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Genes (Basel). 2019 Aug 9;10(8). pii: E602. doi: 10.3390/genes10080602.

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.

Liu Q1,2, Muglia LJ2,3, Huang LF4,5,6.

Author information

1
Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
2
Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45229, USA.
3
Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
4
Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA. Frank.Huang@cchmc.org.
5
Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45229, USA. Frank.Huang@cchmc.org.
6
Department of Information Science, School of Mathematical Sciences and LAMA, Peking University, Beijing 100871, China. Frank.Huang@cchmc.org.

Abstract

With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover reliable and accurate molecular network-based biomarkers for monitoring cancer treatment. Such efforts will also pave the way toward the realization of biomarker-driven personalized medicine against cancer. Previously, we have reconstructed disease-specific driver signaling networks using multi-omics profiles and cancer signaling pathway data. In this study, we developed a network-based sparse Bayesian machine (NBSBM) approach, using previously derived disease-specific driver signaling networks to predict cancer cell responses to drugs. NBSBM made use of the information encoded in a disease-specific (differentially expressed) network to improve its prediction performance in problems with a reduced amount of training data and a very high-dimensional feature space. Sparsity in NBSBM is favored by a spike and slab prior distribution, which is combined with a Markov random field prior that encodes the network of feature dependencies. Gene features that are connected in the network are assumed to be both relevant and irrelevant to drug responses. We compared the proposed method with network-based support vector machine (NBSVM) approaches and found that the NBSBM approach could achieve much better accuracy than the other two NBSVM methods. The gene modules selected from the disease-specific driver networks for predicting drug sensitivity might be directly involved in drug sensitivity or resistance. This work provides a disease-specific network-based drug sensitivity prediction approach and can uncover the potential mechanisms of the action of drugs by selecting the most predictive sub-networks from the disease-specific network.

KEYWORDS:

cancer signaling pathway; disease-specific driver signaling network; drug resistance; drug sensitivity; network-based sparse Bayesian machine

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