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Wnt, glucocorticoid and cellular prion protein cooperate to drive a mesenchymal phenotype with poor prognosis in colon cancer

Journal of Translational Medicine volume 22, Article number: 337 (2024) Cite this article

Abstract

Background

The mesenchymal subtype of colorectal cancer (CRC), associated with poor prognosis, is characterized by abundant expression of the cellular prion protein PrPC, which represents a candidate therapeutic target. How PrPC is induced in CRC remains elusive. This study aims to elucidate the signaling pathways governing PrPC expression and to shed light on the gene regulatory networks linked to PrPC.

Methods

We performed in silico analyses on diverse datasets of in vitro, ex vivo and in vivo models of mouse CRC and patient cohorts. We mined ChIPseq studies and performed promoter analysis. CRC cell lines were manipulated through genetic and pharmacological approaches. We created mice combining conditional inactivation of Apc in intestinal epithelial cells and overexpression of the human prion protein gene PRNP. Bio-informatic analyses were carried out in two randomized control trials totalizing over 3000 CRC patients.

Results

In silico analyses combined with cell-based assays identified the Wnt-β-catenin and glucocorticoid pathways as upstream regulators of PRNP expression, with subtle differences between mouse and human. We uncover multiple feedback loops between PrPC and these two pathways, which translate into an aggravation of CRC pathogenesis in mouse. In stage III CRC patients, the signature defined by PRNP-CTNNB1-NR3C1, encoding PrPC, β-catenin and the glucocorticoid receptor respectively, is overrepresented in the poor-prognosis, mesenchymal subtype and associates with reduced time to recurrence.

Conclusions

An unleashed PrPC-dependent vicious circle is pathognomonic of poor prognosis, mesenchymal CRC. Patients from this aggressive subtype of CRC may benefit from therapies targeting the PRNP-CTNNB1-NR3C1 axis.

Background

With over 900,000 deaths in 2020, colorectal cancer (CRC) remains the third most frequent cancer and the second cause of cancer-related deaths worldwide [1]. Despite tremendous progress in screening, diagnosis and therapy, the 5-years relative survival rate remains 65% (all disease stages), and even drops below 15% when patients are diagnosed at the metastatic stage (stage IV). Achieving a better understanding of the complex molecular mechanisms that orchestrate CRC pathogenesis still remains among the greatest challenges to improve therapeutic strategies.

Ever since the seminal identification of APC gene mutations associated with familial adenomatous polyposis over 30 years ago [2], the Wnt-β-catenin has remained the top-most scrutinized signaling pathway in CRC [3]. Around 90% of CRC cases have somatic mutations in the APC gene or other components of the Wnt pathway [3]. Although disturbed Wnt signaling is a constant feature in CRC, our knowledge of the co-regulatory pathways that cooperate with Wnt-β-catenin to promote CRC progression or that dictate specific transcriptional programs according to CRC molecular subtypes is far from complete. Indeed, the consensus molecular classification of CRC enables to divide tumors into one major or a combination of consensus molecular subtypes (CMS) based on bulk transcriptome profiling [4, 5]. Because APC mutations are prevalent in all CMS, although less frequent in the CMS1 subtype that corresponds to hypermutated and microsatellite unstable tumors [4], it can be surmised that CMS-specific signaling cascades orientate the Wnt response. In the CMS4 subtype that is associated with dismal prognosis, two pathways, namely TGFβ [6] and YAP/TAZ [7], have gathered special interest. These pathways are well known to crosstalk with Wnt-β-catenin signaling [8], and, therefore, are good candidates for influencing the Wnt-β-catenin output. Recently, we documented that the cellular prion protein PrPC, encoded by the PRNP gene, is an upstream regulator of these two major pathways in CMS4 CRC [9], and that its targeting is a promising approach to treat CMS4 patients [10]. Long confined to the field of neurodegenerative diseases, PrPC is now attracting a great deal of interest in cancer research [11]. Its location at the cell membrane [12], coupled to its capacity to instruct downstream cell signaling events [13] make it an ideal candidate for fine-tuning the cellular response to environmental signals. One unanswered question relates to the induction of PRNP expression along CRC initiation and progression. By integrating in silico analyses on mouse and human datasets with cell-based experiments, we provide evidence that PRNP expression is jointly controlled by the Wnt and glucocorticoid signaling pathways. Using mice that combine Apc inactivation in intestinal epithelial cells and PRNP overexpression, we uncover a positive Wnt-PrPC feedback loop that unleashes a vicious circle. Analyses carried out in several mouse models of β-catenin-driven liver cancer corroborated the occurrence of a Wnt-PrPC axis. Our data further point to species differences in the regulation of PRNP expression, with glucocorticoid signaling playing a critical role in human as compared to mouse. Finally, we show in two randomized clinical trials (RCT) of stage III CRC, altogether encompassing over 3000 patients, that the PrPC-dependent axis is pathognomonic of the CMS4 subtype and is associated with dismal prognosis.

Methods

Gene expression analyses

The following datasets were retrieved from public sources: GSE200908 [14], GSE208372 [15], GSE167008 [16], GSE20916 [17], GSE8671 [18], GSE4183 [19], GSE39852 [20], GSE11406 [21], PRJEB44400 [22]. Due to missing values, some analyses could not be performed with the GSE20916 dataset. Kinetic RNAseq data from sorted Apc∆hep hepatocytes are deposited on GEO with accession number GSE210482 [23].

Patients cohorts and analyses

The IDEA-France cohort is composed of 1248 patients having signed informed consent within a phase III randomized trial comparing 3 months to 6 months of mFOLFOX6 (infusional fluorouracil, leucovorin, and oxaliplatin) or CAPOX (capecitabine, oxaliplatin) after curative resection of stage III CC [24], and for which we obtained RNAseq on punch biopsies, as described in [25].

The PETACC8 cohort is composed of 1733 patients having signed informed consent within a phase III randomized trial comparing FOLFOX4 (infusional fluorouracil, leucovorin, and oxaliplatin) to FOLFOX4 + cetuximab in 2550 patients after curative resection of stage III CC [26], and for which we obtained RNAseq on macrodissected formalin-fixed, paraffin-embedded (FFPE) tissue sections, as described in [25].

The demographics of the IDEA-France and the PETACC8 cohorts are summarized in Additional file 1: Tables S1 and S2, respectively.

3’RNAseq and bioinformatics analyses are detailed in Additional file 1.

For survival analyses, the optimal cutpoint value was determined to predict time to recurrence (TTR), using the surv_cutpoint function from R package survminer. TTR analyses were performed using the coxph function of the survival R package. All analyses were carried out using R studio (version 4.2.2).

Statistical analysis

RNAseq datasets (GSE200908, GSE208372, GSE167008, PRJEB44400) were analyzed using the DESeq2 package version 1.38.3. All analyses were performed with R studio 4.2.2. All statistical analyses were performed in R studio (version 4.2.2) using the stat_compare_means function from the ggpubr package. Correlation analyses were performed in R studio (version 4.2.2) using the stat_cor function from the ggpubr package. The results from experimental data in cell lines are reported as the means ± standard errors of the means (s.e.m.) with graphs generated using GraphPad PRISM version 9.4.1. Analyses involving two groups were carried out using the Shapiro test followed by Student’s t test or Mann–Whitney rank-sum test according to normality. Results from RT-qPCR in mouse tissue and RNA analysis in public datasets or patient cohorts are expressed as median and interquartile range with graphs generated with ggplot2 in R studio. Statistical analysis was performed using the Mann–Whitney rank-sum test for two groups or one-way ANOVA followed by Wilcoxon rank-sum tests with Holm’s correction for multiple comparisons for > 2 groups.

