The ‘Pokemon’ ( ZBTB7 ) Gene: No Evidence of Association with Sporadic Breast Cancer

Introduction

It has been suggested that breast cancer, together with prostate and colorectal, are the cancers with the highest heritable components. A substantial proportion of familiar breast cancer (~25%) is explained by mutations in the BRCA1 and BRCA2 genes [1],[2]. By contrast, the excess of familial risk associated with sporadic breast cancer (as well as the unexplained genetic risk in familial breast cancer) may be better explained by the effect of multiple weakly predisposing alleles [3],[4]. The identification of common alleles conferring modest susceptibility to cancer (as opposed to the known high penetrance BRCA1/2 genes) is a field of growing interest, especially with the development of new genotyping techniques and SNP database facilities [5].

Hence, there is much interest in the search for low penetrance gene/variants for breast cancer, which could exist with relatively high prevalence in the general population. Many polymorphisms have been proposed as candidates for susceptibility to sporadic breast cancer but reported positive associations have rarely been replicated in independent studies [6]–[9].

Recently, Maeda et al. [10] identified the transcriptional repressor FBI1, namely Pokemon (POK erythroid myeloid ontogenic factor), as a critical factor in oncogenesis. This protein is encoded by the ZBTB7 gene (“zing finger and BTB domain containing 7”; Gene ID: 51341). Mouse embyronic fibroblasts lacking ZBTB7 are completely refractory to oncogene-mediated cellular transformation. Conversely, FBI1 over-expression led to overt oncogenic transformation both in vitro and in vivo in transgenic mice. FBI1 can specifically repress the transcription of the tumor suppressor gene ARF (600160). Maeda et al. [10] found that FBI1 is aberrantly over-expressed in human cancers, and its expression levels predict biologic behaviour and clinical outcome. On the other hand, tissue microarray (TMA) analysis in breast carcinomas has revealed high levels of Pokemon expression in a subset of these tumours. In addition, the genomic region where the ZBTB7 gene resides (19p13.3) is a hotspot for chromosomal translocations (The Cancer Genome Anatomy Project; http://cgap.nci.nih.gov/). ZBTB7 is therefore a good candidate low penetrance breast cancer susceptibility gene.

Here we aimed to study the potential implications of common ZBTB7 variants in sporadic breast cancer in a sample of cases and controls from Spain. To do this, we selected a set of 19 SNPs covering the whole extension of ZBTB7 and flanking regions at high density.

Material and Methods

SNP selection

SNPs were selected from different sources: the International HapMap Project (The International HapMap Consortium, 2003; 2004; http://www.hapmap.org/), Ensembl (Birney et al. 2004; http://www.ensemble.org/), the Sequenom RealSNP database (https://www.realsnp.com/default.asp), and PupaSNP (Conde et al. 2004; http://www.pupasnp.org/). All 22 SNPs described at the time of selection were included, which yielded an average coverage of 1 SNP/1.7 Kb. These SNPs cover the upstream and downstream flanking regions (10000 bp) and the introns of ZBTB7, and include only one coding non-synonymous SNPs (Table 1).

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Table 1

ZBTB7 SNPs successfully genotyped.

ID	SNP	Alleles	MA	MAF (%)	Chr. 19 location in Ensembl v.31	aa change	Position (bp)	Intermarker Distance (bp)	Distance from v01 (bp)
–	rs10405522	C/G	G	2.4	3'-downstream	–	3990056	–	–
v1	rs11882361	A/G	G	2.9	3'-downstream	–	3993858	3802	3802
v2	rs2121137	C/T	T	2.6	3'-downstream	–	3993923	65	3867
v3	rs2121136	C/T	T	2.6	3'-downstream	–	3994024	101	3968
v4	rs2121135	A/G	A	0.2	3'-downstream	–	3994038	14	3982
v5	rs10414035	A/G	G	0.3	3'-downstream	–	3996434	2396	6378
v6	rs10412825	A/G	A	0.1	3'-downstream	–	3996962	528	6906
v7	rs4807540	C/T	T	4.0	intronic	–	4001399	4437	11343
v8	rs7251080	C/T	T	0.8	coding	341syn	4005208	3809	15152
v9	rs3745985	C/T	T	0.3	coding	A177T	4005702	494	15646
v10	rs10409301	C/T	T	4.7	intronic	–	4007346	1644	17290
v11	rs895331	G/T	G	2.1	intronic	–	4011222	3876	21166
–	rs895330	C/G	C	22.7	intronic	–	4011707	485	21651
v12	rs350842	C/T	C	1.1	intronic	–	4012781	1074	22725
v13	rs350841	A/G	A	0.9	intronic	–	4014067	1286	24011
–	rs350840	C/G	C	1.9	intronic	–	4015418	1351	25362
v14	rs1992710	C/G	C	0.2	intronic	–	4015672	254	25616
–	rs350832	C/T	C	22.9	5'-upstream	–	4020426	4754	30370
v15	rs11880023	C/T	T	21.1	5'-upstream	–	4025697	5271	35641

Abbreviations: MA: minor allele; MAF: control minor allele frequency; aa: aminoacid.

