Background

B-cell non-Hodgkin lymphoma represents a diverse group of hematological malignancies, of which follicular lymphoma (FL) is one of the most common subtypes. Family and epidemiological studies suggest an important genetic role in the etiology of FL. In recent genome-wide association studies (GWAS) of FL, several genetic susceptibility loci have been identified on chromosome 6p21.33 (rs6457327) and 6p21.32 (rs10484561, rs2647012) in the human leukocyte antigen class I and class II regions. To identify new genetic variants and further elucidate the genetic basis of FL, a meta-analysis was performed of the top 1000 SNPs associated with FL risk from two GWAS in the US, Denmark and Sweden (592 cases, 1541 controls), with independent validation in 107 cases and 681 controls.

Results

rs9275517 and rs3117222 in the HLA class II region were validated and inversely associated with FL risk (rs9275517: OR = 0.63, 95% CI = 0.55-0.73, p = 4.03 × 10-11; rs3117222: OR = 0.66, 95% CI = 0.57-0.77, p = 1.45 × 10-7). rs9275517, which is in high linkage disequilibrium with rs2647012 (r2 = 0.9), was no longer associated with FL after conditioning on rs2647012. The rs3117222 association was independent of established FL SNPs, but not of the HLA-DPB1*0301 allele. Using publicly available gene expression profiles with matching genotype information, we found that rs3117222 also was significantly correlated with increased HLA-DPB1 expression.

Conclusions

By performing a meta-analysis of two GWAS of FL, we further validated the relevance of HLA-DPB1*0301 as a protective allele in the pathogenesis of FL. Moreover, the protective rs3117222 A allele correlated with increased levels of HLA-DPB1, suggesting a possible disease mechanism involving HLA-DPB1 expression regulation. Our results add further support to the major role of HLA genetic variation in the pathogenesis of FL.

Given that the transcriptional regulatory activity of estrogen receptor (ER) is modulated by its biochemical cofactors, genetic variation within the ER cofactor genes may alter cellular response to estrogen exposure and consequently modify the risk for endometrial cancer. We genotyped 685 tagging SNPs within 60 ER cofactor genes in 564 endometrial cancer cases and 1,510 controls from Sweden, and tested their associations with the risk of endometrial cancer. We investigated the associations of individual SNPs by using a trend test as well as multiple SNPs within a gene or gene complex by using multi-variant association analysis. No significant association was observed for any individual SNPs or genes, but a marginal association of the cumulative genetic variation of the NCOA2 complex as a whole (NCOA2, CARM1, CREBBP, PRMT1 and EP300) with endometrial cancer risk was observed (Padjusted = 0.033). However, the association failed to be replicated in an independent European dataset of 1265 cases and 5190 controls (P = 0.71). The results indicate that common genetic variants within ER cofactor genes are unlikely to play a significant role in endometrial cancer risk in European population.

Introduction

Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.

Methods

We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.

Results

The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.

Conclusions

Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.

Background

Previous studies have suggested that breast cancer risk factors are associated with estrogen receptor (ER) and progesterone receptor (PR) expression status of the tumors.

Methods

We pooled tumor marker and epidemiological risk factor data from 35 568 invasive breast cancer case patients from 34 studies participating in the Breast Cancer Association Consortium. Logistic regression models were used in case–case analyses to estimate associations between epidemiological risk factors and tumor subtypes, and case–control analyses to estimate associations between epidemiological risk factors and the risk of developing specific tumor subtypes in 12 population-based studies. All statistical tests were two-sided.

Results

In case–case analyses, of the epidemiological risk factors examined, early age at menarche (≤12 years) was less frequent in case patients with PR− than PR+ tumors (P = .001). Nulliparity (P = 3 × 10−6) and increasing age at first birth (P = 2 × 10−9) were less frequent in ER− than in ER+ tumors. Obesity (body mass index [BMI] ≥ 30 kg/m2) in younger women (≤50 years) was more frequent in ER−/PR− than in ER+/PR+ tumors (P = 1 × 10−7), whereas obesity in older women (>50 years) was less frequent in PR− than in PR+ tumors (P = 6 × 10−4). The triple-negative (ER−/PR−/HER2−) or core basal phenotype (CBP; triple-negative and cytokeratins [CK]5/6+ and/or epidermal growth factor receptor [EGFR]+) accounted for much of the heterogeneity in parity-related variables and BMI in younger women. Case–control analyses showed that nulliparity, increasing age at first birth, and obesity in younger women showed the expected associations with the risk of ER+ or PR+ tumors but not triple-negative (nulliparity vs parity, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.75 to 1.19, P = .61; 5-year increase in age at first full-term birth, OR = 0.95, 95% CI = 0.86 to 1.05, P = .34; obesity in younger women, OR = 1.36, 95% CI = 0.95 to 1.94, P = .09) or CBP tumors.

