title: Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome.
abstract: 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.
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.|Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, United States of America.
authors.forename: Erik
authors.initials: E
authors.lastname: Andrews
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.|School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Tian
authors.initials: T
authors.lastname: Xia
authors.affiliation: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China.
authors.forename: Wenqing
authors.initials: W
authors.lastname: Cheng
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.|Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, United States of America.|Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, United States of America.
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism; D066289:Data Curation; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D011379:Prognosis