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

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 (APCmut-) 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 APCmut- and APC mutation-positive (APCmut+) microsatellite stable CRCs. Transcriptionally, APCmut- 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. APCmut- CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. APCmut- 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.

Introduction

Colorectal cancer (CRC) is the second deadliest cancer in the United States, with an estimated 147,950 individuals diagnosed and 53,200 deaths in 2020[1]. Although there have been great reductions in CRC incidence and mortality widely attributed to increased screening[2], the incidence of CRC has been increasing in individuals less than 50 years of age at a rate of 2% per year since 1994[3]. Molecular analysis has shown that < 20% of early-onset CRC cases are explained by genetically determined hereditary syndromes[4] and a variety of environmental factors have been postulated to underlie its increase[5], suggesting that a unitary cause of early-onset CRC will be elusive. With early-onset CRC manifesting as a heterogenous disease caused by a multitude of factors, there is a pressing need to identify the distinct molecular subtypes of CRC that are overrepresented in early-onset cases.

Somatic mutation of the adenomatous polyposis coli (APC) gene is the initiating event in approximately 80% of all CRCs, but APC mutations are significantly less frequent in early-onset CRCs[6-8]. APC is a structural and regulatory component of a destruction complex, which responds to WNT stimulation by inhibition of degradation of the stem cell-promoting transcription factor β-catenin, encoded by the CTNNB1 gene[9]. Failure to regulate β-catenin by degradation due to mutational inactivation of APC throws colorectal epithelial cells into a continuous “WNT-activated” state; they no longer require activation by WNTs to maintain the stem cell compartment[10]. The fact that early-onset CRCs more frequently lack an APC mutation suggests that many of these tumors depend on alternative molecular mechanisms. In mismatch repair-deficient CRCs, which exhibit microsatellite instability (MSI) and constitute 12–15% of all CRCs, APC mutations are also significantly less frequent and BRAF mutations constitute a dominant driver mechanism[11]. What initiates and drives the carcinogenetic process in microsatellite stable (MSS) CRCs that lack APC mutations? Here, we comprehensively compare molecular profiles of MSS APC mutation-positive CRCs (APC) and MSS APC mutation-negative (APC) CRCs to identify novel APC-independent mechanisms driving CRC subtypes.

Methods

Analyses of genomic alterations in case series

To formulate a discovery series, we obtained colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) data from The Cancer Genome Atlas (TCGA) in the Genomic Data Commons portal (Supplementary File 1). Curated somatic nucleotide variant and copy number data were extracted using TCGAbiolinks and FireBrowse, respectively[12]. Deep deletions, amplifications, and gene fusions[13] were identified. We excluded hypermutable cases by removing MSI-high cases based on clinical data and by removing cases with > 700 mutations. CRC samples were classified as APC if they lacked a non-silent mutation or deep (homozygous) deletion in APC or lacked a mutation in CTNNB1[14]. With these filtration steps, we had 63 APC samples and 362 APC samples. We compared genomic alterations between APC and APC CRCs by Fisher’s exact test and tested mutual exclusivity by CoMEt[15]. For more details on the bioinformatics analysis, see Supplementary Methods.

For validation series, we used the CPTAC-2[16] and GSE35896[17] datasets, because they were the only CRC datasets with APC mutation status and gene expression data available from the cBioPortal, the International Cancer Genome Consortium or studies utilized by Guinney et al. to determine consensus molecular subtypes of CRC[18]. CPTAC-2 was downloaded from cBioPortal and GSE3896 from synapse.org[18]. In the CPTAC-2 dataset, we identified 11 APC CRCs and 70 APC CRCs. Because GSE35896 did not have whole exome sequencing or copy number data, we could not filter for hypermutation, APC deep deletion, or CTNNB1 mutations. Based on the data available, we classified 16 out of 56 MSS CRC samples as APC.

