Polymorphisms in steroid hormone biosynthesis genes and risk of breast cancer and fibrocystic breast conditions in Chinese women

Association Between Single Nucleotide Polymorphisms (rs1484215 and rs6495096) in CYP11A1 Gene in Iranian Women with Polycystic Ovary Syndrome

Background

Genetic factors are significantly have important role in the etiology of the polycystic ovary syndrome (PCOS). This study examined the possible relation of rs1484215 and rs6495096 polymorphisms of CYP11A1 gene in Iranian women with PCOS.

Methods

The population of the case-control research included 100 women presenting with PCOS and 100 women as a control group who were referred to Infertility Center in Qom, Iran. The genotypes of rs1484215 and rs6495096 polymorphisms in CYP11A1 gene were detected with the tetra-ARMS PCR method. The independent segregation of alleles was tested for the Hardy-Weinberg equilibrium (HWE). Differences in quantitative traits were assessed between each group with a single PCOS feature and control group using Mann-Whitney U test for categorical variables and student's t-test for continuous variables. Statistical analysis of allele and genotype frequencies between women with PCOS and control was performed using the chi-square test. Significance level was defined as p<0.05.

Results

There was a significant association of C with G alleles in rs6495096 polymorphism and susceptibility to PCOS (p=0.001), but no significant relation was found between C and T alleles in rs1484215 polymorphism and susceptibility to PCOS. Also, GG genotype of rs6495096 was significantly associated with the waist-to-hip ratio (WHR) and infertility duration compared to CG and CC (p<0.01). However, rs1484215 showed no association with these variables.

Conclusion

The results from the research indicated that rs6495096 polymorphism of CYP11A1 gene is related to the PCOS risk in Iranians women.

Personalized analysis of breast cancer using sample-specific networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

Role of hydroxysteroid (17beta) dehydrogenase type 1 in reproductive tissues and hormone-dependent diseases

Abnormal synthesis and metabolism of sex steroids is involved in the pathogenesis of various human diseases, such as endometriosis and cancers arising from the breast and uterus. Steroid biosynthesis is a multistep enzymatic process proceeding from cholesterol to highly active sex steroids via different intermediates. Human Hydroxysteroid (17beta) dehydrogenase 1 (HSD17B1) enzyme shows a high capacity to produce the highly active estrogen, estradiol, from a precursor hormone, estrone. However, the enzyme may also play a role in other steps of the steroid biosynthesis pathway. In this article, we have reviewed the literature on HSD17B1, and summarize the role of the enzyme in hormone-dependent diseases in women as evidenced by preclinical studies.

CYP17 polymorphisms are associated with decreased risk of breast cancer in Chinese Han women: a case-control study

Introduction

CYP17 is the second most important enzyme in estradiol synthesis. Epidemiological studies have shown the associations between CYP17 polymorphisms and cancer risk. We conducted a case–control study to evaluate the relationship between CYP17 polymorphisms (rs743572 and rs2486758) and breast cancer (BC) risk.

Patients and methods

This case–control study included 560 BC patients and 583 age-matched healthy controls from Northwest China. Two polymorphisms (rs743572 and rs2486758) of CYP17 were genotyped by using Sequenom MassARRAY. ORs and 95% CIs were used to evaluate the relationship.

Results

Compared with the wild genotype of rs743572, we found a significantly reduced risk of BC associated with the variant genotypes (heterozygote model: OR=0.69, 95% CI=0.53–0.89; homozygote model: OR=0.68, 95% CI=0.49–0.95; dominant model: OR=0.69, 95% CI=0.54–0.87; overdominant model: OR=0.78, 95% CI=0.62–0.98; allele model: OR=0.79, 95% CI=0.66–0.93). For rs2486758 polymorphism, we did not find any difference in any of the genetic models. Further stratification analysis by clinical characteristics showed rs743572 was associated with estrogen receptor status (heterozygote model: OR=2.13, 95% CI=1.47–3.08; homozygote model: OR=3.29, 95% CI=1.94–5.58; dominant model: OR=2.39, 95% CI=1.69–3.37) and progesterone receptor status (homozygote model: OR=3.17, 95% CI=1.82–5.55), but there was no association between rs2486758 and clinical characteristics of BC. Haplotype analysis showed that G_rs743572C_rs2486758 haplotype was a protective factor of BC (OR=0.52, 95% CI=0.40–0.67). Survival analysis did not find that CYP17 rs743572 polymorphism was associated with triple-negative BC, either in terms of overall survival or progression-free survival.

