Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis

Le résumé fourni par la source

Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically characterize potential shuttling-attacking mutations (SAMs) across cancers through developing the deep learning model pSAM for the ab initio decoding of the sequence determinants of nucleocytoplasmic shuttling. Leveraging cancer mutations across 11 cancer types, we find that SAMs enrich functional genetic variations and critical genes in cancer. We experimentally validate a dozen SAMs, among which R14M in PTEN, P255L in CHFR, etc. are identified to disrupt the nuclear localization signals through interfering their interactions with importins. Further studies confirm that the nucleocytoplasmic shuttling altered by SAMs in PTEN and CHFR rewire the downstream signaling and eliminate their function of tumor suppression. Thus, this study will help to understand the molecular traits of nucleocytoplasmic shuttling and their dysfunctions mediated by genetic variants. Genetic variants can affect protein function and promote tumour progression by driving aberrant subcellular localization. Here, the authors characterise potential shuttling-attacking mutations (SAMs) across cancers using a deep learning model and experimental validation, and find how SAMs disrupt critical signalling interactions involved in cancer progression.

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