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Recovering drug-induced apoptosis subnetwork from Connectivity Map data.

Yu J, Putcha P, Silva JM - Biomed Res Int (2015)

Bottom Line: We then 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 the 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Precision Medicine, Oncology Research Unit, Pfizer Inc., Pearl River, NY 10965, USA.

ABSTRACT
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. Since the therapeutic goal of most anticancer drugs is 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 CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then 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 the 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.

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Marginal distributions of the 13 selected drug-responsive apoptotic genes across all samples in CMAP data.
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fig4: Marginal distributions of the 13 selected drug-responsive apoptotic genes across all samples in CMAP data.

Mentions: A Bayesian network represents the dependence structure of a joint probability distribution of multiple variables, which can be factorized into a product of distributions of each individual node conditioning on its parents. To model the local distribution of each node conditioned on its parents, a commonly used method for continuous data is to discretize data points into bins and then fit a multinomial distribution to the discretized data. However, data discretization results in a loss of information and can be highly sensitive to the number of bins the data is split into. Furthermore, due to the continuous nature of microarray data and the marginal normality of many genes in this study as shown in Figure 4, we determined it would be more accurate to employ a continuous model. We therefore used a conditional linear Gaussian model [23] for the local distribution of each node as shown below:(1)gi ∣ parentsgi,β,αi,σi2,G ~N∑jβi∗parentjgi+αi,σi2.


Recovering drug-induced apoptosis subnetwork from Connectivity Map data.

Yu J, Putcha P, Silva JM - Biomed Res Int (2015)

Marginal distributions of the 13 selected drug-responsive apoptotic genes across all samples in CMAP data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389823&req=5

fig4: Marginal distributions of the 13 selected drug-responsive apoptotic genes across all samples in CMAP data.
Mentions: A Bayesian network represents the dependence structure of a joint probability distribution of multiple variables, which can be factorized into a product of distributions of each individual node conditioning on its parents. To model the local distribution of each node conditioned on its parents, a commonly used method for continuous data is to discretize data points into bins and then fit a multinomial distribution to the discretized data. However, data discretization results in a loss of information and can be highly sensitive to the number of bins the data is split into. Furthermore, due to the continuous nature of microarray data and the marginal normality of many genes in this study as shown in Figure 4, we determined it would be more accurate to employ a continuous model. We therefore used a conditional linear Gaussian model [23] for the local distribution of each node as shown below:(1)gi ∣ parentsgi,β,αi,σi2,G ~N∑jβi∗parentjgi+αi,σi2.

Bottom Line: We then 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 the 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Precision Medicine, Oncology Research Unit, Pfizer Inc., Pearl River, NY 10965, USA.

ABSTRACT
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. Since the therapeutic goal of most anticancer drugs is 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 CMAP microarray data from breast cancer cell line MCF7, we applied a Gaussian Bayesian network modeling approach and identified apoptosis as a major drug-induced cellular-pathway. We then 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 the 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.

Show MeSH
Related in: MedlinePlus