Literature DB >> 30699286

Mutation Hotspots in the β-Catenin Gene: Lessons from the Human Cancer Genome Databases.

Sewoon Kim1, Sunjoo Jeong1.   

Abstract

Mutations in the β-catenin gene (CTNNB1) have been implicated in the pathogenesis of some cancers. The recent development of cancer genome databases has facilitated comprehensive and focused analyses on the mutation status of cancer-related genes. We have used these databases to analyze the CTNNB1 mutations assembled from different tumor types. High incidences of CTNNB1 mutations were detected in endometrial, liver, and colorectal cancers. This finding agrees with the oncogenic role of aberrantly activated β-catenin in epithelial cells. Elevated frequencies of missense mutations were found in the exon 3 of CTNNB1, which is responsible for encoding the regulatory amino acids at the N-terminal region of the protein. In the case of metastatic colorectal cancers, inframe deletions were revealed in the region spanning exon 3. Thus, exon 3 of CTNNB1 can be considered to be a mutation hotspot in these cancers. Since the N-terminal region of the β-catenin protein forms a flexible structure, many questions arise regarding the structural and functional impacts of hotspot mutations. Clinical identification of hotspot mutations could provide the mechanistic basis for an oncogenic role of mutant β-catenin proteins in cancer cells. Furthermore, a systematic understanding of tumor-driving hotspot mutations could open new avenues for precision oncology.

Entities:  

Keywords:  cancer genome database; hotspot mutations; β-catenin

Mesh:

Substances:

Year:  2019        PMID: 30699286      PMCID: PMC6354055          DOI: 10.14348/molcells.2018.0436

Source DB:  PubMed          Journal:  Mol Cells        ISSN: 1016-8478            Impact factor:   5.034


INTRODUCTION

β-Catenin is an important co-activator downstream of the oncogenic Wnt signaling pathway, so mutations in the β-catenin gene (CTNNB1) have been implicated in oncogenesis (Korinek et al., 1997; Morin et al., 1997; Polakis, 2012b). Recently, large-scale cancer databases, such as The Cancer Genome Atlas (TCGA) pan-cancer analysis project, have leveraged systemic analyses on genome, exome, and transcriptome data from all types of cancers (Blum et al., 2018; Hutter and Zenklusen, 2018; Tomczak et al., 2015). Multidimensional cancer genome data are available on cBioPortal, an open platform for cancer genome analysis and visualization (Cerami et al., 2012; Gao et al., 2013). In this review, we have employed pan-cancer genome databases to analyze the current status of β-catenin gene (CTNNB1) mutations to identify mutation hotspots and to re-evaluate the oncogenic roles of specific β-catenin mutant proteins. An extensive review on the clinical aspects of the β-catenin protein is beyond the scope of this mini review, so we have provided a brief introduction regarding the basic biology of the β-catenin protein.

A BRIEF INTRODUCTION TO THE β-CATENIN PROTEIN

β-Catenin is a multitasking protein involved in transcription and cell adhesion (Hur and Jeong, 2013; Kumar and Bashyam, 2017; Valenta et al., 2012). In particular, β-catenin is an important co-activator of Wnt target genes, such as cyclin D1 and c-myc (Korinek et al., 1997; Morin et al., 1997). However, in differentiated cells, where Wnt signaling is off, the central regulatory mechanism for β-catenin is sequential phosphorylation in the N-terminal region followed by ubiquitin-mediated proteolysis (Fig. 1A). Casein Kinase-1α phosphorylates the S45 residue and primes subsequent phosphorylation on T41/S37/S33 by GSK-3β, leading to the binding of ubiquitin E3 ligase β-transducin repeats-containing proteins (β-TrCP) at the N-terminal region (D32 to S37) in a phosphorylation-dependent manner (Hart et al., 1998; Liu et al., 2002). Specific phosphorylation and ubiquitination occur in the APC/Axin complex, termed as the β-catenin destruction complex (Stamos and Weis, 2013). In contrast, the destruction complex functions no more, so the level of the β-catenin protein in the cytoplasm increases following Wnt activation (Fig. 1B). The mechanism by which Wnt signaling stabilizes β-catenin needs to be better understood in the aspect of the β-catenin destruction complex (Kim et al., 2013; 2015; Li et al., 2012; Taelman et al., 2010). Finally, Wnt-stimulated β-catenin is translocated into the nucleus, where it acts as transcriptional co-activator with DNA binding TCF/LEF proteins and activates many developmentally important, cancer-related and pathogenic genes (Nusse and Clevers, 2017).
Fig. 1