Additional materials and methods are described in Additional file 1.

Results

PRNP is a target of Wnt-β-catenin signaling in various models of β-catenin-activated intestinal tumors

A still unresolved question regarding the overexpression of the cellular prion protein in colon cancer relates to the molecular pathways involved. By interrogating public datasets, we found that Apc inactivation in several mouse experimental paradigms, i.e. in the murine MC38 colon cancer cell line (GSE200908 [14]), mouse colonic tumor organoids (GSE208372 [15]), or mouse intestinal tumors (GSE167008 [16]), was systematically associated with Prnp gene upregulation (Fig. 1A–C). Assuming that PRNP might be a target of the canonical Wnt-β-catenin pathway, we subsequently mined ChIPseq datasets for β-catenin binding. As shown in Fig. 1D, we found that the PRNP promoter is occupied by β-catenin and harbors the active H3K4me3 mark (H3 histone tri-methylated on Lysine 4) in SW480 and DLD1 colon cancer cells in the GSE156083 dataset [27]. Likewise, searching the ENCODE database, we found that the PRNP promoter is bound by the TCF7L2 gene-encoded TCF4 transcription factor, the main β-catenin co-factor in colon and liver, as well as H3K27ac (H3 histone acetylated on Lysine 27) and H3K4me3, which both mark active chromatin, in HCT116 colon cancer cells (Fig. 1E). We further identified one TCF4 binding site in the promoter of PRNP by analyzing predicted transcription factor binding sites using the JASPAR database [28] (Fig. 1F). In agreement with the above data, PRNP expression was significantly reduced in β-catenin-silenced MDST8 colon cancer cells (Fig. 1G, H). Altogether these data show that β-catenin/TCF4 bind Wnt-responsive element (WRE)-encompassing enhancers specifically active in PRNP in human β-catenin activated tumor cells.

Fig. 1
figure 1

The PRNP gene is a target of Wnt-β-catenin signaling in colon cancer models. A–C Analysis of the GSE200908 (A) GSE208372 (B) and GSE167008 (C) datasets reveals increased Prnp expression in cellular, organoid and in vivo mouse models of Apc inactivation. D Analysis of the GSE156083 ChIPseq dataset reveals enrichment of β-catenin and the H3K4me3 active histone mark at the promoter of the PRNP gene in SW480 and DLD-1 human colon cancer cell lines. E Analysis from the ENCODE database reveals binding of the TCF7L2-encoded TCF4 factor, together with the H3K27ac and H3K4me3 active histone marks at the promoter of the PRNP gene in the HCT116 human colon cancer cell line. F Predicted TCF7L2 binding site within the human PRNP gene promoter. G, H Relative mRNA levels of CTNNB1 (G) and PRNP (H) in CTNNB1-silenced versus control MDST8 cells, as determined in qPCR analysis. Results are expressed as means of n = 2 independent triplicates of cell preparations ± s.e.m. (***p < 0.001, **p < 0.01, Student’s t-test)

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PRNP is a target of the glucorticoid receptor in CRC cells

Turning to CRC patients, we analyzed the expression profile of PRNP along disease progression from normal to adenoma to carcinoma. In three distinct datasets GSE8671 [18], GSE20916 [17] and GSE4183 [19], we recurrently found decreased expression of PRNP transcripts in adenoma versus normal tissue (Fig. 2A–C), contrasting with the increase in the expression of several Wnt target genes including AXIN2 [18]. On another hand, colon cancer tissue exhibited higher amounts of PRNP as compared to both adenoma and normal tissue (Fig. 2B, C), confirming the previous report by de Wit et al. that PrPC is induced along the adenoma-carcinoma sequence [29]. It is well established that the recruitment of β-catenin to target genes is not always sufficient to induce transcription and that β-catenin-dependent transcriptional regulation is highly context-specific and often involves co-factors [30]. Because the Wnt pathway is switched on in adenomas but is not accompanied by an increase in PRNP mRNAs, in contrast to our observations in mouse models, we thus surmised that β-catenin may cooperate with (an)other regulatory factor(s) to promote PRNP transcription in human cells. To identify potential candidates, we selected the transcription factors (TF) that fulfil the following criteria: (1) binding to the PRNP gene in the ENCODE database and (2) featuring in the HIPPIE (Human Integrated Protein–Protein Interaction rEference, [31]) database of β-catenin interactors. We further filtered the list of 11 candidates to keep TF whose expression is significantly correlated to that of PRNP in the two patient datasets GSE4183 and GSE39852 [20] (Fig. 2D). NR3C1, which encodes the glucocorticoid receptor, GR, was more strongly correlated with PRNP in the GSE39852 dataset than was ETS1 (R = 0.45 versus R = 0.32) and was selected for further analysis (Fig. 2E). Using the JASPAR database, we identified a consensus palindromic binding motif for NR3C1 in the PRNP promoter (Fig. 2F). Of note, this sequence is not conserved in the mouse Prnp promoter sequence (Additional file 1: Fig. S1A), although another GR responsive element (GRE) is found further upstream, according to JASPAR analysis (Additional file 1: Fig. S1B), in contrast to the WRE that is conserved from human to mouse (Additional file 1: Fig. S1A). The expression profile of NR3C1 is highly compatible with a GR-dependent regulation of PRNP gene expression since it is most abundantly expressed in the CMS4 subtype of CRC (Fig. 2G), it globally follows the pattern of PRNP expression along the normal to adenoma to adenocarcinoma sequence in patients (Fig. 2H–J), and NR3C1 and PRNP levels are significantly correlated in various datasets (Fig. 2K–M).

Fig. 2
figure 2

The GR encoded by the NR3C1 gene is predicted to regulate PRNP gene expression in human CRC. A–C Analysis of the GSE8671 (A) GSE20916 (B) and GSE4183 (C) datasets reveals decreased PRNP expression across the normal to adenoma sequence, followed by an increase from the adenoma to carcinoma sequence in human CRC. D Schematic representation of the selection of ETS1 and NR3C1 as candidate transcription factors regulating the expression of PRNP in human CRC. E Scatter plot showing the correlation between PRNP and NR3C1 mRNA levels in the GSE39582 dataset of human CRC. F Predicted NR3C1 binding sites within the human PRNP gene promoter. G Relative NR3C1 mRNA levels according to the CMS classification in the GSE39582 dataset of human CRC. NT = non tumor. H–J Analysis of the GSE8671 (H) GSE20916 (I) and GSE4183 (J) datasets reveals decreased NR3C1 expression across the normal to adenoma sequence, followed by an increase from the adenoma to carcinoma sequence in human CRC. K–M Scatter plots showing the correlation between PRNP and NR3C1 mRNA levels in GSE8671 (K) GSE20916 (L) and GSE4183 (M) datasets

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Because the expression of NR3C1 mRNA cannot be considered as a readout of GR activation, we reasoned that if PRNP is indeed a GR target, then it should be co-expressed with well-established GR-regulated genes. Indeed, TSC22D3, also known as Glucocorticoid-Inducible Leucine Zipper (GILZ), features among the top-50 PRNP positively correlated genes in the GSE4183 dataset (Fig. 3A). As observed with NR3C1, TSC22D3 transcripts are reduced in adenoma versus normal tissue but further induced at the adenoma-to-carcinoma progression (Fig. 3B, C); they strongly correlate with PRNP levels, including in the large GSE39582 dataset (Fig. 3D–F) and are enriched in CMS4 CRC (Fig. 3G). We then assessed whether PRNP transcription is induced upon GR activation. In the GSE11406 dataset [21], Prnp levels were induced in oligodendrocyte progenitor cells (OPC) after exposure to Dexamethasone (Dex) in a time-dependent manner (Fig. 3H). Likewise, we found increased expression of PRNP in the MDST8 CRC cell line upon treatment with Dex (1 µM, 24 h), accompanying increased TSC22D3 expression (Fig. 3I-J). Reciprocally, PRNP levels were reduced in NR3C1-silenced MDST8 cells (Fig. 3K, L). Combined with our previous report that PrPC controls the expression of NR3C1 in CRC cells [9], these data provide evidence for a positive feedback loop linking PrPC to the GR and suggest that the GR may cooperate with β-catenin to regulate PRNP gene expression in CRC.