Conclusion

Our results suggest that CYP17 polymorphisms may reduce the susceptibility to BC in Chinese women.

Association between CYP17 T-34C rs743572 and breast cancer risk

Association between CYP17 T-34C (rs743572) polymorphism and breast cancer (BC) risk was controversial. In order to derive a more definitive conclusion, we performed this meta-analysis. We searched in the databases of PubMed, EMBASE and Cochrane for eligible publications. Pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were used to assess the strength of association between CYP17 T-34C polymorphism and breast cancer risk. Forty-nine studies involving 2,7104 cases and 3,4218 control subjects were included in this meta-analysis. In overall, no significant association between CYP17 T-34C polymorphism and breast cancer susceptibility was found among general populations. In the stratified analysis by ethnicity and source, significant associations were still not detected in all genetic models; besides, limiting the analysis to studies with controls in agreement with HWE, we also observed no association between CYP17 T-34C polymorphism and breast cancer risk. For premenopausal women, we didn't detect an association between rs743572 and breast cancer risk; however, among postmenopausal women, we observed that the association was statistically significant under the allele contrast genetic model (OR = 1.10, 95% CI = 1.03-1.17, P = 0.003), but not in other four models. In conclusion, rs743572 may increase breast cancer risk in postmenopausal individuals, but not in premenopausal folks and general populations.

Pathway-based discovery of genetic interactions in breast cancer

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

Polymorphism of HSD17B1 Ser312Gly with Cancer Risk: Evidence from 66,147 Subjects

Hydroxysteroid (17-beta)dehydrogenase 1(HSD17B1) plays a central role in sex steroid hormone metabolism. HSD17B1 polymorphic variants may contribute to cancer susceptibility. Numerous investigations have been conducted to assess the association between HSD17B1 Ser312Gly polymorphism and cancer risk in multiple ethnicities, yet these have produced inconsistent results. We therefore performed this comprehensive meta-analysis to attempt to provide a quality assessment of the association of interest. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the strength of associations. After a systematic literature search of several major public databases, 20 studies involving 29,460 cases and 36,687 controls were included in this meta-analysis. No significant association was found between HSD17B1 Ser312Gly polymorphism and cancer risk. However, Ser312Gly polymorphism showed a significantly decreased risk for Caucasians (there were 44,284 Caucasians for analysis, comprising 19,889 cases and 24,395 controls) in the subgroup analysis by ethnicity (dominant: OR = 0.958, 95% CI = 0.919–0.998; and allele comparing: OR = 0.973, 95% CI = 0.947–0.999). And there was the same trend towards risk in the population-based (PB) controls (homozygous: OR = 0.951, 95% CI = 0.908–0.997 and allele comparing: OR = 0.976, 95% CI = 0.954–0.999), but not among Asians or hospital-based (HB) controls. In addition, no association was observed in the stratified analysis for breast cancer studies by source of control, ethnicity and quality score. These findings suggested that the HSD17B1 Ser312Gly polymorphism might confer genetic cancer susceptibility in an ethnic-dependent manner, especially among Caucasians. Well-designed, large-scale studies are warranted to validate these findings.

The identification of specific methylation patterns across different cancers

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.

CYP11A1 microsatellite (tttta)n polymorphism in PCOS women from South India

BACKGROUND

Polycystic ovary syndrome (PCOS) is a condition with central feature of hyperandrogensism that affects 5-12 % of women worldwide. P450sec the cholesterol side chain cleavage enzyme encoded by CYP11A1 gene is instrumental in the synthesis of sex hormones. A promoter pentanucleotide repeat (tttta)(n) polymorphism of this gene is reported to be associated with several hormone related diseases including PCOS. Here we aimed to examine the involvement of CYP11A1 polymorphism with PCOS susceptibility in a case-control study conducted among South Indian women.