Conclusions

This study shows that reproductive factors and BMI are most clearly associated with hormone receptor–positive tumors and suggest that triple-negative or CBP tumors may have distinct etiology.

Non-Hodgkin lymphoma (NHL) represents a diverse group of hematological malignancies, of which follicular lymphoma (FL) is a prevalent subtype. A previous genome-wide association study has established a marker, rs10484561 in the human leukocyte antigen (HLA) class II region on 6p21.32 associated with increased FL risk. Here, in a three-stage genome-wide association study, starting with a genome-wide scan of 379 FL cases and 791 controls followed by validation in 1,049 cases and 5,790 controls, we identified a second independent FL–associated locus on 6p21.32, rs2647012 (ORcombined = 0.64, Pcombined = 2×10−21) located 962 bp away from rs10484561 (r2<0.1 in controls). After mutual adjustment, the associations at the two SNPs remained genome-wide significant (rs2647012:ORadjusted = 0.70, Padjusted = 4×10−12; rs10484561:ORadjusted = 1.64, Padjusted = 5×10−15). Haplotype and coalescence analyses indicated that rs2647012 arose on an evolutionarily distinct haplotype from that of rs10484561 and tags a novel allele with an opposite (protective) effect on FL risk. Moreover, in a follow-up analysis of the top 6 FL–associated SNPs in 4,449 cases of other NHL subtypes, rs10484561 was associated with risk of diffuse large B-cell lymphoma (ORcombined = 1.36, Pcombined = 1.4×10−7). Our results reveal the presence of allelic heterogeneity within the HLA class II region influencing FL susceptibility and indicate a possible shared genetic etiology with diffuse large B-cell lymphoma. These findings suggest that the HLA class II region plays a complex yet important role in NHL.

Author Summary

Earlier studies have established a marker rs10484561, in the HLA class II region on 6p21.32, associated with increased follicular lymphoma (FL) risk. Here, in a three-stage genome-wide association study of 1,428 FL cases and 6,581 controls, we identified a second independent FL–associated marker on 6p21.32, rs2647012, located 962 bp away from rs10484561. The associations at two SNPs remained genome-wide significant after mutual adjustment. Haplotype and coalescence analyses indicated that rs2647012 arose on an evolutionarily distinct lineage from that of rs10484561 and tags a novel allele with an opposite, protective effect on FL risk. Moreover, in an analysis of the top 6 FL–associated SNPs in 4,449 cases of other NHL subtypes, rs10484561 was associated with risk of diffuse large B-cell lymphoma. Our results reveal the presence of allelic heterogeneity at 6p21.32 in FL risk and suggest a shared genetic etiology with the common diffuse large B-cell lymphoma subtype.

To identify susceptibility loci for non-Hodgkin lymphoma (NHL) subtypes, we conducted a three-stage genome-wide association study. We identified two variants associated with follicular lymphoma (FL) in 1,465 FL cases/6,958 controls at 6p21.32 (rs10484561, rs7755224, r2=1.0; combined p-values=1.12×10-29, 2.00×10-19), providing further support that MHC genetic variation influences FL susceptibility. Confirmatory evidence of a previously reported association was also found between chronic lymphocytic leukemia/small lymphocytic lymphoma and rs735665 (combined p-value=4.24×10-9).

Introduction

Given the role of estrogen in breast carcinogenesis and the modification of estrogen receptor (ER) activity by its biochemical cofactors, we hypothesize that genetic variation within ER cofactor genes alters cellular response to estrogen exposure and consequently modifies the risk for ER-positive breast cancer.

Methods

We genotyped 790 tagging SNPs within 60 ER cofactor genes in 1,257 cases and 1,464 controls from Sweden and in 2,215 cases and 1,265 controls from Finland, and tested their associations with either ER-positive or ER-negative breast cancer.

Results

Seven SNPs showed consistent association with ER-positive breast cancer in the two independent samples, and six of them were located within PPARGC1B, encoding an ER co-activator, with the strongest association at rs741581 (odds ratio = 1.41, P = 4.84 × 10-5) that survived Bonferroni correction for multiple testing in the combined ER-positive breast cancer sample (Pcorrected = 0.03). Moreover, we also observed significant synergistic interaction (Pinteraction = 0.008) between the genetic polymorphisms within PPARGC1B and ESR1 in ER-positive breast cancer. By contrast, no consistent association was observed in ER-negative breast cancer. Furthermore, we found that administration of estrogen in the MCF-7 cell line induced PPARGC1B expression and enhanced occupancies of ER and RNA polymerase II within the region of SNP association, suggesting the upregulation of PPARGC1B expression by ESR1 activation.

