title: A bioinformatics approach reveals novel interactions of the OVOL transcription factors in the regulation of epithelial - mesenchymal cell reprogramming and cancer progression.
abstract: BACKGROUND
  Mesenchymal to Epithelial Transition (MET) plasticity is critical to cancer progression, and we recently showed that the OVOL transcription factors (TFs) are critical regulators of MET. Results of that work also posed the hypothesis that the OVOLs impact MET in a range of cancers. We now test this hypothesis by developing a model, OVOL Induced MET (OI-MET), and sub-model (OI-MET-TF), to characterize differential gene expression in MET common to prostate cancer (PC) and breast cancer (BC).
  
  
  RESULTS
  In the OI-MET model, we identified 739 genes differentially expressed in both the PC and BC models. For this gene set, we found significant enrichment of annotation for BC, PC, cancer, and MET, as well as regulation of gene expression by AP1, STAT1, STAT3, and NFKB1. Focusing on the target genes for these four TFs plus the OVOLs, we produced the OI-MET-TF sub-model, which shows even greater enrichment for these annotations, plus significant evidence of cooperation among these five TFs. Based on known gene/drug interactions, we prioritized targets in the OI-MET-TF network for follow-on analysis, emphasizing the clinical relevance of this work. Reflecting these results back to the OI-MET model, we found that binding motifs for the TF pair AP1/MYC are more frequent than expected and that the AP1/MYC pair is significantly enriched in binding in cancer models, relative to non-cancer models, in these promoters. This effect is seen in both MET models (solid tumors) and in non-MET models (leukemia). These results are consistent with our hypothesis that the OVOLs impact cancer susceptibility by regulating MET, and extend the hypothesis to include mechanisms not specific to MET.
  
  
  CONCLUSIONS
  We find significant evidence of the OVOL, AP1, STAT1, STAT3, and NFKB1 TFs having important roles in MET, and more broadly in cancer. We prioritize known gene/drug targets for follow-up in the clinic, and we show that the AP1/MYC TF pair is a strong candidate for intervention.
authors.forename: Hernan
authors.initials: H
authors.lastname: Roca
authors.forename: Manjusha
authors.initials: M
authors.lastname: Pande
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Huo
authors.forename: James
authors.initials: J
authors.lastname: Hernandez
authors.forename: James D
authors.initials: JD
authors.lastname: Cavalcoli
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. kpienta1@jhmi.edu.
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Pienta
authors.forename: Richard C
authors.initials: RC
authors.lastname: McEachin
mesh_terms: D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D058750:Epithelial-Mesenchymal Transition / Q000235:genetics; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D008954:Models, Biological; D058977:Molecular Sequence Annotation; D058990:Molecular Targeted Therapy; D011401:Promoter Regions, Genetic / Q000235:genetics; D011471:Prostatic Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D011485:Protein Binding; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors