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 http://mulv.lms.mrc.ac.uk.
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
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.
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
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