The kinome 'at large' in cancer

• Published in: Nature reviews. Cancer Vol. 16; no. 2; pp. 83 - 98
• Main Authors: Fleuren, Emmy D. G., Zhang, Luxi, Wu, Jianmin, Daly, Roger J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.02.2016; Nature Publishing Group
• Subjects: 631/208/191; 631/337/458/1733; 631/337/475; 631/45/275; 631/553; 631/67/395; 631/67/69; analysis; Antineoplastic drugs; Antitumor agents; Biomedicine; Cancer; Cancer Research; Carcinogenesis; Gene Expression Regulation, Enzymologic; Gene Expression Regulation, Neoplastic; Genetic aspects; Genomics; Health aspects; High-Throughput Screening Assays; Histone Acetyltransferases - genetics; Histone Acetyltransferases - metabolism; Humans; Kinases; Malignancy; Molecular targeted therapy; Molecular Targeted Therapy - methods; Mutation; Neoplasms - enzymology; Neoplasms - genetics; Neoplasms - metabolism; NIMA-Related Kinases; Pharmacological research; Protein kinase; Protein kinases; Protein Kinases - genetics; Protein Kinases - metabolism; Protein-Serine-Threonine Kinases - genetics; Protein-Serine-Threonine Kinases - metabolism; Proteomics; Proteomics - methods; TATA-Binding Protein Associated Factors - genetics; TATA-Binding Protein Associated Factors - metabolism; Transcription Factor TFIID - genetics; Transcription Factor TFIID - metabolism
• ISSN: 1474-175X; 1474-1768; 1474-1768
• DOI: 10.1038/nrc.2015.18

Key Points Application of high-throughput omics technologies has provided important and complementary insights into dysregulation of protein kinases in human cancer and their functional roles. By interrogating a series of recent studies aimed at identifying cancer 'driver' genes causally implicated in cancer development from analysis of cancer genomes, we have identified a list of 91 protein kinases that represent likely cancer drivers. However, the number of protein kinases subject to functionally relevant mutations is likely to be greater, in part reflecting the incidence of non-recurrent mutations that affect signalling networks in a highly context-dependent manner. The list of high-confidence protein kinase cancer drivers includes kinases with well-established roles in cancer development, such as various receptor tyrosine kinases, as well as novel oncogenes and tumour suppressors, such as TATA box binding protein-associated factor 1 (TAF1) and never in mitosis A-related kinase 9 (NEK9). Approximately three-quarters of the high-confidence drivers are either targets for US Food and Drug Administration-approved therapies or amenable to therapeutic targeting through repurposing of existing therapies or further development of those at the preclinical stage. However, there is a substantial need to develop therapeutic strategies that exploit the vulnerabilities conferred by loss of function alterations in specific tumour suppressor kinases. High-throughput analysis of cancer proteomes and sub-proteomes by application of reverse phase protein arrays and mass spectrometry has identified cancer-associated perturbations in protein kinase expression and activity that would not be detected by genomic and transcriptomic approaches. This reflects post-transcriptional regulation of kinase activity at the level of protein expression and post-translational modification. Novel insights provided by proteomics approaches include identification of patterns of kinase activation conserved across various different cancers, the determination of specific tyrosine kinases implicated in non-small-cell lung cancer and basal breast cancer, and characterization of kinome remodelling in response to administration of particular targeted therapies. Functional genomic approaches enable characterization of context-dependent cellular dependency on particular protein kinases and identification of protein kinases that sensitize cancer cells to specific therapies. Integration of particular omics approaches often provides novel insights not provided by individual methodologies, and enables a network-level understanding of kinase signalling that can lead to novel therapeutic opportunities. This Analysis article discusses characterization of the kinome in human cancers through genomic, proteomic and functional genomic analyses. In particular, it presents an analysis of cancer genomic data to derive a new census of protein kinase cancer drivers. Over the past decade, rapid advances in genomics, proteomics and functional genomics technologies that enable in-depth interrogation of cancer genomes and proteomes and high-throughput analysis of gene function have enabled characterization of the kinome 'at large' in human cancers, providing crucial insights into how members of the protein kinase superfamily are dysregulated in malignancy, the context-dependent functional role of specific kinases in cancer and how kinome remodelling modulates sensitivity to anticancer drugs. The power of these complementary approaches, and the insights gained from them, form the basis of this Analysis article.