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Regulators of genetic risk of breast cancer identified by integrative network analysis.

Castro MA, de Santiago I, Campbell TM, Vaughn C, Hickey TE, Ross E, Tilley WD, Markowetz F, Ponder BA, Meyer KB - Nat. Genet. (2015)

Bottom Line: To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms.We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology.The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Systems Biology Laboratory, Federal University of Paraná (UFPR), Polytechnic Center, Curitiba, Brazil.

ABSTRACT
Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

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Related in: MedlinePlus

Regulatory network for breast cancer, showing clustering of breast cancer riskThe network is depicted on the basis of the overlap of regulons, with risk association shown in yellow to red (based on cohort I of METABRIC). The 36 consensus risk-TFs identified in both cohorts are labelled. The colouring of the edges (shown in light green to blue) indicates the overlap as measured by the Jaccard coefficient (JC) and the size of circles represents the size of each regulon. Only regulons with JC ≥ 0.4 are shown in the diagram. All regulons and a heatmap depicting the overlap of the regulons of risk-TFs is shown in Supplementary Figure 13. BCa risk: Bonferroni adjusted p-values obtained for each regulon when calculating the enrichment with breast cancer GWAS loci in the EVSE analysis, using cohort I for calculating the network and eQTLs. P-values are based on  distributions from 1,000 random AVSs.
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Figure 3: Regulatory network for breast cancer, showing clustering of breast cancer riskThe network is depicted on the basis of the overlap of regulons, with risk association shown in yellow to red (based on cohort I of METABRIC). The 36 consensus risk-TFs identified in both cohorts are labelled. The colouring of the edges (shown in light green to blue) indicates the overlap as measured by the Jaccard coefficient (JC) and the size of circles represents the size of each regulon. Only regulons with JC ≥ 0.4 are shown in the diagram. All regulons and a heatmap depicting the overlap of the regulons of risk-TFs is shown in Supplementary Figure 13. BCa risk: Bonferroni adjusted p-values obtained for each regulon when calculating the enrichment with breast cancer GWAS loci in the EVSE analysis, using cohort I for calculating the network and eQTLs. P-values are based on distributions from 1,000 random AVSs.

Mentions: To examine whether the different risk-TFs converge on common mechanisms, we used ARACNe to calculate the breast cancer regulatory network and mapped onto this network the p-values for risk-association (shown in orange to red) using METABRIC cohort I. The network was visualised by the degree of overlap of regulons (Fig. 3, Supplementary Fig. 13). The enriched regulons mostly cluster together, suggesting that the risk-TFs share biological function.


Regulators of genetic risk of breast cancer identified by integrative network analysis.

Castro MA, de Santiago I, Campbell TM, Vaughn C, Hickey TE, Ross E, Tilley WD, Markowetz F, Ponder BA, Meyer KB - Nat. Genet. (2015)

Regulatory network for breast cancer, showing clustering of breast cancer riskThe network is depicted on the basis of the overlap of regulons, with risk association shown in yellow to red (based on cohort I of METABRIC). The 36 consensus risk-TFs identified in both cohorts are labelled. The colouring of the edges (shown in light green to blue) indicates the overlap as measured by the Jaccard coefficient (JC) and the size of circles represents the size of each regulon. Only regulons with JC ≥ 0.4 are shown in the diagram. All regulons and a heatmap depicting the overlap of the regulons of risk-TFs is shown in Supplementary Figure 13. BCa risk: Bonferroni adjusted p-values obtained for each regulon when calculating the enrichment with breast cancer GWAS loci in the EVSE analysis, using cohort I for calculating the network and eQTLs. P-values are based on  distributions from 1,000 random AVSs.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4697365&req=5

Figure 3: Regulatory network for breast cancer, showing clustering of breast cancer riskThe network is depicted on the basis of the overlap of regulons, with risk association shown in yellow to red (based on cohort I of METABRIC). The 36 consensus risk-TFs identified in both cohorts are labelled. The colouring of the edges (shown in light green to blue) indicates the overlap as measured by the Jaccard coefficient (JC) and the size of circles represents the size of each regulon. Only regulons with JC ≥ 0.4 are shown in the diagram. All regulons and a heatmap depicting the overlap of the regulons of risk-TFs is shown in Supplementary Figure 13. BCa risk: Bonferroni adjusted p-values obtained for each regulon when calculating the enrichment with breast cancer GWAS loci in the EVSE analysis, using cohort I for calculating the network and eQTLs. P-values are based on distributions from 1,000 random AVSs.
Mentions: To examine whether the different risk-TFs converge on common mechanisms, we used ARACNe to calculate the breast cancer regulatory network and mapped onto this network the p-values for risk-association (shown in orange to red) using METABRIC cohort I. The network was visualised by the degree of overlap of regulons (Fig. 3, Supplementary Fig. 13). The enriched regulons mostly cluster together, suggesting that the risk-TFs share biological function.

Bottom Line: To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms.We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology.The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Systems Biology Laboratory, Federal University of Paraná (UFPR), Polytechnic Center, Curitiba, Brazil.

ABSTRACT
Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

Show MeSH
Related in: MedlinePlus