Novel Genetic Markers of Breast Cancer Survival
Novel Genetic Markers of Breast Cancer Survival
The overall results were based on 37954 case patients with 2900 deaths from breast cancer (Supplementary Table 2, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. The results of the subtype-specific analyses were based on 23059 ER-positive case patients (1333 deaths) from five studies and 6881 ER-negative case patients (920 deaths) from eight studies.
In the overall analysis, we identified 28 SNPs associated with breast cancer–specific survival at P values of less than 5x10 (Table 1; Supplementary Figures 1 and 2, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. All 28 SNPs were located in the same region on chromosome 2 and had been imputed in all eight datasets. The strongest association was for rs148760487 (hazard ratio [HR] = 1.88, 95% confidence interval [CI] = 1.51 to 2.34), P = 1.5x10) (risk allele frequency = 0.01). This SNP was associated with breast cancer–specific survival in both ER-positive (HR = 2.07, 95% CI = 1.47 to 2.91, P = 3.1x10) and ER-negative case patients (HR = 1.87, 95% CI = 1.27 to 2.75, P = .002). The imputation efficiency for these SNPs varied between an r of 0.69 and 0.997 for the eight data sets. The inflation factor λ for the overall analysis was 1.01.
A single imputed SNP, rs2059614, located on chromosome 11, was associated with breast cancer–specific mortality at genome-wide statistical significance in patients with ER-negative disease (HR = 1.90, 95% CI = 1.54 to 2.33, P = 1.3x10) (risk allele frequency = 0.06) (Table 1; Figures 1 and 2). The imputation r ranged from 0.75 to 0.82 across eight studies with ER-negative cases. The inflation factor λ for analysis based on ER-negative cases was 1.03. No SNP reached nominal genome-wide statistical significance in the analysis of case patients with ER-positive disease (Supplementary Figures 3 and 4, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1, for which the strongest association was for rs7149859 in chromosome 14 (HR = 1.22, 95% CI = 1.13 to 1.33, P = 7.0x10). There was very little between study heterogeneity for the overall analysis for rs148760487 (I = 0%, P = .59) (Supplementary Figure 5, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1 or the ER-negative analysis for rs2059614 (I = 0%, P = .50) (Supplementary Figure 6, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1.
(Enlarge Image)
Figure 1.
Association plot for combined GWAS and COGS analyses for estrogen receptor (ER)–negative cases. The P values of the association between each single nucleotide polymorphism (SNP) and breast cancer survival were obtained by cox regression analyses with adjustment for principle components for each study and then combined. The y-axis shows the -log10P values of each SNP analyzed, and the x-axis shows their chromosome position. The red horizontal line represents P = 5x10. All statistical tests were two-sided.
(Enlarge Image)
Figure 2.
Quantile-Quantile (Q-Q) plot for the combined GWAS and COGS analyses for estrogen receptor (ER)–negative cases. The y-axis represents the observed -log10P value, and the x-axis represents the expected -log10P value. The red line represents the expected distribution under the null hypothesis of no association. All statistical tests were two-sided.
We conducted follow-up imputation on the two regions around rs148760487 and rs2059614 using the IMPUTE2 Markov chain Monte Carlo algorithm with 80 iterations without prephasing, as omitting the prephasing step should maximize imputation accuracy. We reimputed all SNPs in the genomic regions 500 KB pairs on either side of the two SNPs of interest. The association for rs148760487 was somewhat weaker (HR = 1.75, 95% CI = 1.39 to 2.20, P = 1.44 x 10). A highly correlated SNP, rs114860916 (r = 0.97) was now the most strongly associated SNP in the region (HR = 1.74, 95% CI = 1.39 to 2.18, P = 1.16 x 10). In contrast, rs2059614 remained the most strongly associated SNP with survival of ER-negative disease (HR = 1.95, 95% CI = 1.55 to 2.47, P = 1.91 x 10). Again there was no evidence of heterogeneity in the meta-analysis of these SNPs (data not shown).
We genotyped rs148760487 and rs2059614 in 2113 breast cancer case patients from the Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH) in order to confirm the quality of the imputation. The correlation between the imputed and observed genotypes was 0.63 for rs148760487 and 0.68 for rs2059614. This compares with an estimated imputation r of 0.76 and 0.79 for the genotypes imputed with prephasing using genotype data from the COGS custom array. We then compared the results of association analyses for the SEARCH data set using the imputed and observed genotypes. For rs148760487, there were 133 breast cancer deaths. In this subset the association based on genotyped data was weaker than the association based on the imputed data (HR = 1.66, 95% CI = 0.75 to 3.69, P = .21 and HR = 2.06, 95% CI = 0.84 to 5.04, P = .11, respectively), but this difference was not statistically significant (P = .72). For rs2059614, there were genotyped and imputed data for 300 ER-negative samples with 45 deaths. The association with genotyped data was stronger than that for imputed data (HR = 1.80, 95% CI = 0.99 to 3.25, P = .05 and HR = 1.44, 95% CI = 0.51 to 4.12, P = .49, respectively), as would be expected for a true positive association. Again, this difference was not statistically significant (P = .72). We also conducted multivariable analysis for these two SNPs using the pooled data within the COGS dataset, stratified by study and adjusting for principal components, age, lymph node status, tumor size, stage, grade, ER status (where applicable), and adjuvant treatment; the results were similar to the main findings (data not shown). Finally, we compared the hazard ratios for rs148760487 in all case patients and rs2059614 for ER-negative case patients in premenopausal (defined as age at diagnosis younger than 45 years) and postmenopausal (age at diagnosis of 55 years or older). There was no statistically significant difference (P = .96 and .24, respectively).
