Polo-like kinase 4 or PLK4 is a member of a conserved family of serine/threonine protein kinases that regulate multiple cellular processes, such as cell division and checkpoint regulation of mitosis. These kinases are often deregulated in cancer. PLK4 is the most structurally divergent PLK, localizes to centrosomes and is a critical regulator of centriole duplication. Over-expression of PLK4 leads to centrosome amplification and results in chromosome instability (CIN), a common characteristic observed in many types of cancers. We found that PLK4 is upregulated in breast cancer, specifically in the basal-like subtype. PLK4 expression is induced by hypoxia and suppressed by p53 in cancer cells. Consistent with this observation, PLK4 expression in cancer cells is upregulated when they are implanted and grown as xenografts in vivo. Furthermore, RNAi-mediated depletion of PLK4 inhibits the growth of cancer cells, but not normal cells (HMEC), in vitro, and tumor growth in vivo. Interestingly, siRNA knockdown of PLK4 sensitizes cancer cells to hypoxia. These findings suggest that targeting PLK4 may be a good therapeutic strategy in treating certain cancers. To this end, we initiated a discovery program that resulted in the identification of potent PLK4 inhibitors. These novel inhibitors are potent anti-prolifeartives, cause loss of mitotic checkpoint followed by apoptotic cell death, and suppress tumor growth in xenograft models. Mechanistically, inhibition of PLK4 suppresses phosphorylation of PLK4 and Histone H3, leads to failure of centrosome clustering and formation of multipolar spindles. Interestingly, breast cancer cell response to PLK4 inhibition appears to differ among subtypes of breast cancer cells and to be influenced by receptor and mutation status, such as ER and PTEN. Since multipolar division in cancer cells is not viable, due to massive missegregation of chromosomes, inhibition of PLK4 and formation of multipolar division followed by cell death may be a unique strategy for killing cancer cells. Implications of these findings in treating cancer will also be discussed. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-215. doi:10.1158/1538-7445.AM2011-LB-215

We propose novel radical features from automatic translation for event extraction. Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging. We derive meaningful subword features from automatic translations into target language. Results suggest this method is particularly useful when using languages with writing systems that facilitate easy decomposition into subword features, e.g., logograms and Cangjie. The best result combines logogram features from Chinese and Japanese with syllable features from Korean, providing an additional 3.0 points f-score when added to state-of-the-art generalisation features on the TAC KBP 2015 Event Nugget task.

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