title: E2F4 regulatory program predicts patient survival prognosis in breast cancer.
abstract: BACKGROUND
  Genetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures.
  
  
  METHODS
  We defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score (RAS) of E2F4 in cancer tissues, and examined how E2F4 RAS correlates with patient survival.
  
  
  RESULTS
  Genes in our E2F4 signature were 21-fold more likely to be correlated with breast cancer patient survival time compared to randomly selected genes. Using eight independent breast cancer datasets containing over 1,900 unique samples, we stratified patients into low and high E2F4 RAS groups. E2F4 activity stratification was highly predictive of patient outcome, and our results remained robust even when controlling for many factors including patient age, tumor size, grade, estrogen receptor (ER) status, lymph node (LN) status, whether the patient received adjuvant therapy, and the patient's other prognostic indices such as Adjuvant! and the Nottingham Prognostic Index scores. Furthermore, the fractions of samples with positive E2F4 RAS vary in different intrinsic breast cancer subtypes, consistent with the different survival profiles of these subtypes.
  
  
  CONCLUSIONS
  We defined a prognostic signature, the E2F4 regulatory activity score, and showed it to be significantly predictive of patient outcome in breast cancer regardless of treatment status and the states of many other clinicopathological variables. It can be used in conjunction with other breast cancer classification methods such as Oncotype DX to improve clinical outcome prediction.
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA. sari.s.khaleel.dm@dartmouth.edu.
authors.forename: Sari S
authors.initials: SS
authors.lastname: Khaleel
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA. erik.h.andrews.dm@dartmouth.edu.
authors.forename: Erik H
authors.initials: EH
authors.lastname: Andrews
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA. matthew.ung@dartmouth.edu.
authors.forename: Matthew
authors.initials: M
authors.lastname: Ung
authors.affiliation: Department of Pharmacology & Toxicology, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA. james.direnzo@dartmouth.edu.
authors.forename: James
authors.initials: J
authors.lastname: DiRenzo
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, 1 Rope Ferry Road, Hanover, NH, 03755, USA. chao.cheng@dartmouth.edu.|Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH, 03766, USA. chao.cheng@dartmouth.edu.|Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH, 03766, USA. chao.cheng@dartmouth.edu.
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000401:mortality; D002277:Carcinoma / Q000235:genetics* / Q000378:metabolism / Q000401:mortality; D047369:Chromatin Immunoprecipitation; D050704:E2F4 Transcription Factor / Q000235:genetics* / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D011379:Prognosis; D016016:Proportional Hazards Models; D015996:Survival Rate; D059467:Transcriptome
chemical_list: D050704:E2F4 Transcription Factor; C493762:E2F4 protein, human