Translational genomics in personalized medicine – scientific challenges en route to clinical practice

In the area of omics and translational bio(medical)sciences, there is an increasing need to integrate, normalize, analyze, store and protect genomics data. Large datasets and scientific knowledge are rationally combined into valuable clinical information that ultimately will benefit human healthcare and are en route to clinical practice. Data from biomarker discovery and Next Generation Sequencing (NGS) are very valuable and will combine in comprehensive analyses to stratify medicine and guide therapy planning and ultimately benefit patients. However, the combination into useful and applicable information and knowledge is not trivial. Personalized medicine generally promises to result in both higher quality in treatment for individual patients and in lower costs in health care since patients will be offered only such therapies that are more effective for them and treatments that will not be safe or effective will be avoided. Recent advancements in biomedical and genomic sciences have paved the way to translate such research into clinical practice and health policies. However, the move towards greater personalization of medicine also comes along with challenges in the development of novel diagnostic and therapeutic tools in a complex framework that assumes that the use of genomic information is part of a translational continuum, which spans from basic to clinical research, preclinical and clinical trials, to policy research and the analysis of health and economic outcomes. The use of next-generation genomic technologies to improve the quality of life and efficiency of healthcare delivered to patients has become a mainstay theme in the field as benefits derived from such approaches include reducing a patient’s need to go through ineffective therapies, lowering side- and off-target effects of drugs, prescribing prophylactic therapies before acute exacerbations, and reducing expenditures. As such, personalized medicine promises to increase the quality of clinical care and, in some cases, to decrease health care costs. Besides the scientific challenges, there are several economic hurdles. For instance, healthcare providers need to know, whether the approach of personalized healthcare is affordable and worth the expenses. In addition, the economic rationale of personalized healthcare includes not only the reduction of the high expense of hospitalizations, the predictive diagnostics that will help to reduce cost through prevention or the increased efficacy of personalized therapies needs to offset prices of drugs. There are also several factors that influence payer adoption, coverage and reimbursement; the strength of evidence drives payers‘ decisions about coverage and reimbursement, varies widely depending on the personalized healthcare technology applied and regulation and cost-effectiveness seem to be increasingly associated with reimbursement, which is strongly influenced by professional society guidelines. In general, we see the following main obstacles to the advancement of personalized medicine: (i) the scientific challenges (a poor understanding of molecular mechanisms or a lack of molecular markers associated with some diseases, for example), (ii) the economic challenges (poorly aligned incentives), and (iii) operational issues in public healthcare systems. The operational issues can often be largely resolved within a particular stakeholder group, but correcting the incentive structure and modifying the relationships between stakeholders is more complex. This article focuses on the scientific difficulties that remain to translate genomics technologies into clinical practice and reviews recent technological advances in genomics and the challenges and potential benefits of translating this knowledge into clinical practice, with a particular focus on their applications in oncology.