BARTweb: a web server for transcriptional regulator association analysis.

Identifying active transcriptional regulators (TRs) associating with cis-regulatory elements in the genome to regulate gene expression is a key task in gene regulation research. TR binding profiles from numerous public ChIP-seq data can be utilized for association analysis with query data for TR identification, as an alternative to DNA sequence motif analysis. However, integration of the massive ChIP-seq datasets has been a major challenge in such approaches. Here we present BARTweb, an interactive web server for identifying TRs whose genomic binding patterns associate with input genomic features, by leveraging over 13 000 public ChIP-seq datasets for human and mouse. Using an updated binding analysis for regulation of transcription (BART) algorithm, BARTweb can identify functional TRs that regulate a gene set, have a binding profile correlated with a ChIP-seq profile or are enriched in a genomic region set, without a priori information of the cell type. BARTweb can be a useful web server for performing functional analysis of gene regulation. BARTweb is freely available at http://bartweb.org and the source code is available at https://github.com/zanglab/bart2.

Combined inhibition of Aurora A and p21-activated kinase 1 as a new treatment strategy in breast cancer.

PURPOSE: The serine-threonine kinases Aurora A (AURKA) and p21-activated kinase 1 (PAK1) are frequently overexpressed in breast tumors, with overexpression promoting aggressive breast cancer phenotypes and poor clinical outcomes. Besides the well-defined roles of these proteins in control of cell division, proliferation, and invasion, both kinases support MAPK kinase pathway activation and can contribute to endocrine resistance by phosphorylating estrogen receptor alpha (ERα). PAK1 directly phosphorylates AURKA and its functional partners, suggesting potential value of inhibiting both kinases activity in tumors overexpressing PAK1 and/or AURKA. Here, for the first time, we evaluated the effect of combining the AURKA inhibitor alisertib and the PAK inhibitor FRAX1036 in preclinical models of breast cancer. METHODS: Combination of alisertib and FRAX1036 was evaluated in a panel of 13 human breast tumor cell lines and BT474 xenograft model, with assessment of the cell cycle by FACS, and signaling changes by immunohistochemistry and Western blot. Additionally, we performed in silico analysis to identify markers of response to alisertib and FRAX1036. RESULTS: Pharmacological inhibition of AURKA and PAK1 synergistically decreased survival of multiple tumor cell lines, showing particular effectiveness in luminal and HER2-enriched models, and inhibited growth and ERα-driven signaling in a BT474 xenograft model. In silico analysis suggested cell lines with dependence on AURKA are most likely to be sensitive to PAK1 inhibition. CONCLUSION: Dual targeting of AURKA and PAK1 may be a promising therapeutic strategy for treatment of breast cancer, with a particular effectiveness in luminal and HER2-enriched tumor subtypes.