The risk allele of rs2059614 was associated with increased expression of EI24 and CHEK1 in normal breast epithelium adjacent to tumor from the METABRIC study (P = .002 and .007, respectively) (Supplementary Figure 7, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. EI24 is a tumor suppressor gene involved in TP53 dependent apoptosis. CHEK1 is required for checkpoint-mediated cell cycle arrest in response to DNA damage. Other SNPs in the region were more strongly associated with both EI24 and CHEK1 expression, but were not associated with prognosis. There were no statistically significant eQTLs for rs148760487 and rs114860916 in normal breast epithelium. None of the three SNPs had statistically significant eQTLs in tumors from the TCGA study. We also explored the association between gene expression for all genes in the 1 MB region spanning the associated SNPs and breast cancer–specific mortality using KM plotter. Data were available for 575 ER-negative breast cancer patients. CHEK1 expression was not associated with relapse-free survival in ER-negative case patients (HR = 0.86, 95% CI = 0.65 to 1.12, P = .25) (Supplementary Figure 8, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1 but had statistically significant associations in ER-positive case patients (HR = 1.59, 95% CI = 1.31 to 1.91, P = 1.2x10). EI24 expression was associated with relapse-free survival in both ER-positive case patients (HR = 0.75, 95% CI = 0.63 to 0.90, P = .002) and ER-negative case patients (HR = 1.38, 95% CI = 1.07 to 1.77, P = .01). It is interesting that the direction of association is consistent: The risk allele G of rs2059614 is associated both with poor breast cancer–specific survival in ER-negative case patients and with higher levels of EI24 expression in normal breast epithelium, which in turn is associated with poorer relapse-free survival in breast cancer. Expression of neither of the genes near rs148760487 was associated with relapse.
Both the two top SNPs lie in putative enhancer sequences for which promoter interactions have been predicted (Supplementary Figure 9, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1.IFIH or FAP might be the target of rs148760487, and EI24 might be the target of rs2059614 because the SNP is in an enhancer in endothelial cells that is predicted to regulate EI24.
Results
The overall results were based on 37954 case patients with 2900 deaths from breast cancer (Supplementary Table 2, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. The results of the subtype-specific analyses were based on 23059 ER-positive case patients (1333 deaths) from five studies and 6881 ER-negative case patients (920 deaths) from eight studies.
In the overall analysis, we identified 28 SNPs associated with breast cancer–specific survival at P values of less than 5x10 (Table 1; Supplementary Figures 1 and 2, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. All 28 SNPs were located in the same region on chromosome 2 and had been imputed in all eight datasets. The strongest association was for rs148760487 (hazard ratio [HR] = 1.88, 95% confidence interval [CI] = 1.51 to 2.34), P = 1.5x10) (risk allele frequency = 0.01). This SNP was associated with breast cancer–specific survival in both ER-positive (HR = 2.07, 95% CI = 1.47 to 2.91, P = 3.1x10) and ER-negative case patients (HR = 1.87, 95% CI = 1.27 to 2.75, P = .002). The imputation efficiency for these SNPs varied between an r of 0.69 and 0.997 for the eight data sets. The inflation factor λ for the overall analysis was 1.01.
A single imputed SNP, rs2059614, located on chromosome 11, was associated with breast cancer–specific mortality at genome-wide statistical significance in patients with ER-negative disease (HR = 1.90, 95% CI = 1.54 to 2.33, P = 1.3x10) (risk allele frequency = 0.06) (Table 1; Figures 1 and 2). The imputation r ranged from 0.75 to 0.82 across eight studies with ER-negative cases. The inflation factor λ for analysis based on ER-negative cases was 1.03. No SNP reached nominal genome-wide statistical significance in the analysis of case patients with ER-positive disease (Supplementary Figures 3 and 4, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1, for which the strongest association was for rs7149859 in chromosome 14 (HR = 1.22, 95% CI = 1.13 to 1.33, P = 7.0x10). There was very little between study heterogeneity for the overall analysis for rs148760487 (I = 0%, P = .59) (Supplementary Figure 5, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1 or the ER-negative analysis for rs2059614 (I = 0%, P = .50) (Supplementary Figure 6, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1.
(Enlarge Image)
Figure 1.
Association plot for combined GWAS and COGS analyses for estrogen receptor (ER)–negative cases. The P values of the association between each single nucleotide polymorphism (SNP) and breast cancer survival were obtained by cox regression analyses with adjustment for principle components for each study and then combined. The y-axis shows the -log10P values of each SNP analyzed, and the x-axis shows their chromosome position. The red horizontal line represents P = 5x10. All statistical tests were two-sided.
