American Association for Cancer Research
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Figure S8 from Mutational Mechanisms That Activate Wnt Signaling and Predict Outcomes in Colorectal Cancer Patients

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posted on 2023-03-31, 01:23 authored by William Hankey, Michael A. McIlhatton, Kenechi Ebede, Brian Kennedy, Baris Hancioglu, Jie Zhang, Guy N. Brock, Kun Huang, Joanna Groden

SERPINE2 expression is stable following knockdown of endogenous APC or induction of exogenous APC at three time points examined.

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Pelotonia Fellowship Program

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ARTICLE ABSTRACT

APC biallelic loss-of-function mutations are the most prevalent genetic changes in colorectal tumors, but it is unknown whether these mutations phenocopy gain-of-function mutations in the CTNNB1 gene encoding β-catenin that also activate canonical WNT signaling. Here we demonstrate that these two mutational mechanisms are not equivalent. Furthermore, we show how differences in gene expression produced by these different mechanisms can stratify outcomes in more advanced human colorectal cancers. Gene expression profiling in Apc-mutant and Ctnnb1-mutant mouse colon adenomas identified candidate genes for subsequent evaluation of human TCGA (The Cancer Genome Atlas) data for colorectal cancer outcomes. Transcriptional patterns exhibited evidence of activated canonical Wnt signaling in both types of adenomas, with Apc-mutant adenomas also exhibiting unique changes in pathways related to proliferation, cytoskeletal organization, and apoptosis. Apc-mutant adenomas were characterized by increased expression of the glial nexin Serpine2, the human ortholog, which was increased in advanced human colorectal tumors. Our results support the hypothesis that APC-mutant colorectal tumors are transcriptionally distinct from APC-wild-type colorectal tumors with canonical WNT signaling activated by other mechanisms, with possible implications for stratification and prognosis.Significance: These findings suggest that colon adenomas driven by APC mutations are distinct from those driven by WNT gain-of-function mutations, with implications for identifying at-risk patients with advanced disease based on gene expression patterns. Cancer Res; 78(3); 617–30. ©2017 AACR.

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