Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.

蛋白激酶（Protein kinases）是与癌症关联最为密切的一类常见蛋白质结构域，已知体细胞获得性突变（somatically acquired mutations）与多种癌症的发生存在功能性关联。针对蛋白激酶编码区的重测序研究凸显了致癌激酶突变的序列与结构决定因素在解析突变依赖型激酶激活过程中的重要意义。本研究开发了一款整合式生物信息学资源平台，该平台将当前所有公开的蛋白激酶基因遗传修饰（genetic modifications）相关信息，与序列、结构及功能数据进行了整合与映射。多类数据的整合为激酶组（kinome）范围内基于序列与结构的癌症突变特征研究提供了便捷的分析框架。基于数据库的分析揭示了单核苷酸多态性（Single Nucleotide Polymorphisms, SNPs）的不同类别在激酶结构域功能区域的差异化富集，表明大量癌症突变可位于催化核心内结构等价的位置（突变热点（mutational hotspots））。本研究还发现，结构保守的突变热点可在多个蛋白激酶基因中共享，且通常富集着具有高致癌活性的癌症驱动突变（cancer driver mutations）。对突变热点的结构建模与能量分析揭示了癌症突变介导激酶激活的共有分子机制，同时契合了现有实验数据。根据本研究提出的机制，具有高致癌潜力的激酶突变，其结构效应可表现为对自抑制型激酶构象（autoinhibited kinase form）的显著去稳定化，这在一定程度上可驱动肿瘤发生。基于结构的蛋白激酶癌症突变功能注释与效应预测，可助力解析突变依赖型激酶激活过程，并为探索肿瘤发生分子病理机制的实验研究提供参考。