Mutational Genomics for Cancer Pathway Discovery

  • Jeroen de Ridder
  • Jaap Kool
  • Anthony G. Uren
  • Jan Bot
  • Johann de Jong
  • Alistair G. Rust
  • Anton Berns
  • Maarten van Lohuizen
  • David J. Adams
  • Lodewyk Wessels
  • Marcel Reinders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)


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.


Boolean Network Mutational Genomic Boolean Matrix Expression Quantitative Trait Locus Insertion Locus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011)CrossRefGoogle Scholar
  2. 2.
    van’t Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A.M., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)Google Scholar
  3. 3.
    van de Vijver, M.J., He, Y.D., van’t Veer, L.J., Dai, H., Hart, A.A.M., et al.: A gene-expression signature as a predictor of survival in breast cancer. N Engl. J. Med. 347, 1999–2009 (2002)Google Scholar
  4. 4.
    Sørlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10869–10874 (2001)CrossRefGoogle Scholar
  5. 5.
    Kool, J., Berns, A.: High-throughput insertional mutagenesis screens in mice to identify oncogenic networks. Nature Reviews Cancer 9, 389–399 (2009)CrossRefGoogle Scholar
  6. 6.
    Kool, J., Uren, A.G., Martins, C.P., Sie, D., de Ridder, J., et al.: Insertional mutagenesis in mice deficient for p15ink4b, p16ink4a, p21cip1, and p27kip1 reveals cancer gene interactions and correlations with tumor phenotypes. Cancer Res. 70, 520–531 (2010)CrossRefGoogle Scholar
  7. 7.
    Uren, A.G., et al.: Retroviral insertional mutagenesis: past, present and future. Oncogene 24, 7656–7672 (2005)CrossRefGoogle Scholar
  8. 8.
    Mikkers, H., Berns, A.: Retroviral insertional mutagenesis: tagging cancer pathways. Adv. Cancer Res. 88, 53–99 (2003)CrossRefGoogle Scholar
  9. 9.
    Jansen, R.C., Nap, J.P.: Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001)CrossRefGoogle Scholar
  10. 10.
    Gerrits, A., Dykstra, B., Otten, M., Bystrykh, L., de Haan, G.: Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny. Immunogenetics 60, 411–422 (2008)CrossRefGoogle Scholar
  11. 11.
    Li, J., Burmeister, M.: Genetical genomics: combining genetics with gene expression analysis. Hum. Mol. Genet. 14(spec. 2), R163–R169 (2005)Google Scholar
  12. 12.
    Schadt, E.E., Monks, S.A., Drake, T.A., Lusis, A.J., Che, N., et al.: Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)CrossRefGoogle Scholar
  13. 13.
    Bystrykh, L., Weersing, E., Dontje, B., Sutton, S., Pletcher, M.T., et al.: Uncovering regulatory pathways that affect hematopoietic stem cell function using ’genetical genomics’. Nat. Genet. 37, 225–232 (2005)CrossRefGoogle Scholar
  14. 14.
    Gerrits, A., Li, Y., Tesson, B.M., Bystrykh, L.V., Weersing, E., et al.: Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet 5, e1000692 (2009)Google Scholar
  15. 15.
    Erkeland, S.J., Verhaak, R.G.W., Valk, P.J.M., Delwel, R., Löwenberg, B., et al.: Significance of murine retroviral mutagenesis for identification of disease genes in human acute myeloid leukemia. Cancer Res. 66, 622–626 (2006)CrossRefGoogle Scholar
  16. 16.
    Jonkers, J., Berns, A.: Retroviral insertional mutagenesis as a strategy to identify cancer genes. Biochim. Biophys. Acta 1287, 29–57 (1996)Google Scholar
  17. 17.
    de Ridder, J., Gerrits, A., Bot, J., de Haan, G., Reinders, M., et al.: Inferring combinatorial association logic networks in multimodal genome-wide screens. Bioinformatics 26, i149–157 (2010)Google Scholar
  18. 18.
    Uren, A.G., Kool, J., Matentzoglu, K., de Ridder, J., Mattison, J., et al.: Large-scale mutagenesis in p19(arf)- and p53-deficient mice identifies cancer genes and their collaborative networks. Cell 133, 727–741 (2008)CrossRefGoogle Scholar
  19. 19.
    Hirvonen, H., Hukkanen, V., Salmi, T.T., Pelliniemi, T.T., Alitalo, R.: L-myc and n-myc in hematopoietic malignancies. Leuk Lymphoma 11, 197–205 (1993)CrossRefGoogle Scholar
  20. 20.
