Predicting transcription factor activity using prior biological information
William M. Yashar,
Joseph Estabrook,
Hannah D. Holly,
Julia Somers,
Olga Nikolova,
Özgün Babur,
Theodore P. Braun,
Emek Demir
Affiliations
William M. Yashar
Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
Joseph Estabrook
Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
Hannah D. Holly
Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
Julia Somers
Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
Olga Nikolova
Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
Özgün Babur
Computer Science Department, University of Massachusetts, Boston, MA 02125, USA
Theodore P. Braun
Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
Emek Demir
Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Division of Oncologic Sciences, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA; Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; Pacific Northwest National Laboratories, Richland, WA 99354, USA; Corresponding author
Summary: Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.
