How to Translate Complex Genomic Data to Clinically-Oriented Personalized Cancer Care: a Real-World Experience

The next-generation sequencing (NGS) technologies have led to shifting paradigms for hereditary cancers genetic testing. With the increasing sensitivity of new technologies and the reduction of sequencing costs, there was an increase in the number of genes that could be evaluated simultaneously. NGS-based tests encompassing multi-gene panels, whole-exome (WES), and whole-genome sequencing (WGS)  entered the market in a number of laboratories becoming an integral part of the clinical diagnostics in oncology. This approach offers a huge potential to improve the detection rate of hereditary cancer syndromes and to reveal associations between mutations in different genes and clinical phenotypes.

However, by increasing the number of genes that we evaluate, we generate a vast number of genetic variants per individual. Sometimes, our ability to generate massive amounts of genetic data has far outpaced our ability to interpret their clinical significance. Results misinterpretation is also an important issue to consider especially when variants of unknown significance (VUS) are involved.

In this webinar, Dr. Ana Krivokuca presents a real-world use-case of how the Institute for Oncology and Radiology of Serbia (IORS), a National Cancer Research Center that provides routine NGS testing for cancer patients, uses the QCI Interpret in their NGS testing pipeline to annotate, assess, and better understand the clinical significance of detected germline variants in the context of hereditary cancers. 

This webinar will be dedicated to:
• The importance of adequate variant classification for inherited cancer syndromes
• The challenges in variant classification especially with the limited bioinformatics capabilities and end-to-end workflows
• Manual data curation and its downsides
• The advantages of bioinformatics solutions and computational variant classification

Attendees will:
• Learn how IORS uses QCI Interpret to evaluate genomic profiles of hereditary cancers, discriminate between variant classes, re-evaluate VUS, evaluate novel guideline recommendations, accelerate germline data analysis pipeline, confidently identify those variants that have clinical utility, and generate comprehensive clinical reports