    Chipuk, J.E., Kuwana, T., Bouchier-Hayes, L., Droin, N.M., Newmeyer, D.D., et al.: Direct activation of bax by p53 mediates mitochondrial membrane permeabilization and apoptosis. Science 303, 1010–1014 (2004)CrossRefGoogle Scholar
  21. 21.
    Dulić, V., Kaufmann, W.K., Wilson, S.J., Tlsty, T.D., Lees, E., et al.: p53-dependent inhibition of cyclin-dependent kinase activities in human fibroblasts during radiation-induced g1 arrest. Cell 76, 1013–1023 (1994)CrossRefGoogle Scholar
  22. 22.
    Komarova, E.A., Diatchenko, L., Rokhlin, O.W., Hill, J.E., Wang, Z.J., et al.: Stress-induced secretion of growth inhibitors: a novel tumor suppressor function of p53. Oncogene 17, 1089–1096 (1998)CrossRefGoogle Scholar
  23. 23.
    Lam, D.C.L., Girard, L., Ramirez, R., Chau, W.S., Suen, W.S., et al.: Expression of nicotinic acetylcholine receptor subunit genes in non-small-cell lung cancer reveals differences between smokers and nonsmokers. Cancer Res 67, 4638–4647 (2007)CrossRefGoogle Scholar
  24. 24.
    Rouault, J.P., Rimokh, R., Tessa, C., Paranhos, G., Ffrench, M., et al.: Btg1, a member of a new family of antiproliferative genes. EMBO J. 11, 1663–1670 (1992)Google Scholar
  25. 25.
    van Galen, J.C., Kuiper, R.P., van Emst, L., Levers, M., Tijchon, E., et al.: Btg1 regulates glucocorticoid receptor autoinduction in acute lymphoblastic leukemia. Blood 115, 4810–4819 (2010)CrossRefGoogle Scholar
  26. 26.
    Morin, R.D., Mendez-Lago, M., Mungall, A.J., Goya, R., Mungall, K.L., et al.: Frequent mutation of histone-modifying genes in non-hodgkin lymphoma. Nature 476, 298–303 (2011)CrossRefGoogle Scholar
  27. 27.
    Tavor, S., Park, D.J., Gery, S., Vuong, P.T., Gombart, A.F., et al.: Restoration of c/ebpalpha expression in a bcr-abl+ cell line induces terminal granulocytic differentiation. J. Biol. Chem. 278, 52651–52659 (2003)CrossRefGoogle Scholar
  28. 28.
    Duan, Z., Horwitz, M.: Targets of the transcriptional repressor oncoprotein gfi-1. Proc. Natl. Acad. Sci. U S A 100, 5932–5937 (2003)CrossRefGoogle Scholar
  29. 29.
    Katoh, M., Katoh, M.: Integrative genomic analyses on hes/hey family: Notch-independent hes1, hes3 transcription in undifferentiated es cells, and notch-dependent hes1, hes5, hey1, hey2, heyl transcription in fetal tissues, adult tissues, or cancer. Int. J. Oncol. 31, 461–466 (2007)Google Scholar
  30. 30.
    Margolin, A.A., Palomero, T., Sumazin, P., Califano, A., Ferrando, A.A., et al.: Chip-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes. Proc. Natl. Acad. Sci. U S A 106, 244–249 (2009)CrossRefGoogle Scholar
  31. 31.
    Dudley, D.D., Wang, H.C., Sun, X.H.: Hes1 potentiates t cell lymphomagenesis by up-regulating a subset of notch target genes. PLoS One 4, e6678 (2009)Google Scholar
  32. 32.
    Mattison, J., van der Weyden, L., Hubbard, T., Adams, D.J.: Cancer gene discovery in mouse and man. Biochim. Biophys. Acta 1796, 140–161 (2009)Google Scholar
  33. 33.
    de Jong, J., de Ridder, J., van der Weyden, L., Sun, N., van Uitert, M., et al.: Computational identification of insertional mutagenesis targets for cancer gene discovery. Nucleic Acids Res 39, e105 (2011)Google Scholar
  34. 34.
    Lin, S.: Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jeroen de Ridder
    • 1
    • 2
    • 3
  • Jaap Kool
    • 4
    • 6
  • Anthony G. Uren
    • 5
    • 6
  • Jan Bot
    • 1
    • 3
  • Johann de Jong
    • 2
  • Alistair G. Rust
    • 7
  • Anton Berns
    • 6
  • Maarten van Lohuizen
    • 6
  • David J. Adams
    • 7
  • Lodewyk Wessels
    • 1
    • 2
    • 3
  • Marcel Reinders
    • 1
    • 3
  1. 1.Delft Bioinformatics LabDelft University of TechnologyThe Netherlands
  2. 2.Bioinformatics and Statistics, Dept. Molecular Biology, Netherlands Cancer InstituteThe Netherlands
  3. 3.Netherlands Bioinformatics CentreThe Netherlands
  4. 4.MSD Animal Health, Merck/Intervet B.V.The Netherlands
  5. 5.MRC Clinical Sciences CentreImperial College Faculty of MedicineUK
  6. 6.Dept. Molecular GeneticsNetherlands Cancer InstituteThe Netherlands
  7. 7.Experimental Cancer GeneticsWellcome Trust Sanger InstituteUK

Personalised recommendations