Abstract
BackgroundIndividualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.MethodsIn this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.ResultsTotally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response.ConclusionsTaken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.
Highlights
Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward
Inferring patient‐specific subpathway activities at different omics levels To weight each of these selected subpathways in training set, we introduced a measure to infer where n is the number of genes in the subpathway and N is the number of all genes detected in sample p. Xi represents the expression value (CNV and methylation value respectively) of the gene i in sample p. βi represents the estimated regression coefficient of gene i in the univariate logistic regression model. σp is the standard deviation of all the genes values Xi multiplying βi in sample p
The performance of subpathway signatures for predicting individualized drug response We applied our method to five anticancer drug response datasets
Summary
Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Zhang et al [8] presented a method to identify significantly associated biomarkers and developed ordinal genomic classifier using the hierarchical ordinal logistic model for predicting drug response. He et al [11] provided a comprehensive review of the clinical relevance of CNVs to drug efficacy. Whereas the entire pathway is often too large to accurately interpret relevant pathological phenomena, a pivotal subpathway region representative of the corresponding entire pathway may be more effective and sensitive for dissecting the related phenomena [22, 23] These existing methods mainly focused on only single omics data. A few large scale cancer genome projects provide diverse molecular data and drug response information of cancer patients such as The Cancer Genome Atlas (TCGA) (https://gdc-portal.nci. nih.gov/) and cancer cell lines [24], which provide new opportunities to identify signatures for individualized drug response prediction
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