Common germline polymorphisms in COMT, CYP19A1, ESR1, PGR, SULT1E1 and STS and survival after a diagnosis of breast cancer

Genetic Polymorphisms as Predictors of Survival in Breast Cancer: Future Lessons in Historical Data

Introduction

Breast cancer is the most common cancer among women worldwide and one of the main causes of death in the female sex. Genetic polymorphisms in the mu-opioid receptor (OPRM1) and catechol-o-methyltransferase (COMT) genes have been shown to increase breast cancer risk. Variants in these genes may carry a prognostic impact in breast cancer. Long follow-up intervals are critical to adequately analyze prognosis in diseases with prolonged survival times and late relapses.

Objective

To analyze the impact of genetic polymorphisms on the survival of a cohort of breast cancer patients with very long follow-up.

Methods

This was a retrospective study of patients treated at Portuguese Oncology Institute of Porto (IPO Porto), a Portuguese comprehensive cancer center, with invasive carcinoma of the breast with very long follow-up, with analysis of genetic polymorphisms OPMR1 rs1799971 (AA vs. G allele) and COMT rs4680 (CC vs T allele) on biological samples. Statistical analysis of survival was performed using the Kaplan-Meier method, log-rank test, and Cox regression method.

Results

A total of 143 patients with invasive breast cancer were included, with a median follow-up of 21.5 years. There was a statistically significant difference in overall survival (OS) at 30 years according to the OPMR1 polymorphism, with lower survival in patients with the AA genotype (p<0.05). The difference in OS according to the COMT polymorphism was also statistically significant, with worse survival in patients with genotype T allele (p<0.05). The genetic variants were not associated with patient age, stage at diagnosis, or tumor grade.

Discussion

The genetic polymorphisms of OPRM1 and COMT affected the overall survival of breast cancer patients, in concordance with previous research. Further investigation is needed in order to clarify the prognostic impact of these genetic alterations on breast cancer.

A Method for Analyzing Two-locus Epistasis of Complex Diseases based on Decision Tree and Mutual Entropy

Background:

Epistasis makes complex diseases difficult to understand, especially when heterogeneity also exists. Heterogeneity of complex diseases makes the distribution of case population more confused. However, the traditional methods proposed to detect epistasis often ignore heterogeneity, resulting in low power of association studies.

Methods:

In this study, we firstly use rank information in the Classification Decision Tree and Mutual Entropy (CTME) to construct two different evaluation scores, namely multiple objectives. In addition, we improve the calculation of joint entropy between SNPs and disease label, which elevates the efficiency of CTME. Then, the ant colony algorithm is applied to search two-locus epistatic combination space. To handle the potential heterogeneity, all candidate two-locus SNPs are merged to recognize multiple different epistatic combinations. Finally, all these solutions are tested by χ2 test.

Results and Conclusion:

Experiments show that our method CTME improves the power of association study. More importantly, CTME also detects multiple epistatic SNPs contributing to heterogeneity. The experimental results show that CTME has advantages on power and efficiency.

Elevated Aromatase (CYP19A1) Expression Is Associated with a Poor Survival of Patients with Estrogen Receptor Positive Breast Cancer

Genetic variants in CYP19A1, the gene encoding aromatase, have been reported to be associated with circulating estrogen concentrations, a key risk factor for breast cancer. The mechanism underlying this association is still unclear; it has been suggested that some of these variants may alter the expression and/or activity of aromatase. Here we analyzed the expression of intra-tumoral CYP19A1 messenger RNA (mRNA) and the genotypes of rs10046, a well-characterized single nucleotide polymorphism in CYP19A1, in 138 breast cancer patients and 15 breast cancer cell lines. The genotype TT was detected in 36 patients and six cell lines, genotype CT in 55 patients and five cell lines, and genotype CC in 28 patients and four cell lines. We found no evidence for a significant association of CYP19A1 levels with rs10046 genotypes, although expression tended to be higher in tumors and cell lines with the homozygous risk genotype TT. We also found no evidence for a significant association of rs10046 genotypes with breast cancer prognosis. In contrast, high CYP19A1 expression was highly significantly associated with a poor overall, disease-free, and metastasis-free survival in estrogen receptor-positive but not negative breast cancer patients. Moreover, CYP19A1 mRNA was significantly elevated in postmenopausal patients and in patients older than 50 years, and a trend towards a positive correlation with ER status and ESR1 mRNA expression was observed. These findings highlight the key role of aromatase in estrogen receptor-positive breast cancer biology.

