Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings.

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

SMART on FHIR Genomics: facilitating standardized clinico-genomic apps.

BACKGROUND: Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient's electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. METHODS: A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. RESULTS: Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. CONCLUSION: This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.

OncDRS: An integrative clinical and genomic data platform for enabling translational research and precision medicine.

We live in the genomic era of medicine, where a patient's genomic/molecular data is becoming increasingly important for disease diagnosis, identification of targeted therapy, and risk assessment for adverse reactions. However, decoding the genomic test results and integrating it with clinical data for retrospective studies and cohort identification for prospective clinical trials is still a challenging task. In order to overcome these barriers, we developed an overarching enterprise informatics framework for translational research and personalized medicine called Synergistic Patient and Research Knowledge Systems (SPARKS) and a suite of tools called Oncology Data Retrieval Systems (OncDRS). OncDRS enables seamless data integration, secure and self-navigated query and extraction of clinical and genomic data from heterogeneous sources. Within a year of release, the system has facilitated more than 1500 research queries and has delivered data for more than 50 research studies.

Some experiences and opportunities for big data in translational research.

Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

Emerging landscape of genomics in the Electronic Health Record for personalized medicine.

The Information Technology (IT) roadmap for personalized medicine requires Electronic Health Records (EHRs), extension of Healthcare IT (HIT) standards, and understanding of how genetics/genomics should be integrated into the clinical applications. For reduced overall costs and development times, these three initiatives should run in parallel. EHRs must contain structured data and infrastructure that enables quality analysis, Clinical Decision Support (CDS) and messaging within the healthcare information network. Fortunately, as a result of sustained financial commitment to nongenetic-based healthcare, the industry has HIT data standards and understanding of EHR functionality that improves patient safety and outcomes while reducing overall healthcare costs. However, the HIT standards and EHR functional requirements, needed for personalized medicine, are only beginning to support simple genetic tests and need significant extension. In addition, our understanding of the clinical implications of genomic data is evolving and translation of new discovery into clinical care remains a challenge. Therefore, priority areas include CDS, educational resources, and knowledgebases for the EHR, clinical and research data warehouses, messaging frameworks, and continued review of healthcare policies and regulations supporting personalized medicine. Where core infrastructure remains to be developed and implemented, funding is needed for pilot projects, data standards, policy, and stakeholder collaboration.