Bayesian Network to Infer Drug-Induced Apoptosis Circuits from Connectivity Map Data

  • Jiyang Yu
  • Jose M. Silva
Part of the Methods in Molecular Biology book series (MIMB, volume 1783)


The Connectivity Map (CMAP) project profiled human cancer cell lines exposed to a library of anticancer compounds with the goal of connecting cancer with underlying genes and potential treatments. As most targeted anticancer therapeutics aim to induce tumor-selective apoptosis, it is critical to understand the specific cell death pathways triggered by drugs. This can help to better understand the mechanism of how cancer cells respond to chemical stimulations and improve the treatment of human tumors. In this study, using Connectivity MAP microarray-based gene expression data, we applied a Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular pathway. We focused on 13 apoptotic genes that showed significant differential expression across all drug-perturbed samples to reconstruct the apoptosis network. In our predicted subnetwork, 9 out of 15 high-confidence interactions were validated in literature, and our inferred network captured two major cell death pathways by identifying BCL2L11 and PMAIP1 as key interacting players for the intrinsic apoptosis pathway, and TAXBP1 and TNFAIP3 for the extrinsic apoptosis pathway. Our inferred apoptosis network also suggested the role of BCL2L11 and TNFAIP3 as “gateway” genes in the drug-induced intrinsic and extrinsic apoptosis pathways.

Key words

Microarray Gene expression Drug discovery Bayesian network CMAP Apoptosis Cancer 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computational BiologySt. Jude Children’s Research HospitalMemphisUSA
  2. 2.Department of Pathology, Icahn School of Medicine at Mount SinaiThe Mount Sinai HospitalNew YorkUSA

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