(Enlarge Image)
Figure 2.
Quantile-Quantile (Q-Q) plot for the combined GWAS and COGS analyses for estrogen receptor (ER)–negative cases. The y-axis represents the observed -log10P value, and the x-axis represents the expected -log10P value. The red line represents the expected distribution under the null hypothesis of no association. All statistical tests were two-sided.
We conducted follow-up imputation on the two regions around rs148760487 and rs2059614 using the IMPUTE2 Markov chain Monte Carlo algorithm with 80 iterations without prephasing, as omitting the prephasing step should maximize imputation accuracy. We reimputed all SNPs in the genomic regions 500 KB pairs on either side of the two SNPs of interest. The association for rs148760487 was somewhat weaker (HR = 1.75, 95% CI = 1.39 to 2.20, P = 1.44 x 10). A highly correlated SNP, rs114860916 (r = 0.97) was now the most strongly associated SNP in the region (HR = 1.74, 95% CI = 1.39 to 2.18, P = 1.16 x 10). In contrast, rs2059614 remained the most strongly associated SNP with survival of ER-negative disease (HR = 1.95, 95% CI = 1.55 to 2.47, P = 1.91 x 10). Again there was no evidence of heterogeneity in the meta-analysis of these SNPs (data not shown).
We genotyped rs148760487 and rs2059614 in 2113 breast cancer case patients from the Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH) in order to confirm the quality of the imputation. The correlation between the imputed and observed genotypes was 0.63 for rs148760487 and 0.68 for rs2059614. This compares with an estimated imputation r of 0.76 and 0.79 for the genotypes imputed with prephasing using genotype data from the COGS custom array. We then compared the results of association analyses for the SEARCH data set using the imputed and observed genotypes. For rs148760487, there were 133 breast cancer deaths. In this subset the association based on genotyped data was weaker than the association based on the imputed data (HR = 1.66, 95% CI = 0.75 to 3.69, P = .21 and HR = 2.06, 95% CI = 0.84 to 5.04, P = .11, respectively), but this difference was not statistically significant (P = .72). For rs2059614, there were genotyped and imputed data for 300 ER-negative samples with 45 deaths. The association with genotyped data was stronger than that for imputed data (HR = 1.80, 95% CI = 0.99 to 3.25, P = .05 and HR = 1.44, 95% CI = 0.51 to 4.12, P = .49, respectively), as would be expected for a true positive association. Again, this difference was not statistically significant (P = .72). We also conducted multivariable analysis for these two SNPs using the pooled data within the COGS dataset, stratified by study and adjusting for principal components, age, lymph node status, tumor size, stage, grade, ER status (where applicable), and adjuvant treatment; the results were similar to the main findings (data not shown). Finally, we compared the hazard ratios for rs148760487 in all case patients and rs2059614 for ER-negative case patients in premenopausal (defined as age at diagnosis younger than 45 years) and postmenopausal (age at diagnosis of 55 years or older). There was no statistically significant difference (P = .96 and .24, respectively).
The risk allele of rs2059614 was associated with increased expression of EI24 and CHEK1 in normal breast epithelium adjacent to tumor from the METABRIC study (P = .002 and .007, respectively) (Supplementary Figure 7, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1. EI24 is a tumor suppressor gene involved in TP53 dependent apoptosis. CHEK1 is required for checkpoint-mediated cell cycle arrest in response to DNA damage. Other SNPs in the region were more strongly associated with both EI24 and CHEK1 expression, but were not associated with prognosis. There were no statistically significant eQTLs for rs148760487 and rs114860916 in normal breast epithelium. None of the three SNPs had statistically significant eQTLs in tumors from the TCGA study. We also explored the association between gene expression for all genes in the 1 MB region spanning the associated SNPs and breast cancer–specific mortality using KM plotter. Data were available for 575 ER-negative breast cancer patients. CHEK1 expression was not associated with relapse-free survival in ER-negative case patients (HR = 0.86, 95% CI = 0.65 to 1.12, P = .25) (Supplementary Figure 8, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1 but had statistically significant associations in ER-positive case patients (HR = 1.59, 95% CI = 1.31 to 1.91, P = 1.2x10). EI24 expression was associated with relapse-free survival in both ER-positive case patients (HR = 0.75, 95% CI = 0.63 to 0.90, P = .002) and ER-negative case patients (HR = 1.38, 95% CI = 1.07 to 1.77, P = .01). It is interesting that the direction of association is consistent: The risk allele G of rs2059614 is associated both with poor breast cancer–specific survival in ER-negative case patients and with higher levels of EI24 expression in normal breast epithelium, which in turn is associated with poorer relapse-free survival in breast cancer. Expression of neither of the genes near rs148760487 was associated with relapse.
Both the two top SNPs lie in putative enhancer sequences for which promoter interactions have been predicted (Supplementary Figure 9, available online) http://jnci.oxfordjournals.org/content/107/5/djv081/suppl/DC1.IFIH or FAP might be the target of rs148760487, and EI24 might be the target of rs2059614 because the SNP is in an enhancer in endothelial cells that is predicted to regulate EI24.
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