Statistical Analysis

We tested for differences in allele frequencies between cases and controls using Pearson's Chisquared test (the best model is provided in Table 2). We adjusted for age in categories <45, 44–49, 50–54, 55–59, and ≥60 via logistic regression using Stata v8. Disequilibrium coefficients (D’) for adjacent SNPs were calculated using Haploview v3.11 [11]. We used Gold software [12] to graphically summarized patterns of linkage disequilibrium in ZBTB7 because it is well suited to the analysis of dense genetic maps. Assuming a minimum allele frequency (MAF) of 3% (the average MAF of our SNP set) and a genetic effect of 2, the a priori power to detect association under a dominant model is above 70%.

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Table 2

OR and P-value for the best fitting model.

SNP	Rare Allele	Best Model	OR 1	95% CI	Un-adjusted P-value
rs11882361	G	Dom. 2	1.30	0.70–2.42	0.4
rs2121137	A	Dom. 2	1.32	0.69–2.53	0.4
rs2121136	T	Dom. 2	1.36	0.69–2.68	0.4
rs2121135	T	Dom. 2	0.54	0.05–5.96	0.6
rs10414035	G	Dom. 2	2.18	0.20–24.1	0.5
rs10412825	A	Dom. 2	1.11	0.07–17.7	0.9
rs4807540	T	Dom. 2	0.95	0.54–1.70	0.9
rs7251080	T	Dom. 2	1.91	0.51–7.18	0.3
rs3745985	T	Dom. 2	1.08	0.15–7.71	0.9
rs10409301	T	Rec.	0.27	0.06–1.29	0.1
rs895331	C	Dom. 2	0.99	0.48–2.06	0.9
rs350842	C	Dom. 2	0.70	0.24–2.08	0.5
rs350841	A	Dom.	0.78	0.24–2.47	0.7
rs1992710	C	Dom. 2	0.52	0.05–5.81	0.6
rs11880023	T	Rec.	0.69	0.32–1.48	0.3

1

Using common-allele homozygotes as reference. The P-value refers to a Pearson's Chisquared test.

2

Only model that could be fit due to zero counts for rare homozygotes.

Haploview v3.32 (http://www.broad.mit.edu/mpg/haploview) was used for estimating the genotyping coverage of the selected SNPs (see below) and haplotype block structure.

The Cocaphased program of the Unphased software package [13] was used to check for single SNP and haplotype associations. We tested all two-, three-, four-, and five-SNP haplotypes for association in a sliding window across the gene. The option ‘drop rare haplotypes’ was used in order to restrict the analysis to the haplotypes with a frequency > 1%. We followed the permutation test procedure implemented in Unphased which provides P-values corrected for the multiple haplotypes tested. The EM algorithm was used to impute missing data.

Evaluation of stratification was carried out based on the genotyping of 28 neutral SNPs, as previously described in a separate study that targeted a different set of low penetrance breast cancer genes in overlapping samples [14].

Results and Discussion

Three out of the 22 SNPs selected failed genotyping. Four out of the 19 remaining SNPs (namely, rs10405522, rs895330, rs350840, and rs350832) were successfully genotyped in less than 75% of the samples and were therefore excluded from association analyses. The average call rate for these 15 SNPs was 95% (see also preliminary results in [15]–[17]) and none gave evidence of deviation from Hardy Weinberg equilibrium. Table 1 summarizes their location and allele frequencies.

We computed D’ values between all 19 markers, and detected moderate levels of LD (Fig. 1). However, under the ‘four gamete rule’ model (see Haploview for more information) we identified a haplotype blocks nearly covering the entire extension of the gene (Fig. 2). This characterization of LD along the ZTBT7 region could be useful for future association study designs in cancer.

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Figure 1

D’ pairwise linkage disequilibrium values of ZBTB7 markers in control individuals.

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Figure 2

Haplotype block structure in our control individuals and HapMap information for the CEPH dataset (top).

In order to measure the percentage of variability captured by the our selected SNPs, we first collected the HapMap data from the CEPH subset (http://www.hapmap.org) and the same chromosome range explored in the present study (chromosome 19: positions 3990056–4025697). Then, we estimated the number of SNPs un-captured in the CEPH-HapMap using our SNP selection under an r² threshold of 0.8 and a model of ‘aggressive tagging’. Only one SNP in the HapMap dataset would remain untagged by our selected SNPs, indicating that our set of SNPs covers well the whole gene region under analysis.

No statistically significant differences between cases and controls were observed for individual SNPs based on comparisons of allele frequencies (see Table 2 for the best fitting models) whether or not age was adjusted for. Four- and three-SNPs haplotypes carrying markers rs350842 and rs350841 had associated P-values below 0.05 but were not significant after correction for multiple testing. Note also that these adjusted P-values overestimates the real value since the software employed (cocaphase) does not correct for the multiple hypothesis tested running different sliding windows.

To our knowledge, this is the first time that ZBTB7 has been evaluated as a candidate sporadic breast cancer susceptibility gene. We have not found evidence of an association for ZBTB7 SNPs nor haplotypes with breast cancer risk. It should be mentioned that most of the ZBTB7 variants studied are rare in our sample. We are aware that the main drawback in detecting positive associations of rare variants (or haplotypes) is the need for large sample sizes. Therefore, the present result needs further validation in future studies of independent case-control series before a role for ZBTB7 in breast cancer can be completely ruled out.

Web Site References

HapMap: http://www.hapmap.org/; Ensembl homepage: http://www.ensemble.org/;

GO website: http://www.geneontology.org/;

The Cancer Genome Anatomy Project (CGAP): http://cgap.nci.nih.gov/Chromosomes