A schematic diagram of the Wnt signaling pathway

(A) Wnt-off. In the absence of Wnt stimulation, β-catenin is phosphorylated by CK1α and GSK3β followed by ubiquitin-proteasome mediated proteolysis. (B) Wnt-on. Upon Wnt stimulation, the destruction complex is not functional, so the β-catenin protein is translocated into the nucleus and acts as a transcriptional co-activator to regulate oncogenic target genes. APC, Adenomatous polyposis; DVL, Disheveled.

FREQUENCY OF GENOMIC ALTERATIONS IN THE CTNNB1 GENE IN CANCERS

Small-scale targeted gene analysis demonstrates mutations in the β-catenin gene (CTNNB1 ) in some cancers (Polakis, 2007; 2012b). Large-scale β-catenin mutational landscape was revealed from clinical sequencing of 10,000 prospective cancer patients by the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) (Zehir et al., 2017). Figure 2 shows the frequency of CTNNB1 alterations across tumor types. A high frequency of CTNNB1 mutations are found in endometrial (16%), hepatobiliary (12%), melanoma (7%), and colorectal (6%) cancers. It is noteworthy that the frequency of APC mutations is much higher (approximately 70%) than that of CTNNB1 in colorectal cancer, but CTNNB1 and APC mutations exist in the exclusive manner. The alteration frequency of CTNNB1 in endometrial, liver, and colorectal cancer from other genomic analysis networks is compiled in Table 1. In addition, cancer cell lines with mutations in CTNNB1 are summarized in Table 2.
Fig. 2

The alteration frequency of CTNNB1 and APC across cancer types

Data obtained from the MSK-IMPACT pan-cancer study on cBioportal (www.cbioportal.org).

Table 1

The alteration frequency of CTNNB1 in endometrial, liver, and colorectal cancer

Cancer typeSequencing data sourceNo. SequencedNo. Alteration (%)No. Exon3-mut (%)Reference
Endometrial cancerEndometrial Cancer (MSK, 2018)18727 (14.4)25 (13.4)Soumerai et al., 2018
Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013)24071 (29.6)63 (26.3)Cancer Genome Atlas Research et al., 2013a
Uterine Carcinosarcoma (TCGA, PanCancer Atlas)561 (1.8)0 (0.0)Cancer Genome Atlas Research et al., 2013b
Uterine Clear Cell Carcinoma (NIH, Cancer 2017)160 (0.0)0 (0.0)Le Gallo et al., 2017
Liver cancerLiver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)35395 (26.9)78 (22.1)Cancer Genome Atlas Research et al., 2013b
Liver Hepatocellular Carcinoma (AMC, Hepatology 2014)23153 (22.9)46 (19.9)Ahn et al., 2014
Liver Hepatocellular Carcinoma (RIKEN, Nat Genet 2012)253 (12.0)3 (12.0)Fujimoto et al., 2012
Hepatocellular Carcinomas (Inserm, Nat Genet 2015)24387 (35.8)76 (31.3)Schulze et al., 2015
Hepatocellular Adenoma (Inserm, Cancer Cell 2014)3013 (43.3)11 (36.7)Pilati et al., 2014
Colorectal cancerColorectal Adenocarcinoma (TCGA, Nature 2012)21211 (5.2)1 (0.5)Cancer Genome Atlas, N. et al., 2012
Colorectal Adenocarcinoma (Genentech, Nature 2012)725 (6.9)2 (2.8)Seshagiri et al., 2012
Colorectal Adenocarcinoma (DFCI, Cell Reports 2016)61931 (5.0)8 (1.3)Giannakis et al., 2016
Metastatic colorectal cancer (MSK, Cancer Cell 2018)109984 (7.6)19 (1.7)Yaeger et al., 2018
Colon Adenocarcinoma (TCGA, PanCancer Atlas)38927 (6.9)15 (3.9)Cancer Genome Atlas Research et al., 2013b
Rectum Adenocarcinoma (TCGA, PanCancer Atlas)1378 (5.8)0 (0.0)Cancer Genome Atlas Research et al., 2013b

