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Aggregating and Sharing Clinical Evidence to Improve the Diagnosis of Rare Diseases

Speaker

Abstract

The introduction of improved, more cost efficient target capture technologies has accelerated the adoption of Whole Exome Sequencing (WES) in clinical diagnostics. The ability to provide more complete answers to patients with rare diseases in turn has increased the interest of physicians in genetic testing. Clinical genetic testing today is generating more real world evidence than any large scale research sequencing project has or will ever have. However, this valuable real world evidence remains underutilized due to the lack of proper technology and due to legal obstacles. In practice WES generates large amounts of information that need to be processed and interpreted. If used in routine diagnostics, the data processing and the interpretation must be standardized, clinically validated and ultimately automated to a high degree. The hands-on time of creating a diagnosis must be a matter of minutes and not of days or weeks. Taking together the genotype and phenotype information from a large number of patients can improve the success rate in diagnostic testing significantly. Healthcare institutions address these challenges to a variable degree by creating and maintaining proprietary in-house software or by using a patchwork of third-party solutions. A single, streamlined solution for these challenges is varvis, a diagnostic software that covers the entire workflow from raw data to interpretation and reporting. Using varvis, diagnostic laboratories across Europe, Canada and the USA are aggregating and sharing real-world evidence and improving patient care in compliance with data privacy regulations. The aggregated database now contains information about 19,000,000 unique variants from 35,000 cases. I will present case examples how patients benefit from this knowledgebase and how it enables clinical labs to quickly scale up WES diagnostics.

Learning Objectives:

1. How Whole Exome Sequencing has matured to become a first-line diagnostic method in clinical genetics and what its benefit is for the patient

2. Which tools and processes are necessary to successfully deploy this powerful method in routine diagnostics at scale

3. How every data point from every single patient can be leveraged to continuously improve the quality and success of the diagnostic process.


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