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

Abstract

Abstract 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 EPHB1mutations 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 recurring hotspot mutations in EPHreceptors. From these, 15 recurring EPHB1mutations were stably expressed in colorectal cancer followed by 3D confocal microscopy based in vitro compartmentalisation assays and phospho-proteome analysis using the Sciomics platform. Results: 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. Furthermore, the 3D-protein structure-based bioinformatics analysis showed comparatively more robustness by identifying 63% (5 out of 8 selected 3D-anlysed mutants) vs the 43% (3 out of 7 selected 2D-analysed mutants) EPHB1mutants with compartmentalisation phenotypes. 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, and demonstrated the utility for 3D-protein structure-based mutation analysis in characterization of putative cancer genes.