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Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Kim K, Yang W, Lee KS, Bang H, Jang K, Kim SC, Yang JO, Park S, Park K, Choi JK - Nucleic Acids Res. (2015)

Bottom Line: Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model.Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects.Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.

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Tumor subclass and drug response analysis based on the global transcription network. (A) Frequency of subclass-specific genes present in the GATA3 or FOXM1 pathways. (B) Connectivity between small molecules (columns) and responsible TFs (rows) computed based on the mapping of transcription response signatures to the network. Unsupervised clustering was performed using the normalized connection scores, leading to three major clusters, a anti-cancer cluster (red), an epigenetic-drugs cluster (green) and an estrogen-receptor cluster (orange). (C) Expression perturbation of gene under BRCA1 in the global transcription network according to the mutation status of BRCA1 (D) Relative frequency of subclass-specific genes present in the GATA3 pathway as a ratio to the FOXM1 pathway in the two test networks based on the complete TF priors or proximal TF priors. (E) Correlations of the drug-TF connections scores between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors. (F) Comparison of the connection scores for the selected proper drug-TF pairs (red, orange and green arrows in B) between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors.
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Figure 3: Tumor subclass and drug response analysis based on the global transcription network. (A) Frequency of subclass-specific genes present in the GATA3 or FOXM1 pathways. (B) Connectivity between small molecules (columns) and responsible TFs (rows) computed based on the mapping of transcription response signatures to the network. Unsupervised clustering was performed using the normalized connection scores, leading to three major clusters, a anti-cancer cluster (red), an epigenetic-drugs cluster (green) and an estrogen-receptor cluster (orange). (C) Expression perturbation of gene under BRCA1 in the global transcription network according to the mutation status of BRCA1 (D) Relative frequency of subclass-specific genes present in the GATA3 pathway as a ratio to the FOXM1 pathway in the two test networks based on the complete TF priors or proximal TF priors. (E) Correlations of the drug-TF connections scores between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors. (F) Comparison of the connection scores for the selected proper drug-TF pairs (red, orange and green arrows in B) between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors.

Mentions: We next performed clinical evaluations. Breast cancer can be classified based on the status of three receptor proteins as luminal A, luminal B, HER2-enriched, or basal-like. The genes that were differentially expressed between tumor and normal tissue in a specific subclass were mapped to our transcription network to identify upstream regulators. GATA3, FOXA1 and FOXM1 were identified as key subclass regulators (Supplementary Figure S7), in agreement with previous findings based on annotated pathways (24). Because FOXA1 is a direct descendant of GATA3 in the network, we only used GATA3 as a representative regulator. The percentage of the subclass-specific descendants of GATA3 or FOXM1 was quantitatively correlated with the expected prognosis of the four subtypes (Figure 3A). For example, the highest percentage of the basal-like genes was specifically related to FOXM1, highlighting the role of this regulator in contributing to the aggressive nature of this subtype.


Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Kim K, Yang W, Lee KS, Bang H, Jang K, Kim SC, Yang JO, Park S, Park K, Choi JK - Nucleic Acids Res. (2015)

Tumor subclass and drug response analysis based on the global transcription network. (A) Frequency of subclass-specific genes present in the GATA3 or FOXM1 pathways. (B) Connectivity between small molecules (columns) and responsible TFs (rows) computed based on the mapping of transcription response signatures to the network. Unsupervised clustering was performed using the normalized connection scores, leading to three major clusters, a anti-cancer cluster (red), an epigenetic-drugs cluster (green) and an estrogen-receptor cluster (orange). (C) Expression perturbation of gene under BRCA1 in the global transcription network according to the mutation status of BRCA1 (D) Relative frequency of subclass-specific genes present in the GATA3 pathway as a ratio to the FOXM1 pathway in the two test networks based on the complete TF priors or proximal TF priors. (E) Correlations of the drug-TF connections scores between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors. (F) Comparison of the connection scores for the selected proper drug-TF pairs (red, orange and green arrows in B) between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4499150&req=5

Figure 3: Tumor subclass and drug response analysis based on the global transcription network. (A) Frequency of subclass-specific genes present in the GATA3 or FOXM1 pathways. (B) Connectivity between small molecules (columns) and responsible TFs (rows) computed based on the mapping of transcription response signatures to the network. Unsupervised clustering was performed using the normalized connection scores, leading to three major clusters, a anti-cancer cluster (red), an epigenetic-drugs cluster (green) and an estrogen-receptor cluster (orange). (C) Expression perturbation of gene under BRCA1 in the global transcription network according to the mutation status of BRCA1 (D) Relative frequency of subclass-specific genes present in the GATA3 pathway as a ratio to the FOXM1 pathway in the two test networks based on the complete TF priors or proximal TF priors. (E) Correlations of the drug-TF connections scores between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors. (F) Comparison of the connection scores for the selected proper drug-TF pairs (red, orange and green arrows in B) between the full-scale full-prior network and the two test networks based on the complete or proximal TF priors.
Mentions: We next performed clinical evaluations. Breast cancer can be classified based on the status of three receptor proteins as luminal A, luminal B, HER2-enriched, or basal-like. The genes that were differentially expressed between tumor and normal tissue in a specific subclass were mapped to our transcription network to identify upstream regulators. GATA3, FOXA1 and FOXM1 were identified as key subclass regulators (Supplementary Figure S7), in agreement with previous findings based on annotated pathways (24). Because FOXA1 is a direct descendant of GATA3 in the network, we only used GATA3 as a representative regulator. The percentage of the subclass-specific descendants of GATA3 or FOXM1 was quantitatively correlated with the expected prognosis of the four subtypes (Figure 3A). For example, the highest percentage of the basal-like genes was specifically related to FOXM1, highlighting the role of this regulator in contributing to the aggressive nature of this subtype.

Bottom Line: Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model.Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects.Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.

Show MeSH
Related in: MedlinePlus