title: Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.
abstract: Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.
authors.affiliation: Algorithmic Bioinformatics, Institute for Computer Science, c/o Bonn-Aachen International Center for IT (B-IT), University of Bonn, Bonn, Germany.
authors.forename: Holger
authors.initials: H
authors.lastname: Fröhlich
authors.affiliation: Algorithmic Bioinformatics, Institute for Computer Science, c/o Bonn-Aachen International Center for IT (B-IT), University of Bonn, Bonn, Germany.
authors.forename: Gloria
authors.initials: G
authors.lastname: Bahamondez
authors.affiliation: Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Götschel
authors.affiliation: Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Ulrike
authors.initials: U
authors.lastname: Korf
mesh_terms: D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000378:metabolism; D045744:Cell Line, Tumor; D049109:Cell Proliferation; D019295:Computational Biology; D066246:ErbB Receptors / Q000378:metabolism*; D005786:Gene Expression Regulation; D015972:Gene Expression Regulation, Neoplastic; D053823:Hedgehog Proteins / Q000378:metabolism*; D006801:Humans; D008527:Medulloblastoma / Q000378:metabolism; D015233:Models, Statistical; D011336:Probability; D038762:Ribosomal Protein S6 Kinases, 70-kDa / Q000378:metabolism; D015398:Signal Transduction; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D053823:Hedgehog Proteins; C509239:SHH protein, human; D014157:Transcription Factors; C512478:EGFR protein, human; D066246:ErbB Receptors; D038762:Ribosomal Protein S6 Kinases, 70-kDa