SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care

Abstract

Background: Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations.

Methods: Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient's diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app.

Results: The PCM prototype can rapidly present a visualization that compares a patient's somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype's strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations.

Discussion: PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases.

Keywords: electronic health records; genomics; health information management; information science; mobile health; neoplasms.

Figure 1:

A snippet of the JSON FHIR code for a patient with lung cancer and a p.T790M mutation detected in the epidermal growth factor receptor gene. Three extensions to the FHIR Observation Resource are shown: (1) assessed.gene, which uses the NCI Thesaurus to represent the gene name in HGNC-compliant format; (2) assessed.referenceSeq, which uses the CCDS database to represent the gene reference sequence; (3) assessed.variant, which represents the observed gene mutation (c.2369C>T) and predicted protein alteration (p.T790M) directly in Human Genome Variant Society syntax. The full code for this patient is available in Supplementary Table 2.

Figure 2:

Example output of the SMART PCM app, showing a lung cancer patient with KRAS p.G12C mutation in the context of other lung cancer patients tested at VUMC. Further information is available to the user through interaction with the pie charts, all pieces of which are activated by touch. On the left, a pie chart shows the population distribution of gene mutations. In this example, it is evident that slightly more than half the patients have no mutation detected, whereas KRAS is the most commonly mutated gene. On the right, the distribution of variants of the mutated gene is shown, where it is evident that p.G12C is the most common KRAS mutation. In a case where a patient has no mutation detected, the variant pie chart is suppressed. Patient details (name, age, gender) are redacted to preserve PHI.

Figure 3:

The SMART PCM app allows for user interaction, in order to obtain a quantitative view of the mutation spectrum. In the continued example of a KRAS-mutated lung cancer patient, the user can see that KRAS is the most frequent mutation, and can also see the distribution of other mutations quantified. This information is not displayed in the first visualization (Figure 2) because of the visual clutter.

Figure 4:

The SMART PCM app allows for access to external knowledge sources that would otherwise be unavailable to the user through their native EHR system. Shown here is the Gene Wiki page for the gene KRAS, embedded within the app.

Figure 5:

An example of a synthesized NGS panel result with many detected alterations and 200+ genes with detected alterations in the population.

Figure 6:

The local outlier factor (LOF) distribution for the population; x-axis and y-axis represent the LOF distribution and the proportion of patients of certain LOF interval in all patients, respectively. The further the LOF value is from 1.0, the more possibility that the genetic mutation observed in that patient is an outlier. The patient represented in Figure 5 (“Mary 1 Smith”) had an LOF of 0.925, suggesting that she is somewhat similar to the other patients in the population.