TY - JOUR
T1 - Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response
AU - Choi, Minsoo
AU - SHI, Jue
AU - Zhu, Yanting
AU - Yang, Ruizhen
AU - Cho, Kwang Hyun
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science, ICT & Future Planning (2017R1A2A1A17069642 and 2015M3A9A7067220) to K.-H.C., and Hong Kong Research Grant Council (#N_HKBU215/13 and #T12-710/16-R) to J.S.
Project title:
Dynamic Regulation of the p53 Pathway and Its Control over Cell Fate at the Single-cell Level
PY - 2017/12/5
Y1 - 2017/12/5
N2 - Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Here we present a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combination. We select the p53 network as an example and analyze its cancer-specific state transition dynamics under distinct anticancer drug treatments by attractor landscape analysis. Our results not only enable stratification of cancer into distinct drug response groups, but also reveal network-specific drug targets that maximize p53 network-mediated cell death, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes.
AB - Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Here we present a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combination. We select the p53 network as an example and analyze its cancer-specific state transition dynamics under distinct anticancer drug treatments by attractor landscape analysis. Our results not only enable stratification of cancer into distinct drug response groups, but also reveal network-specific drug targets that maximize p53 network-mediated cell death, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes.
UR - http://www.scopus.com/inward/record.url?scp=85037067180&partnerID=8YFLogxK
U2 - 10.1038/s41467-017-02160-5
DO - 10.1038/s41467-017-02160-5
M3 - Journal article
C2 - 29208897
AN - SCOPUS:85037067180
SN - 2041-1723
VL - 8
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1940
ER -