Abstract
Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.
Highlights
Benefiting from the development of high-throughput sequencing technology and the improvement of bioinformatics, the precision medicine has become a novel and burgeoning concept.[1]
Based on the predicted and observed response data, we calculated the Pearson correlation coefficient (PCC) and root-mean-square error (RMSE) for each drug to estimate the capability of weighted graph regularized matrix factorization (WGRMF) on predicting drug response in cell lines
The PCC value indicates the extent of correlation between the predicted and observed response profiles of a drug, which could be formulated as PCC = rffiPffiffiffiffiPffiniffi=dffiffi1ffiniffiðffi=dffirffi1ffiiffiðffiÀffirffiiffiffiffiÀrffiffiÞffi2ffirffiPffiÞffiffiffiffiniffibrffi=dffiiffi1ffiÀffiffiffibffirffibrffiiffiffiÀffiffiffiffiffibrffiffiffiffiffi2ffi; (Equation 1)
Summary
Benefiting from the development of high-throughput sequencing technology and the improvement of bioinformatics, the precision medicine has become a novel and burgeoning concept.[1]. Many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects.[5,6,7] As the basis of in-depth researches, tremendous genomic and pharmacological data have been collected and categorized in large scale for diverse cancer cell lines.[4,8,9,10,11,12] The consequential mission is to develop powerful methods to extract useful information from those complicated datasets and find the connections between the cancer information and the drug response
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