The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally

Interferon regulatory factor-1 is a major regulator of epidermal growth factor receptor gene expression

AbstractOverexpression of the epidermal growth factor receptor (EGFR) occurs in many tumors and in breast cancer correlates with poor prognosis for treatment. Here, we report that interferon regulatory factor-1 (IRF-1) induces EGFR promoter activity up to 200-fold compared to 3–10-fold induction by other regulators. The region of the promoter that is required for this induction was defined using deletion mutants. In addition, we found that IRF-1 and tricostatin A, a deacetylase inhibitor, have a synergistic effect on EGFR promoter activity. This indicates that the increase in EGFR promoter activity by IRF-1 may also involve changes in chromatin structure. These results identify IRF-1 as a major regulator of EGFR gene expression

The GRDR-GUID: a model for global sharing of patients de-identified data

<p><strong>The GRDR-GUID: a model for global sharing of patients de-identified data</strong></p> <p><strong><em>Yaffa R. Rubinstein1; Matthew McAuliffe2; Manuel Posada3; Domenica Taruscio4; Stephen C. Groft1</em></strong></p> <p><em>1Office of Rare Disease Research/NCATS/NIH, 2Center for Information Technology/NIH, 3 Institute of Rare Diseases Research, IIER-ISCIII, 4National Centre for Rare Diseases, Istituto Superiore di Sanità (Rome, Italy)</em></p> <p>The Global Rare Diseases Patient Registry Data Repository-GRDR aggregates de-identified patient data, using CDEs, from various rare disease registries utilizing a Global Unique Identifier (GUID)(1). The GUID is a unique random alpha-numeric set of characters assignedto each patient-data that GUID is NOT directly generated from personally identifiable information (PII). The GUID system allows the patient to be followed across studies and registries can be used also to link to biospecimens.</p> <p>GUID Process</p> <p>User executes the GUID tool client locallyPII is enteredPII is combined and one-way hash codes are generatedThe one-way hash codes are sent to the GUID serverIf the hash codes match the server's hash codes for an existing GUID, then that GUID is returnedIf the hash codes do not match, then a new random GUID is generated and returned</p> <p>To generate a GUID for the subject, the following PII is required (these elements are included in the ORDR/GRDR list of CDEs) (PII is never sent to the GRDR system):</p> <p>Complete legal given (first) name of subject at birthComplete Legal additional name of subject at birth (if the subject has a middle name)Complete legal family (last) name of the subject at birthDay of birth (1-31)Month of birth (1-12)Year of birth (####)Name of city/municipality in which subject was bornCountry of birthPhysical sex of subject at birth (M/F)</p> <p>The GUID is an ID that allows researchers to share data specific to a study participant without exposing PII and to track participants longitudinally, across multiple research sites and across multiple studies. RD-CONNECT (IRDiRC framework) is assessing possibilities to implement a GUID strategy in their work and the NIH-GUID is one of the possibilities under consideration.</p> <p>(1) Johnson SB, Whitney G, McAuliffe M, etal. Using Global Unique Identifiers to Link Autism Collections. J. Am. Med. Inform. Assoc. July 17, 2010. 10.1136/jamia.2009.002063.</p