Data obtained from the listed cancer studies on cBioportal (www.cbioportal.org)

Table 2

Status of mutations in cancer cell lines harboring activating mutations of CTNNB1

Cancer typeCell LineMutations

CTNNB1APCTP53BRAFKRAS
Colorectal cancerSW48S33YR2714C
CCK81T41AY159CP278HS273N
SNU407T41AR726CG12D
HCT116S45delG13D
LS180S45FR1788CD211GG12D
Gastric cancerKE39D32NV272L
AGSG34EG12D
SNU719G34V
OCUM1S45C
Endometrial cancerHEC265D32V, X561_spliceP1233L
HEC6D32VV160A
HEC108S37P, D207GS678G, A2388V, T2514IP151H
JHUEM2S37C
SNGMS37PA2VG12V
Lung cancerMORCPRS33LP865L, A2122dupP152Rfs*18G13C
SW1573S33FG12C
LXF289T41AR248W
HCC15S45F, Y670*D2796GD259V
Liver cancerHUH6G34VN239D, A159D
SNU398S37C
MelanomaSKMEL1S33CV600E
COLO783S45delP27LV600E

Mutation data obtained from Cancer Cell Line Encyclopedia (Novartis/Broad, Nature, 2012) on cBioportal (www.cbioportal.org).

Abbreviation: del, deletion; dup, duplication; fs, frame shift; splice, splice site mutation;

stop codon

Endometrial cancer

Wnt/β-catenin pathway has been linked to endometrial cancer. Loss of APC function in the mouse endometrium induces nuclear β-catenin accumulation in uterine hyperplasia and squamous cell metaplasia. Although APC loss alone does not lead to malignant transformation, APC loss enhances endometrial tumorigenesis driven by PTEN loss (van der Zee et al., 2013). The majority of CTNNB1 alterations occur in endometrial carcinoma, but not in carcinosarcoma or clear cell carcinoma as indicated in Table 1 (Cancer Genome Atlas Research et al., 2013a; Cancer Genome Atlas Research et al., 2013b; Le Gallo et al., 2017; Soumerai et al., 2018). In endometrial cancer cases from TCGA, the alterations of CTNNB1 or APC genes are 30% or 12% of 240 patients, respectively (Cancer Genome Atlas Research et al., 2013a). Loss of APC function arises from truncation of the gene, but the frequency is only 6% in endometrial cancer. Thus, in endometrial cancer, CTNNB1 mutations, rather than APC mutations, might be direct driver mutations. Recently, 245 endometrial cancer patient samples were sequenced using 46–200 gene panels. CTNNB1 mutations appear more frequently in low-grade (grades 1–2) and early-stage (stages I–II) patients. More significantly, the patients harboring CTNNB1 mutations are associated with worse recurrence-free survival (Kurnit et al., 2017).