Method for generating multiple risky barcodes of complex diseases using ant colony algorithm

Background

Susceptible barcode recognition plays an important role in the diagnosis and treatment of complex diseases. Numerous approaches have been proposed to identify risky barcodes involved in the progress of complex diseases. However, some methods only consider differences in barcode frequencies between the control and disease groups; as such, these methods may be partial or even wrong. For example, some barcodes with a high risk ratio yield a low frequency on cases or exhibit a high frequency on controls, which may unreasonable from a statistical point.

Results

In our study, a stricter criteria, maximum discrepancy and maximum constituency, is designed to evaluate each barcode and ant colony algorithm is used to search combination space of epistasis. For complex diseases with multi-subtypes, our method can list several potential barcodes contributing to different subtypes of complex diseases. Another contribution of this work is to introduce a method for determining the length of barcodes and excluding noisy barcodes whose frequencies are abnormal. In addition, common pathogenic genes shared by different risky barcodes are also recognized, which may provide key clue for further study, such as gene function analysis.

Conclusions

Experimental results reveal that our method can find multiple risky barcodes whose risk ratio and odds ratio are >1. These barcodes could be related to different subtypes of complex diseases.

Genetically predicted 17beta-estradiol, cognitive function and depressive symptoms in women: A Mendelian randomization in the Guangzhou Biobank Cohort Study

OBJECTIVE

The role of estrogen in cognitive function and depressive symptoms is controversial due to discrepancies between results from randomized controlled trials (RCT) and observational studies. Mendelian randomization analysis may provide further insights concerning the role of estrogen in these outcomes as it assesses the effect of lifelong endogenous exposure but is less vulnerable to confounding than observational studies.

METHOD

We used separate sample instrumental variable analysis to estimate the association of log 17β estradiol with cognitive function (Delayed 10 word recall, and Mini Mental State Examination (MMSE)) and depressive symptoms (Geriatric Depression Scale (GDS)) in older Chinese women of the Guangzhou Biobank Cohort Study (GBCS, n=3086). The estimate was derived based on the Wald estimator, the ratio of the association of genetic determinants (rs1008805 and rs2175898) of log 17β-estradiol with cognitive function and depressive symptoms in GBCS and the association of log 17β-estradiol with genetic determinants in the sample of young women in Hong Kong (n=236).

RESULTS

Genetically predicted 17β-estradiol was not associated with delayed 10-word recall (0.42 words per log increase in 17β-estradiol (pmol/L), 95% confidence interval (CI) -0.49 to 1.34) MMSE (0.39 per log increase in 17β-estradiol (pmol/L), 95% CI -0.87 to 1.65) or GDS (0.24 per log increase in 17β-estradiol (pmol/L), 95% CI -0.57 to 1.05).

CONCLUSION

These results were largely consistent with evidence from RCTs and did not show any beneficial effect of estrogen on cognitive function and depressive symptoms. However, larger Mendelian randomization analyses are needed to identify any minor effects.

Genetically predicted 17beta-estradiol and cardiovascular risk factors in women: a Mendelian randomization analysis using young women in Hong Kong and older women in the Guangzhou Biobank Cohort Study

PURPOSE

The role of estrogen in cardiovascular health remains contested with discrepancies between findings from randomized controlled trials and observational studies. Mendelian randomization, which assesses the effect of lifelong endogenous exposure, may help elucidate these discrepancies.

