MAVEs Inform Variant Interpretation in Clinical Oncology Practice

The landscape of precision oncology relies heavily on the precise classification of genetic variants to inform targeted therapies. However, a persistent challenge is the high number of Variants of Uncertain Significance (VUS) identified through next-generation sequencing. These VUS cannot be definitively linked to disease causation or therapeutic response, leading to diagnostic uncertainty and limiting the application of genotype-driven treatments. Current variant interpretation guidelines, such as those from the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP), integrate various lines of evidence, including population frequency, computational predictions, and functional studies. However, traditional functional studies are often low-throughput and cannot keep pace with the volume of variants discovered.¹

Multiplex Assays of Variant Effects (MAVEs), also known as deep mutational scanning, represent a high-throughput experimental approach designed to quantify the functional impact of thousands of genetic variants within a gene or protein. These assays involve generating large libraries of variants, expressing them in a relevant biological system, and then measuring their functional consequences through various readouts, such as protein stability, enzyme activity, or cellular fitness. By systematically assessing the effect of each variant, MAVEs generate comprehensive functional maps that can directly inform variant classification. For example, a MAVE might assess the impact of every possible single amino acid substitution within a tumor suppressor gene on its ability to suppress cell growth. Variants that significantly impair this function would be flagged as likely pathogenic, while those with no discernible effect would be considered benign.²

Integrating MAVE Data into Clinical Practice

The integration of MAVE data into clinical oncology practice holds promise for reducing the VUS rate and improving the precision of therapeutic decisions. MAVE-derived functional scores can be incorporated into existing variant interpretation frameworks, providing strong evidence for pathogenicity or benignity. For instance, a variant previously classified as VUS might receive a functional score indicating severe loss of function, thereby reclassifying it as pathogenic according to ACMG/AMP criteria. This reclassification can directly impact treatment selection, allowing clinicians to consider targeted therapies for which the patient's tumor was previously not deemed eligible due to variant uncertainty. Conversely, a VUS reclassified as benign could prevent unnecessary or ineffective treatments.³

The utility of MAVEs extends beyond single-gene analyses. They can be applied to characterize variants in genes associated with drug resistance or sensitivity, providing insights into patient response to specific chemotherapies or targeted agents. For example, MAVEs could be used to map the functional effects of variants in genes like EGFR or BRAF, identifying specific mutations that confer resistance to tyrosine kinase inhibitors. This information could guide the selection of alternative therapies or inform the design of combination regimens. Furthermore, MAVE data can contribute to the development of more accurate computational prediction algorithms by providing a large, experimentally validated dataset for training and validation.⁴

Despite their potential, the clinical implementation of MAVEs faces several challenges. Standardization of assay design, execution, and data analysis is critical to ensure reproducibility and comparability across different laboratories. The biological relevance of the functional assays must be carefully considered; an in vitro assay may not perfectly recapitulate the complex cellular environment in vivo. Furthermore, the sheer volume of data generated by MAVEs necessitates robust bioinformatics pipelines and clear guidelines for interpreting and reporting results in a clinical context. Regulatory bodies will need to establish frameworks for validating MAVE-derived evidence for clinical use. The cost-effectiveness of integrating MAVE technology into routine clinical sequencing workflows also requires evaluation.⁵