Hepatocellular carcinoma (HCC)

Liver cancer is the seventh most common cancer and the fourth leading cause of cancer mortality worldwide (Bray et al., 2018). However, treatment options are still limited for patients with advanced HCC due to the heterogeneity of genome alterations. Genome-wide studies have been carried out to identify driver genes responsible for tumorigenesis. SNP array analysis of 125 HCC cases have identified that four genes (CTNNB1 (32.8%), TP53 (20.8%), ARID1A (16.8%), and AXIN1 (15.2%)) are altered in more than 10% of the samples (Guichard et al., 2012). Whole exome sequencing analysis with 231 early-stage HCC Korean patient samples identified recurrent somatic mutations in CTNNB1 (23%) and TP53 (32%) (Ahn et al., 2014). In addition, CTNNB1 and TP53 were found to be frequently altered in a large cohort of HCC patient samples (Cancer Genome Atlas Research et al., 2013b; Fujimoto et al., 2012; Schulze et al., 2015). The CTNNB1 gene is also frequently altered in hepatocellular adenoma (HCA) (Pilati et al., 2014). β-catenin transgenic mouse models have been used to define a function of β-catenin in HCC tumorigenesis (Nejak-Bowen and Monga, 2011). Ectopic expression of either wild-type or mutant β-catenin is not sufficient to induce tumorigenesis (Harada et al., 2002; Nejak-Bowen et al., 2010). In some cases, β-catenin may accelerate tumorigenesis in cooperating with activated Ha-Ras (Harada et al., 2004) or heterozygote deleted Lkb1 (Miyoshi et al., 2009).

Colorectal cancer (CRC)

Wnt/β-cat signaling plays an important role in the tumorigenesis of CRC (Polakis, 2012b). In particular, alteration of APC, a negative regulator in Wnt signaling, is found in approximately 70% of CRC patients. Most APC alterations are truncation mutations, which cannot facilitate the proteolysis of β-catenin. In addition, loss of heterozygosity is frequently found in colorectal cancers. As shown in Table 1, genetic alterations also occurred in the CTNNB1 gene (5% of TCGA, 5% of DFCI, 6.9% of Genentech) (Giannakis et al., 2016; Seshagiri et al., 2012). Several studies reported that β-catenin has oncogenic activity in CRC cells, so the inhibition of β-catenin by gene targeting or knockdown resulted in growth inhibition of colorectal cancer cells (Cancer Genome Atlas, 2012; Green et al., 2001; Kim et al., 2002; Roh et al., 2001).

MUTATION HOTSPOTS IN EXON 3 (ENCODING THE N-TERMINAL REGION) OF THE β-CATENIN GENE

The β-catenin protein is composed of three domains: an N-terminal domain (~130 aa), a central domain (residue 141-664) made of 12 Armadillo (Arm) repeats and a C-terminal domain (~100 aa) (Fig. 3A). The central domain of the protein, the Arm repeats domain, forms a rigid rod-like structure and interacts with many binding proteins (Xu and Kimelman, 2007). However, it has been difficult to determine the structure of the terminal regions (N- and C-terminals) of β-catenin, so they are likely to be flexible and could be intrinsically disordered (Xing et al., 2008). Interestingly, the N-terminal region of the β-catenin protein is encoded by exon 3 (amino acid residues 5–80) of CTNNB1, so the N-terminal mutations can also be referred to as exon 3 mutations. CTNNB1 mutation hotspots were statistically analyzed by the Sorting Intolerant From Tolerant (SIFT) and the Polymorphism Phenotyping (PolyPhen) (Adzhubei et al., 2010; Naus, 1982; Sim et al., 2012).
Fig. 3

Diagram of β-catenin protein domains and hotspot mutations

(A) A schematic diagram of the β-catenin protein and mRNA. UTR, untranslated region; CDS, coding sequence; ATG, translation start codon; TAA, translation stop codon. (B) Exon 3 hotspot mutations of CTNNB1 are marked on the lollipop plot downloaded from the MSK-IMPACT pan-cancer study on cBioportal. Deep deletions near Exon 3 of CTNNB1 pre-mRNA are indicated as red lines. Deletion data were obtained from metastatic colorectal cancer study (MSK) on cBioportal.

