Supplementary Material for: Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method
2018-12-06T14:49:18Z (GMT) by
<b><i>Background/Aims:</i></b> 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. <b><i>Methods:</i></b> Here we present the L<i>q</i> penalized network-constrained logistic regression (L<i>q</i>-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 <i>in vivo</i> drug sensitivity prediction. <b><i>Results:</i></b> 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 <i>in vitro</i> drug response and patient’s <i>in vivo</i> 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. <b><i>Conclusion:</i></b> The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.