Transcriptomic analyses

For TCGA and CPTAC-2 series, we obtained HTSeq count files and used the edgeR and limma pipeline to normalize the counts matrix. For GSE35896, we used the RMA normalized data. Genes that had less than one count per million in more than half the samples were discarded. MBatch analysis showed no evidence of batch effects.

We used limma to identify differentially expressed genes between APC and APC CRCs (Padj < 0.05). PathView was used to map differentially expressed genes onto the KEGG WNT canonical signaling pathway, with the node sum parameter set to “max.abs”[19]. Gene set enrichment analysis (GSEA) was performed using fgsea[20]. For GSEA input, we used the Gene Ontology (GO) biological process gene sets from MSigDB and ranked the genes by the t-statistic from our differential expression analysis. The Cytoscape application EnrichmentMap was used to visualize all statistically significant GO terms (Padj < 0.05)[21]. CIBERSORTx was used to impute the fraction of immune cells based on gene expression data from the TCGA, GSE35896, and CPTAC-2 datasets[22].

To characterize the theoretical WNT ligand sensitivity of APC tumors, we defined a score by the normalized expression of RSPO3 minus the sum of the normalized expression levels of RNF43 and ZNRF3:where the subscript mRNA-z indicates the z-score of the expression value relative to all samples including tumors and normals. The WNTLS score for each tumor was then compared to the maximum WNT ligand expression over all 12 WNTs in the same tumor.

DNA methylation analyses

Methylation was assayed by TCGA using Illumina Human Methylation 450 arrays and data was accessed using TCGAbiolinks. Preprocessing and normalization were carried out with the R package minfi[23]. MBatch analysis showed no evidence of batch effects. Differentially methylated regions (DMRs) were identified using DMRcate and annotated with annotatr[24,25]. For DMRs that spanned multiple gene regions, we selected the gene with the most significant beta-values. To quantify methylation of a DMR, we took the average of all the statistically significant beta-values associated with the DMR.

DepMap data analyses

DepMap data was obtained from https://depmap.org/portal/download/[26]. CRC cell lines were selected excluding those with MSI and with > 800 mutations. To distinguish APC from APC CRC cell lines, we used the same filtering steps we used for the TCGA dataset. To assess the effect of CRISPR knockouts, we applied a Welch’s two-sample t-test statistic to the dependency scores of APC and APC cell lines. Dependency scores were extracted from the file “Achilles_gene_dependency.csv” on the DepMap portal.

Ethics statement

Ethics approval is not required for this study because it does not involve human participants or animal subjects.

Results

Age effect in CRCs

To identify characteristics that distinguished APC from APC CRCs, we compared molecular profiles between the two groups in a discovery series from the TCGA, then validated the results in two additional publicly available series. CRC samples that exhibited MSI or were hypermutated were excluded from our study, because tumors with these characteristics constitute a well-defined subtype[11]. In addition to separating MSS and non-hypermutated CRCs by APC mutation status, samples that contained a CTNNB1 mutation[14] or deep deletion of APC were also classified as APC. After applying these filtration steps, we classified 63 of 425 (15%) of the MSS CRCs in the TCGA dataset as APC. In the GSE35896 validation dataset, 16 out of 56 (29%) CRCs were classified as APC and in the CPTAC-2 dataset, 11 out of 81 (14%) CRCs were classified as APC.

We tested clinical features that might be statistically associated with TCGA APC CRCs (Table 1). In agreement with previous studies[6-8], APC CRCs were diagnosed at a younger age (61.4 in APC vs. 66.4 in APC), and 63% of tumors diagnosed < 50 were APC. APC CRCs were also younger in the CPTAC-2 dataset (61.5 in APC vs. 65.5 in APC), but this observation did not reach statistical significance (p = 0.24). (Age of diagnosis was not available for the GSE35896 dataset.) In addition to age, TCGA APC CRCs were more prevalent in Asians (p = 0.005), were more likely to be classified as CpG island methylator phenotype (CIMP) high (p = 0.02), and were more likely to be diagnosed later than stage one (p = 0.035).

