TY - GEN
T1 - Predicting drug efficacy based on the integrated breast cancer pathway model
AU - Huang, Hui
AU - Wu, Xiaogang
AU - Ibrahim, Sara
AU - McKenzie, Marianne
AU - Chen, Jake Y.
PY - 2011
Y1 - 2011
N2 - This study is based on a simple hypothesis - "ideal" drugs for a patient can cure the patient's disease by modulating the gene expression profile of the patient to a similar level with those in healthy people, on the pathway level. To verify this hypothesis, we present a computational framework to evaluate drug effects on gene expression profiles in breast cancer. First, a breast cancer pathway model has been constructed by utilizing a computational connectivity maps (C-Maps) approach. This model includes important protein and drug information. In this pathway, specific drug-protein interactions (i.e. activation/inhibition) are annotated as edge attributes. Thus, we get a novel Pharmacology Effect Network, or PEN. We then develop a ranking algorithm called PET (i.e. Pharmacological Effect on Target) to combine gene expression information and our constructed PEN to evaluate specific drugs' efficacies. Finally, we applied PET and PEN to evaluate 23 breast cancer drugs. The ranking results clearly show the validity of our framework.
AB - This study is based on a simple hypothesis - "ideal" drugs for a patient can cure the patient's disease by modulating the gene expression profile of the patient to a similar level with those in healthy people, on the pathway level. To verify this hypothesis, we present a computational framework to evaluate drug effects on gene expression profiles in breast cancer. First, a breast cancer pathway model has been constructed by utilizing a computational connectivity maps (C-Maps) approach. This model includes important protein and drug information. In this pathway, specific drug-protein interactions (i.e. activation/inhibition) are annotated as edge attributes. Thus, we get a novel Pharmacology Effect Network, or PEN. We then develop a ranking algorithm called PET (i.e. Pharmacological Effect on Target) to combine gene expression information and our constructed PEN to evaluate specific drugs' efficacies. Finally, we applied PET and PEN to evaluate 23 breast cancer drugs. The ranking results clearly show the validity of our framework.
KW - Algorithms development
KW - Cancer pathway modeling
KW - Data integration
KW - Drug efficacy prediction
UR - http://www.scopus.com/inward/record.url?scp=84863646903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863646903&partnerID=8YFLogxK
U2 - 10.1109/gensips.2011.6169437
DO - 10.1109/gensips.2011.6169437
M3 - Conference contribution
AN - SCOPUS:84863646903
SN - 9781467304900
T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
SP - 42
EP - 45
BT - Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
PB - IEEE Computer Society
T2 - 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
Y2 - 4 December 2011 through 6 December 2011
ER -