Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.

BACKGROUND: Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear. METHODS: The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. RESULTS: Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles. CONCLUSIONS: Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. IMPACT: The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

Observational health research in Europe: understanding the General Data Protection Regulation and underlying debate.

Insights into the incidence and survival of cancer, the influence of lifestyle and environmental factors and the interaction of treatment regimens with outcomes are hugely dependent on observational research, patient data derived from the healthcare system and from volunteers participating in cohort studies, often non-selective. Since 25th May 2018, the European General Data Protection Regulation (GDPR) applies to such data. The GDPR focusses on more individual control for data subjects of 'their' data. Yet, the GDPR was preceded by a long debate. The research community participated actively in that debate, and as a result, the GDPR has research exemptions as well. Some of those apply directly; other exemptions need to be implemented into national law. Those exemptions will be discussed together with a general outline of the GDPR. I propose a substantive definition of research-absent in the GDPR-which can warrant its special status in the GDPR. The debate is not over yet. Most legal texts exhibit ambiguity and are interpreted against a background of values. In this case, those could be subsumed under informational self-determination versus solidarity and the deeper meaning of autonomy. Values will also guide national implementation and their interpretation. The value of individual control or informational self-determination should be balanced by nuanced visions about our mutual dependency in healthcare, as an ever-learning system, especially in the European solidarity-based healthcare systems. Good research governance might be a way forward to escape the consent or anonymise dichotomy.

FDA's Activities Supporting Regulatory Application of "Next Gen" Sequencing Technologies.

Applications of next-generation sequencing (NGS) technologies require availability and access to an information technology (IT) infrastructure and bioinformatics tools for large amounts of data storage and analyses. The U.S. Food and Drug Administration (FDA) anticipates that the use of NGS data to support regulatory submissions will continue to increase as the scientific and clinical communities become more familiar with the technologies and identify more ways to apply these advanced methods to support development and evaluation of new biomedical products. FDA laboratories are conducting research on different NGS platforms and developing the IT infrastructure and bioinformatics tools needed to enable regulatory evaluation of the technologies and the data sponsors will submit. A High-performance Integrated Virtual Environment, or HIVE, has been launched, and development and refinement continues as a collaborative effort between the FDA and George Washington University to provide the tools to support these needs. The use of a highly parallelized environment facilitated by use of distributed cloud storage and computation has resulted in a platform that is both rapid and responsive to changing scientific needs. The FDA plans to further develop in-house capacity in this area, while also supporting engagement by the external community, by sponsoring an open, public workshop to discuss NGS technologies and data formats standardization, and to promote the adoption of interoperability protocols in September 2014. LAY ABSTRACT: Next-generation sequencing (NGS) technologies are enabling breakthroughs in how the biomedical community is developing and evaluating medical products. One example is the potential application of this method to the detection and identification of microbial contaminants in biologic products. In order for the U.S. Food and Drug Administration (FDA) to be able to evaluate the utility of this technology, we need to have the information technology infrastructure and bioinformatics tools to be able to store and analyze large amounts of data. To address this need, we have developed the High-performance Integrated Virtual Environment, or HIVE. HIVE uses a combination of distributed cloud storage and distributed cloud computations to provide a platform that is both rapid and responsive to support the growing and increasingly diverse scientific and regulatory needs of FDA scientists in their evaluation of NGS in research and ultimately for evaluation of NGS data in regulatory submissions.