Fig. 3
figure 3

PRNP gene expression is regulated by the GR in human CRC. A Heatmap showing the top 20 positively (in red) and negatively (in blue) correlated genes to PRNP expression in the GSE4183 dataset. Highlighted is the GR target TSC22D3. B, C Analysis of the GSE8671 (B) and GSE4183 (C) datasets reveals decreased TSC22D3 expression across the normal to adenoma sequence, followed by an increase from the adenoma to carcinoma sequence in human CRC. D, E Scatter plots showing the correlation between PRNP and TSC22D3 mRNA levels in GSE8671 (D) and GSE4183 (E) datasets. F Scatter plot showing the correlation between PRNP and TSC22D3 mRNA levels in the GSE39582 dataset of human CRC. G Relative TSC22D3 mRNA levels according to the CMS classification in the GSE39582 dataset of human CRC. NT = non tumor. H Boxplots showing the time-dependent increase in Prnp expression in oligodendrocyte progenitor cells (OPCs) exposed to Dexamethasone (Dex) (GSE11406 dataset). I, J Relative mRNA levels of TSC22D3 (I) and PRNP (J) in MDST8 cells exposed to Dex (1 µM, 24 h) versus vehicle (water), as determined in qPCR analysis. n = 3 cell preparations per condition. (***p < 0.001, *p < 0.05, Student’s t-test). K, L Relative mRNA levels of NR3C1 (K) and PRNP (L) in NR3C1-silenced versus control MDST8 cells, as determined in qPCR analysis. n = 2 independent triplicates of cell preparations. (***p < 0.001, Student’s t-test)

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PrP C overexpression boosts Wnt signaling in mouse intestine

According to our previous findings and the above data, PrPC sustains positive feedback loops with multiple effectors in colon cancer cells: TGFβ1 [9], but also the integrin linked kinase (ILK) [32], the amyloid precursor protein (APP)-derived Aβ [10], or the GR (cf above). This prompted us to investigate whether this would also apply to the Wnt pathway. This hypothesis is notably supported by the demonstration by Besnier et al. that PrPC interacts with β-catenin and its cofactor TCF7L2 and upregulates the transcriptional activity of the β-catenin/TCF7L2 complex in SW480 cells [33]. Corroborating this finding, we found that PrPC silencing in MDST8 or SW480 colon cancer cells reduced the levels of AXIN2 mRNA, while its overexpression in LoVo cells was associated to an opposite increase in AXIN2 transcripts (Additional file 1: Fig. S2). To test our hypothesis, we sought to generate mice combining inducible Apc inactivation with PrPC overexpression with the idea that a PrPC-Wnt positive feedback loop would translate into an increase in Wnt targets in normal tissue of PrPC overexpressing mice and sustain a vicious circle amplifying the expression of PrPC itself as well as its targets in Apc-mutant tumors. Mice containing a floxed mutant allele of the Apc gene (Apcfl/+) [34] as well as tamoxifen-dependent Cre-recombinase under the control of the Villin promoter (Vill-cre) [35], i.e. VilCreERT2Apcfl/+ mice [36], were crossed with mice overexpressing the human PRNP gene under its own regulatory sequences (through the use of a transgene composed of a large human genomic insert purified from a P1-derived artificial chromosome), in an endogenous mouse Prnp-null context (PRNP+/+Prnp−/−, hereafter referred to as PRNP+/+) [37], as outlined in Additional file 1: Fig. S3A. Importantly, the latter so-called tg650 mice have been extensively used in prion studies and were not reported to spontaneously overdevelop tumors compared to wild-type mice (personal communication JLV), indicating that PrPC overexpression per se does not initiate tumorigenesis. On another hand, these mice moderately overexpress PrPC in the heterozygous state (PRNP+/−) and overexpress PrPC sixfold versus WT in the homozygous state (PRNP+/+) [37]. They further allow to capture regulatory events impacting PRNP expression, contrary to the tga20 mouse model that overexpresses murine Prnp (eightfold) in an unregulated fashion [38]. To induce Apc loss of function, VilCreERT2Apcfl/+-PRNP+/− and VilCreERT2Apcfl/+-PRNP+/+ mice were treated with tamoxifen (see Materials and Methods) and maintained until they showed signs of intestinal illness. Several tamoxifen-untreated VilCreERT2Apcfl/+-PRNP+/− or Cre-negative Apcfl/+-PRNP+/− mice remained healthy at comparable time points, confirming that the loss of function of Apc is mandatory to initiate tumorigenesis in this mouse model (data not shown). We then harvested tissue from VilCreERT2Apcfl/+-PRNP+/− and VilCreERT2Apcfl/+-PRNP+/+ mice and assessed the expression of several markers of interest through RT-qPCR. As shown in Additional file 1: Fig. S3B, PRNP levels were comparable in control (tamoxifen-untreated VilCreERT2Apcfl/+-PRNP+/− or Cre-negative Apcfl/+-PRNP+/−) and tamoxifen-treated VilCreERT2Apcfl/+-PRNP+/− normal tissue, indicating that PRNP gene expression is insensitive to tamoxifen treatment. Then, we compared the normal tissues of PRNP+/− and PRNP+/+ mice. Elevated expression of PRNP in homozygous mice (Additional file 1: Fig. S4A) was accompanied by increased expression of a set of genes that we previously identified as PrPC gene targets in colon cancer cells [10], namely App, Bace1, Dkk3, Pdgfc and Tgfb1 (Additional file 1: Fig. S4B–F), thus validating our model as a robust PRNP-overexpressing paradigm. We further recorded increased expression of the canonical Wnt target genes Axin2, Lgr5 and Ccnd1 in homozygous PRNP+/+ mice versus their heterozygous counterparts (Additional file 1: Fig. S4G–I). As observed for PRNP, PrPC-dependent target genes (Additional file 1: Fig. S3C–G) and canonical Wnt target genes (Additional file 1: Fig. S3H–J) were not sensitive to tamoxifen treatment per se. As a whole, these data support our hypothesis that PrPC boosts Wnt signaling.

PrP C overexpression aggravates oncogenic Apc-induced colon tumorigenesis

We went on to examine the impact of PrPC overexpression on the same set of genes in intestinal tumors. We first found that PRNP mRNA levels were elevated in intestinal tumors versus normal tissue, whatever the genetic status (hetero- or homozygous) of the mice (Fig. 4A). The mean PRNP levels measured in the tumors of PRNP+/− mice however remained inferior to that measured in the normal tissue of PRNP+/+ mice. In the end, the mean PRNP mRNA level found in PRNP+/+ tumors reached 3.8 fold that of PRNP+/− tumors. In agreement with the above findings, we also found that the expression of the PrPC target genes App, Bace1, Dkk3, Pdgfc and Tgfb1 were all increased in the tumor versus normal tissue of both types of mice, and that the tumor levels were higher in homozygous versus heterozygous mice (Fig. 4B–F). We actually found that the expression levels of these genes were highly correlated to those of PRNP (Fig. 4G–K). In our mouse model, we further found that the levels of the Wnt target genes Axin2, Lgr5 and Ccnd1 were also strongly correlated to those of PRNP (Fig. 4L–N). As a result, the mean levels of Axin2 and Ccdn1 were much higher in tumors from homozygous than heterozygous mice (1.7 fold and 2.3 fold for Axin2 and Ccdn1, respectively, Fig. 4O, P). At histological examination, VilCreERT2Apcfl/+-PRNP+/− mice exhibited a small number of adenomas or in situ adenocarcinomas (Fig. 5A–C), while VilCreERT2Apcfl/+-PRNP+/+ mice were found to harbor numerous high-grade adenomas, in situ and even infiltrating adenocarcinomas, (Fig. 5A and D–E) suggesting that the overexpression of PrPC promotes colon cancer aggressiveness. In agreement, PRNP+/+ tumors exhibited intense nuclear or cytoplasmic β-catenin staining (Fig. 5E, lower panels) in contrast to PRNP+/− tumors that present a lower accumulation of β-catenin (Fig. 5C, lower panel).

