Chapter

Pattern Recognition in Bioinformatics

Volume 7986 of the series Lecture Notes in Computer Science pp 35-46

Mutational Genomics for Cancer Pathway Discovery

  • Jeroen de RidderAffiliated withCarnegie Mellon UniversityDelft Bioinformatics Lab, Delft University of TechnologyBioinformatics and Statistics, Dept. Molecular Biology, Netherlands Cancer InstituteNetherlands Bioinformatics Centre
  • , Jaap KoolAffiliated withCarnegie Mellon UniversityMSD Animal Health, Merck/Intervet B.V.Dept. Molecular Genetics, Netherlands Cancer Institute
  • , Anthony G. UrenAffiliated withCarnegie Mellon UniversityMRC Clinical Sciences Centre, Imperial College Faculty of MedicineDept. Molecular Genetics, Netherlands Cancer Institute
  • , Jan BotAffiliated withCarnegie Mellon UniversityDelft Bioinformatics Lab, Delft University of TechnologyNetherlands Bioinformatics Centre
  • , Johann de JongAffiliated withCarnegie Mellon UniversityBioinformatics and Statistics, Dept. Molecular Biology, Netherlands Cancer Institute
  • , Alistair G. RustAffiliated withUniversity of SurreyExperimental Cancer Genetics, Wellcome Trust Sanger Institute
  • , Anton BernsAffiliated withCarnegie Mellon UniversityDept. Molecular Genetics, Netherlands Cancer Institute
  • , Maarten van LohuizenAffiliated withCarnegie Mellon UniversityDept. Molecular Genetics, Netherlands Cancer Institute
  • , David J. AdamsAffiliated withUniversity of SurreyExperimental Cancer Genetics, Wellcome Trust Sanger Institute
    • , Lodewyk WesselsAffiliated withCarnegie Mellon UniversityDelft Bioinformatics Lab, Delft University of TechnologyBioinformatics and Statistics, Dept. Molecular Biology, Netherlands Cancer InstituteNetherlands Bioinformatics Centre
    • , Marcel ReindersAffiliated withCarnegie Mellon UniversityDelft Bioinformatics Lab, Delft University of TechnologyNetherlands Bioinformatics Centre

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Abstract

We propose mutational genomics as an approach for identifying putative cancer pathways. This approach relies on expression profiling tumors that are induced by retroviral insertional mutagenesis. Akin to genetical genomics, this provides the opportunity to search for associations between tumor-initiating events (the viral insertion sites) and the consequent transcription changes, thus revealing putative regulatory interactions. An important advantage is that in mutational genomics the selective pressure exerted by the tumor growth is exploited to yield a relatively small number of loci that are likely to be causal for tumor formation. This is unlike genetical genomics which relies on the natural occurring genetic variation between samples to reveal the effects of a locus on gene expression.

We performed mutational genomics using a set of 97 lymphoma from mice presenting with splenomegaly. This identified several known as well as novel interactions, including many known targets of Notch1 and Gfi1. In addition to direct one-to-one associations, many multilocus networks of association were found. This is indicative of the fact that a cell has many parallel possibilities in which it can reach a state of uncontrolled proliferation. One of the identified networks suggests that Zmiz1 functions upstream of Notch1. Taken together, our results illustrate the potential of mutational genomics as a powerful approach to dissect the regulatory pathways of cancer.