METHODS

A total of 542 subjects comprised of 267 PCOS patients and 275 controls were recruited. DNA was extracted from blood and CYP11A1 (tttta)(n) polymorphism was genotyped by PCR-PAGE.

RESULTS

Fifteen different alleles ranging between 2-16 repeats were identified in the studied group and the most frequent allele observed in controls was of 8 repeats. The presence of >8 repeat allele was common in patients (64 % vs. 38 %) and showed a three-fold risk for PCOS susceptibility than controls (OR = 2.93; p < 0.05). PCOS women with higher BMI were markedly elevated in early quartile (p < 0.05).

CONCLUSION

CYP11A1 (tttta)(n) repeat polymorphism appeared to be a potential molecular marker for PCOS risk in our population. Gene-gene and gene-environmental interactions with respect to obesity may play a role in the early onset of this multifactorial condition. This is the first report from South India; however, replicative studies considering other probable causative factors for PCOS risk are warranted.

MspA1 polymorphism of the CYP17 gene in breast cysts

OBJECTIVE

To study the prevalence of breast cysts in several age groups of the general female population and their association with the MspAI polymorphism of the gene CYP17.

RESULTS

In 204 ultrasound tests, cysts were present in 22% of the studied population. The epidemiological-clinical profile of these women was Caucasian, aged 41-50 years, regular menstrual cycles, multiparous and complaining of mastalgia. The genetic distribution of polymorphisms of the gene displayed Hardy-Weinberg equilibrium and the wild homozygous phenotype was observed in 36.4% of the case group and in 37.6% of the control groups; the heterozygous phenotype was observed in 50% of the study group and 46.3% of control group and a mutated homozygous phenotype was seen in 13.6% of the study group and 16.1% of the controls. There was no statistically significant difference between the groups (p = 0.92).

CONCLUSION

The prevalence and most of the epidemiological profile of breast cysts were in agreement with the literature. There was no statistically significant difference among the genotypic groups (wild homozygous, heterozygous and mutated homozygous), despite a slightly increased frequency of the mutated genotype in the control group. This difference indicates a trend of the MspAI polymorphism of the gene CYP17 to act as a protective factor against the development of breast cysts.

Risk-association of CYP11A1 polymorphisms and breast cancer among Han Chinese women in Southern China

Exposure to endogenous sex hormones has been reported as a risk factor for breast cancer. The CYP11A1 gene encodes the key enzyme that catalyzes the initial and rate-limiting step in steroid hormone synthesis. In this study, the associations between single nucleotide polymorphisms (SNPs) in CYP11A1 and breast cancer susceptibility were examined. Six SNPs in CYP11A1 were genotyped using the MassARRAY IPLEX platform in 530 breast cancer patients and 546 healthy controls. Association analyses based on a χ² test and binary logistic regression were performed to determine the odds ratio (OR) and 95% confidence interval (95% CI) for each SNP. Two loci (rs2959008 and rs2279357) showed evidence of associations with breast cancer risk. The variant genotype C/T-C/C of rs2959008 was significantly associated with a decreased risk (age-adjusted OR, 0.75; 95% CI, 0.58–0.96; P = 0.023) compared with the wild-type TT. However, the homozygous TT variant of rs2279357 exhibited increased susceptibility to breast cancer (age-adjusted OR, 1.44; 95% CI, 1.05–1.98; P = 0.022). The locus rs2959003 also showed an appreciable effect, but no associations were observed for three other SNPs. Our results suggest that polymorphisms of CYP11A1 are related to breast cancer susceptibility in Han Chinese women of South China.

