Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR.Author Summary: Gene expression regulators, such as transcription factors and microRNAs, are critical actors in cellular physiology and pathophysiology and act by modulating the expression levels of sets of target genes. Given their significance, numerous experiments have sought to characterize the specific target genes of specific regulators, which in turn has led to regulator target gene list databases. Unfortunately, these lists are plagued by poor curation and validation. Further, all lists suffer from the fundamental issue that regulator targets vary across tissue type and physiological state, or “context”, making them poor for conducting downstream, context-specific analyses. To address this issue, here we present a method called icTAIR that contextually-refines regulator target gene lists. To demonstrate its value, we use icTAIR to take the largest-available database of regulator target gene lists, refine it for the breast cancer context, and use both the pre-refined and refined lists for downstream survival analyses in over 3,400 tumors. We find that icTAIR improves the statistical power of the analyses by multiple orders of magnitude. This in turn lets us map the relational network of breast cancer regulators and identify regulators with prognostic effects even after clinicopathological adjustment. We anticipate icTAIR will be broadly useful in regulator studies.