Comparison of somatic variant oncogenicity classification using ClinGen/CGC/VICC guidelines and QIAGEN Clinical Insight Interpret decision support software
Accurate clinical interpretation of somatic cancer variants is critical for diagnosis and guidance of precision oncology treatment. As the depth and breadth of genomic sequencing increased, laboratories developed independent standards for the classification of somatic variants. In response, a set of standards for classification were published by a collaboration among Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC) and Variant Interpretation for Cancer Consortium (VICC). This study evaluated these standards and compared the resulting classifications to the classifications generated by a clinical decision support software system, QIAGEN Clinical Insight (QCI) Interpret One, a system using a version of the 2015 consensus guidelines by the American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) customized for somatic assessment. The published variant set for validation was utilized and expanded by conducting a retrospective analysis using real-world cancer variants drawn from oncology cases tested at Mayo Clinic. For "oncogenic" and "likely oncogenic" variants in the combined datasets, automated classifications by the QCI system were 97.2% concordant with those assessed using the ClinGen/CGC/VICC system. The ClinGen/CGC/VICC standards led to more conservative variant classifications, with a larger proportion of variants assigned to the "variant of unknown significance" and "likely benign" designations. This study demonstrates that the ClinGen/CGC/VICC guidelines and clinical decision support tools can be effectively used together to facilitate somatic variant classification and interpretation.
Authors
- Lisa Mullineaux
- Kevin C. Halling (ORCID: https://orcid.org/0000-0002-2914-2526)
- Beth A. Pitel (ORCID: https://orcid.org/0000-0002-5138-824X)
- Claire Teigen
- Sheryl K. Elkin (ORCID: https://orcid.org/0000-0002-2283-3842)
- Amber Pryzbylski
- Aarthi Goverdhan
Institutions
- Maxygen (United States) (US)
- Mayo Clinic (US)
Publication Details
- Journal
- Cancer Genetics
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.cancergen.2025.09.009
- Primary Topic
- Health Systems, Economic Evaluations, Quality of Life
- Type
- article
- Field-Weighted Citation Impact
- 0.00