Neptune: an environment for the delivery of genomic medicine

Abstract

Purpose: Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process.

Methods: We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration.

Results: Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow.

Conclusion: Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

Figure 1:. Overview of Neptune functionality.

A. Neptune manages the variant review process and brings together disparate data from multiple external systems in order to create a final report file, in either json, html or FHIR format. Central to this process is the ‘VIP’ database of genetic variation. For each sample, novel genomic variants are added to this database and curated as needed according to project-specific rules. B. The contents of the VIP database includes curated variants. VIP database variants are predominantly VUS or Likely Benign.

Figure 2:. Variant Review Burden Over Time.

The plot shows the number of variants per sample requiring review in 68 eMERGE III consensus reportable genes, starting with an empty database. As additional samples are reviewed from a data freeze of 7258, the number of variants per sample that are selected quickly decreases. In eMERGE III, the number of variants that require review plateaus at around 1 variant per sample.

Figure 3:. eMERGE III reanalysis activities.

Neptune supported two parallel reanalysis activities during the eMERGE III project. First was a project with the goal of providing updated reports when variant classifications change (3A) over time. To accomplish this, we used Neptune’s reanalysis module to compare a ClinVar snapshot to local variant categorizations. We identified upgrades and downgrades by detecting either unreported variants with a new Pathogenic / Likely pathogenic classification in ClinVar or a reported variant with a new VUS, Benign or Likely benign classification. There were 26 upgrades for review, resulting in 3 updated reports (all initially VUS) and 86 downgrades for review, resulting in 2 updated reports. Next, we collected a set of VUS variants that were lacking one ACMG/AMP subcategory to reach an overall classification of likely pathogenic (3B). We then contacted clinical sites requesting more detailed patient phenotype information, in order to be able to apply the PP4 ACMG/AMP subcategory (Patient phenotype or family history highly specific for gene). In four cases we were able to issue updated reports, all due to the new clinical information. In a separate study, we reanalyzed 83 variants based on additional clinical information requested from clinical sites for variants that were VUS but which could be reclassified as Likely Pathogenic with the application of one ACMG subcategory. This resulted in four updated reports and highlights the importance of detailed clinical information during review by clinical geneticists.