Abstract
Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.
Highlights
Elucidating the relationships between genetic alterations and cancer vulnerabilities is a major task for current cancer genome projects
The cancer genomic and drug response data used in this work are available from the Cancer Cell Line Encyclopedia (CCLE)
The mean of MRSs (PCC) by iterative sure independence screening (ISIS) is 0.1924, whereas that by simple top features (STF) is 0.4963, suggesting that the feature redundancy is significantly removed by ISIS compared with STF (p-value
Summary
Elucidating the relationships between genetic alterations and cancer vulnerabilities is a major task for current cancer genome projects. Cancers are induced by the accumulation of genetic alterations within a cell, including inherited genetic mutations chromosome translocations, and copy number alterations [1,2]. Association analysis between genetic alterations and anticancer drug sensitivity could provide new insights for biomarker discovery and drug sensitivity predictions. The huge diversity of different cancer types, even tumors from the same tissue, makes the above aim very challenging. Many efforts on elucidating biomarkers for some kinds of anticancer drugs have been seen in literatures ever since the outcome of high-throughput genomic technique, and most of them are based on expression profile data. Based on the same data, Riddick et al built an ensemble regression model using Random Forest [4], and Lee et al developed a co-expression extrapolation algorithm to infer drug signature by comparing differential gene expression
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