Table 1

Comparison of clinical features in APC mutation-positive (APC) and APC mutation-negative (APC) colorectal cancers.

Feature	APCmut+ (N = 362, 85%)	APCmut– (N = 63, 15%)	P value
Age	66.4	61.4	.004
Non-silent mutations	121.4	112.4	.21
Male/female	194/165 (54%)	30/33 (48%)	.41
COAD/READ	250/109 (70%)	51/12 (81%)	.07
Proximal/distal	104/195 (35%)	24/25 (49%)	.08
White	305/359 (85%)	53/63 (84%)	.85
African American	46/359 (13%)	5/63 (8%)	.40
Asian	4/359 (1%)	5/63 (8%)	.005
American Indian	4/359 (1%)	0/63 (0%)	1.0
Stage I	62/346 (18%)	4/59 (7%)	.035
Stage II	106/346 (31%)	23/59 (39%)	.23
Stage III	119/346 (34%)	18/59 (31%)	.66
Stage IV	59/346 (17%)	14/59 (23%)	.27
CIMP-0	177/251 (70%)	30/50 (60%)	.18
CIMP-low	57/251 (23%)	11/50 (22%)	1.0
CIMP-high	17/234 (7%)	9/50 (18%)	.02
CMS1	5/291 (2%)	2/49 (4%)	.27
CMS2	149/291 (51%)	20/49 (41%)	.22
CMS3	41/290 (14%)	7/49 (14%)	1.0
CMS4	96/290 (33%)	20/49 (41%)	.33

P values were calculated for comparisons between APC CRCs and APC CRCs from the TCGA dataset. A two-sample t-test with a two-tailed p value was performed for continuous features and a Fisher’s exact test with a two-tailed p value was performed for categorical data. A p value threshold of 0.05 was considered significant. CIMP, CpG island methylator phenotype was defined by unsupervised clustering as reported by Guinney et al. CMS, consensus molecular subtypes of colorectal cancer determined by Guinney et al. Significant values are in bold.

WNT signaling mutations in CRCs

To identify distinctive somatic mutations, we compared non-silent nucleotide variants, gene amplifications, deep gene deletions, and gene fusions in APC and APC CRCs (Fig. 1A). The top three most statistically different genomic alterations specific to APC CRCs were PTPRK-RSPO3 gene fusions (p = 1.3 × 10–5), RNF43 mutations (p = 4.7 × 10–5) and BRAF mutations (p = 1.9 × 10–4). These genetic alterations have been identified in CRC previously with evidence for mutual exclusivity with APC mutations[27-30]. (The RNF43 mutation G659Vfs*41, which is associated with MSI CRCs, was not present in the tumors analyzed here as MSI tumors were excluded from this analysis[31]). Eight of nine BRAF mutated APC CRCs had the oncogenic V600E BRAF mutation. Six of eight RNF43 mutated APC CRCs had mutations that caused premature protein truncations, whilst one sample had a previously identified missense mutation, R554G. These findings suggested that BRAF and RNF43 mutations are associated with tumor progression in MSS APC CRCs.

Figure 1

WNT signaling mutations in APC CRCs. (A) Fraction of APC CRCs from the TCGA dataset with gene mutations, amplifications, deep deletions, and fusions that were significantly more common in APC in comparison to APC 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 CRCs and the gene fusion PTPRK-RSPO3 for the 63 APC CRCs.