METHODS

We used separate sample instrumental variable analysis to estimate the association of log 17β-estradiol with factors related to cardiovascular disease risk (systolic and diastolic blood pressure, lipids, fasting glucose, body mass index, waist hip ratio, and waist circumference) and Framingham score, a predictor of 10-year risk of ischemic heart disease events, in older Chinese women from the Guangzhou Biobank Cohort Study (GBCS, n = 3092). The estimate was derived using the Wald estimator, that is, the ratio of the association of genetic determinants (rs1008805 and rs2175898) of log 17β-estradiol with cardiovascular disease risk factors and Framingham score in GBCS and the association of these genetic determinants with log 17β-estradiol in a sample of young women from Hong Kong (n = 236).

RESULTS

Genetically, higher 17β-estradiol was not associated with any cardiovascular disease-related risk factor or with Framingham score (-0.01, 95% confidence interval = -1.34 to 1.31).

CONCLUSIONS

Lifetime exposure to estrogen does not appear to be cardioprotective via the cardiovascular disease-related risk factors examined.

Polymorphism at 19q13.41 Predicts Breast Cancer Survival Specifically after Endocrine Therapy

PURPOSE

Although most patients with estrogen receptor (ER)-positive breast cancer benefit from endocrine therapies, a significant proportion do not. Our aim was to identify inherited genetic variations that might predict survival among patients receiving adjuvant endocrine therapies.

EXPERIMENTAL DESIGN

We performed a meta-analysis of two genome-wide studies; Helsinki Breast Cancer Study, 805 patients, with 240 receiving endocrine therapy and Prospective study of Outcomes in Sporadic versus Hereditary breast cancer, 536 patients, with 155 endocrine therapy patients, evaluating 486,478 single-nucleotide polymorphisms (SNP). The top four associations from the endocrine treatment subgroup were further investigated in two independent datasets totaling 5,011 patients, with 3,485 receiving endocrine therapy.

RESULTS

A meta-analysis identified a common SNP rs8113308, mapped to 19q13.41, associating with reduced survival among endocrine-treated patients [hazard ratio (HR), 1.69; 95% confidence interval (CI), 1.37-2.07; P = 6.34 × 10(-7)] and improved survival among ER-negative patients, with a similar trend in ER-positive cases not receiving endocrine therapy. In a multivariate analysis adjusted for conventional prognostic factors, we found a significant interaction between the rs8113308 and endocrine treatment, indicating a predictive, treatment-specific effect of the SNP rs8113308 on breast cancer survival, with the per-allele HR for interaction 2.16 (95% CI, 1.30-3.60; Pinteraction = 0.003) and HR = 7.77 (95% CI, 0.93-64.71) for the homozygous genotype carriers. A biologic rationale is suggested by in silico functional analyses.

CONCLUSIONS

Our findings suggest carrying the rs8113308 rare allele may identify patients who will not benefit from adjuvant endocrine treatment.

Association of breast cancer risk loci with breast cancer survival

The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele =0.70; 95% CI: 0.58-0.85; ptrend  = 2.84 × 10(-4) ; HRheterozygotes  = 0.71; 95% CI: 0.55-0.92; HRhomozygotes  = 0.48; 95% CI: 0.31-0.76; p2DF  = 1.45 × 10(-3) ). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04-1.15; ptrend  = 6.6 × 10(-4) ; HRheterozygotes  = 0.96 95% CI: 0.90-1.03; HRhomozygotes  = 1.21; 95% CI: 1.09-1.35; p2DF =1.25 × 10(-4) ). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662.

Identification of novel genetic markers of breast cancer survival

BACKGROUND

Survival after a diagnosis of breast cancer varies considerably between patients, and some of this variation may be because of germline genetic variation. We aimed to identify genetic markers associated with breast cancer-specific survival.

METHODS

We conducted a large meta-analysis of studies in populations of European ancestry, including 37954 patients with 2900 deaths from breast cancer. Each study had been genotyped for between 200000 and 900000 single nucleotide polymorphisms (SNPs) across the genome; genotypes for nine million common variants were imputed using a common reference panel from the 1000 Genomes Project. We also carried out subtype-specific analyses based on 6881 estrogen receptor (ER)-negative patients (920 events) and 23059 ER-positive patients (1333 events). All statistical tests were two-sided.

