Molecular drivers of tumor progression in microsatellite stable APC mutation-negative colorectal cancers

Abstract

The tumor suppressor gene adenomatous polyposis coli (APC) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative (APC^mut-) CRCs. We analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APC^mut- and APC mutation-positive (APC^mut+) microsatellite stable CRCs. Transcriptionally, APC^mut- CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43, increased expression of the WNT agonist RSPO3, activating mutation of BRAF, or increased methylation and decreased expression of AXIN2. APC^mut- CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. APC^mut- CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.

Figure 1

WNT signaling mutations in APC^mut– CRCs. (A) Fraction of APC^mut– CRCs from the TCGA dataset with gene mutations, amplifications, deep deletions, and fusions that were significantly more common in APC^mut– in comparison to APC^mut+ CRCs. The top 10 are shown by p-value ranking, most significant (left) to least (right). (B) OncoPrint diagram showing the top 10 statistically significant mutations associated with APC^mut– CRCs and the gene fusion PTPRK-RSPO3 for the 63 APC^mut– CRCs.

Figure 2

Enhanced sensitivity to extracellular WNT in APC^mut– CRCs. (A) Volcano plot representing the results from differential expression analysis between APC^mut– and APC^mut+ CRCs. Labeled points are the genes with an P_adj < 0.0005. Blue points were downregulated in APC^mut– CRCs and red points upregulated. (B) Comparison of RNF43 gene expression in APC^mut– CRCs, APC^mut+ CRCs, and normal colon samples in the TCGA, GSE35896 and CPTAC-2 datasets. Two-sample t-tests with a two tailed p-value were used to test statistical significance. (C) Differentially expressed genes (P_adj < 0.05) between APC^mut– and APC^mut+ CRCs from TCGA were mapped onto the KEGG canonical WNT signaling pathway. Blue labeling represents genes downregulated in APC^mut–; red labeling represents upregulated genes. (D) Unsupervised clustering analysis of APC^mut– CRCs from the TCGA dataset using differentially expressed genes (P_adj < 0.05). (E) Scatter plot showing estimation of activation potential of extracellular WNT signaling. Each point is the mean for individual groups. The y-axis represents a group’s apparent sensitivity to extracellular WNT signaling using the WNT ligand sensitivity score. The x-axis represents a group’s WNT stimulation potential by quantifying each sample’s maximum WNT ligand expression.

Figure 3

APC^mut– CRCs associated with immune infiltration. (A) GSEA results of differential gene expression analysis of APC^mut– versus APC^mut+ CRCs from the TCGA dataset. Red clusters represent GO terms enriched among upregulated genes in APC^mut– CRCs and blue clusters correspond to down-regulated processes. (B) CIBERSORTx absolute score in CRCs from the TCGA, GSE35896 and CPTAC-2 datasets. Two-sample t-tests with a two-tailed p-value were used to test statistical significance. (C) Violin plot of CIBERSORTx absolute score across subtypes of APC^mut– CRCs. (D) Expression of RSPO3 in APC^mut– and APC^mut+ CRCs plotted against their individual M2 macrophage scores identified from the CIBERSORTx algorithm. Pearson correlation was performed to determine statistical significance.

Figure 4

APC^mut– CRCs have higher AXIN2 methylation. (A) Bar plot comparing total number of hyper-methylated and hypo-methylated differentially methylated regions (DMRs) between APC^mut– and APC^mut+ CRCs from the TCGA dataset. (B) Top 10 APC^mut– hypermethylated and hypomethylated DMRs between APC^mut– and APC^mut+ CRCs from TCGA. Red bars represent APC^mut– CRC differentially hypermethylated genes and blue bars represent APC^mut– CRC differentially hypomethylated genes. (C) Bar plot representing DMRs with strongest correlations with RNF43. Blue bars represent the top 10 DMRs with the highest Pearson gene expression correlation with RNF43 gene expression. Red bars represent the Pearson correlation between the average differentially methylated beta values and RNF43 expression for these differentially methylated regions. (D) Scatter plots of RNF43 expression and AXIN2 expression of both APC^mut– and APC^mut+ CRCs in the TCGA, GSE35896, and CPTAC-2 datasets. Pearson correlation was performed to determine statistical significance. (E) Matched comparison between Z-normalized AXIN2 average beta values and Z-normalized RNF43 expression of APC^mut– CRCs. (F) Scatter plot of AXIN2 average beta values and the CIBERSORTx M2 macrophage score of APC^mut– and APC^mut+ CRCs from the TCGA dataset. Pearson correlation was performed to measure statistical significance.

Figure 5

AP2M1 gene expression associated with earlier-onset in APC^mut– CRCs. (A) A comparison of age between APC^mut– clusters identified from Fig. 2D. A two-sample t-test with a two-tailed p-value was used to determine statistical significance. (B) Top 10 statistically significant genes based on a logrank test whose median gene expression best separates age of CRC diagnosis of APC^mut– CRCs from the TCGA dataset. (C) Kaplan–Meier plot representing association between the age at CRC diagnosis and median separation of AP2M1 expression in APC^mut– and APC^mut+ CRCs from the TCGA dataset. (D) Flowchart of two molecular mechanisms that may be involved in the development of APC^mut– CRC.