Cancer Genomics

Cancers arise when normal cells accumulate mutations that drive uncontrollable proliferation. Tumour cells can harbour thousands of these mutations, but only a fraction of them cause cancer. Typically, most studies of cancer mutations only study the “clonal” mutations that are present in the majority of the tumour. Including subclonal mutations in tumour analysis can add confidence to identification of cancer drivers. We use insertional mutagenesis screens as a tool to compare the full spectrum of mutations in premalignant cells to mutations at different stages of tumour development.

In a recent study using mouse models of BCL2 driven lymphoma we isolated 700,000 MuLV mutations from more than 500 malignancies. Only 3,000 of these mutations were clonal, but by examining all mutations at different time points throughout tumour development, we identified hundreds of loci that contribute to disease that could not have been identified through the sole use of clonal mutations. Comparing these regions to human cancer genome datasets, shows significant overlap with more than 100 known genes and regions implicated in non-Hodgkin lymphoma. Several hundred additional regions were also implicated suggesting further study of these in human datasets is warranted.

These data have been used to construct an online genome-wide map of regions that contribute to tumour development

Cancer Genomics

Distribution of integrations over the Myc/Pvt1 locus. Each row of coloured vertical lines represents the forward and reverse strand integrations of each category of mice. Grey bands below each coloured row represent the level of selection evidenced by contingency table tests. Late-stage specific integrations are evident throughout the region; however, integrations upstream of Myc are primarily on the forward strand and T cell specific whereas integrations within the Pvt1 gene are in the reverse orientation and somewhat biased toward wild-type mice

Cancer Genomics

The Venn diagram demonstrates the overlap between four sets of cancer genes identified by different statistical criteria. Genes implicated by selection occurring between early and late stage tumours (yellow) overlap substantially with groups of genes identified based on the predisposing mutations in the mice (BCL2 overexpression (blue) or normal BCL2 expression (red)) and with the genes identified by looking at mutation orientation (green). Combining these criteria gives a richer dataset for prioritization of candidate genes that can be studied further.

Cancer Genomics

Multiple criteria indicate selection of both clonal and subclonal mutations at CIS loci. Four heat maps representing the relative levels of selection observed between different categories of integrations. Fisher’s exact tests were performed counting the inserts within 100kb windows surrounding each of the top 50 clonal insert loci. Blue indicates comparisons between early and late-stage integrations. Red represents integration orientation bias (forward or reverse strand). Yellow represents specificity for B cell (>50% CD19) versus T cell lymphomas. Green represents specificity between different genotypes. p-Values for Fisher’s exact tests are indicated by colour intensity

Selected Publications

Philip Webster, Joanna C. Dawes, Hamlata Dewchand, Katalin Takacs, Barbara Iadarola, Bruce J. Bolt, Juan J. Caceres, Jakub Kaczor, Gopuraja Dharmalingam, Marian Dore, Laurence Game, Thomas Adejumo, James Elliott, Kikkeri Naresh, Mohammad Karimi, Katerina Rekopoulou, Ge Tan, Alberto Paccanaro & Anthony G. Uren (2018). Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates Nature Communications 9, 2649, 10.1038/s41467-018-05069-9

Edward Curry, Ian Green, Nadine Chapman-Rothe, Elham Shamsaei, Sarah Kandil, Fanny L Cherblanc, Luke Payne, Emma Bell, Thota Ganesh, Nitipol Srimongkolpithak, Joachim Caron, Fengling Li, Anthony G Uren, James P Snyder, Masoud Vedadi, Matthew J Fuchter, Robert Brown (2015)Dual EZH2 and EHMT2 histone methyltransferase inhibition increases biological efficacy in breast cancer cells Clinical epigenetics, 7:84, 10.1186/s13148-015-0118-9

Bart A Westerman, Marleen Blom, Ellen Tanger, Martin van der Valk, Ji-Ying Song, Marije van Santen, Jules Gadiot, Paulien Cornelissen-Steijger, John Zevenhoven, Haydn M Prosser, Anthony Uren, Eleonora Aronica, Maarten van Lohuizen (2012). GFAP-Cre-Mediated Transgenic Activation of Bmi1 Results in Pituitary Tumors PloS one, 7(5):e35943 10.1371/journal.pone.0035943

Kool, J., Uren, A. G., Martins, C. P., Sie, D., de Ridder, J., Turner, G., van Uitert, M., Matentzoglu, K., Lagcher, W., Krimpenfort, P., Gadiot, J., Pritchard, C., Lenz, J., Lund, A. H., Jonkers, J., Rogers, J., Adams, D. J., Wessels, L., Berns, A., & van Lohuizen, M. (2010). Insertional mutagenesis in mice deficient for p15Ink4b, p16Ink4a, p21Cip1, and p27Kip1 reveals cancer gene interactions and correlations with tumor phenotypes. Cancer Research, 70(2), 520–531.

Uren, A. G., Kool, J., Matentzoglu, K., de Ridder, J., Mattison, J., van Uitert, M., Lagcher, W., Sie, D., Tanger, E., Cox, T., Reinders, M., Hubbard, T. J., Rogers, J., Jonkers, J., Wessels, L., Adams, D. J., van Lohuizen, M., & Berns, A. (2008). Large-scale mutagenesis in p19(ARF)- and p53-deficient mice identifies cancer genes and their collaborative networks. Cell, 133(4), 727–741.

de Ridder, J., Uren, A., Kool, J., Reinders, M., & Wessels, L. (2006). Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLoS Computational Biology, 2(12), e166+.

Uren, A. G., Wong, L., Pakusch, M., Fowler, K. J., Burrows, F. J., Vaux, D. L., & Choo, K. H. (2000b). Survivin and the inner centromere protein INCENP show similar cell-cycle localization and gene knockout phenotype. Current Biology : CB, 10(21), 1319–1328.

Uren, A. G., O’Rourke, K., Aravind, L., Pisabarro, Seshagiri, S., Koonin, E. V., & Dixit, V. M. (2000a). Identification of paracaspases and metacaspases. Molecular Cell, 6(4), 961–967.