RESULTS

We identified one new locus (rs2059614 at 11q24.2) associated with survival in ER-negative breast cancer cases (hazard ratio [HR] = 1.95, 95% confidence interval [CI] = 1.55 to 2.47, P = 1.91 x 10(-8)). Genotyping a subset of 2113 case patients, of which 300 were ER negative, provided supporting evidence for the quality of the imputation. The association in this set of case patients was stronger for the observed genotypes than for the imputed genotypes. A second locus (rs148760487 at 2q24.2) was associated at genome-wide statistical significance in initial analyses; the association was similar in ER-positive and ER-negative case patients. Here the results of genotyping suggested that the finding was less robust.

CONCLUSIONS

This is currently the largest study investigating genetic variation associated with breast cancer survival. Our results have potential clinical implications, as they confirm that germline genotype can provide prognostic information in addition to standard tumor prognostic factors.

Associations between CYP19A1 polymorphisms, Native American ancestry, and breast cancer risk and mortality: the Breast Cancer Health Disparities Study

The cytochrome p450 family 19 gene (CYP19A1) encodes for aromatase, which catalyzes the final step in estrogen biosynthesis and conversion of androgens to estrogens. Genetic variation in CYP19A1 is linked to higher circulating estrogen levels and increased aromatase expression. Using data from the Breast Cancer Health Disparities Study, a consortium of three population-based case-control studies in the United States (n = 3,030 non-Hispanic Whites; n = 2,893 Hispanic/Native Americans (H/NA) and Mexico (n = 1,810), we examined influence of 25 CYP19A1 tagging single-nucleotide polymorphisms (SNPs) on breast cancer risk and mortality, considering NA ancestry. Odds ratios (ORs) and 95 % confidence intervals (CIs) and hazard ratios estimated breast cancer risk and mortality. After multiple comparison adjustment, none of the SNPs were significantly associated with breast cancer risk or mortality. Two SNPs remained significantly associated with increased breast cancer risk in women of moderate to high NA ancestry (≥29 %): rs700518, ORGG 1.36, 95 % CI 1.11-1.67 and rs11856927, ORGG 1.35, 95 % CI 1.05-1.72. A significant interaction was observed for rs2470144 and menopausal status (p adj = 0.03); risk was increased in postmenopausal (ORAA 1.22, 95 % CI 1.05-1.14), but not premenopausal (ORAA 0.78, 95 % CI 0.64-0.95) women. The absence of an overall association with CYP19A1 and breast cancer risk is similar to previous literature. However, this analysis provides support that variation in CYP19A1 may influence breast cancer risk differently in women with moderate to high NA ancestry. Additional research is warranted to investigate the how variation in an estrogen-regulating gene contributes to racial/ethnic disparities in breast cancer.

No association between germline variation in catechol-O-methyltransferase and colorectal cancer survival in postmenopausal women

OBJECTIVE

Sex steroid hormones play a role in colorectal cancer (CRC) development, but little is known about their influence on tumor progression and metastasis. Because catechol-O-methyltransferase (COMT; 22q11.21) activity is an important component of estrogen-mediated carcinogenesis, we hypothesized that germline variation in COMT may be associated with CRC survival.

METHODS

We identified 10 single nucleotide polymorphisms that tagged variation across two isoforms of COMT in 2,458 women with CRC from the Nurses' Health Study, Postmenopausal Hormones Supplementary Study to the Colon Cancer Family Registry, VITamins And Lifestyle Study, and Women's Health Initiative. All four studies participated in the Genetics and Epidemiology of Colorectal Cancer Consortium.

