High-Throughput Functional Evaluation of Variants of Unknown Significance in ERBB2

Masaaki Nagano1,2, Shinji Kohsaka3, Toshihide Ueno1

  • 1Department of Cellular Signaling, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Insights

This study reveals new activating mutations in Erb-B2 receptor tyrosine kinase 2 (ERBB2) and shows varying drug sensitivities for different ERBB2 mutations, guiding personalized cancer therapy.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Next-generation sequencing identifies numerous ERBB2 mutations in various cancers.
  • The clinical significance of less common ERBB2 mutations remains under-investigated.

Purpose of the Study:

  • To comprehensively assess the functional significance of ERBB2 mutations using a high-throughput approach.
  • To evaluate the transforming activities and drug sensitivities of 55 nonsynonymous ERBB2 mutations.

Main Methods:

  • Utilized the mixed-all-nominated-in-one (MANO) method to assess 55 nonsynonymous ERBB2 mutations.
  • Analyzed transforming activities and drug sensitivities, including responses to afatinib, neratinib, and osimertinib.
  • Investigated the prevalence of concurrent ERBB2 mutations with gene amplification in urothelial carcinomas.

Main Results:

  • Identified G776V, G778_S779insG, and L841V as novel activating ERBB2 mutations.
  • Osimertinib showed efficacy against L755P and L755S mutations (common in breast cancer), while afatinib and neratinib were more effective against A775_776insYVMA (common in lung cancer).
  • Approximately 30% of ERBB2-amplified urothelial carcinomas had concurrent ERBB2 mutations, affecting trastuzumab sensitivity. Compound mutations involving L755S responded to osimertinib but not afatinib or neratinib.

Conclusions:

  • ERBB2 mutations exhibit differential sensitivities to targeted inhibitors.
  • A comprehensive understanding of ERBB2 variant function is crucial for tailoring cancer treatment strategies.
  • This research provides a foundational database for customizing therapies in ERBB2-driven cancers.

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