Fig. 4
figure 4

PrPC sustains a vicious circle in an oncogenic Apc mouse model. A–F Boxplots showing the mRNA levels of human PRNP (A), and mouse App (B), Bace1 (C), Dkk3 (D), Pdgfc (E) and Tgfb1 (F) in normal or tumor tissue from VilCreERT2Apcfl/+-PRNP+/− (PRNP_het) or VilCreERT2Apcfl/+-PRNP+/+ (PRNP_hom) mice as measured through qRT-PCR. G-N Scatter plots showing the correlation between the mRNA levels of human PRNP and those of mouse App (G), Bace1 (H), Dkk3 (I), Pdgfc (J), Tgfb1 (K), Axin2 (L), Lgr5 (M) and Ccnd1 (N) in normal or tumor tissue from VilCreERT2Apcfl/+-PRNP+/− or VilCreERT2Apcfl/+-PRNP+/+ mice. O, P Boxplots showing the mRNA levels of mouse Axin2 (O) and Ccnd1 (P) in normal or tumor tissue from VilCreERT2Apcfl/+-PRNP+/− (PRNP_het) or VilCreERT2Apcfl/+-PRNP+/+ (PRNP_hom) mice as measured through qRT-PCR. Mice experiments were conducted as described in Additional file 1

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Fig. 5
figure 5

PrPC overexpression aggravates the development of mutant Apc driven CRC in mouse. A Relative quantification of lesion types according to genotype. B, C H&E staining sections from PRNP+/− mice, showing representative zones of high grade dysplasia (B, bottom panel) and low grade dysplasia (c, middle panel), as well as β-catenin staining (C, bottom panel). D, E H&E staining sections from PRNP+/+ mice, showing representative zones of infiltrating adenocarcinoma (D, bottom panels) and high grade dysplasia (E, middle panels), as well as β-catenin staining (E, bottom panels). Arrows indicate: a transition zone between normal cells and high grade dysplasia cells (B, bottom panel), mitoses (E, middle right panel), nuclear β-catenin (E, bottom right panel). Mice experiments were conducted as described in Additional file 1

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PRNP expression is induced in mouse models of liver cancer with β-catenin activation

To possibly extend our observations, we turned to liver cancer models, in which activation of β-catenin is one the most frequent mutational event [39]. We first exploited a mouse model based on Cre-Lox inactivation of Apc in hepatocytes (Apc∆hep) that recapitulates both hepatocellular carcinoma and hepatoblastoma with aberrant β-catenin activation [22, 40, 41]. In the pre-tumoral setting, analysis of our ChIPseq data [41] indicated that Tcf7l2-encoded TCF4 bound to the Prnp promoter in hepatocytes shortly after β-catenin activation, which was not observed when β-catenin was invalidated (Fig. 6A), thus confirming our earlier data that TCF4 regulates Prnp expression through direct binding to its promoter region. In line with this, ATACseq analysis revealed a time-dependent opening of chromatin at the Prnp promoter in GFP + Apc∆hep-sorted hepatocytes, accompanied by Prnp transcription, as inferred through RNAseq (Fig. 6B). In qPCR experiments, we confirmed the induction of Prnp gene expression in tumors from Apc∆hep mice (Fig. 6C), which was not observed in β-catenin-independent liver tumors induced by diethylnitrosamine (DEN) (Additional file 1: Fig. S5A). Of note, there was a tendency towards higher levels of Prnp in undifferentiated tumors, which resemble hepatoblastoma and display an epithelial to mesenchymal transition signature [22], as opposed to differentiated tumors that are similar to human hepatocellular carcinoma from G5-G6 groups (Fig. 6D). Similar results were obtained using RNAseq data from our previous study [22], combining liver tumors from either mutant Ctnnb1 deleted of exon 3 obtained by in vivo CRISPR/Cas9 editing or Apc∆hep mice (Fig. 6E). Interestingly, Nr3c1 transcripts followed the same pattern of expression as Prnp (Fig. 6F), further reinforcing the link between PrPC and glucocorticoid signaling found in the context of colon cancer (see above), and the two Wnt target genes Axin2 and Ccnd1 were higher in undifferentiated as compared to differentiated tumors (Additional file 1: Fig. S5C, D). Finally, in the same dataset, we found that Prnp mRNA levels were significantly correlated to those of App, Dkk3, Pdgfc, Tgfb1, Axin2, Ccnd1 and Nr3c1 (Fig. 6G–M). Altogether, these results provide further evidence that Prnp is a target of the Wnt-β-catenin pathway in multiple tissues and that it defines a signaling axis that is conserved from colon to liver cancer in mice.

Fig. 6
figure 6

The Prnp gene is a target of Wnt-β-catenin signaling in mouse models of liver cancer. A Analysis of our GSE35213 ChIPseq dataset reveals enrichment of the Tcf7l2-encoded TCF4 transcription factor at the promoter of the Prnp gene in Apc-inactivated (Apc∆hep), but not control or Ctnnb1-deficient hepatocytes. B Analysis of our GSE242267 ATACseq dataset and our GSE210482 RNAseq showing the promoter accessibility (left) and RNA transcription (right) of the Prnp gene along the kinetics (days 6, 15 and 21) of Apc inactivation in hepatocytes as compared to GFP− control. C Boxplots showing the mRNA levels of Prnp in Apc∆hep tumor versus Apc∆hep normal tissue, as measured through qRT-PCR. D Boxplots showing the mRNA levels of Prnp in undifferentiated versus differentiated liver tumors from Apc∆hep mice, as measured through qRT-PCR. E, F Analysis of our PRJEB44400 dataset reveals increased Prnp (E) and Nr3c1 (F) expression in differentiated and mostly undifferentiated liver tumors from mutant Ctnnb1 and Apc mice. G–M Scatter plots showing the correlation between Prnp and App (G), Dkk3 (H), Pdgfc (I), Tgfb1 (J), Axin2 (K), Ccnd1 (L) and Nr3c1 (M) mRNA levels in the PRJEB44400 dataset

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The PRNP-CTNNB1-NR3C1 axis defines a group of CRC patients with dismal prognosis

