Genomes in clinical care

Abstract

In the era of precision medicine, genome sequencing (GS) has become more affordable and the importance of genomics and multi-omics in clinical care is increasingly being recognized. However, how to scale and effectively implement GS on an institutional level remains a challenge for many. Here, we present Genome First and Ge-Med, two clinical implementation studies focused on identifying the key pillars and processes that are required to make routine GS and predictive genomics a reality in the clinical setting. We describe our experience and lessons learned for a variety of topics including test logistics, patient care processes, data reporting, and infrastructure. Our model of providing clinical care and comprehensive genomic analysis from a single source may be used by other centers with a similar structure to facilitate the implementation of omics-based personalized health concepts in medicine.

Fig. 1. Diagnostic power of srGS compared to ES.

Different areas of application are shown (genomics, transcriptomics, and epigenomics). The shade of the color represents the coverage/potential detection rate of the respective variants/genome/transcriptome regions.

Fig. 2. Overall approach and concept of Ge-Med.

Genetic diagnostics (brown boxes) starts with whole genome analysis (srGS) and is associated with transcriptome sequencing (srTS). Data analysis for actionable genes and selected PRS are offered to the patients after genetic counseling. Exceptions from this Genome First approach are indicated (light brown). Unsolved patients may be enrolled into the Genome+ research concept allowing extended research NGS tools such as lrGS, extended srTS, epigenome, and metabolome analysis if patients give consent, fit research criteria, and request extended analysis (depicted in green).

Fig. 3. Complexity of the interaction of various individuals involved in the genome diagnostic process.

Before diagnostic GS can be initiated, the full complexity and dimension of GS have to be communicated with the patient and the referring physician. In unclear and unsolved cases interdisciplinary boards decide whether to extend diagnostics to GS. In the process of sample collection, several caretakers need to be involved besides the clinical geneticist to ensure high-quality samples and complete documentation. Information and consent of patients have to be discussed at the end of the diagnostic process, as to where to store data, and who gets access to it. Using diagnostically generated data for research requires careful consideration of each stage of the diagnostic process. This model could support policymakers in developing novel diagnostic core centers ensuring widespread scientific use of diagnostic data in the healthcare system. KLINSE: Clinical Information Office on Rare Diseases (https://www.medizin.uni-tuebingen.de/de/das-klinikum/einrichtungen/zentren/zentrum-fuer-seltene-erkrankungen-zse/klinse).