Profiling of protein-protein interactions via single-molecule techniques predicts the dependence of cancers on growth-factor receptors

Hong Won Lee, Byoungsan Choi, Han Na Kang, Hyunwoo Kim, Ahrum Min, Minkwon Cha, Ji Young Ryu, Sangwoo Park, Jinyoung Sohn, Kihyuk Shin, Mi Ran Yun, Joo Yeun Han, Min Ju Shon, Cherlhyun Jeong, Junho Chung, Seung Hyo Lee, Seock Ah Im, Byoung Chul Cho, Tae Young Yoon

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


The accumulation of genetic and epigenetic alterations in cancer cells rewires cellular signalling pathways through changes in the patterns of protein-protein interactions (PPIs). Understanding these patterns may facilitate the design of tailored cancer therapies. Here, we show that single-molecule pull-down and co-immunoprecipitation techniques can be used to characterize signalling complexes of the human epidermal growth-factor receptor (HER) family in specific cancers. By analysing cancer-specific signalling phenotypes, including post-translational modifications and PPIs with downstream interactions, we found that activating mutations of the epidermal growth-factor receptor (EGFR) gene led to the formation of large protein complexes surrounding mutant EGFR proteins and to a reduction in the dependency of mutant EGFR signalling on phosphotyrosine residues, and that the strength of HER-family PPIs is correlated with the strength of the dependence of breast and lung adenocarcinoma cells on HER-family signalling pathways. Furthermore, using co-immunoprecipitation profiling to screen for EGFR-dependent cancers, we identified non-small-cell lung cancers that respond to an EGFR-targeted inhibitor. Our approach might help predict responses to targeted cancer therapies, particularly for cancers that lack actionable genomic mutations.

Original languageEnglish
Pages (from-to)239-253
Number of pages15
JournalNature biomedical engineering
Issue number4
Publication statusPublished - 2018 Apr 1

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications


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