From the above data, we may hypothesize that, in CRC patients, β-catenin and glucocorticoid pathway activation cooperatively induce PRNP expression and may, as a consequence, instigate a detrimental vicious circle. Accordingly and in line with our previous findings [9, 10] and those illustrated in Fig. 2G and 3G, PRNP, BACE1, DKK3, PDGFC, NR3C1 and TSC22D3 levels were all enriched in the CMS4 subgroups of tumors from the IDEA France cohort (Fig. S6A-F), uniquely composed of stage III CRC cases [24]. However, this was not the case for the canonical Wnt targets AXIN2 and LGR5 (Additional file 1: Fig. S6G, H), in line with data reported in cell lines [42]. In addition, the expression levels of BACE1, DKK3, PDGFC, NR3C1 and TSC22D3 were all significantly correlated with those of PRNP (Additional file 1: Fig. S6I–M). Finally, we found that genes from this network were all enriched in the CMS combinations that we identified as associated with a notable dismal prognosis in our recent work [5] (Additional file 1: Fig. S6N–S). Again, opposite results were obtained for the two canonical Wnt targets AXIN2 and LGR5 (Additional file 1: Fig. S6T–U). Similar analyses in the PETACC8 cohort [26] yielded comparable results (Additional file 1: Fig. S7), although the correlations between PRNP and BACE1 or PDGFC were modest as compared to those between PRNP and DKK3, NR3C1 and TSC22D3, whose expression is majorly controlled by the GR (data not shown). The differences between the two cohorts may be accounted for by the different material used for 3’RNAseq (punch biopsies for the IDEA cohort and macro-dissected tissue sections for the PETACC8 cohort). Next, we calculated a PRNP-CTNNB1-NR3C1 score and we found this score to be significantly higher in CMS4 patients from the IDEA France cohort (Fig. 7A). This score was also increased in the group of patients with a pejorative CMS combination (Fig. 7B) or in patients with high-risk disease (pT4 and/or N2) (Fig. 7C). Furthermore, major correlations were observed between the PRNP-CTNNB1-NR3C1 score and a mesenchymal score derived from the study by de Reyniès et al. [43] (Fig. 7D), or a fibroblast score based on the MCP counter deconvolution algorithm [44] (Fig. 7E). As shown in Fig. 7F, the correlation between the mesenchymal and the PRNP-CTNNB1-NR3C1 score featured among the highest in the IDEA-France cohort, arguing that the PrPC-Wnt-GR axis is a genuine hallmark of mesenchymal CRC.

Fig. 7
figure 7

The PRNP-dependent axis is over-represented in poor-prognosis subtypes of CRC in the IDEA-France randomized clinical trial and predicts dismal outcome. A–C Relative PRNP-CTNNB1-NR3C1 score according to the CMS classification (A), CMS combination (B) or TNM risk (C) in the IDEA France cohort. Patients with stage pT1-T3 and pN1 were classified as low TNM risk and those with pT4 and / or pN2 as high TNM risk. D–F Scatter plots showing the correlation between the PRNP-CTNNB1-NR3C1 score and the mesenchymal (D) or the fibroblast (E) scores in the IDEA France cohort. F Density plot showing the distribution of correlation coefficients with the PRNP-CTNNB1-NR3C1 score in the IDEA France cohort highlighting the top-ranked correlation with the mesenchymal score. G Sankey plots showing the correspondence between low or high PRNP-CTNNB1-NR3C1 score and CMS subclasses. H, I Kaplan–Meier curves comparing time to recurrence in patients with low or high PRNP-CTNNB1-NR3C1 score in the entire IDEA France cohort (H) or in the subset of patients having received chemotherapy during 3 months (I)

Full size image

A higher PRNP-CTNNB1-NR3C1 score was also found in PETACC8 patients belonging to the CMS4 or the dismal prognosis CMS combination subgroups (Additional file 1: Fig. S8A, B). When we dichotomized patients from the IDEA France cohort according to this PRNP-CTNNB1-NR3C1 score, we observed that patients with a high score segregated into the CMS4 subgroup (Fig. 7G and Additional file 1: Table S3) and were significantly associated with the poor CMS combination or high TNM risk subgroups (Additional file 1: Table S3). Patients with a high PRNP-CTNNB1-NR3C1 score in the PETACC8 cohort were also more represented in the CMS4 and pejorative CMS combination groups (Additional file 1: Fig. S8D). Finally, a high PRNP-CTNNB1-NR3C1 score in the IDEA France cohort was associated with a significantly shorter time to recurrence than a low score (HR = 1.44, CI = 1.16–1.79, p < 0.001) (Fig. 7H), which was even more apparent within the group of patients who received chemotherapy for 3 months instead of 6 months (HR = 1.70, CI = 1.26–2.29, p < 0.001) (Fig. 7I). This was also true when considering only patients having been treated with FOLFOX (Additional file 1: Fig. S9A). Of note, the prognostic value of the 3 gene-combination was superior to those obtained when combining two out of three genes (Additional file 1: Fig. S9B–D). A higher PRNP-CTNNB1-NR3C1 score was also associated with a worse prognosis in the entire PETACC8 cohort (HR = 1.23, CI = 1.01–1.50, p = 0.04) (Additional file 1: Fig. S8E) or within each arm (Additional file 1: Fig. S8F, G). Altogether, these results exemplify the PRNP-CTNNB1-NR3C1 axis as a poor prognosis trait of CRC.

Discussion

This study uncovers a new gene regulatory network involving PrPC, the Wnt and Glucocorticoid signaling pathways that pertains to CRC progression and dismal outcome (Fig. 8) We have recently documented that PrPC is overexpressed in the mesenchymal, poor-prognosis subtype of CRC, and have shed some mechanistic insight into how it contributes to the mesenchymal phenotype of CRC cells [9, 32]. Going one step further, we have provided the proof of concept that antibody-mediated neutralization of PrPC may represent a novel therapeutic strategy to target mesenchymal CRC [10]. However, an integrated view of the mechanisms leading to the upregulation of PrPC was lacking. Several cancer studies have documented an induction of PrPC expression upon ER stress [45], hypoxia [46] or DNA damage [47]. From a molecular point of view, the PRNP promoter has been shown to be positively regulated by several transcription factors including sXBP1 [45], AP1 [48] as well as NFIL3 [49]. Here, we first uncovered that PrPC is a downstream target of the canonical Wnt-β-catenin pathway through in silico and molecular analyses in several models of colon and liver cancer based on β-catenin overactivation. One major finding is that, in human, PRNP gene regulation is also highly modulated by glucocorticoid signaling. This observation fully fits in with the notion that the transcriptional response to Wnt-β-catenin signaling is highly context- and co-factor-dependent [30], and with the recent report that β-catenin partners with the GR to promote stemness properties of prostate cancer cells [50]. That PrPC levels are sensitive to corticoids has been previously documented in neutrophils by Mariante et al. [51], but, to our knowledge, this is the first report that this also holds true in the context of cancer. Our results also align well with the current view that the specificity of gene expression regulation is highly dependent upon TF combinations [52]. In this respect, we may envision that additional regulatory factors beyond β-catenin and the GR combine with those to regulate PRNP gene expression. Potential candidates include SMADs, since they both interact with β-catenin [8] and the GR [53] and relay the action of TGFβ, itself known to induce the expression of PrPC [9, 51]. Further exploration is required to obtain an integrated view of the complex regulation of the PRNP gene in cancer.

Fig. 8
figure 8

Graphical summary

Full size image

While the study from Mariante and colleagues was performed in mice, our results obtained in human cohorts and mouse models, combined with comparative genomics of the regulatory sequences in the PRNP promoter, suggest that some aspects of PRNP regulation are specific to human, as reported for its regulation by the FOXP2 transcription factor [54]. This finding is reminiscent of the divergence of the p53 gene regulatory network between mouse and human emphasized by Fischer [55]. Incidentally, the identification of apparent inconsistencies after interrogating mouse and human datasets has sparked our search for potential β-catenin co-factors and led us to focus on the GR, whose response element differs between the mouse Prnp and human PRNP promoters. Besides, regardless of the differences between mouse and human, our generation of a PRNP-overexpressing Apc mouse model has proven instrumental to uncover the occurrence of a vicious circle deriving from the positive feedback loop linking PrPC to the Wnt signaling pathway. Thus, despite limitations inherent to mouse studies, molecular and histological examination of our autochthonous mouse model has successfully informed us on the deleterious impact of the cooperation between PrPC and aberrant Wnt signaling. Such observations were made possible by the unique characteristics of our model of PrPC overexpression, insuring proper regulation of expression and gain of function. Of note, Tgfb1 features among the genes that are induced downstream from PrPC (see Fig. 4F, K), suggesting that TGFβ1 may also fuel the vicious circle fostering PRNP gene expression, since we [9] and others [51] have documented a positive regulation of PRNP by TGFβ1.

