Comprehensive functional analysis of the tousled-like kinase 2 frequently amplified in aggressive luminal breast cancers

2016-10-03 19:44:16

Nature Communications; 3 October 2016: DOI:10.1038/ncomms12991

Jin-Ah Kim, Ying Tan, Xian Wang, Xixi Cao, Jamunarani Veeraraghavan, Yulong Liang, Dean P. Edwards, Shixia Huang, Xuewen Pan, Kaiyi Li, Rachel Schiff & Xiao-Song Wang


More aggressive and therapy-resistant oestrogen receptor (ER)-positive breast cancers remain a great clinical challenge. Here our integrative genomic analysis identifies tousled-like kinase 2 (TLK2) as a candidate kinase target frequently amplified in ~10.5% of ER-positive breast tumours. The resulting overexpression of TLK2 is more significant in aggressive and advanced tumours, and correlates with worse clinical outcome regardless of endocrine therapy. Ectopic expression of TLK2 leads to enhanced aggressiveness in breast cancer cells, which may involve the EGFR/SRC/FAK signalling. Conversely, TLK2 inhibition selectively inhibits the growth of TLK2-high breast cancer cells, downregulates ERα, BCL2 and SKP2, impairs G1/S cell cycle progression, induces apoptosis and significantly improves progression-free survival in vivo. We identify two potential TLK2 inhibitors that could serve as backbones for future drug development. Together, amplification of the cell cycle kinase TLK2 presents an attractive genomic target for aggressive ER-positive breast cancers.


A vast majority of breast cancers express the oestrogen receptor (ER+) and can be treated with endocrine therapy; however, the clinical outcome varies radically between different patients. ER+ breast cancers are also known as luminal breast cancers and can be subdivided into A and B subtypes. The luminal B tumours are more aggressive ER+ breast cancers characterized by poorer tumour grade, larger tumour size and higher proliferation index. Clinically, such tumours are prone to develop endocrine resistance, which poses a great challenge to clinical management. Identifying the genetic aberrations underlying the enhanced aggressiveness of these tumours, and developing effective therapeutic strategies to target them, are in high demand. Recent prominent success of the CDK4/6-specific inhibitors in clinical trials for advanced breast cancers have attracted wide-spread attention to the potential of cell cycle kinases as viable drug targets in breast cancer. Thus, discovering new cell cycle kinase targets that can tackle the more aggressive ER+ breast cancers will be of critical clinical significance

Genomic amplifications lead to deregulations of oncogenes to which cancer cells become often addicted in specific tumours. Such events, however, usually affect a large number of genes in cancer genomes, which make it difficult to identify the primary oncogene targets of these amplifications. In our previous study, we discovered that cancer genes possess distinctive yet complicated ‘gene concept signature’, which include cancer-related signalling pathways, molecular interactions, transcriptional motifs, protein domains and gene ontologies. Based on this observation, we developed a Concept Signature (or ConSig) analysis that prioritizes the biological importance of candidate genes underlying cancer via computing their strength of association with those cancer-related signature concepts. In our previous study, we have applied this analysis to reveal the primary target genes of chromosome 17q amplifications in breast cancer. Here we postulate that the ConSig analysis may be used to effectively nominate dominantly acting cancer genes from the genomic amplifications in cancer at a genome-wide scale, which can be further translated into viable therapeutic targets by interrogating pharmacological databases. Toward this end, we have assembled a genome-wide analysis called ‘ConSig-Amp’ to discover viable therapeutic targets in cancer from multi-dimensional genomic data sets.

Empire Genomics TLK2 (Red 5-Rox dUTP) and centromere 17 (Green 5-Fluorescein dUTR) probes were used in this publication.

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Key Words

Breast cancer | Cancer genetics | Cancer genomics