RESULTS

During a median follow-up of 7 years across all studies, there were 799 deaths, including 566 deaths from CRC. Based on multiple comparisons, no associations between single nucleotide polymorphisms and CRC-specific or overall survival reached statistical significance, including the well-characterized Val108/158Met polymorphism (rs4680; CRC-specific survival: hazard ratio per minor allele, 1.04; 95% CI, 0.92-1.17; overall survival: hazard ratio per minor allele, 1.01; 95% CI, 0.90-1.14).

CONCLUSIONS

In this large study of women with CRC, we find no evidence that common inherited variation in COMT is associated with survival time after diagnosis.

Breast cancer susceptibility risk associations and heterogeneity by E-cadherin tumor tissue expression

E-cadherin is involved in cell-cell adhesion and epithelial-to-mesenchymal transitions. In cancers, loss or inactivation of E-cadherin is associated with epithelial cell proliferation and invasion. Here, we sought to determine, if risk associations for 18 breast cancer susceptibility single nucleotide polymorphisms (SNPs) differed by E-cadherin tumor tissue expression in the Polish Breast Cancer Study (PBCS), using data on 1,347 invasive breast cancer cases and 2,366 controls. E-cadherin expression (low/high) was assessed using immunohistochemical staining of tumor tissue microarrays. Replication data on 2,006 cases and 6,714 controls from the Study of Epidemiology and Risk Factors in Cancer Heredity was used to follow-up promising findings from PBCS. In PBCS, we found the rs11249433 SNP at the 1p11.2 locus to be more strongly associated with risk of E-cadherin low tumors (OR = 1.30, 95 % CI = 1.08-1.56) than with E-cadherin high tumors [OR = 1.06, 95 % CI = 0.95-1.18; case-only p-heterogeneity (p-het) = 0.05]. Findings in PBCS for rs11249433 were replicated in SEARCH. Combined analyses of the two datasets for SNP rs11249433 revealed significant heterogeneity by E-cadherin expression (combined case-only p-het = 0.004). Further, among carriers of rs11249433, the highest risk was seen for E-cadherin low tumors that were ER-positive and of lobular histology. Our results in two independent data sets suggest that rs11249433, which is located between the NOTCH2 and FCGR1B genes within the 1p11.2 locus, is more strongly associated with risk of breast tumors with low or absent E-cadherin expression, and suggest that evaluation of E-cadherin tumor tissue expression may be useful in clarifying breast cancer risk factor associations.

The seed and soil hypothesis revisited: current state of knowledge of inherited genes on prognosis in breast cancer

The crucial event in the course of malignancies such as breast cancer is its metastatic spread from the primary tumor of origin to distant organs. The natural history of a tumor is determined by the expression of its genes, and in this sense, knowledge has advanced dramatically in recent decades. However, much less is known about the role that the patient plays in the behavior of a tumor. In this article, we review the evidence regarding the genetic background of the host in metastatic tumor dissemination, providing information from epidemiological studies as well as from animal models and human studies. Undoubtedly, the elucidation of possible interpersonal variability in susceptibility to developing metastases would significantly contribute to improve management of cancer patients.

Sulfotransferase genetic variation: from cancer risk to treatment response

Cytosolic sulfotransferases (SULTs) are phase II detoxification enzymes that are involved in the biotransformation of a wide variety of structurally diverse endo- and xenobiotics. Single-nucleotide polymorphisms (SNPs) in SULTs can alter the phenotype of the translated proteins. SNPs in some SULTs are fairly uncommon in the population, but some, most notably for SULT isoform 1A1, are commonly found and have been associated with cancer risk for a variety of tumor sites and also with response to therapeutic agents. SNPs in many SULTs vary by ethnicity, another factor that could influence SULT-associated disease risk and pharmacogenetics. This review surveys the current knowledge of SULT genetic variability in relation to cancer risk and response to therapy, focusing primarily on SULT1A1.