Adding yet another layer of complexity, it is now recognized that the action of the GR can differ according to whether it is liganded or not [56]. This prompted us to include the GR target gene TSC22D3 in our analyses, as a readout of GR activation. Given the reciprocal relations between stress and cancer [57, 58], an endogenous activation of the GR in CRC patients is very likely. Incidentally, we obtained prominent correlations between the expression of TSC22D3 and that of PRNP (see Additional file 1: Fig. S6M) or the PRNP-CTNNB1-NR3C1 score (R = 0.55, Spearman) in the IDEA France cohort, indicating that tumors with high PrPC expression exhibit intrinsic GR activation. Furthermore, beyond the circulation, one source of glucocorticoids may be the tumor itself, as emphasized recently [59]. A major finding in the later study is that tumor-derived glucocorticoids exert a paracrine action on Tregs and foster tumor growth through Treg activation [59]. It can thus be surmised that the mobilization of the PRNP-CTNNB1-NR3C1 axis in tumor cells is associated with the emergence of an immune-suppressive tumor microenvironment. Another potential deleterious trait connected to the PRNP-CTNNB1-NR3C1 axis would be the interference with therapy-induced antitumor immunity, as the induction of TSC22D3 in dendritic cells (DCs) by glucocorticoids was shown to block interferon responses and subsequent T cell activation [60]. Altogether, these different processes may account for the dismal prognosis exemplified in patients with a high PRNP-CTNNB1-NR3C1 score.

Importantly, we obtained a set of observations that consistently conspire to define high PRNP-CTNNB1-NR3C1 as a pejorative trait in CRC, in addition to its prognostic value. Indeed, we found the PRNP-CTNNB1-NR3C1 score to be higher in CRC tumors of the CMS4 subtype, those with a pejorative CMS combination, or those associated with a higher risk of relapse based on TNM staging. One key finding is the very robust correlation between the PRNP-CTNNB1-NR3C1 score and the mesenchymal score defined by [43], arguing that the PRNP-CTNNB1-NR3C1 axis may be a hallmark of mesenchymal tumors.

Conclusion

In summary, the integration of in silico, cellular, mouse and human data allowed us to uncover an unprecedented cooperation between Wnt, GR and PrPC signaling, which perpetuates a vicious circle in CRC. We found that the regulation of PrPC expression is not strictly transposable from mouse to human, but that the manipulation of PrPC expression in mouse can nonetheless help uncover regulatory networks, which subsequently proved to have clinical value. Our work thus advocates for the implementation of multimodal approaches to decipher the complexity of CRC. Finally, given the premises of PrPC targeting [10], this study may pave the way towards new therapeutic strategies to treat mesenchymal CRC.

Data availability

Materials, data, and protocols described in the manuscript will be made available upon reasonable request.

Abbreviations

APP:

Amyloid precursor protein

CMS:

Consensus molecular subtype

CRC:

Colorectal cancer

DEN:

Dimethylnitrosamine

Dex:

Dexamethasone

GR:

Glucocorticoid receptor

GRE:

GR responsive element

HR:

Hazard ratio

ILK:

Integrin linked kinase

OPC:

Oligodendrocyte progenitor cell

PrPC :

Cellular prion protein

TF:

Transcription factor

WRE:

Wnt-responsive element

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49.

    Article  PubMed  Google Scholar 

  2. Kinzler KW, Nilbert MC, Su LK, Vogelstein B, Bryan TM, Levy DB, et al. Identification of FAP locus genes from chromosome 5q21. Science. 1991;253:661–5.

    Article  CAS  PubMed  Google Scholar 

  3. Parsons MJ, Tammela T, Dow LE. WNT as a driver and dependency in cancer. Cancer Discov. 2021;11:2413–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Marisa L, Blum Y, Taieb J, Ayadi M, Pilati C, Le Malicot K, et al. Intratumor CMS heterogeneity impacts patient prognosis in localized colon cancer. Clin Cancer Res. 2021;27:4768–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tauriello DVF, Sancho E, Batlle E. Overcoming TGFβ-mediated immune evasion in cancer. Nat Rev Cancer. 2022;22:25–44.

    Article  CAS  PubMed  Google Scholar 

  7. Mouillet-Richard S, Laurent-Puig P. YAP/TAZ signalling in colorectal cancer: lessons from consensus molecular subtypes. Cancers. 2020;12:3160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Qin K, Yu M, Fan J, Wang H, Zhao P, Zhao G, et al. Canonical and noncanonical Wnt signaling: multilayered mediators, signaling mechanisms and major signaling crosstalk. Genes Dis. 2024;11:103–34.

    Article  CAS  PubMed  Google Scholar 

  9. Le Corre D, Ghazi A, Balogoun R, Pilati C, Aparicio T, Martin-Lannerée S, et al. The cellular prion protein controls the mesenchymal-like molecular subtype and predicts disease outcome in colorectal cancer. EBioMedicine. 2019;46:94–104.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mouillet-Richard S, Martin-Lannerée S, Le Corre D, Hirsch TZ, Ghazi A, Sroussi M, et al. A proof of concept for targeting the PrPC - Amyloid β peptide interaction in basal prostate cancer and mesenchymal colon cancer. Oncogene. 2022;41:4397–404.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mouillet-Richard S, Ghazi A, Laurent-Puig P. The cellular prion protein and the hallmarks of cancer. Cancers. 2021;13:5032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hirsch TZ, Martin-Lannerée S, Mouillet-Richard S. Functions of the prion protein. Prog Mol Biol Transl Sci. 2017;150:1–34.

    Article  CAS  PubMed  Google Scholar 

  13. Mouillet-Richard S, Ermonval M, Chebassier C, Laplanche JL, Lehmann S, Launay JM, et al. Signal transduction through prion protein. Science. 2000;289:1925–8.

    Article  CAS  PubMed  Google Scholar 

  14. Lee R, Li J, Li J, Wu C-J, Jiang S, Hsu W-H, et al. Synthetic essentiality of tryptophan 2,3-dioxygenase 2 in APC-mutated colorectal cancer. Cancer Discov. 2022;12:1702–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marei H, Tsai W-TK, Kee Y-S, Ruiz K, He J, Cox C, et al. Antibody targeting of E3 ubiquitin ligases for receptor degradation. Nature. 2022;610:182–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Flanagan DJ, Pentinmikko N, Luopajärvi K, Willis NJ, Gilroy K, Raven AP, et al. NOTUM from Apc-mutant cells biases clonal competition to initiate cancer. Nature. 2021;594:430–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Skrzypczak M, Goryca K, Rubel T, Paziewska A, Mikula M, Jarosz D, et al. Modeling oncogenic signaling in colon tumors by multidirectional analyses of microarray data directed for maximization of analytical reliability. PLoS ONE. 2010;5: e13091.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sabates-Bellver J, Van der Flier LG, de Palo M, Cattaneo E, Maake C, Rehrauer H, et al. Transcriptome profile of human colorectal adenomas. Mol Cancer Res. 2007;5:1263–75.

