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Updated: Jun 21, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Understanding variants of unknown significance and classification of genomic alterations
Dean C Pavlick1, Garrett M Frampton1, Jeffrey R Ross2,3
1Department of Computational Discovery, Foundation Medicine, Inc., Boston, MA, United States.
Abstract:
Despite recent efforts to issue clinical guidelines outlining strategies to define the pathogenicity of genomic variants, there is currently no standardized framework for which to make these assertions. This review does not present a step-by-step methodology, but rather takes a holistic approach to discuss many aspects which should be taken into consideration when determining variant pathogenicity. Categorization should be curated to reflect relevant findings within the scope of the specific medical context. Functional characterization should evaluate all available information, including results from literature reviews, different classes of genomic data repositories, and applicable computational predictive algorithms. This article further proposes a multidimensional view to infer pathogenic status from many genomic measurements across multiple axes. Notably, tumor suppressors and oncogenes exhibit fundamentally different biology which helps refine the importance of effects on splicing, mutation interactions, copy number thresholds, rearrangement annotations, germline status, and genome-wide signatures. Understanding these relevant datapoints with thoughtful perspective could aid in the reclassification of variants of unknown significance (VUS), which are ambiguously understood and currently have uncertain clinical implications. Ongoing assessments of VUS examining these relevant biological axes could lead to more accurate classification of variant pathogenicity interpretation in diagnostic oncology.
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