Phase two steroid metabolism and its roles in breast and prostate cancer patients

Breast and prostate cancer are diseases in which steroids and steroid metabolism could markedly influence clinical outcomes for patients. In both malignancies the modification of ketone and hydroxyl groups attached to the steroid backbone (phase one metabolism) has been examined in detail but the conjugation reactions (phase two metabolism) have not been extensively studied. Therefore, in this review we aim to summarize phase two metabolism in breast and prostate cancers from a number of perspectives, including the impact of variation in serum levels of conjugated steroids, tissue, and pathology specific expression of phase two enzymes, and consequences of genetic variations of these conjugation enzymes. In addition to this biological perspective, we will also address current pharmacological efforts to manipulate phase two metabolism as a potential therapy for hormone dependent cancers, including clinical trials of STS inhibitors and preclinical STS inhibitor development. While this review is not intended to cover any one particular area in great technical depth, it is intended as an introduction to and/or update on the importance of variance in phase two metabolic pathways in breast and prostate cancers.

The relationship between eight GWAS-identified single-nucleotide polymorphisms and primary breast cancer outcomes

BACKGROUND

Several single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified through genome-wide association studies (GWAS). We investigated whether eight risk SNPs identified in GWAS were associated with breast cancer disease-free survival (DFS) and overall survival (OS) rates.

PATIENTS AND METHODS

A cohort of 739 white women with early-stage breast cancer was genotyped for eight GWAS-identified SNPs (rs2981582, rs1219648 [FGFR2], rs3803662, rs12443621, rs8051542 [TOX3], rs999737 [RAD51L1], rs6504950 [17q23], and rs4973768 [3p24]). Relationships between SNPs and breast cancer outcomes were evaluated using Cox proportional hazard regression models. The cumulative effects of SNPs on breast cancer outcomes were assessed by computing the number of at-risk genotypes.

RESULTS

At a median follow-up of 121 months (range: 188-231 months) for survivors, 237 deaths (32%) and 186 breast cancer events (25%) were identified among the 739 patients. After adjusting for age, clinical stage, and treatment, rs12443621 (16q12; p = .03) and rs6504950 (17q23; p = .008) were prognostic for OS but not DFS. A higher risk for death was also found in the multivariable analysis of patients harboring three or four at-risk genotypes of the GWAS SNPs compared to patients carrying two or less at-risk genotypes (hazard ratio: 1.60, 95% confidence interval: 1.23-2.24; p = .0008).

CONCLUSION

The study results suggest that previously identified breast cancer risk susceptibility loci, rs12443621 (16q12) and rs6504950 (17q23), may influence breast cancer prognosis or comorbid conditions associated with overall survival. The precise molecular mechanisms through which these risk SNPs, as well as others that were not included in the analysis, influence clinical outcomes remain to be determined.

Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.

Epistasis analysis for estrogen metabolic and signaling pathway genes on young ischemic stroke patients

Background

Endogenous estrogens play an important role in the overall cardiocirculatory system. However, there are no studies exploring the hormone metabolism and signaling pathway genes together on ischemic stroke, including sulfotransferase family 1E (SULT1E1), catechol-O-methyl-transferase (COMT), and estrogen receptor α (ESR1).

Methods

A case-control study was conducted on 305 young ischemic stroke subjects aged ≦ 50 years and 309 age-matched healthy controls. SULT1E1 -64G/A, COMT Val158Met, ESR1 c.454−397 T/C and c.454−351 A/G genes were genotyped and compared between cases and controls to identify single nucleotide polymorphisms associated with ischemic stroke susceptibility. Gene-gene interaction effects were analyzed using entropy-based multifactor dimensionality reduction (MDR), classification and regression tree (CART), and traditional multiple regression models.

Results

COMT Val158Met polymorphism showed a significant association with susceptibility of young ischemic stroke among females. There was a two-way interaction between SULT1E1 -64G/A and COMT Val158Met in both MDR and CART analysis. The logistic regression model also showed there was a significant interaction effect between SULT1E1 -64G/A and COMT Val158Met on ischemic stroke of the young (P for interaction = 0.0171). We further found that lower estradiol level could increase the risk of young ischemic stroke for those who carry either SULT1E1 or COMT risk genotypes, showing a significant interaction effect (P for interaction = 0.0174).

