Clinical evaluation and performance of QIAGEN Clinical Insight Interpret (QCI-I) as a reporting tool for a comprehensive cancer panel (TSO 500).

Abstract

e14048 Background: The rapid adoption of next-generation sequencing (NGS) methods into clinical diagnostic laboratories have revolutionized the field of genetic testing. To interpret the exhaustive NGS data, bioinformatics has become an inevitable component of any laboratory implementing a clinical NGS test. However, there is an unmet need for accurate, efficient and standardized variant interpretation for actionable insights. In this effort, AMP/ASCO/CAP released a joint consensus recommendation proposing a four-tiered system to categorize somatic alterations based on their clinical significance in cancer diagnosis, prognosis, or therapeutics for standardization of interpretation and reporting of results among laboratories. Thus, the aim of the study was to assess and validate QIAGEN Clinical Insight (QCI) Interpret software for clinical laboratory test interpretation. Methods: 67 malignant cases (myeloid neoplasm and lung) were analyzed using the TSO-500 NGS assay and secondary analysis platform (Illumina, San Diego, CA). The data was uploaded and analyzed using QCI Interpret. Patient case draft reports were produced using a TS0-500 customizable workflow with automated variant filtering, and variant classifications. Results: Data analysis on 67 cases resulted in a total of 251 alterations (SNVs and Indels). A total of 77 and 174 alterations were reported as pathogenic/ likely pathogenic into tier based classification by the QCI Interpret for myeloid and lung malignancies, respectively. For myeloid malignancies, the QCI Interpret was able to detect accurately all the clinically relevant variants and listed them into appropriate tier-based classification compared to the expertly reported alterations. For lung malignancies, 16 alteration were classified in tier 1, 96 alteration in tier 2, 61 alteration in tier 3, and 1 alteration in tier 4. The QCI interpret was able to detect 100% of the alterations being validated, and reported them into tier based classification. Conclusions: Systematic evaluation of an automated classification based on AMP/ASCO/CAP using an up to date knowledge base is critical to accurately classify actionable mutations and detect emerging relevant mutations. The QCI Interpret seems to be a critical bioinformatics tool that adheres to the AMP/ASCO/CAP classification system and incorporates TMB and MSI values from the TSO500 assay, which can be used across clinical laboratories for accurate, efficient and standardized variant interpretation for actionable insights.