    Article  CAS  PubMed  Google Scholar 

  19. Galamb O, Györffy B, Sipos F, Spisák S, Németh AM, Miheller P, et al. Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature. Dis Markers. 2008;25:1–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Marisa L, de Reynies A, Duval A, Selves J, Gaub MP, Vescovo L, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med. 2013;10: e1001453.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gobert RP, Joubert L, Curchod M-L, Salvat C, Foucault I, Jorand-Lebrun C, et al. Convergent functional genomics of oligodendrocyte differentiation identifies multiple autoinhibitory signaling circuits. Mol Cell Biol. 2009;29:1538–53.

    Article  CAS  PubMed  Google Scholar 

  22. Loesch R, Caruso S, Paradis V, Godard C, Gougelet A, Renault G, et al. Deleting the β-catenin degradation domain in mouse hepatocytes drives hepatocellular carcinoma or hepatoblastoma-like tumor growth. J Hepatol. 2022;77:424–35.

    Article  CAS  PubMed  Google Scholar 

  23. Sanceau J, Poupel L, Joubel C, Lagoutte I, Caruso S, Pinto S, et al. DLK1/DIO3 locus upregulation by a β-catenin-dependent enhancer drives cell proliferation and liver tumorigenesis. Mol Ther. 2024;S1525–0016(24):00037–46.

    Google Scholar 

  24. André T, Vernerey D, Mineur L, Bennouna J, Desrame J, Faroux R, et al. Three versus 6 months of oxaliplatin-based adjuvant chemotherapy for patients with stage III colon cancer: disease-free survival results from a randomized, open-label, international duration evaluation of adjuvant (IDEA) France, Phase III. Trial J Clin Oncol. 2018;36:1469–77.

    Article  PubMed  Google Scholar 

  25. Gallois C, Sroussi M, Andre T, Mouillet-Richard S, Agueeff N, Vernerey D, et al. Predictive models of recurrence from transcriptomic signatures of the tumor microenvironment and cell cycle in stage III colon cancer from PETACC-8 and IDEA France trials. J Clin Oncol. 2023;41:3523.

    Article  Google Scholar 

  26. Taieb J, Tabernero J, Mini E, Subtil F, Folprecht G, Van Laethem J-L, et al. Adjuvant FOLFOX4+/- Cetuximab in KRAS wild-type patients with resected stage III colon cancer results from the PETACC8 intergroup trial. Ann Oncol. 2012;23:17–17.

    Article  Google Scholar 

  27. Wan C, Mahara S, Sun C, Doan A, Chua HK, Xu D, et al. Genome-scale CRISPR-Cas9 screen of Wnt/β-catenin signaling identifies therapeutic targets for colorectal cancer. Sci Adv. 2021;7:eabf2567.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sandelin A, Alkema W, Engström P, Wasserman WW, Lenhard B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2004;32:D91–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. de Wit M, Jimenez CR, Carvalho B, Belien JAM, Delis-van Diemen PM, Mongera S, et al. Cell surface proteomics identifies glucose transporter type 1 and prion protein as candidate biomarkers for colorectal adenoma-to-carcinoma progression. Gut. 2012;61:855–64.

    Article  PubMed  Google Scholar 

  30. Nakamura Y, de Paiva AE, Veenstra GJC, Hoppler S. Tissue- and stage-specific Wnt target gene expression is controlled subsequent to β-catenin recruitment to cis-regulatory modules. Development. 2016;143:1914–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017;45:D408–14.

    Article  CAS  PubMed  Google Scholar 

  32. Ghazi A, Le Corre D, Pilati C, Taieb J, Aparicio T, Didelot A, et al. Prognostic value of the PrPC-ILK-IDO1 axis in the mesenchymal colorectal cancer subtype. Oncoimmunology. 2021;10:1940674.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Besnier LS, Cardot P, Da Rocha B, Simon A, Loew D, Klein C, et al. The cellular prion protein PrPc is a partner of the Wnt pathway in intestinal epithelial cells. Mol Biol Cell. 2015;26:3313–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Colnot S, Niwa-Kawakita M, Hamard G, Godard C, Le Plenier S, Houbron C, et al. Colorectal cancers in a new mouse model of familial adenomatous polyposis: influence of genetic and environmental modifiers. Lab Invest. 2004;84:1619–30.

    Article  CAS  PubMed  Google Scholar 

  35. el Marjou F, Janssen K-P, Chang BH-J, Li M, Hindie V, Chan L, et al. Tissue-specific and inducible Cre-mediated recombination in the gut epithelium. Genesis. 2004;39:186–93.

    Article  PubMed  Google Scholar 

  36. Lévy J, Cacheux W, Bara MA, L’Hermitte A, Lepage P, Fraudeau M, et al. Intestinal inhibition of Atg7 prevents tumour initiation through a microbiome-influenced immune response and suppresses tumour growth. Nat Cell Biol. 2015;17:1062–73.

    Article  PubMed  Google Scholar 

  37. Béringue V, Le Dur A, Tixador P, Reine F, Lepourry L, Perret-Liaudet A, et al. Prominent and persistent extraneural infection in human PrP transgenic mice infected with variant CJD. PLoS ONE. 2008;3: e1419.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fischer M, Rulicke T, Raeber A, Sailer A, Moser M, Oesch B, et al. Prion protein (PrP) with amino-proximal deletions restoring susceptibility of PrP knockout mice to scrapie. Embo J. 1996;15:1255–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Laurent-Puig P, Zucman-Rossi J. Genetics of hepatocellular tumors. Oncogene. 2006;25:3778–86.

    Article  CAS  PubMed  Google Scholar 

  40. Colnot S, Decaens T, Niwa-Kawakita M, Godard C, Hamard G, Kahn A, et al. Liver-targeted disruption of Apc in mice activates beta-catenin signaling and leads to hepatocellular carcinomas. Proc Natl Acad Sci U S A. 2004;101:17216–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Gougelet A, Torre C, Veber P, Sartor C, Bachelot L, Denechaud P-D, et al. T-cell factor 4 and β-catenin chromatin occupancies pattern zonal liver metabolism in mice. Hepatology. 2014;59:2344–57.

    Article  CAS  PubMed  Google Scholar 

  42. Linnekamp JF, van Hooff SR, Prasetyanti PR, Kandimalla R, Buikhuisen JY, Fessler E, et al. Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models. Cell Death Differ. 2018;25:616–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. de Reyniès A, Jaurand M-C, Renier A, Couchy G, Hysi I, Elarouci N, et al. Molecular classification of malignant pleural mesothelioma: identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition. Clin Cancer Res. 2014;20:1323–34.

    Article  PubMed  Google Scholar 

  44. Becht E, de Reyniès A, Giraldo NA, Pilati C, Buttard B, Lacroix L, et al. Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin Cancer Res. 2016;22:4057–66.

    Article  CAS  PubMed  Google Scholar 

  45. Dery MA, Jodoin J, Ursini-Siegel J, Aleynikova O, Ferrario C, Hassan S, et al. Endoplasmic reticulum stress induces PRNP prion protein gene expression in breast cancer. Breast Cancer Res. 2013;15:R22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Park J-Y, Jeong J-K, Lee J-H, Moon J-H, Kim S-W, Lee Y-J, et al. Induction of cellular prion protein (PrPc) under hypoxia inhibits apoptosis caused by TRAIL treatment. Oncotarget. 2015;6:5342–53.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bravard A, Auvré F, Fantini D, Bernardino-Sgherri J, Sissoëff L, Daynac M, et al. The prion protein is critical for DNA repair and cell survival after genotoxic stress. Nucleic Acids Res. 2015;43:904–16.