Conclusions

Our findings support that a significant epistasis effect exists among estrogen metabolic and signaling pathway genes and gene-environment interactions on young ischemic stroke subjects.

An improved PSO algorithm for generating protective SNP barcodes in breast cancer

Background

Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions.

Methodology/Principal Findings

In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification.

Conclusions/Significance

Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.

The role of genetic breast cancer susceptibility variants as prognostic factors

Recent genome-wide association studies identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. We investigated these and 62 other SNPs for their prognostic relevance. Confirmed BC risk SNPs rs17468277 (CASP8), rs1982073 (TGFB1), rs2981582 (FGFR2), rs13281615 (8q24), rs3817198 (LSP1), rs889312 (MAP3K1), rs3803662 (TOX3), rs13387042 (2q35), rs4973768 (SLC4A7), rs6504950 (COX11) and rs10941679 (5p12) were genotyped for 25 853 BC patients with the available follow-up; 62 other SNPs, which have been suggested as BC risk SNPs by a GWAS or as candidate SNPs from individual studies, were genotyped for replication purposes in subsets of these patients. Cox proportional hazard models were used to test the association of these SNPs with overall survival (OS) and BC-specific survival (BCS). For the confirmed loci, we performed an accessory analysis of publicly available gene expression data and the prognosis in a different patient group. One of the 11 SNPs, rs3803662 (TOX3) and none of the 62 candidate/GWAS SNPs were associated with OS and/or BCS at P<0.01. The genotypic-specific survival for rs3803662 suggested a recessive mode of action [hazard ratio (HR) of rare homozygous carriers=1.21; 95% CI: 1.09-1.35, P=0.0002 and HR=1.29; 95% CI: 1.12-1.47, P=0.0003 for OS and BCS, respectively]. This association was seen similarly in all analyzed tumor subgroups defined by nodal status, tumor size, grade and estrogen receptor. Breast tumor expression of these genes was not associated with prognosis. With the exception of rs3803662 (TOX3), there was no evidence that any of the SNPs associated with BC susceptibility were associated with the BC survival. Survival may be influenced by a distinct set of germline variants from those influencing susceptibility.

Genetic polymorphisms in telomere pathway genes, telomere length, and breast cancer survival

The impact of genetic variants in telomere pathway genes on telomere length and breast cancer survival remains unclear. We hypothesized that telomere length and genetic variants of telomere pathway genes are associated with survival among breast cancer patients. A population-based cohort study of 1,026 women diagnosed with a first primary breast cancer was conducted to examine telomere length and 52 genetic variants of 9 telomere pathway genes. Adjusted Cox regression analysis was employed to examine associations between telomere length, genetic variants and all-cause and breast cancer-specific mortality. Longer telomere length was significantly correlated with all-cause mortality in the subgroup with HER-2/neu negative tumors (HR=1.90, 95% CI: 1.12-3.22). Carrying the PINX1-33 (rs2277130) G-allele was significantly associated with increased all-cause mortality (HR=1.45, 95% CI: 1.06-1.98). Three SNPs (TERF2-03 rs35439397, TERT-14 rs2853677, and TERT-67 rs2853669) were significantly associated with reduced all-cause mortality. A similar reduced trend for breast cancer-specific mortality was observed for carrying the TERT-14 (rs2853677) T-allele (HR=0.57, 95% CI: 0.39-0.84), while carrying the POT1-18 (rs1034794) T-allele significantly increased breast cancer-specific mortality (HR=1.48, 95% CI: 1.00-2.19). However, none of the associations remained significant after correction for multiple tests. A significant dose-response effect was observed with increased number of unfavorable alleles/genotypes (PINX1-33 G-allele, POT1-18 T-allele, TERF2-03 GG, TERT-14 CC, and TERT-67 TT genotypes) and decreased survival. These data suggest that unfavorable genetic variants in telomere pathway genes may help to predict breast cancer survival.

