clusters. The results demonstrated that C2, composed of patients with DNMT3A and PPM1D single and co-mutated CH, correlated with reduced stem cell yields and delayed platelet count recovery following ASCT. Also, the benefit of maintenance therapy was particularly strong in C2 patients. Taken together, these data indicate an impaired regenerative potential of hematopoietic stem cell grafts harboring CH with DNMT3A and PPM1D mutations.

BBMRI-ERIC Negotiator: Implementing Efficient Access to Biobanks.

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data.

Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events.

Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level. Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank. Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles. Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries.

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC.

BACKGROUND: High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. OBJECTIVES: To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. METHODS: Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. RESULTS: The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. CONCLUSION: A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.

A Decentralized IT Architecture for Locating and Negotiating Access to Biobank Samples.

There is a need among researchers for the easy discoverability of biobank samples. Currently, there is no uniform way for finding samples and negotiate access. Instead, researchers have to communicate with each biobank separately. We present the architecture for the BBMRI-CS IT platform, whose goal is to facilitate sample location and access. We chose a decentral approach, which allows for strong data protection and provides the high flexibility needed in the highly heterogeneous landscape of European biobanks. This is the first implementation of a decentral search in the biobank field. With the addition of a Negotiator component, it also allows for easy communication and a follow-through of the lengthy approval process for accessing samples.

Models in Translational Oncology: A Public Resource Database for Preclinical Cancer Research.

The devastating diseases of human cancer are mimicked in basic and translational cancer research by a steadily increasing number of tumor models, a situation requiring a platform with standardized reports to share model data. Models in Translational Oncology (MiTO) database was developed as a unique Web platform aiming for a comprehensive overview of preclinical models covering genetically engineered organisms, models of transplantation, chemical/physical induction, or spontaneous development, reviewed here. MiTO serves data entry for metastasis profiles and interventions. Moreover, cell lines and animal lines including tool strains can be recorded. Hyperlinks for connection with other databases and file uploads as supplementary information are supported. Several communication tools are offered to facilitate exchange of information. Notably, intellectual property can be protected prior to publication by inventor-defined accessibility of any given model. Data recall is via a highly configurable keyword search. Genome editing is expected to result in changes of the spectrum of model organisms, a reason to open MiTO for species-independent data. Registered users may deposit own model fact sheets (FS). MiTO experts check them for plausibility. Independently, manually curated FS are provided to principle investigators for revision and publication. Importantly, noneditable versions of reviewed FS can be cited in peer-reviewed journals. Cancer Res; 77(10); 2557-63. ©2017 AACR.

The perfectly motivated nurse and the others: workplace and personal characteristics impact preference of nursing tasks.

AIMS: To identify whether motivation of nurses coincides with personal values, workplace or personal characteristics. BACKGROUND: Shortage of nursing workforce compromises patient care. Motivation and job satisfaction are factors considered to make nurses quit. Little is known about measurement and variation of nurses' motivation. Funding for human resource programmes is limited - effective programmes could focus on nurses in need of motivational support. METHODS: Exploratory study with nurses using questionnaires in an academic hospital in Germany. Work motivation was approximated through preference of nursing tasks. Questionnaires measured personal values, preference of generic nursing tasks, and workplace and personal characteristics. RESULTS: A total of 212 questionnaires were usable. Higher motivation was found in groups of nurses with the dominant personal value 'Benevolence', with high self-rated expertise, in the middle of their career or working in surgical or general wards. Motivation was low in nurses with the dominant value 'Hedonism', or nurses in internal medicine or with low to medium self-rated expertise or who used computers infrequently. CONCLUSIONS: Motivation coincided with dominant personal values, workplace and personal characteristics. The results should be validated in other settings. IMPLICATIONS FOR NURSING MANAGEMENT: Human resource programmes could focus on nurses whose motivation is at risk. Prospectively highly motivated individuals should be hired with priority.