Missense mutations affecting the N-terminal region of the β-catenin protein

In most cancers, mutations are found in the N-terminal region of β-catenin, especially in exon 3 of β-catenin mRNA (Fig. 3B). In endometrial cancers, integrated analysis showed that exon 3 mutations in β-catenin mRNA are associated with an aggressive phenotype of low-grade and low-stage in younger women (Liu et al., 2014). These studies suggest that β-catenin mutations can be a prognostic marker for aggressive endometrial cancer. Additionally, in liver cancer, hotspot mutations in CTNNB1 were deeply analyzed in a large cohort of patients from HCA to carcinoma (HCC). S45, K335, and N387 mutations result in weak activation of β-catenin and are frequently found in HCA. T41 mutations show relatively moderate activation. Exon 3 deletion and β-TrCP binding site (D32-S37) mutations show strong activation and are enriched in HCA/HCC borderline and HCC, respectively. Highly activated β-catenin is associated with malignant tumors, as evaluated by glutamine synthase staining. Although S45 mutations show weak activation, most S45 mutant alleles in HCC are duplicated, resulting in strong activation of β-catenin. This study suggests that HCA harboring high/moderate mutants or S45 mutants may be associated with malignant transformation (Rebouissou et al., 2016). Accelerated liver regeneration and hepatocarcinogenesis was also observed in mouse overexpressing S45 mutant β-catenin (Nejak-Bowen et al., 2010). In colorectal cancers, most somatic mutations are observed at D32, S33, G34, S37, T41, and S45 in exon 3 of β-catenin mRNA. These hotspot mutations have been shown to stabilize β-catenin by disturbing the phosphorylation-dependent ubiquitination, leading to tumorigenesis. S45 is a priming-phosphorylation site for Casein Kinase I alpha (CK1α) (Liu et al., 2002). S33, S37, and T41 are further phosphorylated by GSK3β. D32 and G34 is required to bind with β-TrCP, a component of ubiquitin E3 ligase for phosphorylated β-catenin (Aberle et al., 1997; Hart et al., 1998).

Exon 3-spanning in-frame deletion in metastatic colorectal cancers

Recently, prospective targeted sequencing was reported with metastatic and early-stage colorectal cancer patients of a large cohort study (Yaeger et al., 2018). In this MSK study, the frequency of CTNNB1 alterations (8%) is slightly higher than that in TCGA cohort (5% of TCGA pan-cancer atlas), but in-frame deletion is highly enriched in the MSK cohort. This difference may be due to the distinct features between MSK and TCGA cohorts. The MSK cohort includes 47% of metastases that were not included in TCGA cohort, representing more aggressive and advanced disease. Activating hotspot mutations of β-catenin are more frequently occurred in microsatellite instability-high (MSI-H) tumors than in microsatellite stable (MSS) tumors (25% of MSI-H, 6% of MSS). Interestingly, however, exon 3-spanning in-frame deletions were identified only in MSS tumors and the nuclear staining of β-catenin was observed in tumors harboring inframe deletions in CTNNB1 (Yaeger et al., 2018).

CONCLUSION

Large-scale analysis of pan-cancer genomic database revealed a high frequency of CTNNB1 mutations in endometrial, liver, and colorectal cancers. In addition, mutations are frequently located near exon 3 of CTNNB1, which encode for the regulatory amino acids (D32, S33, G34, S37, T41, and S45) at the N-terminal region of the protein. Since the N-terminal region is highly unstructured and flexible, the contributions of N-terminal hotspot mutations from a structural perspective are not easy to comprehend (Dar et al., 2017; Gottardi and Peifer, 2008; Xing et al., 2008). Rather, their contribution to cancer development should be understood in terms of their roles in normal and pathogenic epithelial cell states.

FUTURE PERSPECTIVES

Re-evaluating hotspot mutations

The high frequency of mutations affecting the GSK3β and β-TrCP-binding sites (D32, S33, G34, S37) can be explained by their roles in the β-catenin destruction complex (Megy et al., 2005; Stamos and Weis, 2013). However, higher frequencies of S45 and T41 mutations cannot be easily explained in terms of the residues for priming and relay kinases, respectively. In fact, recent study suggested the uncoupling of CK1α phosphorylation on S45 residue to GSK3β phosphorylation on S37/S33 residues. The phosphorylations on the T41/S45 residues of β-catenin were spatially uncoupled from the phosphorylated S33/S37/T41 (Maher et al., 2010). In addition, a previous study reported that the phosphorylations on S33/S37/T41 can occur in the absence of the phospho-S45 in colon cancer cells (Wang et al., 2003). In desmoid-type fibromatosis, protein stability and target genes for the S45F mutant are different from those of the wild-type β-catenin (Colombo et al., 2017). Moreover, the S45F mutation is associated with low efficacy of a cyclooxygenase-2 inhibitor in desmoid tumors (Hamada et al., 2014). It will be important to determine the oncogenic role of the S45 mutant β-catenin protein, as a type of mutation distinct from other mutant β-catenin proteins.

β-catenin in multiple protein complexes

β-Catenin protein was first discovered as a component of the adherens junction (Ozawa et al., 1989). Later, it is considered as a multitasking protein involved in transcription as well as in cell adhesion (Hur and Jeong, 2013; Kumar and Bashyam, 2017; Valenta et al., 2012). However, it should be noted that most β-catenin proteins reside in the adhesion complex near the plasma membrane in which it interacts with E-cadherin and α-catenin with high affinities (Huber and Weis, 2001). Multiple roles of β-catenin protein may come from multiprotein assembly forming distinct complexes in different intracellular locations (Xu and Kimelman, 2007). In the nucleus, β-catenin associates with DNA binding proteins, such as TCF/LEF and BCL9 (Graham et al., 2001; Sampietro et al., 2006). Collectively, the N-terminal region of β-catenin is critical for regulating the adhesion and transcription functions of the protein. Thus, the regulatory mechanism of phosphorylation may differ in distinct β-catenin complexes (Dar et al., 2016). Therefore, many questions arise as to whether the specific mutant β-catenin proteins can form a previously unknown complex, in addition to the adhesion, destruction, and transcription complexes (Fig. 4). We hope that the clinical information gained from the large cancer genome databases could facilitate the study of novel functions of β-catenin in RNA metabolism as an RNA-binding protein (Hur and Jeong, 2013; Kim et al., 2009; Kim et al., 2012; Lee and Jeong, 2006). To enhance our understanding of such novel functions, a systematic mutant β-catenin library could be developed to link the differential functional impacts to specific mutations in cancer. More functional studies on specific mutant β-catenin proteins will open up new avenues for elucidating the mechanisms underlying mutant β-catenin-mediated oncogenesis.
Fig. 4

Proposed model for the role of mutant β-catenin proteins in distinct complexes

The green indicates wild-type β-catenin protein and the pink indicates exon 3-mutated β-catenin proteins. In addition to the adhesion, destruction and transcription complexes with the indicated proteins, additional unknown protein complexes are likely to be formed by the mutant β-catenin proteins.

Novel therapeutic approach for mutant β-catenin proteins

β-Catenin protein has been a prime target for anti-cancer drug development, but some limitations may suspend successful drug development. In most cases, wild-type β-catenin protein have been utilized as a target protein and Wnt signaling activated transcription is used as a screening read-out (Cui et al., 2018; Krishnamurthy and Kurzrock, 2018; Polakis, 2012a). As a novel strategy, the information obtained for mutant β-catenin can be implemented for mutant-specific anti-cancer therapeutics, as utilized for mutant p53 proteins (Bykov et al., 2018; Kotler et al., 2018). Large-scale clinical analysis could provide important information on the functions of cancer-related proteins and cancer signaling, as shown here (Hyman et al., 2017). Therefore, future research should be directed toward a precision oncology strategy by identifying the molecular signature of cancer-related genes and exploiting cancer genome databases (Zehir et al., 2017).
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