10.6084/m9.figshare.7429829.v1 Huang H.-H. Huang H.-H. Dai J.-G. Dai J.-G. Liang Y. Liang Y. Supplementary Material for: Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method Karger Publishers 2018 Personalized medicine Drug response prediction Regularization Variable selection 2018-12-06 14:49:18 Dataset https://karger.figshare.com/articles/dataset/Supplementary_Material_for_Clinical_Drug_Response_Prediction_by_Using_a_Lq_Penalized_Network-Constrained_Logistic_Regression_Method/7429829 <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.