A mapping study of the biomedical ontologies in the RD-Connect project framework

<p>RD-Connect is an unique global infrastructure project that links up databases, registries, biobanks and clinical bioinformatics data used in rare diseases research into a central resource for researchers worldwide. One of the aims of this project is to achieve the interoperability among patient registries. In order to allow data sharing, some strategies are being developing, like the implementation of ontologies for phenotypic description.</p> <p>Numerous ontologies already exist for the biomedical community. A review of existing biomedical ontologies and their characteristics is needed to identify gaps, avoid duplication of efforts and be able to make a recommendation.</p> <p>Our general objective was the definition of a strategy for the searching of ontologies useful to be implemented in RD Connect and other IRDiRC-funded projects. This searching strategy consisted of a multi-stage process:</p> <p>The two leading repositories of biomedical ontologies, the BioPortal and the OBO Foundry, were consulted for collecting a preliminary inventory of existing biomedical ontologies, which was composed by 377 ontologies.After the revision of the inventory, a list formed by the selected biomedical ontologies was obtained. This list contained 71 records.Afterwards, the selected biomedical ontologies were classified into the following groups: phenotype (n=3), clinic (n=7), classifications & nomenclatures (n=14), disorder/disease-specific (n=17), anatomy (n=7), patients-related data (n=7), pharmacy (n=6), epidemiology (n=3), health indicators (n=2), miscellaneous (n=1) and discarded (n=4).Because the degree of relationship among the selected ontologies is a very important aspect to consider for our objectives, a mappings study was performed.</p> <p>A special mention has to be aimed to Orphanet Ontology and Human Phenotype Ontology. Both are phenotype ontologies which fit exactly in RD-Connect aims. In addition to these ontologies, we propose the use of some ontologies focused on specific rare diseases, as well as some other ontologies as support tools.</p

Data quality in rare diseases registries

In the field of rare diseases, registries are considered power tool to develop clinical research, to facilitate the planning of appropriate clinical trials, to improve patient care and healthcare planning. Therefore high quality data of rare diseases registries is considered to be one of the most important element in the establishment and maintenance of a registry. Data quality can be defined as the totality of features and characteristics of data set that bear on its ability to satisfy the needs that result from the intended use of the data. In the context of registries, the 'product' is data, and quality refers to data quality, meaning that the data coming into the registry have been validated, and ready for use for analysis and research. Determining the quality of data is possible through data assessment against a number of dimensions: completeness, validity; coherence and comparability; accessibility; usefulness; timeliness; prevention of duplicate records. Many others factors may influence the quality of a registry: development of standardized Case Report Form and security/safety controls of informatics infrastructure. With the growing number of rare diseases registries being established, there is a need to develop a quality validation process to evaluate the quality of each registry. A clear description of the registry is the first step when assessing data quality or the registry evaluation system. Here we report a template as a guide for helping registry owners to describe their registry

Current trends in biobanking for rare diseases: a review

Rare diseases (RD) refer to a collection of approximately 5,000–8,000 individual diseases that have a low prevalence and are often genetic in origin. While RD can manifest throughout life, they frequently affect children and newborns. Common characteristics include being severe, disabling, life-threatening, degenerative and affecting different organ systems. The burden of RD is often exacerbated by a lack of specific treatments. Whilst there is etiological heterogeneity, there is overlap in cellular and molecular pathways. Amongst specialists, there is legitimate hope that based on genetic knowledge and pathway definition, a new medical classification system, currently called “precision medicine”, will be developed, which may change our view on how to apply shared therapeutic targets. Thus, collection of clinical and genetic data and biospecimens (in biobanks) will play an increasing role in diagnoses and development of therapies for RD. Biobanks are maintained collaboratively by researchers or their institutions, and involve a delicate balance between health policy objectives, academic research, public good outcomes, and community trust. Due to the nature of RD, international cooperation is critical for sharing limited numbers of RD samples and achieving a critical mass. Here we review the current and future direction of RD biobanks and discuss research and development stemming from the use of biospecimens to improve management of RD.The authors acknowledge the financial support of the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement numbers 305444 (RD-Connect), 305608 (EURenOmics), and 305121 (NeurOmics); TREAT-NMD operating grants, FP6 LSHM-CT-2006-036825, 20123307 UNEW_FY2013; Telethon grant GTB12001 to TNGB, and AFM 16104; EUROPLAN 2012–2015, coordinated by Italian National Institute of Health-Italian National Centre for Rare Diseases, included in the EUCERD Joint Action (grant number 2011 22 01, cofunded by the European Union Commission (DG-SANCO); Office of Rare Diseases Research at National Center for Advancing Translational Sciences/National Institutes of Health; European Organisation for Rare Diseases; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-ERIC); ISBER/ESBB Rare Diseases Working Group; and Public Population Project in Genomics and Society (P3G).S

The case for open science : rare diseases

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally