Hai-Hui Huang1,2, Jing-Guo Dai3, Yong Liang4. 1. School of Information Science and Engineering & Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, Chinatomyhwang@163.com. 2. Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Chinatomyhwang@163.com. 3. School of Information Science and Engineering & Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, China. 4. Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China.
Abstract
BACKGROUND/AIMS: One of the most important impacts of personalized medicine is the connection between patients' genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. METHODS: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. RESULTS: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient's in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient's gene-expression profile. CONCLUSION: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.
BACKGROUND/AIMS: One of the most important impacts of personalized medicine is the connection between patients' genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. METHODS: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. RESULTS: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient's in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient's gene-expression profile. CONCLUSION: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.