B279: Regulators of breast cancer risk identified by integrative network analysis

Mauro Castro1,2,Ines de Santiago1,Thomas Campbell1,Courtney Vaughn1,Florian Markowetz1,Bruce Ponder1,Kerstin Meyer1

1Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK,2Bioinformatics & Systems Biology Lab, Federal University of Paraná, 1225 Curitiba, Brazil

Presenting date: Tuesday 3 November
Presenting time: 12.20-13.10

Background

 

About 90 breast cancer risk loci have so far been identified. Hundreds more loci are likely to make small contributions to risk. However, it is still poorly understood how these loci combine to influence risk at the cellular level, how heterogeneous the possible mechanisms are, and whether the same processes are affected by germline and somatic events.

Method

To address these questions, we created a breast cancer gene regulatory network between transcription factors (TFs) and putative target genes (regulon). We asked whether specific regulons are enriched for genes associated with the known GWAS loci, and if so, how these regulons map within the network.

Results

We identified 36 overlapping regulons that were enriched for an association with GWAS loci and formed a distinct cluster within the network. The TFs controlling these regulons are frequently mutated in cancer and we refer to these as risk-TFs. ‘Guilt by association’ suggests that genes in these regulons may form a disease module for breast cancer, affected by both germline and somatic variation. Starting our analysis from GWAs loci, suggests a causal link between the identified risk-TF and disease etiology. We find that risk TFs lie in two opposing subgroups, which relate to ER+ luminal A/B and to ER- basal-like cancers and to different self-renewing, luminal epithelial cell populations in the adult mammary gland, potentially identifying the cell types in which risk genes mediate their effect. Furthermore the identification of the two opposing risk clusters has lead to the identification of novel regulators of the ER? receptor, a key player in breast cancer risk.

Conclusion

 

Our approach provides a foundation for an integrated model of how germline and somatic genetic events combine to influence cancer development, to identify targets for intervention, and to evaluate the cellular effects of those interventions.

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