Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers  

作　　者：Rui Liu Jinzeng Wang Masao Ukai Ki Sewon Pei Chen Yutaka Suzuki Haiyun Wang Kazuyuki Aihara Mariko Okada-Hatakeyama Luonan Chen 

机构地区：[1]School of Mathematics, South China University of Science and Technology, Guangzhou 510640, China [10]Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China [11]School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China [12]Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China [2]School of Life Sciences and Technology, Tongji University, Shanghai 200092, China [3]National Research Center for Translational Medicine (Shanghai), Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China [4]Graduate School of Medical Life Science, Yokohama City University, Yokohama 230-0045, Japan [5]Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan [6]Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China [7]Department of Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan [8]Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan [9]Laboratory of Cell Systems, Osaka University, Osaka 565-0871, Japan

出　　处：《Journal of Molecular Cell Biology》2019年第8期649-664,共16页分子细胞生物学报（英文版）

基　　金：This work was supported by grants from the National Key R&D Program of China (2017YFA0505500);Strategic Priority Research Program of the Chinese Academy of Sciences (XDBl3040700);the National Natural Science Foundation of China (11771152,91529303,31771476,31571363,31771469,91530320,61134013,81573023,81501203,and 11326035);Pearl River Science and Technology Nova Program of Guangzhou (201610010029);FISRT,Aihara Innovative Mathematical Modeling Project from Cabinet Office,Japan;Fundamental Research Funds for the Central Universities (2017ZD095);JSPS KAKENHI (15H05707);Grant-in-Aid for Scientific Research on Innovative Areas (3901) and SPS KAKENHI (15KT0084,17H06299,17H06302,and 18H04031);RIKEN Epigenome and Single Cell Project Grants to M.O.-H.This work was performed in part under the International Cooperative Research Program of Institute for Protein Research,Osaka University (ICRa-17-01 to L.C.and M.O.-H.).

摘　　要：Acquired drug resistance is the major reason why patients fail to respond to cancer therapies.It is a challenging task to deter.mine the tipping point of endocrine resistance and detect the associated molecules.Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance.We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules.The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients.The results provided the detection for the pre-resistance state or early signs of endocrine resistance.Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

关 键 词：drug resistance breast cancer TIPPING POINT dynamic NETWORK biomarker (DNB) molecular NETWORK MRNA-SEQ 

分 类 号：Q[生物学]