HER family kinase domain mutations promote tumor progression and can predict response to treatment in human breast cancer.


Journal Article

Resistance to HER2-targeted therapies remains a major obstacle in the treatment of HER2-overexpressing breast cancer. Understanding the molecular pathways that contribute to the development of drug resistance is needed to improve the clinical utility of novel agents, and to predict the success of targeted personalized therapy based on tumor-specific mutations. Little is known about the clinical significance of HER family mutations in breast cancer. Because mutations within HER1/EGFR are predictive of response to tyrosine kinase inhibitors (TKI) in lung cancer, we investigated whether mutations in HER family kinase domains are predictive of response to targeted therapy in HER2-overexpressing breast cancer. We sequenced the HER family kinase domains from 76 HER2-overexpressing invasive carcinomas and identified 12 missense variants. Patients whose tumors carried any of these mutations did not respond to HER2 directed therapy in the metastatic setting. We developed mutant cell lines and used structural analyses to determine whether changes in protein conformation could explain the lack of response to therapy. We also functionally studied all HER2 mutants and showed that they conferred an aggressive phenotype and altered effects of the TKI lapatinib. Our data demonstrate that mutations in the finely tuned HER kinase domains play a critical function in breast cancer progression and may serve as prognostic and predictive markers.

Full Text

Cited Authors

  • Boulbes, DR; Arold, ST; Chauhan, GB; Blachno, KV; Deng, N; Chang, W-C; Jin, Q; Huang, T-H; Hsu, J-M; Brady, SW; Bartholomeusz, C; Ladbury, JE; Stone, S; Yu, D; Hung, M-C; Esteva, FJ

Published Date

  • March 2015

Published In

Volume / Issue

  • 9 / 3

Start / End Page

  • 586 - 600

PubMed ID

  • 25435280

Pubmed Central ID

  • 25435280

Electronic International Standard Serial Number (EISSN)

  • 1878-0261

International Standard Serial Number (ISSN)

  • 1574-7891

Digital Object Identifier (DOI)

  • 10.1016/j.molonc.2014.10.011


  • eng