Based on the mutated genes in Fig. 1A and the PTPRK-RSPO3 gene fusion, we found that only 37 out of 63 samples (59%) contained a genomic alteration that distinguished APC from APC CRCs (Fig. 1B). No pairwise combination of genes were statistically mutually exclusive. However, PTPRK-RSPO3 gene fusions and RNF43 mutations never co-occurred and were found in 23% of the APC CRCs. After disregarding overlapping genomic alterations, BRAF mutations were the next most abundant (10%), followed by mutations in ADGRL1 (6%), ERBB3 (5%), and ZAP70 (5%). Supporting these findings, we found that BRAF mutations in the GSE35896 dataset and mutations in RNF43, ERBB3, and ZAP70 in the CPTAC-2 dataset were more frequent in APC CRCs than in APC CRCs (Supplementary Fig. 1). (No additional mutation information was provided with the GSE35896 dataset.)

Enhanced sensitivity to extracellular WNT in CRCs

Because a distinctive somatic mutational mechanism was not evident in over 40% of APC CRCs, we examined transcriptomics data for further distinguishing molecular characteristics. Strikingly, in differential gene expression analysis of the TCGA dataset, RNF43 was the most differentially expressed gene between the two tumor groups (Padj = 4.6 × 10–15; Fig. 2A), with a -0.98 log2 fold decrease in mean expression level in APC CRCs. Consistent with these results, RNF43 was also down-regulated in APC CRCs in the GSE35896 and CPTAC-2 validation datasets (Fig. 2B). RNF43 and its family member ZNRF3 are membrane-bound E3 ubiquitin ligases that actuate the degradation of low-density-lipoprotein-related protein (LRP)-FZD WNT receptors. Binding of R-spondins to leucine-rich repeat-containing G-protein coupled receptors (LGR) leads to sequestration and membrane clearance of RNF43 and ZNRF3 from the cell surface[32-34]. The transcriptional down-regulation of RNF43 we found in APC CRCs suggested that these tumors may express higher levels of LRP-FZD receptors at the cell surface, and consequently be more responsive to extracellular WNTs.

Figure 2

Enhanced sensitivity to extracellular WNT in APC CRCs. (A) Volcano plot representing the results from differential expression analysis between APC and APC CRCs. Labeled points are the genes with an Padj < 0.0005. Blue points were downregulated in APC CRCs and red points upregulated. (B) Comparison of RNF43 gene expression in APC CRCs, APC 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 (Padj < 0.05) between APC and APC CRCs from TCGA were mapped onto the KEGG canonical WNT signaling pathway. Blue labeling represents genes downregulated in APC; red labeling represents upregulated genes. (D) Unsupervised clustering analysis of APC CRCs from the TCGA dataset using differentially expressed genes (Padj < 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.

Because WNT signaling was implicated by these results, we sought to determine the extent to which other factors in the canonical WNT signaling pathway were differentially expressed between APC and APC CRCs (Fig. 2C). Consistent with the results above, we observed that other genes involved in extracellular WNT signaling were dysregulated, namely RSPO3 and ZNRF3. Differences in RSPO3 and ZNRF3 mRNA expression showed a similar trend in the validation datasets and were statistically significant in select cases (Supplementary Fig. 2). We did not observe any differential expression of the extracellular WNT regulator genes LGR4, LGR5, LGR6, or LRP-FZD receptors. The fact that LGR4-6 were not differentially expressed between APC and APC CRCs was consistent with the finding that RSPO3 does not require interaction with LGRs to potentiate WNT signaling[35] and LRP-FZD receptor levels are regulated post-transcriptionally[36]. When we compared gene expression of APC and APC CRCs to normal samples and mapped genes onto the canonical WNT signaling pathway, changes in gene expression in WNT signaling were similar between these two tumor types (Supplementary Fig. 3). These results suggested that both types of CRCs exploit changes in WNT signaling. However, based on the mutation and expression data, APC CRCs appear to favor dysregulation of genes involved in response to extracellular WNT signaling, whereas APC CRCs are stuck in the “on” state and are WNT signal-transduction incompetent.