Conclusions

Our study revealed that DNA polymorphisms of PPARGC1B, coding a bona fide ER co-activator, are associated with ER-positive breast cancer risk. The feed-forward transcriptional regulatory loop between PPARGC1B and ESR1 further augments their protein interaction, which provides a plausible mechanistic explanation for the synergistic genetic interaction between PPARGC1B and ESR1 in ER-positive breast cancer. Our study also highlights that biochemically and genomically informed candidate gene studies can enhance the discovery of interactive disease susceptibility genes.

Introduction

Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk.

Methods

We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases.

Results

Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer.

Conclusions

ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.

Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic pathway by interrogating 239 tagSNPs within 35 genes of the pathway in three tumor samples. The discovery sample consisted of 1,596 breast cancer cases, 719 endometrial cancer cases, and 1,730 controls from Sweden; and the validation sample included 2,245 breast cancer cases and 1,287 controls from Finland. We performed admixture maximum likelihood (AML)–based global tests to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three sub-pathways for androgen synthesis, androgen-to-estrogen conversion, and estrogen removal. In the discovery sample, although no single polymorphism was significant after correction for multiple testing, the pathway-based AML global test suggested association with both breast (pglobal = 0.034) and endometrial (pglobal = 0.052) cancers. Further testing revealed the association to be focused on polymorphisms within the androgen-to-estrogen conversion sub-pathway, for both breast (pglobal = 0.008) and endometrial cancer (pglobal = 0.014). The sub-pathway association was validated in the Finnish sample of breast cancer (pglobal = 0.015). Further tumor subtype analysis demonstrated that the association of the androgen-to-estrogen conversion sub-pathway was confined to postmenopausal women with sporadic estrogen receptor positive tumors (pglobal = 0.0003). Gene-based AML analysis suggested CYP19A1 and UGT2B4 to be the major players within the sub-pathway. Our study indicates that the composite genetic determinants related to the androgen–estrogen conversion are important for the induction of two hormone-associated cancers, particularly for the hormone-driven breast tumour subtypes.

Author Summary

Estrogen exposure is the most important risk factor for breast and endometrial cancers. Genetic variation of the genes involved in estrogen metabolism has, however, not been consistently associated with these two cancers. We posited that the genetic risk associated with the estrogen metabolic genes is likely to be carried by multiple variants and is therefore most effectively detected by multi-variant analysis. We carried out a comprehensive association analysis of the estrogen metabolic pathway by interrogating SNPs within 35 genes of the pathway in three tumor samples from Sweden and Finland. Through pathway-based multi-variant association analysis, we showed that the genetic variation within the estrogen metabolic pathway is associated with risk for breast and endometrial cancers and that the genetic variation within the genes involved in androgen-to-estrogen conversion is particularly important for the development of ER–positive and sporadic breast tumors in postmenopausal women. Our study has demonstrated that the influence of genetic variation on hormone exposure has an impact on breast cancer development, especially on the development of hormone-driven breast tumor subtypes. Our study has also highlighted that future genetic studies of the estrogen metabolic genes should focus on the androgen-to-estrogen conversion process.

Introduction

Oestrogen exposure is a central factor in the development of breast cancer. Oestrogen receptor alpha (ESR1) is the main mediator of oestrogen effect in breast epithelia and has also been shown to be activated by epidermal growth factor (EGF). We sought to determine if common genetic variation in the ESR1 and EGF genes affects breast cancer risk, tumour characteristics or breast cancer survival.

Methods

We genotyped 157 single nucleotide polymorphisms (SNPs) in ESR1 and 54 SNPs in EGF in 92 Swedish controls and selected haplotype tagging SNPs (tagSNPs) that could predict both single SNP and haplotype variation in the genes with an R2 of at least 0.8. The tagSNPs were genotyped in 1,590 breast cancer cases and 1,518 controls, and their association with breast cancer risk, tumour characteristics and survival were assessed using unconditional logistic regression models, Cox proportional hazard models and haplotype analysis.

Results

The single tagSNP analysis did not reveal association evidence for breast cancer risk, tumour characteristics, or survival. A multi-locus analysis of five adjacent tagSNPs suggested a region in ESR1 (between rs3003925 and rs2144025) for association with breast cancer risk (p = 0.001), but the result did not withstand adjustment for multiple comparisons (p = 0.086). A similar region was also implicated by haplotype analyses, but its significance needs to be verified by follow-up analysis.

Conclusion

Our results do not support a strong association between common variants in the ESR1 and EGF genes and breast cancer risk, tumour characteristics or survival.