Estrogen-related genes and their contribution to racial differences in breast cancer risk

Racial differences in breast cancer risk, including the risks of hormone receptor subtypes of breast cancer, have been previously reported. We evaluated whether variation in genes related to estrogen metabolism (COMT, CYP1A1, CYP1B1, CYP17A1, CYP19A1, ESR1, GSTM1, GSTP1, GSTT1, HSD17B1, SULT1A1, and UGT1A1) contributes to breast cancer risk and/or racial differences in risk within the CARE study, a multi-centered, population-based case-control study of breast cancer. Genetic variation was assessed as single nucleotide polymorphisms (SNPs), haplotypes, and SNP-hormone therapy (HT) interactions within a subset of 1,644 cases and 1,451 controls, including 949 Black women (493 cases and 456 controls), sampled from the CARE study population. No appreciable associations with breast cancer risk were detected for single SNPs or haplotypes in women overall. We detected SNP-HT interactions in women overall within CYP1B1 (rs1800440; p (het) = 0.003) and within CYP17A1 (rs743572; p (het) = 0.009) in which never users of HT were at a decreased risk of breast cancer, while ever users were at a non-significant increased risk. When investigated among racial groups, we detected evidence of an SNP-HT interaction with CYP1B1 in White women (p value = 0.02) and with CYP17A1 in Black women (p value = 0.04). This analysis suggests that HT use may modify the effect of variation in estrogen-related genes on breast cancer risk, which may affect Black and White women to a different extent.

Genetic and environmental predictors, endogenous hormones and growth factors, and risk of estrogen receptor-positive breast cancer in Japanese women

The incidence of breast cancer in Japanese women has doubled in all age groups over the past two decades. We have recently shown that this marked increase is mostly due to an increase in the estrogen receptor (ER)-positive subtype. It is necessary to establish risk factors capable of predicting the risk of ER-positive breast cancer that will enable the efficient selection of candidates for preventive therapy. We analyzed genetic factors, including 14 single nucleotide polymorphisms (SNPs), environmental risk factors (body mass index, age at menarche, pregnancy, age at first birth, breastfeeding, family history of breast cancer, age at menopause, use of hormone replacement therapy, alcohol intake, and smoking), serum hormones and growth factors (estradiol, testosterone, prolactin, insulin-like growth factor 1 [IGF1] and IGF binding protein 3 [IGFBP3]), and mammographic density in 913 women with breast cancer and 278 disease-free controls. To identify important risk factors, risk prediction models for ER-positive breast cancer in both pre- and postmenopausal women were created by logistic regression analysis. In premenopausal women, one SNP (CYP19A1-rs10046), age, pregnancy, breastfeeding, alcohol intake, serum levels of prolactin, testosterone, and IGFBP3 were considered to be risk predictors. In postmenopausal women, one SNP (TP53-rs1042522), age, body mass index, age at menopause, serum levels of testosterone, and IGF1 were identified as risk predictors. Risk factors may differ between women of different menopausal status, and inclusion of common genetic variants and serum hormone measurements as well as environmental factors might improve risk assessment models. Further validation studies will clarify appropriate risk groups for preventive therapy.

Genetic variation in CYP11A1 and StAR in relation to endometrial cancer risk

OBJECTIVE

Together, steroidogenic acute regulator (StAR) and the cholesterol side chain cleavage enzyme (P450scc), which is encoded by CYP11A1, mediate the initial and rate-limiting step in steroidogenesis. Given the role of estrogens in endometrial carcinogenesis, we hypothesized that genetic variation in StAR and CYP11A1 genes may influence endometrial cancer risk.

METHODS

We genotyped four CYP11A1 tagging single nucleotide polymorphisms (SNPs) and two StAR SNPs in endometrial cancer case-control studies nested within the Nurses' Health Study (553 cases and 1339 controls) and the Women's Health Study (137 cases and 411 controls). We calculated odds ratios and 95% confidence intervals using conditional and unconditional logistic regression adjusted for endometrial cancer risk factors to examine the association between SNPs/haplotypes and endometrial cancer.

RESULTS

We observed an increased risk for women carrying the variant allele for rs4555110 (odds ratio (OR)=1.3, 95% confidence interval (CI)=1.1-1.7), rs3825944 (OR=1.4, 95% CI=1.1-1.8), and rs7173655 (OR=1.3, 95% CI=1.0-1.7) CYP11A1 SNPs but no significant associations with CYP11A1 haplotypes. CYP11A1 SNPs were not predictive of plasma estradiol levels. We observed no associations between StAR SNPs and endometrial cancer risk.

CONCLUSIONS

Genetic variants in CYP11A1 may influence endometrial cancer risk or may be markers for causal variants elsewhere. Polymorphisms in StAR are not associated with endometrial cancer risk, but further research is needed.