To determine the fraction of APC CRCs that operate via enhanced sensitivity of extra-cellular WNT, we performed unsupervised hierarchical clustering using all differentially expressed genes (Padj < 0.05) between APC and APC CRCs in the TCGA dataset (Fig. 2D). APC CRCs clustered into two prominent groups, referred to here as Cluster 1 (CL1) and Cluster 2 (CL2). Most APC CRCs with a PTPRK-RSPO3 fusion, BRAF mutation, or RNF43 mutation were in CL2. To characterize the expression profiles of APC CRCs in the context of extracellular WNT signaling, we computed a summarized score defined as RSPO3 expression minus the sum of RNF43 and ZNRF3 expression. This score represents a theoretical WNT ligand sensitivity (WNTLS) based on the known function of RSPO3 in increasing ligand sensitivity, and RNF43 and ZNRF3 in decreasing ligand sensitivity[33,34]. We examined how the WNTLS score tracked with maximum WNT ligand expression (Fig. 2E; see Methods for more details). Consistent with our expectation, APC CRCs with RNF43 mutations had higher WNTLS scores than APC CRCs and higher maximum WNT expression than normals. Interestingly, APC CRCs with PTPRK-RSPO3 fusions had the highest WNTLS score, but had the lowest expression of WNT ligands compared to other CRCs. Inconsistencies in how CRCs with PTPRK-RSPO3 fusions and CRCs with RNF43 mutations enhance their sensitivity to extracellular WNT signaling may be due to different selective pressures during cancer evolution.

APC CRCs with BRAF mutations also exhibited higher WNTLS and higher WNT ligand expression, similar to APC CRCs with RNF43 mutations. Importantly, APC CRCs from CL2 that did not have BRAF mutations, RNF43 mutations, or PTPRK-RSPO3 fusions exhibited higher WNTLS scores compared to APC CRCs. In contrast, APC CRCs from CL1 exhibited WNTLS scores similar to APC CRCs. APC CRCs from the GSE35896 and CPTAC-2 datasets also clustered into two groups with high and low WNTLS scores (Supplementary Fig. 4). Given the importance of WNT signaling in CRC, these results suggest that other WNT-related mechanisms drive CL1 APC CRCs. By transcriptomic analysis, CL1 APC CRCs were practically indistinguishable from APC CRCs; however, GSEA showed enrichment of oxidative phosphorylation genes (Supplementary Figs. 5 and 6), implicating mitochondrial activation in CL1 APC tumorigenesis. These results were supported by data from the DepMap CRISPR screen that demonstrated dependence of APC CRC cell lines on oxidative phosphorylation complexes in the mitochondria (Supplementary Fig. 5E).

CRCs associated with immune infiltration

GSEA analysis showed that GO terms related to the adaptive immune response were upregulated in APC compared to APC CRCs (Fig. 3A). To further investigate immune system involvement in APC CRCs, we employed the bulk tissue deconvolution method CIBERSORTx[22]. In agreement with the GSEA results, the CIBERSORTx absolute score was increased in APC compared to APC CRCs in all three CRC datasets (Fig. 3B). The CIBERSORTx absolute score was highest in APC CRCs with BRAF or RNF43 mutations and CL2 APC CRCs without mutations (Fig. 3C). Because these APC CRCs had more infiltrating immune cells than those with PTPRK-RSPO3 fusions, we tested whether any of the 22 immune cell types were associated with expression of WNT agonist ligand RSPO3 (Fig. 3D). We found that M2 macrophages had the strongest positive Pearson correlation with RSPO3 expression. M2 macrophages and RSPO3 expression were also significantly correlated in the GSE35896 and the CPTAC-2 datasets. Macrophage expression of RSPO3 was shown in a study of patients with pulmonary fibrosis[37].

Figure 3

APC CRCs associated with immune infiltration. (A) GSEA results of differential gene expression analysis of APC versus APC CRCs from the TCGA dataset. Red clusters represent GO terms enriched among upregulated genes in APC 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 CRCs. (D) Expression of RSPO3 in APC and APC CRCs plotted against their individual M2 macrophage scores identified from the CIBERSORTx algorithm. Pearson correlation was performed to determine statistical significance.

