Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.
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.
Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.Show MeSH
Related in: MedlinePlus
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.
Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.