Translating data derived from cancer genomes into personalized cancer therapy is a holy grail of computational
biology. An important, yet challenging, question in this undertaking is to relate features of tumor cells to clinical outcomes of
anticancer drugs. Recent progress in large pharmacogenomic studies has provided a wealth of data about cancer cell lines,
indicating that many genetic and gene expression candidates might predict the drug response of cancer cells. Unfortunately,
most of the predicted features are inconsistent with current clinical knowledge and lack mutual dependencies that could
explain their molecular mode of action. To address this question, we have developed a new method, named dNetFS, to
prioritize genetic and gene expression features of cancer cell lines that predict drug response, by integrating
genomic/pharmaceutical data, protein-protein interaction network, and prior knowledge of drug-targets interaction with the
techniques of network propagation. Comparing with previous methods, dNetFS is more accurate in cross-validation analysis,
and it is able to reveal the key pathways involved in drug response. It therefore provides a basis to identify the underlying
molecular mechanism for a given compound in different genomic backgrounds.
Keywords: Cancer cell lines, drug sensitivity, pharmacogenomics data, machine learning, network propagation, precision medicine.
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