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.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011)
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)
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)
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)
Kool, J., Berns, A.: High-throughput insertional mutagenesis screens in mice to identify oncogenic networks. Nature Reviews Cancer 9, 389–399 (2009)
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)
Uren, A.G., et al.: Retroviral insertional mutagenesis: past, present and future. Oncogene 24, 7656–7672 (2005)
Mikkers, H., Berns, A.: Retroviral insertional mutagenesis: tagging cancer pathways. Adv. Cancer Res. 88, 53–99 (2003)
Jansen, R.C., Nap, J.P.: Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001)
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)
Li, J., Burmeister, M.: Genetical genomics: combining genetics with gene expression analysis. Hum. Mol. Genet. 14(spec. 2), R163–R169 (2005)
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)
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)
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)
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)
Jonkers, J., Berns, A.: Retroviral insertional mutagenesis as a strategy to identify cancer genes. Biochim. Biophys. Acta 1287, 29–57 (1996)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Duan, Z., Horwitz, M.: Targets of the transcriptional repressor oncoprotein gfi-1. Proc. Natl. Acad. Sci. U S A 100, 5932–5937 (2003)
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)
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)
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)
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)
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)
Lin, S.: Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Ridder, J. et al. (2013). Mutational Genomics for Cancer Pathway Discovery. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-39159-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39158-3
Online ISBN: 978-3-642-39159-0
eBook Packages: Computer ScienceComputer Science (R0)