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The use of histone deacetylase (HDAC) inhibitors has shown promise for a variety of malignancies. In this investigation, we define the activity of this class of inhibitors in combination with traditional cytotoxic chemotherapy in endometrial cancer cells. Significant reductions in growth were observed in Ark2 and KLE endometrial cancer cells following treatment with paclitaxel, doxorubicin, carboplatin, or the HDAC inhibitor trichostatin A (TSA). However, only combined treatment with TSA/paclitaxel caused synergistic inhibition of cell growth. This combination also resulted in significant changes in cell morphology. Using cell cycle analysis, nuclear staining, and Western blot analysis for poly(ADPribose) polymerase and caspase-9 degradation products, TSA/paclitaxel showed the most dramatic activation of the apoptotic cascade. These effects were also observed when the HDAC inhibitors HDAC inhibitor-1 or oxamflatin were substituted for TSA. The anticancer properties of paclitaxel are known to result in part from inhibition of microtubule depolymerization, which results in apoptosis. We show that TSA administration also stabilizes microtubules via A-tubulin acetylation. Furthermore, using Western blot and immunohistochemical analysis, treatment with TSA/paclitaxel led to a significant increase in acetylated tubulin and microtubule stabilization. These effects were confirmed in a mouse xenograft model. Moreover, TSA/paclitaxel resulted in a 50% reduction in tumor weight compared with either agent alone. This study provides in vivo evidence of nonhistone protein acetylation as one possible mechanism by which HDAC inhibitors reduce cancer growth. The TSA/paclitaxel combination seems to hold promise for the treatment of serous endometrial carcinoma and other malignancies with limited sensitivity to paclitaxel.

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The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. These multiple subtasks have topological dependencies, the results of which affect and verify each other. However, existing methods use dependencies of results among multiple subtasks inefficiently. Moreover, for cases with similar descriptions but different penalties, current methods cannot predict accurately because the word collocation information is ignored. In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks. Specifically, we design a multi-perspective forward prediction and backward verification framework to utilize result dependencies among multiple subtasks effectively. To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism. The experimental results show our model achieves significant improvements over baselines on all prediction tasks.

No abstract