WeChat Mini Program
Old Version Features

Abstract B177: Synthetic Lethal Approach Identifies Potent and Selective TTK and CLK Inhibitor with Preclinical Anticancer Activity in Triple-Negative Breast Cancer Model

MOLECULAR CANCER THERAPEUTICS(2018)

Celgene Corp

Cited 0|Views34
Abstract
Abstract Historically, synthetic lethal-based drug discovery has yielded a high percentage of novel, first-in-class drugs while uncovering new tumor biology. Using a synthetic lethal approach, we identified one compound that preferentially induced apoptosis in triple-negative breast cancer (TNBC) cell lines while sparing luminal breast cancer cell lines. The synthetic lethal approach evolved into a targeted approach following the identification of the two kinases that were selectively inhibited by CC-671 in enzyme assays: human protein kinase monopolar spindle 1 (hMps1), also known as TTK, and CDC2-like kinase (CLK2). Detailed CC-671 cellular mechanism of action studies showed phosphorylation inhibition of the direct substrates of TTK and CLK2. The CC-671 IC50 concentration that inhibited phosphorylation of the CLK2 substrate SR protein ranged between 530 and 553 nM in CAL51 cells. Gene Set Enrichment Analyses (GSEA) of RNAseq data suggest that CC-671 CLK2 inhibition induces apoptosis by reducing expression of antiapoptotic isoforms via splicing modification. Cellular CC-671 TTK inhibition was demonstrated by significant reduction of TTK autophosphorylation at T686 and phosphorylation of KNL1, one of the well-characterized TTK substrates. CC-671 treatment also led to a decrease of cyclin B and securin protein level. As a result, the percentage of mitotic cells in nocodazole-arrested CAL51 cells was substantially reduced by CC-671. Furthermore, data from time-lapse video microscopy experiments confirmed the mitotic phase length was reduced by approximately half upon CC-671 treatment. Inhibition of phospho-Histone H3 (Ser 10) following a single CC-671 administration was observed in TNBC in vivo tumor model. More importantly, significant CC-671 in vivo efficacy was demonstrated in a cell line-derived TNBC xenograft model with weekly dosing. The data described here suggest that dual inhibition of TTK and CLK2 represents an attractive therapeutic approach for triple-negative breast cancer. CC-671 presents a unique profile for an anticancer kinase inhibitor. Citation Format: Dan Zhu, Gordafaried Deyanat-Yazdi, David Mikolon, Rama K. Narla, Sophie X. Peng, Yuhong Ning, Jennifer R. Riggs, John F. Boylan. Synthetic lethal approach identifies potent and selective TTK and CLK inhibitor with preclinical anticancer activity in triple-negative breast cancer model [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B177.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined