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Human Molecular Genetics Advance Access originally published online on July 6, 2005
Human Molecular Genetics 2005 14(16):2349-2356; doi:10.1093/hmg/ddi237
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Allelic association of the human homologue of the mouse modifier Ptprj with breast cancer

Fabienne Lesueur1,*, Paul D. Pharoah1, Stewart Laing1, Shahana Ahmed1, Clare Jordan1, Paula L. Smith2, Robert Luben3, Nicholas J. Wareham3, Douglas F. Easton2, Alison M. Dunning1 and Bruce A.J. Ponder1

1 Cancer Research UK Human Cancer Genetics Research Group, Department of Oncology, University of Cambridge, Cambridge CB2 1TN, UK, 2 Cancer Research UK Genetic Epidemiology Group, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 1TN, UK and 3 MRC Epidemiology Unit, Strangeways Research Laboratories, Cambridge CB1 8RN, UK

* To whom correspondence should be addressed at: CNRG, Centre National de Génotypage, 91057 Evry Cedex, France. Tel: +33 160878433; Fax: +33 160878383; Email: fabienne.lesueur{at}cng.fr

Received May 18, 2005; Revised June 21, 2005; Accepted June 29, 2005


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Human homologues of mouse cancer modifier genes may play a role in cancer risk and prognosis. A proportion of the familial risk of common cancers may be attributable to variants in such genes, each contributing to a small effect. The protein tyrosine phosphatase receptor type J (PTPRJ) has been recently identified as being the protein encoded by the Scc1 mouse gene (susceptibility to colon cancer-1). In addition, the PTPRJ gene has been shown to be somatically altered in several human cancer types such as colon, lung and breast cancers and to have the characteristics of a tumour-suppressor gene. The purpose of this study was to determine whether common variants in the PTPRJ gene represent low penetrance breast cancer susceptibility alleles. To test this hypothesis, we assessed single nucleotide polymorphisms (SNPs) tagging the common SNPs and haplotypes of the gene in 4512 cases and 4554 controls from the East Anglian population. We observed a difference in the haplotype frequency distributions between cases and controls (P=0.0023, OR=0.81 [0.72–0.92]). Thus, carrying a specific PTPRJ haplotype confers a protective effect on the risk of breast cancer. This result establishes the principle that mouse cancer modifier genes are candidates for low penetrance human breast cancer susceptibility genes.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Like other common cancers, breast cancer has a significant hereditary component (1Go), which is probably mostly the result of multiple alleles of low to moderate penetrance (2Go). Unlike rarer high penetrance disease alleles, these cannot be found by linkage analysis. Mapping and cloning of quantitative trait loci (QTLs) for cancer susceptibility in animals may help to identify homologous genes in humans. Several cancer susceptibility QTLs have been mapped in mouse and rat [Mom1 (3Go), Scc1 (4Go), Stk6 (5Go)] (reviewed in 6Go) and the demonstration that their human homologues are involved in several cancer types opens the possibility of identifying the cancer susceptibility genes in animals and subsequently in man.

The mouse gene Scc1 (susceptibility to colon cancer-1) was positionally cloned and identified as Ptprj, which encodes a receptor of the protein tyrosine phosphatase (PTP) family (altenative symbols: DEP-1 or CD148 antigen) (4Go). PTPs are known to be signalling molecules that regulate a variety of cellular processes including cell growth, differentiation, mitotic cycle and oncogenic transformation. PTPRJ has several characteristics of a tumour-suppressor gene. In cell lines derived from individuals with breast cancer, the induction of differentiation increases the expression of human PTPRJ, and its transfection into non-differentiated cells induces differentiation and inhibits their growth (7Go,8Go). In rats, it was reported that the expression of PTPRJ suppresses the malignant phenotype in transformed rat thyroid cells (9Go). Finally, it has been shown that human colon, lung and breast cancers frequently present somatic missense mutations, loss of heterozygosity or deletions of the PTPRJ gene indicating a possible role of the gene in carcinogenesis (10Go).

The aim of this study was to determine whether common polymorphic variants in PTPRJ are associated with breast cancer risk in the UK population. Our principal hypothesis is that there are one or more common SNPs in PTPRJ that are associated with an altered risk of breast cancer. When the patterns of allelic association between common variants can be described, it is not necessary to assay all common variants within a candidate gene because genotypes at many of these sites are strongly correlated (11Go). We used a SNP tagging approach to identify a set of SNPs (stSNPs) that efficiently tags all the known SNPs and common haplotypes in the gene. This indirect approach allows the detection of association between a particular candidate gene or region and the disease, whether the SNPs themselves have a functional effect. Thus, we examined an informative set of six common SNPs tagging the other SNPs and the most common haplotypes of the PTPRJ gene, to assess association between the common alleles of this gene and breast cancer risk in our East Anglian population-based breast cancer case–control series.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
The gene PTPRJ on chromosome 11 consists of 25 exons that are distributed over 180 kb of genomic DNA. (CEPH) Centre d'Etude du Polymorphisme Humain trio data were available for 87 SNPs in PTPRJ, of which 39 had minor allele frequency >5%. We divided the gene into four LD blocks for the purpose of tag-SNP selection and used the tagSNPs programme (12Go) to identify six SNPs that capture the known common variation in the gene. These were rs7122335, rs2904315, rs4752894, rs1503185, rs905476 and rs1566729. Sixteen of 39 SNPs were captured with rp2>0.8, rp2 was between 0.6 and 0.8 for four SNPs, rp2 was between 0.4 and 0.6 for 16 SNPs and rp2<0.4 for three SNPs. Minimum rs2 was 0.69 with 36 SNPs having rs2>0.8.

Genotype distribution in the controls did not differ significantly from that expected under Hardy–Weinberg equilibrium for any of the SNPs (Table 1). In the univariate analyses, there were no significant differences in genotype frequency between cases and controls for any of the six polymorphisms (Table 1). All genotypic specific odds ratios (ORs) were close to 1.0, with lower 95% confidence limits >0.6 and upper 95% confidence limits <1.6.


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  		Table 1. Breast cancer risks associated with PTPRJ polymorphism genotype frequencies and genotype specific risks (results for set 1)
 
The six SNPs under study generated six common haplotypes with a frequency ≥5% in controls, representing >95% of all possible haplotypes in the UK population (Table 2). A seventh haplotype was found with a frequency <1%. Haplotype-specific risks are given in Table 2. A difference in the haplotype frequency distributions of the seven haplotypes was found between cases and controls in our initial case–control set (global test P=0.0004). The haplotypes H6 and H7 were under-representated in cases and contributed the most to the global test (P=0.05 for H6 and P=0.02 for H7). The SNPs rs7122335 and rs1566729 allow H6 and H7 to be distinguished from the other haplotypes, so these were genotyped in the additional sample set from the East Anglian population (‘set 2’). Genotype-specific risks for these two SNPs in the two case–control sets analysed separately and together are shown in Table 3. The absence of association of rs1566729 seen in set 1 was confirmed in set 2. However, there was borderline evidence for an increased risk for carriers of the ‘g’ allele of rs7122335 in the complete dataset (Ptrend=0.04). We re-estimated PTPRJ haplotype frequencies and risks on the combined data (set 1+set 2) using these two SNPs and confirmed a significant difference in the haplotype frequency distributions between cases and controls (global test P-value=0.002). This was again due to the under-representation of the haplotypes H6 and H7 in the cases (OR=0.81 [0.72–0.92], P=0.0006; Table 4). We also evaluated this significance level empirically by randomly re-assigning the case–control status among our subjects. A total of 10 000 simulations were carried out and the observed significance level was exceeded 23 times giving a simulated P-value of 0.0023.


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  		Table 2. Estimated PTPRJ haplotype frequencies and risks in cases and controls (results for set 1)
 

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  		Table 3. Breast cancer risks associated with rs7122335 and rs1566729 genotype frequencies and genotype specific risks (results for set 1 and set 2)
 

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  		Table 4. Estimated PTPRJ haplotype frequencies and risks in cases and controls
 
We concluded that this was evidence for the presence of a rare, yet unidentified variant on haplotypes H6 and H7 of PTPRJ, with a frequency ≤6% in the UK population which may confer a moderate protective effect on breast cancer risk.

Exclusion of known coding SNPs
Nine non-synonymous SNPs and five synonymous SNPs have been reported in the coding sequence of PTPRJ (Table 5). Sequence alignments, secondary structure prediction and homology modelling predict that most amino acid substitutions in the extracellular portion of PTPRJ occur in exposed regions available for interactions with ligands or other proteins and could affect the signalling process (4Go). For example, the R326Q substitution (rs1503185) leads to a loss of positive charge in the FNIII-2 domain. This SNP has been included in our study and we did not find a significant association of this variant with the development of breast cancer. A second amino acid substitution, Q276P (rs1566734), results in torsional stress of the same FNIII domain of the protein. A preferential loss of the common ‘a’ allele versus ‘c’ allele in colorectal cancer has been observed for this SNP (4Go). This suggested that, in most cases, the putative ‘cancer-resistance’ ‘a’ allele is lost, whereas the potentially less active ‘c’ allele is retained. We did not genotype this SNP, but we can exclude it because we found that rs905476 has no effect on breast cancer risk and these two SNPs are in perfect LD (D'=1, r2=1) according to the HapMap CEPH trio data.


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  		Table 5. Variants in the coding and 3'UTR of the human PTPRJ gene
 
To further test the possible role of the remaining 12 coding SNPs in predisposition to breast cancer, we re-sequenced the exons 5, 6, 7, 8, 13, 23 and 25 of the gene in 48 UK samples. Their frequency in the UK population is presented in Table 5. Ten of the SNPs are not polymorphic in this population. The last two coding SNPs, T233T and D872E, have a frequency of 29 and 28%, respectively. Because the causal SNP lies exclusively on haplotypes H6 and H7, its expected frequency should be ≤6%. Therefore, none of the proven coding SNPs described here is responsible for the association of the gene with decreased risk of developing breast cancer.

It is also possible that the causal SNP lies within another gene in LD with PTPRJ. Therefore, we used HapMap data to identify SNPs within 1000 kb of PTPRJ that are in strong LD (r2>0.8) with rs7122335 and rs1566729. There was no evidence for extended LD downstream of the PTPRJ. LD extends ~300 kb upstream where there are six SNPs having pairwise r2>0.8 with rs1566729. However, all but one of these is intergenic within the olfactory receptor genes cluster. The other, rs717897, is 2kb from OR4CAP pseudogene (olfactory receptor, family 4, subfamily C, member 4 pseudogene). None of these would be candidates for breast cancer susceptibility.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
By assessing the effect of six common variants of the PTPRJ gene on risk of breast cancer in a large population-based case–control study, we found evidence for the existence of a variant of the gene which may confer a moderate effect in the East Anglian and UK population. The SNPs under study were not selected as functional variants, but they enabled all common variation in the gene to be captured efficiently. As already demonstrated by others (11Go), this study illustrates that looking for susceptibility allele(s) in a candidate gene does not require a priori identification of functional SNPs, nor a full identification of all SNPs in the gene.

Our results must be interpreted with some caution. Firstly, the possibility of a Type I statistical error must be considered. The P-value for the global test of association in the combined data was 0.0023, whereas some authors have suggested that stringent criteria should be applied to statistical tests for genetic association, e.g. P<0.0001, because of the large number of candidate polymorphisms across the human genome. Much larger case–control series will be needed to confirm our results with such levels of significance. Hidden population stratification is an alternative explanation for a spurious association. This occurs when allele frequencies differ between population sub-groups and cases and controls are drawn differentially from those sub-groups. However, it seems unlikely that population stratification is relevant in this investigation because the cases and controls were nearly all white UK. In addition, we have found no evidence for the population substructure in our controls (13Go) and the existence of significant population stratification that has resulted in a false genetic association has never been empirically demonstrated (14Go).

Once an allelic association with the disease has been demonstrated, the identification of the causal variant is less straightforward. Before embarking on such a search, we believe it would be appropriate to confirm the association we have observed in an independent dataset. Nevertheless, we have excluded several known coding SNPs to be responsible for the association, including variations which are predicted to alter the structure of the receptor. We have not screened all the exons or the totality of the introns and regulatory elements and further studies will be needed to discover the putative functional variant with a frequency of ≤7%, which is lying on haplotypes H6 and H7.

A few studies suggest that human homologues of cloned candidate mouse modifier loci play a role in human cancer risk and prognosis. Polymorphisms located in the human Mom1 homologous region have been associated with severity of familial adenomatous polyposis (15Go). However, these results have not been confirmed in subsequent studies (16Go). More recently, two additional mouse modifier genes corresponding to human homologues which have been shown to have effects on cancer susceptibility have been described in the literature. Genetic polymorphisms in the human chromosomal region homologous to the mouse region containing the Pas1 locus have been associated with lung adenocarcinoma risk and prognosis in independent studies(17Go–20Go), and three studies have suggested that the Ile31 variant of STK15, the human homologue of the candidate skin tumour gene Stk6 in mouse, may be a common breast and ovarian low penetrance cancer susceptibility allele (5Go,21Go,22Go). Our study confirms that the cancer gene-mapping strategies in mice can be a fruitful approach to the identification of cancer susceptibility genes in man.

In conclusion, the Ptprj gene was the first successful positional cloning of a low penetrance QTL in mice developing colon cancer. It has been suggested that the effects of cancer modifier loci are tissue specific and restricted to tumour cells (23Go), but many oncogenes and tumour-suppressor genes are altered in tumours from different organs, indicating that similar tumorigenic pathways could operate in various tissues. It is also possible that the same susceptibility gene may affect tumorigenesis at different stages, e.g. Tumour initiation or tumour growth/progression, in different organs. We postulated that Ptprj homologue was such a gene, and we tested the involvement of this candidate in susceptibility to human breast cancer. We found evidence that PTPRJ variants alter the risk of developing the disease. Our study shows that not all mouse modifier genes are cancer type specific, and susceptibility alleles of PTPRJ for other cancer sites, in particular in human colon cancer, should be investigated.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Patients and controls
Cases were drawn from the SEARCH breast study, an ongoing population-based study, with breast cancer cases ascertained through the East Anglian Cancer Registry. Women are eligible for the study if they were diagnosed with invasive breast cancer below age of 55 between 1991 and mid-1996 and still alive in 1996 (prevalent cases, median age 48), together with women with invasive breast cancer diagnosed aged <70 from mid-1996 to the present (incident cases, median age 52). All study participants completed an epidemiological questionnaire and provided a blood sample for DNA analysis. The cases have been split into two sets to save DNA and reduced genotyping costs: the first set (n=2271 cases) is genotyped for all SNPs and the second set (n=2251 cases) is then tested for those SNPs that show marginally significant associations in set 1 (P-heterogeneity or P-trend <0.1). This staged approach substantially reduces genotyping costs without significantly affecting statistical power. Cases with high yields of genomic DNA were selected for set 1. For both sets of cases, female controls were randomly selected from EPIC-Norfolk, a component of the European Prospective Investigation of Cancer. This is a prospective study of diet and cancer being carried out in nine European countries. The EPIC-Norfolk cohort comprises 25 000 individuals resident in Norfolk (East Anglia), ages 45–74 (24Go). The ethnic background of both cases and controls is similar, with >95% being white. A total of 2280 controls were selected for set 1 and 2274 controls were used for set 2. The study is approved by the Eastern multi-centre research ethics committee.

Identification and selection of SNPs
SNPs within PTPRJ were identified through HapMap (http://www.hapmap.org, public data release no. 6 at 2005-03-01). The International HapMap Project is a multi-country collaboration to identify genetic similarities and differences in human beings. The DNA samples for HapMap have come from a total of 270 people from Nigeria, Japan, China and 30 US trios which were collected in 1980 from US Residents with northern and western European ancestry by the CEPH (25Go). We used data from the CEPH trios to select SNPs for this study.

The aim of the SNP tagging was to identify a stSNPs that efficiently tags all the known SNPs and is also expected to tag any unknown SNPs in the gene. The ideal approach would be to resequence the gene and select a stSNPs that efficiently tag all the common variants that are identified. In the absence of full resequencing data, the HapMap consortium provides a reasonably dense SNP map from which stSNPs can be selected. The measure of how well a SNP measures another SNP is the pairwise correlation coefficient rp2 between them. We aimed to measure all known, common SNP (MAF≥0.05) with a minimum rp2 of 0.8. Where a SNP is poorly correlated with any other single SNP, it may be efficiently tagged by a haplotype defined by more than one marker SNP. The correlation between a multi-SNP haplotype and a single SNP is rs2. We used the tagSNPs programme (12Go) to tag all common HapMap SNPs (≥0.05) with is rs2>0.8, assuming that these will represent the range of genetic diversity in the gene and that the selected tagging SNPs will also tag the unidentified SNPs. As tagging SNP selection will be inefficient where there is excessive haplotype diversity, where appropriate we divided the gene into haplotype blocks and selected the stSNPs for each block separately. It is possible to use a variety of formal definitions of haplotype blocks, but we simply used the graphical representations of the pattern of LD based on D' and selected four blocks such that the common haplotypes in each block accounted for at least 80% of all haplotypes observed using the Haploview programme (26Go).

We used denaturing high performance liquid chromatography (Wavemaker 4.1 software, Transgenomics, Crewe, UK) on 48 samples from the EPIC-Norfolk population to confirm the frequency of the chosen SNPs across the PTPRJ gene in the UK population.

Genotyping
We genotyped all samples for the six selected SNPs using the ABI PRISM 7900 sequence detection system or ‘Taqman’ (Applied Biosystems). We carried out PCR on DNA (10 ng) using TaqMan universal PCR master mix (Applied Biosystems), forward and reverse primers and FAM and VIC labelled probes designed by Applied Biosystems (ABI Assay-by-Designs) in a 5 µl reaction. Sequences of primers and probes are shown in Table 6. The polymorphic base is underlined. Amplification conditions on MJ Tetrad thermal cyclers (GRI) were as follows: 1 cycle of 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. We read the completed PCRs on an ABI PRISM 7900 Sequence Detector in endpoint mode using the Allelic Discrimination Sequence Detector Software (Applied Biosystems). For the software to recognize the genotypes, we included two non-template controls in each 384-well plate. Cases and controls were arrayed together in twelve 384-well plates and a thirteenth plate contained eight duplicate samples from each of the twelve plates to ensure a good quality of genotyping. Failed genotypes were not repeated (the rate for failed genotypes did not exceed 8.3% for any of the SNPs under study).


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  		Table 6. PTPRJ polymorphisms selected for the study; primers and probes used for Taqman assays
 
Statistical methods
For each polymorphism, deviation of the genotype frequencies from those expected under Hardy–Weinberg equilibrium was assessed in the controls by {chi}2 tests. Genotype frequencies in cases and controls were compared by {chi}2 test with 2 degrees of freedom (df), and the Armitage trend test ({chi}2 on 1 df) was used to test for a trend in breast cancer risk with number of rare alleles. The relative risks of breast cancer for heterozygotes and for rare homozygotes, relative to common homozygotes, were estimated as ORs with associated 95% confidence intervals (CI).

Haplotype frequencies were estimated using the Estimation Maximization algorithm implemented in Haploscore (27Go). Haploscore also carries out an overall test of differences in haplotype frequencies between cases and controls and provides haplotype-specific score tests which can be used to evaluate individual haplotypes whenever the global test is significant. Each haplotype was compared to all other haplotypes as the reference in calculating the OR. Haplotype-specific ORs were estimated with associated CI to identify which haplotype(s) is associated with the putative causal variant.


   ACKNOWLEDGEMENTS
 
We thank all the subjects who participated in these studies; the EPIC management team (K.-T. Khaw, S. Bingham and N.E. Day) for access to control DNA and the SEARCH team for recruiting the cases. We are grateful to Craig Luccarini, Don Conroy, Oluseun Ajai, Patricia Harrington and Caroline Baynes for their technical help. This work was funded by Cancer Research, UK. B.A.J.P. is a Gibb Fellow, D.F.E. is a Principal Fellow and P.D.P.P. is a Senior Clinical Research Fellow of Cancer Research, UK.

Conflict of Interest statement. None declared.


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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
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