Clinical application of genomic high-throughput data: Infrastructural, ethical, legal and psychosocial aspects.

Genomic high-throughput technologies (GHTT) such as next-generation sequencing represent a fast and cost-effective tool toward a more comprehensive understanding of the molecular background of complex diseases. However, technological advances contrast with insufficient application in clinical practice. Thus, patients, physicians, and other professionals are faced with tough challenges that forestall the efficient and effective implementation. With the increasing application of genetic testing, it is of paramount importance that physicians and other professionals in healthcare recognize the restrictions and potential of GHTT, in order to understand and interpret the complex data in the context of health and disease. At the same time, the growing volume and complexity of data is forever increasing the need for sustainable infrastructure and state-of-the-art tools for efficient data management, including their analysis and integration. The large pool of sensitive information remains difficult to interpret and fundamental questions spanning from billing to legal, social, and ethical issues have still not been resolved. Here we summarize and discuss these obstacles in an interdisciplinary context and suggest ways to overcome them. Continuous discussion with clinicians, data managers, biostatisticians, systems medicine experts, ethicists, legal scholars, and patients illuminates the strengths, weakness, and current practices in the pipeline from biomaterial to sequencing and data management. This discussion also highlights the new, cross-disciplinary working collaborations to realize the wide-ranging challenges in clinical genomics including the exceptional demands placed on the staff preparing and presenting the data, as well as the question as to how to report the data and results to patients.

Potential of big data analytics in the French in vitro diagnostics market.

The new paradigm of the big data raises many expectations, particularly in the field of health. Curiously, even though medical biology laboratories generate a great amount of data, the opportunities offered by this new field are poorly documented. For better understanding the clinical context of chronical disease follow-up, for leveraging preventive and/or personalized medicine, the contribution of big data analytics seems very promising. It is within this framework that we have explored to use data of a Breton group of laboratories of medical biology to analyze the possible contributions of their exploitation in the improvement of the clinical practices and to anticipate the evolution of pathologies for the benefit of patients. We report here three practical applications derived from routine laboratory data from a period of 5 years (February 2010-August 2015): follow-up of patients treated with AVK according to the recommendations of the High authority of health (HAS), use of the new troponin markers HS and NT-proBNP in cardiology. While the risks and difficulties of using algorithms in the health domain should not be underestimated - quality, accessibility, and protection of personal data in particular - these first results show that use of tools and technologies of the big data repository could provide decisive support for the concept of "evidence based medicine".