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Title: Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

Journal Article · · Genes
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  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. National Lab. for Cancer Research, Frederick, MD (United States)
  3. National Cancer Inst., Bethesda, MD (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
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The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.

Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE; National Institutes of Health (NIH)
Grant/Contract Number:
AC02-06CH11357; AC52-07NA27344; AC52-06NA25396; AC05-00OR22725; JDACS4C; HHSN261200800001E
OSTI ID:
1757985
Journal Information:
Genes, Vol. 11, Issue 9; ISSN 2073-4425
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

60 APPLIED LIFE SCIENCES
general drug response prediction model
gene selection
co-expression extrapolation (COXEN)
precision oncology