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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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(a) Top enriched (Pvalue < 0.001, FDR < 0.1) GO BP terms by top differentially expressed genes (FDR < 0.05) with apoptosis-related processes highlighted in red; summary of (b) Fisher's Exact Test and (c) Gene Set Enrichment Analysis (GSEA) to test whether apoptosis pathway with 368 apoptotic genes is enriched in drug-induced signature genes. For GSEA method, absolute mean was used to summarize the enrichment and 10,000 gene permutations were used to produce the significant level.
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fig2: (a) Top enriched (Pvalue < 0.001, FDR < 0.1) GO BP terms by top differentially expressed genes (FDR < 0.05) with apoptosis-related processes highlighted in red; summary of (b) Fisher's Exact Test and (c) Gene Set Enrichment Analysis (GSEA) to test whether apoptosis pathway with 368 apoptotic genes is enriched in drug-induced signature genes. For GSEA method, absolute mean was used to summarize the enrichment and 10,000 gene permutations were used to produce the significant level.

Mentions: As described previously, one of the most important mechanisms induced by oncotherapeutics is cell death programs. More specifically, we hypothesized that the apoptosis pathway may be a major drug-induced program. Enrichment analysis was proposed to validate this hypothesis. Indeed functional enrichment analysis by DAVID [16] confirmed that apoptosis or cell death pathway is a major biological process triggered by anticancer compounds as half of top enriched GO BP terms (FDR < 0.1, P < 0.001) by drug-responsive signature genes are associated with apoptosis (Figure 2(a)). Furthermore, by searching the Gene Ontology database [17], we obtained a list of 380 human genes that were annotated with apoptosis-related GO terms (Table S2). 211 genes were annotated as proapoptotic by induction of apoptosis, positive regulation of apoptosis, and negative regulation of antiapoptosis. 194 genes were annotated as antiapoptotic by negative regulation of apoptosis and positive regulation of antiapoptosis. 25 genes were involved in both positive and negative regulation of apoptosis. We then performed enrichment analysis with differentially expressed genes of drug-perturbation in the apoptosis pathway. Two methods were employed to do this analysis: the first method was Fisher's exact test to validate whether known apoptotic genes were overrepresented in a selected differentially expressed drug-responsive gene set. The second method was to test the known apoptotic genes using Gene Set Enrichment Analysis (GSEA), which does not perform a selection on differentially expressed genes, but instead it considers the entire set of genes and their differential expression as the background. For Fisher's exact test, a set of previously identified 191 signature genes with a threshold of FDR < 0.05 and all 12,632 genes in the microarray were used to fit the hypergeometric distribution. For GSEA, the mean of absolute value of differential expression was used as enrichment score because apoptotic genes could be either up- or downregulated in drug-perturbed samples. The significance of the enrichment scores was tested against 10,000 permutations of gene names.


Recovering drug-induced apoptosis subnetwork from Connectivity Map data.

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

(a) Top enriched (Pvalue < 0.001, FDR < 0.1) GO BP terms by top differentially expressed genes (FDR < 0.05) with apoptosis-related processes highlighted in red; summary of (b) Fisher's Exact Test and (c) Gene Set Enrichment Analysis (GSEA) to test whether apoptosis pathway with 368 apoptotic genes is enriched in drug-induced signature genes. For GSEA method, absolute mean was used to summarize the enrichment and 10,000 gene permutations were used to produce the significant level.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: (a) Top enriched (Pvalue < 0.001, FDR < 0.1) GO BP terms by top differentially expressed genes (FDR < 0.05) with apoptosis-related processes highlighted in red; summary of (b) Fisher's Exact Test and (c) Gene Set Enrichment Analysis (GSEA) to test whether apoptosis pathway with 368 apoptotic genes is enriched in drug-induced signature genes. For GSEA method, absolute mean was used to summarize the enrichment and 10,000 gene permutations were used to produce the significant level.
Mentions: As described previously, one of the most important mechanisms induced by oncotherapeutics is cell death programs. More specifically, we hypothesized that the apoptosis pathway may be a major drug-induced program. Enrichment analysis was proposed to validate this hypothesis. Indeed functional enrichment analysis by DAVID [16] confirmed that apoptosis or cell death pathway is a major biological process triggered by anticancer compounds as half of top enriched GO BP terms (FDR < 0.1, P < 0.001) by drug-responsive signature genes are associated with apoptosis (Figure 2(a)). Furthermore, by searching the Gene Ontology database [17], we obtained a list of 380 human genes that were annotated with apoptosis-related GO terms (Table S2). 211 genes were annotated as proapoptotic by induction of apoptosis, positive regulation of apoptosis, and negative regulation of antiapoptosis. 194 genes were annotated as antiapoptotic by negative regulation of apoptosis and positive regulation of antiapoptosis. 25 genes were involved in both positive and negative regulation of apoptosis. We then performed enrichment analysis with differentially expressed genes of drug-perturbation in the apoptosis pathway. Two methods were employed to do this analysis: the first method was Fisher's exact test to validate whether known apoptotic genes were overrepresented in a selected differentially expressed drug-responsive gene set. The second method was to test the known apoptotic genes using Gene Set Enrichment Analysis (GSEA), which does not perform a selection on differentially expressed genes, but instead it considers the entire set of genes and their differential expression as the background. For Fisher's exact test, a set of previously identified 191 signature genes with a threshold of FDR < 0.05 and all 12,632 genes in the microarray were used to fit the hypergeometric distribution. For GSEA, the mean of absolute value of differential expression was used as enrichment score because apoptotic genes could be either up- or downregulated in drug-perturbed samples. The significance of the enrichment scores was tested against 10,000 permutations of gene names.

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