High-Throughput Functional Evaluation of Variants of Unknown Significance in .

The advent of next-generation sequencing technologies has enabled the identification of several activating mutations of Erb-B2 receptor tyrosine kinase 2 (ERBB2) among various cancers. However, the significance of infrequent mutations has not been fully investigated. Herein, we comprehensively assessed the functional significance of the mutations in a high-throughput manner. We evaluated the transforming activities and drug sensitivities of 55 nonsynonymous mutations using the mixed-all-nominated-in-one (MANO) method. G776V, G778_S779insG, and L841V were newly revealed to be activating mutations. Although afatinib, neratinib, and osimertinib were shown to be effective against most of the mutations, only osimertinib demonstrated good efficacy against L755P and L755S mutations, the most common mutations in breast cancer. In contrast, afatinib and neratinib were predicted to be more effective than other inhibitors for the A775_776insYVMA mutation, the most frequent mutation in lung cancer. We surveyed the prevalence of concurrent mutation with gene amplification and found that approximately 30% of ERBB2-amplified urothelial carcinomas simultaneously carried mutations, altering their sensitivity to trastuzumab, an mAb against ERBB2. Furthermore, the MANO method was applied to evaluate the functional significance of 17 compound mutations within reported in the COSMIC database, revealing that compound mutations involving L755S were sensitive to osimertinib but insensitive to afatinib and neratinib. Several mutations showed varying sensitivities to ERBB2-targeted inhibitors. Our comprehensive assessment of mutations offers a fundamental database to help customize therapy for ERBB2-driven cancers.We identified several mutations as activating mutations related to tumorigenesis. In addition, our comprehensive evaluation revealed that several mutations showed varying sensitivities to ERBB2-targeted inhibitors, and thus, the functional significance of each variant should be interpreted precisely to design the best treatment for each patient. .