    Article  CAS  PubMed  Google Scholar 

  48. Bernardino-Sgherri J, Siberchicot C, Auvré F, Busso D, Brocas C, El Masri G, et al. Tumor resistance to radiotherapy is triggered by an ATM/TAK1-dependent-increased expression of the cellular prion protein. Oncogene. 2021;40:3460–9.

    Article  CAS  PubMed  Google Scholar 

  49. Lin S-C, Lin C-H, Shih N-C, Liu H-L, Wang W-C, Lin K-Y, et al. Cellular prion protein transcriptionally regulated by NFIL3 enhances lung cancer cell lamellipodium formation and migration through JNK signaling. Oncogene. 2020;39:385–98.

    Article  CAS  PubMed  Google Scholar 

  50. Martinez SR, Elix CC, Ochoa PT, Sanchez-Hernandez ES, Alkashgari HR, Ortiz-Hernandez GL, et al. Glucocorticoid receptor and β-catenin interact in prostate cancer cells and their co-inhibition attenuates tumorsphere formation, stemness, and docetaxel resistance. Int J Mol Sci. 2023;24:7130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Mariante RM, Nobrega A, Martins RA, Areal RB, Bellio M, Linden R. Neuroimmunoendocrine regulation of the prion protein in neutrophils. J Biol Chem. 2012;287:35506–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Reiter F, Wienerroither S, Stark A. Combinatorial function of transcription factors and cofactors. Curr Opin Genet Dev. 2017;43:73–81.

    Article  CAS  PubMed  Google Scholar 

  53. Petta I, Dejager L, Ballegeer M, Lievens S, Tavernier J, De Bosscher K, et al. The interactome of the glucocorticoid receptor and its influence on the actions of glucocorticoids in combatting inflammatory and infectious diseases. Microbiol Mol Biol Rev MMBR. 2016;80:495–522.

    Article  CAS  PubMed  Google Scholar 

  54. Konopka G, Bomar JM, Winden K, Coppola G, Jonsson ZO, Gao F, et al. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature. 2009;462:213–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans. Oncogene. 2019;38:4095–109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Cirstea IC, Moll HP, Tuckermann J. Glucocorticoid receptor and RAS: an unexpected couple in cancer. Trends Cell Biol. 2023;33:594–604.

    Article  CAS  PubMed  Google Scholar 

  57. Eckerling A, Ricon-Becker I, Sorski L, Sandbank E, Ben-Eliyahu S. Stress and cancer: mechanisms, significance and future directions. Nat Rev Cancer. 2021;21:767–85.

    Article  CAS  PubMed  Google Scholar 

  58. Ma Y, Kroemer G. The cancer-immune dialogue in the context of stress. Nat Rev Immunol. 2023;24:264–81.

    Article  PubMed  Google Scholar 

  59. Taves MD, Otsuka S, Taylor MA, Donahue KM, Meyer TJ, Cam MC, et al. Tumors produce glucocorticoids by metabolite recycling, not synthesis, and activate Tregs to promote growth. J Clin Invest. 2023;133: e164599.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yang H, Xia L, Chen J, Zhang S, Martin V, Li Q, et al. Stress-glucocorticoid-TSC22D3 axis compromises therapy-induced antitumor immunity. Nat Med. 2019;25:1428–41.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors wish to thank the technical assistance of animal facilities staff at INRAE and Centre de Recherche des Cordeliers.

Funding

This work was supported by INSERM, INRAE, as well as Cancéropôle Ile de France (grant number 2016-1-EMERG-36-UP 5-1), Association pour la Recherche sur le Cancer (grant number PJA 20171206220), SIRIC CARPEM (CAncer Research for PErsonalized Medicine, INCa-DGOS Inserm_12561), Labex Immuno-oncology, Groupement des Entreprises Monégasques dans la Lutte contre le Cancer (GEMLUC), Plan cancer (grant CHROMALIV), Agence Nationale de la Recherche (JCJC DLK1-EPILIV) and the Ligue Nationale Contre le Cancer (Team labellisation for SC, BR and PLP). MS was supported by a fellowship from Fondation pour la Recherche Médicale (FRM grant FDM202006011237) and CJ by a PhD fellowship from the Ligue Nationale Contre le Cancer.

Author information

Author notes

  1. Angélique Gougelet, Bruno Passet, Camille Brochard contributed equally to this work.

Authors and Affiliations

  1. Centre de Recherche Des Cordeliers, INSERM, Sorbonne Université, Université Paris Cité, 75006, Paris, France

    Sophie Mouillet-Richard, Angélique Gougelet, Camille Brochard, Delphine Le Corre, Camille Joubel, Marine Sroussi, Claire Gallois, Julien Lavergne, Camilla Pilati, Aurélien de Reyniès, Fatima Djouadi, Sabine Colnot, Julien Taieb & Pierre Laurent-Puig

  2. University of Paris-Saclay, INRAE, AgroParisTech, UMR1313 GABI, 78350, Jouy-en-Josas, France

    Bruno Passet, Johan Castille, Marthe Vilotte, Nathalie Daniel-Carlier & Jean-Luc Vilotte

  3. Institut du Cancer Paris CARPEM, APHP, Department of Pathology, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France

    Camille Brochard

  4. Université Paris Cité, Institut Cochin, Inserm, CNRS, F-75014, Paris, France

    Caterina Luana Pitasi & Béatrice Romagnolo

  5. Equipe Labellisée Ligue Nationale Contre Le Cancer, Paris, France

    Sophie Mouillet-Richard, Delphine Le Corre, Caterina Luana Pitasi, Camille Joubel, Marine Sroussi, Claire Gallois, Camilla Pilati, Aurélien de Reyniès, Fatima Djouadi, Sabine Colnot, Julien Taieb, Béatrice Romagnolo & Pierre Laurent-Puig

  6. Institut du Cancer Paris CARPEM, APHP, Hepatogastroenterology and GI Oncology Department, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France

    Claire Gallois & Julien Taieb

  7. Histology, Imaging and Cytometry Center (CHIC), Paris, France

    Julien Lavergne

  8. Saint-Antoine Hospital, INSERM, Unité Mixte de Recherche Scientifique 938, Sorbonne Université, Paris, France

    Thierry André

  9. Institut du Cancer Paris CARPEM, APHP, Department of Biology, APHP.Centre-Université Paris Cité, Hôpital Européen G. Pompidou, Paris, France

    Pierre Laurent-Puig

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Contributions

SMR Conceived and supervised the study, performed molecular and bioinformatic analyses, wrote the manuscript. AG Performed animal studies, analyzed and interpreted animal experimental and in silico data. BP Performed animal studies. CB Analyzed and interpreted animal experimental. DLC, CJ and FD Participated in molecular and cellular biology experiments. CLP, JL and MV Participated in histological experiments. JC and NDC Participated in animal studies. MS, CJ, CP and AdR Contributed to assembling patient cohort RNAseq data. SC, JLV, BR Provided animal resources and funding. TA and JT Assembled patient cohorts. PLP Contributed to design the study, assembled patient cohorts, analyzed patient data, provided funding and reviewed the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Sophie Mouillet-Richard or Pierre Laurent-Puig.

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The authors declare no competing interests in the present study.

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Supplementary Information

Additional file 1.

Materials and methods.

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Mouillet-Richard, S., Gougelet, A., Passet, B. et al. Wnt, glucocorticoid and cellular prion protein cooperate to drive a mesenchymal phenotype with poor prognosis in colon cancer. J Transl Med 22, 337 (2024). https://doi.org/10.1186/s12967-024-05164-0

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  • DOI: https://doi.org/10.1186/s12967-024-05164-0

Keywords

Journal of Translational Medicine

ISSN: 1479-5876

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