Literature DB >> 35061901

Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes.

Haozhe Zou1,2, Tao Pan1, Yueying Gao1, Renwei Chen3, Si Li1, Jing Guo1, Zhanyu Tian1, Gang Xu1, Juan Xu2, Yanlin Ma1, Yongsheng Li1,3.   

Abstract

Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein-protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.
© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2022        PMID: 35061901      PMCID: PMC9122534          DOI: 10.1093/nar/gkac028

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   19.160


  67 in total

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