An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

Abstract

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

“…Secondly, we included ICU patients for analysis, which enhanced the heterogeneity of the study population, and thus our results may not be suitable for patients outside the ICU. Third, there are a lot of more widely used methods in feature selection and classification than Lasso, such as elastic net, random forest, and deep neural network ( Huang et al, 2017 ; He et al,2020a,b ; Liang et al, 2020 ; Liu C. et al, 2020 ; Yang J. et al, 2020 ). Model development only uses the general linear regression method, fusing various biological information by multi-information fusion ( Peng et al, 2017 ), bipartite local model ( Peng et al, 2020b ), and the KATZ method ( Zhou et al, 2020 ) should be further studied in the future.…”

A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

“…Very little is known about the pathogenesis of most types of cancers, including liver cancer [ 6 – 8 ]. In the past few decades, about 130-180 anticancer drugs have been approved by the US FDA for use in clinical treatment [ 9 ]. Despite the increasing research on cancer drugs using model species (supported by nonhuman data) and nearly 1,000 drugs having been formulated using combinations of FDA-approved anticancer drugs, there is still uncertainty about the efficacy of these drugs in the treatment of cancer due to insufficient understanding of the molecular mechanisms of the disease [ 10 – 13 ].…”

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

“…The publicly accessible gene expression profiles currently include Connectivity Map (CMap, http://www.broadinstitute.org/ cmap), National Cancer Institute 60 human tumor cell line anticancer drug discovery project (NCI-60 http://dtp.nci.nih.gov/), Library of Integrated Cellular Signatures (LINCS http://www. lincsproject.org/), and Cancer Cell Line Encyclopedia (CCLE http://www.broadinstitute.org/ccle) (2,52).…”

“…Drug repositioning is a new way of applying existing therapeutics to new disease indications. Compared with traditional new drug development methods, the advantage of drug repositioning is that it can reduce the time and cost of drug development, and the drug composition has been proven to be safe in human body, so phase I clinical trials can be skipped (1,2).…”