Building the foundation for genomics in precision medicine

Abstract

Precision medicine has the potential to profoundly improve the practice of medicine. However, the advances required will take time to implement. Genetics is already being used to direct clinical decision-making and its contribution is likely to increase. To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. The building blocks for such a system are already forming and they will accelerate the adoption of precision medicine.

Figure 1

The precision-medicine ecosystem. The precision-medicine ecosystem contains building blocks that optimally connect patients, clinicians, researchers and clinical laboratories to one another. Patients and clinicians access information through portals or EHRs. The ecosystem can include displays or CDS augmented by curated knowledge that is supplied and shared by multiple stakeholders. Case-level databases and biobanks receive case data and samples from clinical and research workflows. Researchers benefit from all of these information sources and also contribute to knowledge sources. Clinical laboratories leverage data and inform the clinical community as they assess genomic variation and its impact on human health

Figure 2

Stages of the genetic interpretation process. Once genetic variants have been identified, they are filtered to select those of interest (step 1). Next, the evidence for each variant is assessed to determine the variant’s clinical impact (step 2). One or more assessed variants are then interpreted with respect to the specific condition for which the patient is being investigated (step 3). Last, the overall genetic assessment is placed into the patient’s clinical and personal context to inform the clinical-care decision-making process (step 4)

Figure 3

Creating and implementing robust standards for the description and structuring of data in laboratory processing and patient-care systems. Professionals with diverse expertise interact with vendors of laboratory-information systems and EHR systems to iteratively design and implement standards that effectively enable techniques to be used in the clinic.

Figure 4

Example of a learning health-care system. Case data can be shared between laboratories to support variant assessment. In this example, the BRCA2 p.Glu1593Asp variant in case D is classified initially as being of ‘uncertain significance’. After accessing genetic and phenotypic patient data from cases A, B and C, in which there are other genetic explanations for the clinical phenotype, the necessary evidence becomes available to classify the BRCA2 p.Glu1593Asp variant as ‘likely benign’