title: A Transcriptional Program for Detecting TGFβ-Induced EMT in Cancer.
abstract: Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. TGFβ is one of the best-studied drivers of EMT, and many drugs are available to target this signaling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFβ-induced EMT which can be used to score TGFβ-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumor datasets, a number of cell lines (including basal B breast cancer and cancers of the central nervous system) show evidence for TGFβ-driven EMT and carry a low mutational burden across the TGFβ signaling pathway. Furthermore, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumor types with evidence of TGFβ-induced EMT. Tumor types with high scores showed significantly lower survival rates than those with low scores and also carry a lower mutational burden in the TGFβ pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFβ.Implications:The TGFβ-induced EMT signature may be useful to identify patients with mesenchymal-like tumors who could benefit from targeted therapeutics to inhibit promesenchymal TGFβ signaling and disrupt the metastatic cascade.Mol Cancer Res; 15(5); 619-31. ©2017 AACR.
authors.affiliation: The University of Melbourne Department of Surgery, St. Vincent's Hospital, Parkville, Victoria, Australia.|Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
authors.forename: Momeneh
authors.initials: M
authors.lastname: Foroutan
authors.affiliation: Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.|Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia.|ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia.
authors.forename: Joseph
authors.initials: J
authors.lastname: Cursons
authors.affiliation: Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
authors.forename: Soroor
authors.initials: S
authors.lastname: Hediyeh-Zadeh
authors.affiliation: The University of Melbourne Department of Surgery, St. Vincent's Hospital, Parkville, Victoria, Australia.|Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Queensland, Australia.|Translational Research Institute, Wooloongabba, Queensland, Australia.
authors.forename: Erik W
authors.initials: EW
authors.lastname: Thompson
authors.affiliation: Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia. davis.m@wehi.edu.au.|Department of Biochemistry and Molecular Biology, Faculty of Medicine, Dentistry and Health, University of Melbourne, Parkville, Victoria, Australia.
authors.forename: Melissa J
authors.initials: MJ
authors.lastname: Davis
mesh_terms: D045744:Cell Line, Tumor; D019295:Computational Biology; D058750:Epithelial-Mesenchymal Transition; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D015398:Signal Transduction; D016019:Survival Analysis; D016212:Transforming Growth Factor beta / Q000378:metabolism*
chemical_list: D016212:Transforming Growth Factor beta