CRCs have higher methylation

Because we found an association between APC CRCs and CIMP-high previously[7], we identified differentially methylated regions (DMRs) between APC and APC CRCs. APC CRCs were globally more hypermethylated than APC CRCs, with a particular excess in promoter regions (Fig. 4A). Comparing the top ten hypermethylated and hypomethylated DMRs, we did not observe the same statistically significant genes as we did in the mutation and expression analyses (Fig. 4B). However, when we tested correlation of RNF43 expression with DNA methylation levels of DMRs and with RNA expression, we found that methylation and gene expression of AXIN2 had the highest correlations (Fig. 4C). RNF43 gene expression was also significantly correlated with AXIN2 expression in the GSE35896 and CPTAC-2 datasets (Fig. 4D; methylation data was not available in these datasets). Increased AXIN2 DNA methylation was associated with decreased RNF43 expression in a subset of APC CRCs that did not have one of the common somatic mutations (Fig. 4E). Similar to our findings with RSPO3 expression, we found that M2 macrophages correlated most with AXIN2 DNA methylation (Fig. 4F).

Figure 4

APC CRCs have higher AXIN2 methylation. (A) Bar plot comparing total number of hyper-methylated and hypo-methylated differentially methylated regions (DMRs) between APC and APC CRCs from the TCGA dataset. (B) Top 10 APC hypermethylated and hypomethylated DMRs between APC and APC CRCs from TCGA. Red bars represent APC CRC differentially hypermethylated genes and blue bars represent APC 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 and APC 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 CRCs. (F) Scatter plot of AXIN2 average beta values and the CIBERSORTx M2 macrophage score of APC and APC CRCs from the TCGA dataset. Pearson correlation was performed to measure statistical significance.

gene expression associated with earlier onset in CRCs

Age of onset was not different in CL1 and CL2 APC CRCs (Fig. 5A). To identify gene expression changes linked to earlier age of onset in APC CRCs, we separated APC CRCs into two groups based on the median expression of each gene and performed a logrank test between these two groups, using the age at diagnosis as the event variable. Expression of AP2M1 best distinguished earlier onset APC CRCs from later onset APC CRCs (Fig. 5B), and higher AP2M1 expression was associated with earlier onset in APC relative to APC CRCs (Fig. 5C).

Figure 5

AP2M1 gene expression associated with earlier-onset in APC CRCs. (A) A comparison of age between APC 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 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 and APC CRCs from the TCGA dataset. (D) Flowchart of two molecular mechanisms that may be involved in the development of APC CRC.

Discussion

Most CRCs are initiated by somatic mutation of the gene APC, leading to ligand-independent, constitutive activity of the WNT pathway. In the present study, we found two alternate ways in which APC CRCs may activate the WNT pathway. APC tumors clustered into two groups according to their transcriptomic profiles (Fig. 5D). One cluster (CL2) exhibited a variety of molecular alterations that were consistent with the hypothesis that these tumors have enhanced sensitivity to extracellular WNT ligands. In particular, the most significant change was downregulation of RNF43, which is expected to result in increased levels of WNT receptors and greater sensitivity to extracellular WNTs. AXIN2 methylation was highly correlated with RNF43 downregulation. AXIN2 and RNF43 are negative regulators of WNT signaling that are transcriptionally activated by nuclear β-catenin, consistent with the notion that epigenetic silencing of negative regulators plays a critical role in tumor formation in ligand-dependent, APC CRCs. Similarly, PTPRK-RSPO3 gene fusions drive R-spondin signaling, which is also expected to reduce RNF43 levels at the cell surface, upregulate WNT receptors, and enhance sensitivity to extracellular WNTs. We defined a WNT ligand sensitivity score to quantify this signature of extracellular WNT signaling in a sample-specific fashion and found a high WNTLS score was associated with CL2 APC CRCs in multiple independent datasets.

