Transposon mutagenesis identifies candidate genes that cooperate with loss of transforming growth factor‐beta signaling in mouse intestinal neoplasms

Abstract

Colorectal cancer (CRC) results from the accumulation of gene mutations and epigenetic alterations in colon epithelial cells, which promotes CRC formation through deregulating signaling pathways. One of the most commonly deregulated signaling pathways in CRC is the transforming growth factor β (TGF-β) pathway. Importantly, the effects of TGF-β signaling inactivation in CRC are modified by concurrent mutations in the tumor cell, and these concurrent mutations determine the ultimate biological effects of impaired TGF-β signaling in the tumor. However, many of the mutations that cooperate with the deregulated TGF-β signaling pathway in CRC remain unknown. Therefore, we sought to identify candidate driver genes that promote the formation of CRC in the setting of TGF-β signaling inactivation. We performed a forward genetic screen in mice carrying conditionally inactivated alleles of the TGF-β receptor, type II (Tgfbr2) using Sleeping Beauty (SB) transposon mediated mutagenesis. We used TAPDANCE and Gene-centric statistical methods to identify common insertion sites (CIS) and, thus, candidate tumor suppressor genes and oncogenes within the tumor genome. CIS analysis of multiple neoplasms from these mice identified many candidate Tgfbr2 cooperating genes and the Wnt/β-catenin, Hippo and MAPK pathways as the most commonly affected pathways. Importantly, the majority of candidate genes were also found to be mutated in human CRC. The SB transposon system provides an unbiased method to identify Tgfbr2 cooperating genes in mouse CRC that are functionally relevant and that may provide further insight into the pathogenesis of human CRC.

A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells

“…Mutagenic SB transposons were developed to modify expression of endogenous genes in specific ways depending on their relative position and orientation, as previously described [8]. This feature, combined with our experience analyzing insertion site data from 15 SB-induced models of cancer [2, 3, 20, 29, 30, 33, 34, 37, 40, 41, 45–47, 52, 55], led us to develop an algorithm to predict the functional impact that recurrent transposon insertion has on a given gene. This approach evaluates clustered transposon insertions within each candidate locus to determine if there is a bias for insertion in the same orientation as the gene, indicative of an over-expression mechanism, or if the transposons are randomly orientated, suggesting a gene disruption mechanism (see Additional file 2: Supplemental Methods).…”

Sleeping Beauty Mouse Models of Cancer: Microenvironmental Influences on Cancer Genetics

“…Most recently, SB mutagenesis was used to identify genes cooperating with loss of TGF-B in intestinal neoplasms (99). Tumors from SB mice with homozygous conditional inactivation of TGF-B receptor, type II ( Tgfbr2 ) were compared with tumors from SB mice on a wildtype background.…”

“…Tumors from SB mice with homozygous conditional inactivation of TGF-B receptor, type II ( Tgfbr2 ) were compared with tumors from SB mice on a wildtype background. Authors found 34% (232/673) and 50% (187/372) of candidate genes identified in Tgfbr2; SB tumors to be unique to the presence of Tgfbr2 inactivation, depending on statistical method used (99). Comparison of the two statistical approaches revealed overlap of 17 genes, with an enrichment in genes responsible for either Wnt/B-catenin or Hippo pathway signaling including Lrp6, Ppp2r1a, Tcf7l2 , and Yap1 (99).…”

A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells

“…Mutagenic SB transposons were developed to modify expression of endogenous genes in specific ways depending on their relative position and orientation, as previously described (Dupuy 2010). This feature, combined with our experience analyzing insertion site data from 15 SB-induced models of cancer Mann et al 2012;Wu et al 2012;Keng et al 2013;Quintana et al 2013;Rogers et al 2013;Zanesi et al 2013;Bard-Chapeau et al 2014;Mann et al 2015;Takeda et al 2015;Montero-Conde et al 2017;Morris et al 2017;Suarez-Cabrera et al 2017;Tschida et al 2017;Riordan et al 2018), led us to develop an algorithm to predict the functional impact that recurrent transposon insertion has on a given gene. This approach evaluates clustered transposon insertions within each candidate locus to determine if there is a bias for insertion in the same orientation as the gene, indicative of an over-expression mechanism, or if the transposons are randomly orientated, suggesting a gene disruption mechanism (see Supplemental Methods).…”