Integrative proximity labeling and multiomic profiling uncover domain-specific interactomes and molecular vulnerabilities of oncogenic PI3K.

Authors

Donghee Lee, Jin Koh, Sun Hee Choi, Jong Hyuk Kim

Published in

The Journal of biological chemistry. Pages 113655. Oct 16, 2026. Epub Oct 16, 2026.

Abstract

Activating mutations in PIK3CA (encoding the p110α kinase) are among the most frequent oncogenic drivers in human tumors. The two major hotspot mutations, E545K (helical domain) and H1047R (kinase domain), both hyperactivate PI3K-AKT signaling, yet they exhibit distinct functional behaviors and differential drug sensitivities. The mutation-specific protein-protein interaction (PPI) networks underlying the differences, however, remain largely unexplored. In this study, we established experimental approaches to identify proteins in proximity to oncogenic PI3K using biotin ligase-based proximity labeling. Our data revealed that the PIK3CA H1047R and E545K mutants fused to BioID2 formed structurally distinct complexes, each associating with unique proximal proteins identified by liquid chromatography-tandem mass spectrometry. Further integration of the PPI networks with CRISPR dependency screens and multiomic profiling mapped the divergent functional interactomes of p110α hotspot mutants. H1047R exhibited a synthetic lethal dependency on the adaptor protein TAB2, which is associated with aggressive clinical phenotypes in breast cancer. Distinctly, E545K interacted with core subunits of the SWI/SNF chromatin remodeling complex, corresponding to a globally repressive chromatin state at distal enhancers. This SWI/SNF association also revealed a mutation-specific epigenetic vulnerability, with E545K-mutant cells displaying increased sensitivity to EZH2 inhibitors. Collectively, these findings demonstrate that the molecular network complexity of oncogenic PI3K can be interrogated through proximity labeling and that distinct PIK3CA point mutations rewire non-overlapping PPI networks, revealing domain-specific dependencies. Our study provides a framework for mapping mutation-specific interactomes and offers molecular insights and potential therapeutic strategies for PIK3CA-mutant tumors.

PMID:
42887778
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.

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