The association between HSD17B1 Ser312Gly polymorphism and breast cancer risk: a meta-analysis including 31,053 subjects

There are increasing evidences that HSD17B1 plays a significant role in the development of breast cancer. However, published data on the association between HSD17B1 Ser312Gly polymorphism and breast cancer risk are inconclusive. In order to derive a more precise estimation of this relationship, a meta-analysis including 9 studies with 31,053 subjects was performed in this study. Crude ORs with 95% CIs were used to assess the strength of association between HSD17B1 Ser312Gly polymorphism and breast cancer risk. The pooled ORs were performed for codominant model (Gly/Gly versus Ser/Ser, Gly/Ser versus Ser/Ser), dominant model (Gly/Gly + Gly/Ser versus Ser/Ser), and recessive model (Gly/Gly versus Gly/Ser + Ser/Ser), respectively. Overall, no significant associations were detected between HSD17B1 Ser312Gly polymorphism and breast cancer susceptibility. However, in the stratified analysis by ethnicity, significant associations were observed in Caucasians for Gly/Gly versus Ser/Ser (OR = 0.91; 95% CI 0.83-1.00), Gly/Ser versus Ser/Ser (OR = 0.92; 95% CI 0.85-0.99), and Gly/Gly + Gly/Ser versus Ser/Ser (OR = 0.92; 95% CI 0.86-0.98). In conclusion, this study suggests that HSD17B1 312Gly allele may be a protective factor for breast cancer development in Caucasians. However, large sample and representative population-based studies with homogeneous breast cancer patients and well-matched controls are warranted to confirm this finding.

Lack of association between CYP17 MspA1 polymorphism and breast cancer risk: a meta-analysis of 22,090 cases and 28,498 controls

Epidemiological studies have evaluated the association between CYP17 MspA1 polymorphism and breast cancer risk. However, the results remain conflicting rather than conclusive. To derive a more precise estimation of the relationship, we performed this meta-analysis. Systematic searches of the PubMed and Medline databases were performed. A total of 35 studies including 22,090 cases and 28,498 controls were identified. Genotype distributions of CYP17 in the controls of all studies were in agreement with Hardy-Weinberg equilibrium (HWE) except for three studies. When all 35 studies were pooled into the meta-analysis, there was no evidence for significant association between CYP17 MspA1 polymorphism and breast cancer risk (for A1/A2 vs. A1/A1: OR = 1.00, 95% CI = 0.96-1.04; for A2/A2 vs. A1/A1: OR = 1.03, 95% CI = 0.97-1.08; for dominant model: OR = 1.01, 95% CI = 0.97-1.05; for recessive model: OR = 1.03, 95% CI = 0.98-1.08). In the subgroup analyses by ethnicity, menopausal status and source of controls, no significant associations were found in all genetic models. When sensitivity analyses were performed by excluding HWE-violating studies, all the results were not materially altered. In summary, the meta-analysis strongly suggests that CYP17 MspA1 polymorphism is not associated with increased breast cancer risk.

No association between CYP17 T-34C polymorphism and breast cancer risk: a meta-analysis involving 58,814 subjects

Breast cancer is one of the most common malignant tumors worldwide. To date, many articles have evaluated the association between Cytochrome P450c17 (CYP17) T-34C polymorphism and breast cancer risk. However, the results remain inconclusive. In order to derive a more precise estimation of the association, a meta-analysis was performed in this study. By searching Medline, ISI Web of Knowledge, Cochrane, ScienceDirect, EBSCO, CNKI, and SinoMed databases, 43 studies including 26,008 cases and 32,806 controls were collected for CYP17 T-34C polymorphism. Crude ORs with 95% CIs were used to assess the strength of association between CYP17 T-34C polymorphism and breast cancer risk. The pooled ORs were performed for codominant model, dominant model, and recessive model, respectively. Overall, no significant associations between CYP17 T-34C polymorphism and breast cancer susceptibility were found for TT versus CC (OR = 0.96; 95% CI: 0.89-1.05), TC versus CC (OR = 0.97; 95% CI: 0.89-1.06), TT + TC versus CC (OR = 0.97; 95% CI: 0.89-1.05) and TT versus TC + CC (OR = 0.98; 95% CI: 0.93-1.03). In the stratified analysis by ethnicity, menopausal status, and sources of controls, significant associations were still not detected in all genetic models. In conclusion, this meta-analysis strongly suggests that CYP17 T-34C polymorphism is not associated with breast cancer risk.

