Recurring EPHB1 mutations in human cancers alter receptor signalling and compartmentalisation of colorectal cancer cells.

Background: Ephrin (EPH) receptors have been implicated in tumorigenesis and metastasis, but the functional understanding of mutations observed in human cancers is limited. We previously demonstrated reduced cell compartmentalisation for somatic EPHB1 mutations found in metastatic colorectal cancer cases. We therefore integrated pan-cancer and pan-EPH mutational data to prioritise recurrent EPHB1 mutations for functional studies to understand their contribution to cancer development and metastasis.

Methods: Here, 79,151 somatic mutations in 9,898 samples of 33 different tumour types were analysed with a bioinformatic pipeline to find 3D-mutated cluster pairs and hotspot mutations in EPH receptors. From these, 15 recurring EPHB1 mutations were stably expressed in colorectal cancer followed by confocal microscopy based in vitro compartmentalisation assays and phospho-proteome analysis.

Results: The 3D-protein structure-based bioinformatics analysis resulted in 63% EPHB1 mutants with compartmentalisation phenotypes vs 43% for hotspot mutations. Whereas the ligand-binding domain mutations C61Y, R90C, and R170W, the fibronectin domain mutation R351L, and the kinase domain mutation D762N displayed reduced to strongly compromised cell compartmentalisation, the kinase domain mutations R743W and G821R enhanced this phenotype. While mutants with reduced compartmentalisation also had reduced ligand induced receptor phosphorylation, the enhanced compartmentalisation was not linked to receptor phosphorylation level. Phosphoproteome mapping pinpointed the PI3K pathway and PIK3C2B phosphorylation in cells harbouring mutants with reduced compartmentalisation.

Conclusions: This is the first integrative study of pan-cancer EPH receptor mutations followed by in vitro validation, a robust way to identify cancer-causing mutations, uncovering EPHB1 mutation phenotypes and demonstrating the utility of protein structure-based mutation analysis in characterization of novel cancer genes. Video Abstract.