TY - JOUR
T1 - An integrated approach to anti-cancer drug sensitivity prediction
AU - Berlow, Noah
AU - Haider, Saad
AU - Wan, Qian
AU - Geltzeiler, Mathew
AU - Davis, Lara E.
AU - Keller, Charles
AU - Pal, Ranadip
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.
AB - A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.
KW - Drug sensitivity prediction
KW - Personalized cancer therapy
UR - http://www.scopus.com/inward/record.url?scp=84919484455&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2014.2321138
DO - 10.1109/TCBB.2014.2321138
M3 - Article
C2 - 26357038
AN - SCOPUS:84919484455
SN - 1545-5963
VL - 11
SP - 995
EP - 1008
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 6
M1 - 6808481
ER -