Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation

Abstract

Background The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging. Objective This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions. Methods On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipeline was applied to data from the 1000 Genomes Project and subsequently to NGS data derived from 206 patients of a local hospital. We further integrated the pipeline into existing structures of data integration centers at the University Hospital Erlangen and combined NGS data with local nongenomic patient-derived data available in Fast Healthcare Interoperability Resources format. Results Using data from the 1000 Genomes Project and from the patient cohort as input, the implemented system produced the same results as already established methodologies. Further, it satisfied all our identified requirements and was successfully integrated into the existing infrastructure. Finally, we showed in an exemplary analysis how the data could be quickly loaded into and analyzed in KETOS, a web-based analysis platform for statistical analysis and clinical decision support. Conclusions This study demonstrates that the GEMINI open source database can be augmented to create an NGS analysis pipeline. The pipeline generates high-quality results consistent with the already established workflows for gene variant annotation and pathological evaluation. We further demonstrate how NGS-derived genomic and other clinical data can be combined for further statistical analysis, thereby providing for data integration using standardized vocabularies and methods. Finally, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany.

Treatment of Cancer Gene Changes in Chronic Myeloid Leukemia by Big Data Analysis Platform-Based Dasatinib

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Fast Healthcare Interoperability Resources (FHIR) in a FAIR Metadata Registry for COVID-19 Research

“…As FHIR complies with reusability, composability, scalability, performance, usability, data fidelity and implementability principles, it is worthwhile to investigate supporting FHIR in a system [5]. FHIR is mainly used in clinical care, but there are also uses in health research [6,7] and a clinical trials registry [8]. To date there is no FHIR based common registry to gather health data and improve cooperation between clinical, epidemiological and Public Health domains.…”

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

“…Of the 49 studies, the majority were conducted in Germany (47%, n=23) [12,26,[28][29][30][31]34,35,[40][41][42][45][46][47]52,53,[56][57][58]60,62,63,69], the United States (27%, n=13) [22,25,36,44,[48][49][50]61,[64][65][66]68,70], and Australia (6%, n=3) [1,43,67]. The remaining studies were performed in Austria (2%, n=1) [32], Canada (2%, n=1) [24], France (2%, n=1) [51], Greece (2%, n=1) [59], Japan (2%, n=1) [27], Pakistan (2%, n=1) [38], Spain (2%, n=1) [55], Switzerland (2%, n=1) [39], Taiwan (2%, n=1) [23], and the United Kingdom (2%, n=1)…”

“…In terms of medical specialty, most (55%, 27/49) of the studies [24,[27][28][29][30][31][32]34,[36][37][38][39][40][41][42]44,46,48,49,53,56,[60][61][62][63]67,70] were using a generic approach-implementable in any kind of specialty (Table 2). Of the remaining studies, 16% (8/49) use cases focused on infectious disease [1,22,23,26,45,47,50,59], whereas 12% (6/49) focused on oncology [25,55,57,58,65,66] and 8% (4/49) on genomics [43,52,68,69]. Further medical specialties were environmental health (2%, 1/49) [64], genomic cancer medicine (2%, 1/49) [51], neur...…”