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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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Schematic model of mammary gland cell populationsIn this model we show the predominant expression of cluster 1 versus cluster 2 risk-TFs with respect to the cell populations found in the mammary gland and the cancer subtypes that arise from them. In the normal mammary gland all three populations have self-renewal capacity. Claudinlow tumours were originally classified as basal in the PAM signature, but are likely to represent a separate lineage arising from myoepithelial cells40. Basal-like cancer is thought to arise from alveolar progenitor cells, (The somewhat misleading term ‘basal-like’ reflects the fact these tumours not only express epithelial, but also mesenchymal cell surface markers that are also highly expressed in the myoepithelial lineage located near the basal membrane.) and luminal A/B cancer from ER+ precursors.
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Figure 8: Schematic model of mammary gland cell populationsIn this model we show the predominant expression of cluster 1 versus cluster 2 risk-TFs with respect to the cell populations found in the mammary gland and the cancer subtypes that arise from them. In the normal mammary gland all three populations have self-renewal capacity. Claudinlow tumours were originally classified as basal in the PAM signature, but are likely to represent a separate lineage arising from myoepithelial cells40. Basal-like cancer is thought to arise from alveolar progenitor cells, (The somewhat misleading term ‘basal-like’ reflects the fact these tumours not only express epithelial, but also mesenchymal cell surface markers that are also highly expressed in the myoepithelial lineage located near the basal membrane.) and luminal A/B cancer from ER+ precursors.

Mentions: The most striking aspect of cluster 1 and 2 TFs is the opposing regulatory effect they exert on their target genes. We postulate that this mutually exclusive activity reflects the decision of a progenitor to commit to either an ER+ ductal or an ER− alveolar cell fate. In line with this hypothesis we find that in primary human mammary cell populations18, those representative of ER− alveolar progenitors show differential upregulation of cluster 2 TFs, whilst ER+ luminal cells display higher expression of cluster 1 TFs (Fig. 8). Recent genetic tracing experiments have shown that the ER+ ductal progenitors and ER− alveolar progenitors are self-renewing in the mouse mammary gland35,36,37. The differential expression of risk-TFs in these two self-renewing populations may suggest that these are the populations where risk genes are effective and cell transformation occurs. In line with this, transcriptional profiles of basal-like tumours most resemble that of ER− alveolar progenitors38,39, while luminal A and B tumours phenocopy ER+ ductal cells39-41,18. Furthermore, the ER− alveolar progenitor population is expanded in BRCA1 mutation carriers39, which are predisposed to develop ER− breast cancer.


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)

Schematic model of mammary gland cell populationsIn this model we show the predominant expression of cluster 1 versus cluster 2 risk-TFs with respect to the cell populations found in the mammary gland and the cancer subtypes that arise from them. In the normal mammary gland all three populations have self-renewal capacity. Claudinlow tumours were originally classified as basal in the PAM signature, but are likely to represent a separate lineage arising from myoepithelial cells40. Basal-like cancer is thought to arise from alveolar progenitor cells, (The somewhat misleading term ‘basal-like’ reflects the fact these tumours not only express epithelial, but also mesenchymal cell surface markers that are also highly expressed in the myoepithelial lineage located near the basal membrane.) and luminal A/B cancer from ER+ precursors.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 8: Schematic model of mammary gland cell populationsIn this model we show the predominant expression of cluster 1 versus cluster 2 risk-TFs with respect to the cell populations found in the mammary gland and the cancer subtypes that arise from them. In the normal mammary gland all three populations have self-renewal capacity. Claudinlow tumours were originally classified as basal in the PAM signature, but are likely to represent a separate lineage arising from myoepithelial cells40. Basal-like cancer is thought to arise from alveolar progenitor cells, (The somewhat misleading term ‘basal-like’ reflects the fact these tumours not only express epithelial, but also mesenchymal cell surface markers that are also highly expressed in the myoepithelial lineage located near the basal membrane.) and luminal A/B cancer from ER+ precursors.
Mentions: The most striking aspect of cluster 1 and 2 TFs is the opposing regulatory effect they exert on their target genes. We postulate that this mutually exclusive activity reflects the decision of a progenitor to commit to either an ER+ ductal or an ER− alveolar cell fate. In line with this hypothesis we find that in primary human mammary cell populations18, those representative of ER− alveolar progenitors show differential upregulation of cluster 2 TFs, whilst ER+ luminal cells display higher expression of cluster 1 TFs (Fig. 8). Recent genetic tracing experiments have shown that the ER+ ductal progenitors and ER− alveolar progenitors are self-renewing in the mouse mammary gland35,36,37. The differential expression of risk-TFs in these two self-renewing populations may suggest that these are the populations where risk genes are effective and cell transformation occurs. In line with this, transcriptional profiles of basal-like tumours most resemble that of ER− alveolar progenitors38,39, while luminal A and B tumours phenocopy ER+ ductal cells39-41,18. Furthermore, the ER− alveolar progenitor population is expanded in BRCA1 mutation carriers39, which are predisposed to develop ER− breast cancer.

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