17Beta-hydroxysteroid dehydrogenase enzymes and breast cancer

Sex steroids play an important role in the development and differentiation in several tissues. Biologically active hormones that are locally converted in endocrine organs in the tissue where they exert their effects without release into extracellular space is a field of endocrinology that has been called intracrinology. In pre-menopausal women the ovary is the main source of estrogens, but in post-menopausal women the estrogen production as main site of synthesis moves to peripheral tissues and almost all of the sex steroids are synthesised from precursors of adrenal origin. In breast cancer 60-80% of the tumors express high levels of oestrogen receptor (ER) alpha which gives estrogen a proliferative effect. Breast tumors tend to have a higher intratumoral estrogen concentration than normal breast tissue and plasma, and in situ synthesis and the metabolism of estrogens is believed to be of great importance for the development and progression of the disease. The activity of estrogen metabolizing enzymes in breast are mainly aromatase, estrone sulfatases and 17HSD enzymes. 17HSD1 and 17HSD2 are the family members known to be of main importance in breast cancer. High expression of 17HSD1 has been associated to poor prognosis in breast cancer and late relapse among patients with ER-positive tumors. One of the mechanisms behind high 17HSD1 expression is gene amplification. Low or absent expression of 17HSD2 is associated to decreased survival in ER-positive breast cancer. 17HSD14 is one of the latest discovered 17HSD enzymes, transfection of 17HSD14 in human breast cancer cells significantly decreased the levels of estradiol in the culture medium. Low expression of 17HSD14 mRNA expression in breast cancer was correlated to decreased survival. The understanding of intratumoral synthesis of sex steroids in breast cancer is crucial to understand the disease both in pre- and post-menopausal women. Further studies are desirable to state the direct role of these enzymes in breast cancer and which patients that may benefit from new therapeutic strategies targeting 17HSD enzymes. The new inhibitors targeting 17HSD1 have shown promising results in pre-clinical studies to have clinical potential in the future.

HSD17B1 genetic variants and hormone receptor-defined breast cancer

HSD17B1 is an important candidate gene in breast cancer via its role in converting estrone to estradiol. A nonsynonymous G-to-A transition (rs605059) and an intronic C-to-A (rs676387) single-nucleotide polymorphism, which captured most common variation in HSD17B1, were evaluated in several breast cancer studies with inconclusive results. We followed up these findings in the Polish Breast Cancer Study (1,995 cases; 2,296 controls) and the British Studies of Epidemiology and Risk Factors in Cancer Heredity study (4,470 cases; 4,560 controls). Meta-analyses of published data and our own were also conducted among Caucasian women. Consistent with previous reports, we found little to no association with overall risk for heterozygotes and minor allele homozygotes compared with major allele homozygotes for rs605059 [summary odds ratios (95% confidence intervals), 0.93 (0.87-0.99) for GA and 0.96 (0.85-1.08), based on 11,762 cases and 14,329 controls from 10 studies] and for rs676387 [summary odds ratios (95% confidence intervals), 1.04 (0.97-1.12) and 1.12 (0.99-1.27), based on analyses of 11,074 cases and 13,605 controls from 8 studies]. Data from the Polish [n=586 estrogen receptor-negative (ER-) cases] and British (n=407) studies did not support the previous findings that ER- tumors were inversely associated with rs676387 AA genotype and positively associated with rs605059 GG genotype, based on subanalyses in 5 prospective cohorts with 354 ER- cases. In conclusion, it is unlikely that common genetic variation in HSD17B1 is associated with a moderate modulation in breast cancer risk overall; however, we cannot exclude the possibility of a very weak effect. Associations between HSD17B1 genotypes and risk for ER- breast cancer were inconsistent across studies and should be studied further.