Update of the UMD-VHL database: classification of 164 challenging variants based on genotype-phenotype correlation among 605 entries

BACKGROUND

The von Hippel-Lindau (VHL) disease is a hereditary tumour syndrome caused by germline mutations in VHL tumour suppressor gene. The identification of VHL variants requires accurate classification which has an impact on patient management and genetic counselling.

METHODS

The TENGEN (French oncogenetics network of neuroendocrine tumors) and PREDIR (French National Cancer Institute network for Inherited predispositions to kidney cancer) networks have collected VHL genetic variants and clinical characteristics of all VHL-suspected patients analysed from 2003 to 2021 by one of the nine laboratories performing VHL genetic testing in France. Identified variants were registered in a locus-specific database, the Universal Mutation Database-VHL database (http://www.umd.be/VHL/).

RESULTS

Here we report the expert classification of 164 variants, including all missense variants (n=124), all difficult interpretation variants (n=40) and their associated phenotypes. After initial American College of Medical Genetics classification, first-round classification was performed by the VHL expert group followed by a second round for discordant and ambiguous cases. Overall, the VHL experts modified the classification of 87 variants including 30 variants of uncertain significance that were as (likely)pathogenic variants for 19, and as likely benign for 11.

CONCLUSION

Consequently, this work has allowed the diagnosis and influenced the genetic counselling of 45 VHL-suspected families and can benefit to the worldwide VHL community, through this review.

Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study

Background

Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine.

Objective

The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice.

Methods

Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations.

Results

The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation.

Conclusions

We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Information Needs in the Precision Medicine Era: How Genetics Home Reference Can Help

Precision medicine focuses on understanding individual variability in disease prevention, care, and treatment. The Precision Medicine Initiative, launched by President Obama in early 2015, aims to bring this approach to all areas of health care. However, few consumer-friendly resources exist for the public to learn about precision medicine and the conditions that could be affected by this approach to care. Genetics Home Reference, a website from the US National Library of Medicine, seeks to support precision medicine education by providing the public with summaries of genetic conditions and their associated genes, as well as information about issues related to precision medicine such as disease risk and pharmacogenomics. With the advance of precision medicine, consumer-focused resources like Genetics Home Reference can be foundational in providing context for public understanding of the increasing amount of data that will become available.