REACTIN: Regulatory activity inference of transcription factors underlying human diseases with application to breast cancer

Abstract

BackgroundGenetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.ResultsHere we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.ConclusionsREACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

“…We have previously developed a method to identify transcriptional regulatory programs that are predictive of cancer prognosis (29, 30). A regulatory program consists of transcription factors and its target genes identified by ChIP-chip or ChIP-seq experiments (31, 32).…”

“…Given a cancer gene expression dataset, we apply a method called Binding Association with Sorted Expression (BASE; ref. 33) to infer the regulatory activities of the transcription factors in all samples, and then examine their correlation with clinical outcomes of patients using the Cox regression model (30). In contrast to multigene prognostic signatures identified by supervised models, regulatory programs are defined on the basis of prior knowledge learned from ChIP-seq/chip data.…”

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

“…These programs were utilized as the input for REACTIN and BASE analysis. More detailed description on defining regulatory programs based on ChIP-seq data can be found in Zhu et al (19). …”

“…We have previously developed computational methods, REACTIN (Regulatory Activity Inference) and BASE (Binding Association with Sorted Expression), to investigate the regulatory programs associated with cancer (19-21). Both of the methods integrate gene expression data with ChIP-seq transcription factor (TF) binding data with the rationale that the regulatory activity of TFs can be reflected by the expression of their target genes.…”

“…Both of the methods integrate gene expression data with ChIP-seq transcription factor (TF) binding data with the rationale that the regulatory activity of TFs can be reflected by the expression of their target genes. REACTIN compares the expression profiles between two groups of samples (e.g., tumor vs. normal) to identify TFs with significantly different regulatory activities (19). BASE can be used to infer the regulatory activity of TFs in each sample of a cancer gene expression dataset, and TFs associated with patient survival can subsequently be identified by survival analysis (20).…”