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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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Effects of risk-TF knock-down on cell proliferationGrowth curves for (a) ER− cell line MCF10A and (b) the ER+ cell line ZR751 after transient transfection of the siRNAs as indicated. Cells transfected with a scrambled siRNA were included as a control. Error bars depict the standard error of the mean of 8 wells each in a minimum of two independent experiments (methods). The statistical analysis (insets) compares the growth curves using 100,000 simulations, with p-values adjusted by the BY correction method.
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Figure 6: Effects of risk-TF knock-down on cell proliferationGrowth curves for (a) ER− cell line MCF10A and (b) the ER+ cell line ZR751 after transient transfection of the siRNAs as indicated. Cells transfected with a scrambled siRNA were included as a control. Error bars depict the standard error of the mean of 8 wells each in a minimum of two independent experiments (methods). The statistical analysis (insets) compares the growth curves using 100,000 simulations, with p-values adjusted by the BY correction method.

Mentions: We examined the effect of siRNA knock-down of cluster 2 risk-TFs (NFIB, YBX1, CBFB and TBX19) in the ER− (MCF10A) and ER+ (ZR751) cell lines. In MCF10A cells, siRNA-targeting of YBX1 strongly reduced proliferation (Fig. 6a), and targeting CBFB, NFIB, TBX19 and LMO4 (Fig. 6a, Supplementary Fig. 20) all had a significant anti-proliferative effect. In contrast, repression of the cluster 2 TFs in ZR751 cells had either no or little effect on proliferation, whilst repression of FOXA1 strongly inhibited growth (Fig. 6b). Interestingly in ZR751 cells siNFIB led to a slight, but significant increase in proliferation, in keeping with the hypothesis that members of the two clusters have opposing effects.


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)

Effects of risk-TF knock-down on cell proliferationGrowth curves for (a) ER− cell line MCF10A and (b) the ER+ cell line ZR751 after transient transfection of the siRNAs as indicated. Cells transfected with a scrambled siRNA were included as a control. Error bars depict the standard error of the mean of 8 wells each in a minimum of two independent experiments (methods). The statistical analysis (insets) compares the growth curves using 100,000 simulations, with p-values adjusted by the BY correction method.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 6: Effects of risk-TF knock-down on cell proliferationGrowth curves for (a) ER− cell line MCF10A and (b) the ER+ cell line ZR751 after transient transfection of the siRNAs as indicated. Cells transfected with a scrambled siRNA were included as a control. Error bars depict the standard error of the mean of 8 wells each in a minimum of two independent experiments (methods). The statistical analysis (insets) compares the growth curves using 100,000 simulations, with p-values adjusted by the BY correction method.
Mentions: We examined the effect of siRNA knock-down of cluster 2 risk-TFs (NFIB, YBX1, CBFB and TBX19) in the ER− (MCF10A) and ER+ (ZR751) cell lines. In MCF10A cells, siRNA-targeting of YBX1 strongly reduced proliferation (Fig. 6a), and targeting CBFB, NFIB, TBX19 and LMO4 (Fig. 6a, Supplementary Fig. 20) all had a significant anti-proliferative effect. In contrast, repression of the cluster 2 TFs in ZR751 cells had either no or little effect on proliferation, whilst repression of FOXA1 strongly inhibited growth (Fig. 6b). Interestingly in ZR751 cells siNFIB led to a slight, but significant increase in proliferation, in keeping with the hypothesis that members of the two clusters have opposing effects.

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