Germline mutations in RNF43 have been previously associated with serrated polyposis families, and somatic mutations in RNF43 and BRAF have been associated with sporadic serrated adenomas[38]. In a preliminary analysis, we found that CL2 APC CRCs expression profiles appear to be more similar to serrated adenomas than CL1 APC CRCs and APC CRCs, according to two published gene signatures[39,40] (data not shown), but these results need further investigation.

We also found that CL2 APC CRCs have a higher level of immune infiltration compared to APC and CL1 APC CRCs, especially in APC CRCs that had RNF43 or BRAF mutations. M2 macrophages had the strongest association with potentiating WNT signaling through its significant correlations with RSPO3 expression and AXIN2 DNA methylation. Previous studies have shown that macrophages have the capability to express RSPO3 and stimulate WNT signaling in response to tissue damage[41,42]. The association of CL2 APC CRCs with M2 macrophages suggests the etiology of this cancer subtype is tied to chronic tissue stress and inflammation that eventually favors a clone with hypersensitivity to WNT. We suggest that CL2 APC CRCs may be sensitive to porcupine inhibitors or anti-WNT/anti-DKK1 biologics. We note that AXIN2 methylation has been previously identified in APC CRCs as a potential biomarker for ligand-dependent tumors that would respond to anti-WNT-based therapies such as porcupine inhibitors[43-45].

In contrast, the other cluster (CL1) of APC CRCs was associated with low WNTLS score and may be dependent on enhanced mitochondrial activation. APC CRC cell lines from the DepMap database had a strong dependency on mitochondrial activation relative to APC CRC cell lines. We are cautious in interpreting these data, because the observed effectiveness of mitochondrial disruption of the APC CRC cell lines may be due to the absence of immune cells in vitro. One potential reason why some APC CRCs become dependent on enhanced mitochondrial activation is because mitochondria can stimulate the WNT pathway independently of WNT ligands[46]. Moreover, intestinal epithelial cell-specific knockout of TFAM, a transcription factor required for replication of mitochondria DNA, drastically reduced tumor formation in APC mouse models[47]. Therefore, we suggest that enhanced activation of mitochondria is a second, independent mechanism by which APC CRCs exploit WNT signaling in tumor progression. These findings also suggest that mitochondria inhibitors may be a promising therapeutic option for CL1 APC CRCs.

Although APC tumors overall exhibit a lower age of onset than APC tumors, we found no difference in age of onset between CL1 and CL2, suggesting that both extracellular WNT sensitivity and mitochondrial activation contribute to the incidence of early-onset CRC. We performed a APCwide analysis to determine what gene expression feature was most associated with age of onset and found that earlier-onset APC CRCs had increased expression of AP2M1. AP2M1 plays an important role in clathrin-mediated endocytosis[48]. A recent study showed that when insulin binds to an insulin receptor, IRS1 and IRS2 recruit AP2M1 to initiate insulin receptor endocytosis[49]. Thus, an increase of AP2M1 may suggest increased insulin signaling. Importantly, insulin can activate both the PI3K pathway and the MAPK pathway, which may in turn play a role in enhancing both mitochondrial activation and immune infiltration, thus contributing to driving both CL1 and CL2 subtypes of APC CRC[50-52]. Other studies have found that individuals with type two diabetes are at a greater risk for early-onset CRC[53,54].

Early-onset CRC is a rapidly advancing public health emergency, and it is associated with a lack of mutation in APC. Our comprehensive genomic analysis has uncovered two classes of APC CRCs, one which potentiates WNT signaling through sensitivity to extracellular signaling, and the other which exhibits mitochondrial activation. Future research should test the effect of anti-WNT biologics and mitochondrial inhibitors in organoid models and in vivo and compare the efficacy of AXIN2 methylation and WNT ligand sensitivity score in identifying anti-WNT sensitive tumors.

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information 3.

Supplementary Figures.