Replication study for reported SNP associations with breast cancer survival

PURPOSE

Nine previously reported associations between single nucleotide polymorphisms (SNPs) and breast cancer outcomes from the Shanghai Breast Cancer Study (Stage 1) were further evaluated in relation to disease-free survival (DFS) and overall survival (OS) among 5,192 additional breast cancer patients (Stage 2).

METHODS

Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by proportional hazards regression in models adjusted for age, disease stage, estrogen and progesterone receptor status, and treatment regimens.

RESULTS

Two SNPs had generally consistent results and significant associations with OS in combined analyses. Compared to women with MMP7 rs11225297 AA genotypes, OS was moderately better for women with AT genotypes (HR: 0.8, 95% CI: 0.7-1.0) and much better for women with TT genotypes (HR: 0.4, 95% CI: 0.2-0.8). Compared to women with MMP8 rs11225395 CC genotypes, OS was slightly better for women with CT genotypes (HR: 0.9, 95% CI: 0.7-1.1) and moderately better for women with TT genotypes (HR: 0.6, 95% CI: 0.4-0.9). Joint analysis showed significant dose-response relationships with increasing numbers of rare alleles for both OS (p < 0.001) and DFS (p = 0.001).

CONCLUSIONS

A functional variant in MMP8 and a SNP in high linkage disequilibrium with a functional variant in MMP7 were significantly associated with breast cancer survival in a large two-stage survival study among Chinese women. This supports the hypothesis that SNPs in matrix metalloproteinase genes may influence breast cancer prognosis; additional research on these and other SNPs in genes important in metastasis, angiogenesis, and the regulation of the tumor microenvironment is warranted.

Steroid sulfatase and estrogen sulfotransferase in human carcinomas

Estrogens are closely involved in the development of hormone-dependent carcinomas. Estrone is locally produced from circulating inactive estrone sulfate by steroid sulfatase (STS), while estrone is inversely inactivated into estrone sulfate by estrogen sulfotransferase (EST). Recent studies suggested importance of this STS pathway in various human carcinomas. Therefore, in this review, we summarized recent results of STS and EST in several estrogen-dependent carcinomas. STS and EST expressions were detected in the breast and endometrial carcinomas, and activation of STS pathway due to increment in STS and/or decrement in EST expressions plays important role in their estrogen-dependent growth. STS expression was also reported in the ovarian and prostate carcinomas. STS/EST status was associated with intratumoral estrogen level in the colon carcinoma, and STS-negative/EST-positive colon carcinoma patients had longer survival. Therefore, STS pathway and estrogen actions may play an important role in the development of these carcinomas, and further investigations are required.

A genome-wide association study of prognosis in breast cancer

BACKGROUND

Traditional clinicopathologic features of breast cancer do not account for all the variation in survival. Germline genetic variation may provide additional prognostic information.

MATERIALS AND METHODS

We conducted a genome-wide association study of survival after a diagnosis of breast cancer by obtaining follow-up data and genotyping information on 528,252 single-nucleotide polymorphisms for 1,145 postmenopausal women with invasive breast cancer (7,711 person-years at risk) from the Nurses' Health Study scanned in the Cancer Genetic Markers of Susceptibility initiative. We genotyped the 10 most statistically significant loci (most significant single-nucleotide polymorphism located in ARHGAP10; P = 2.28 x 10(-7)) in 4,335 women diagnosed with invasive breast cancer (38,148 years at risk) in the SEARCH (Studies of Epidemiology and Risk factors in Cancer Heredity) breast cancer study.

RESULTS

None of the loci replicated in the SEARCH study (all P > 0.10). Assuming a minimum of 10 associated loci, the power to detect at least one with a minor allele frequency of 0.2 conferring a relative hazard of 2.0 at genome-wide significance (P = 5 x 10(-8)) was 99%.

CONCLUSION

We did not identify any common germline variants associated with breast cancer survival overall.

IMPACT

Our data suggest that it is unlikely that there are common germline variants with large effect sizes for breast cancer survival overall (hazard ratio >2). Instead, it is plausible that common variants associated with survival could be specific to tumor subtypes or treatment approaches. New studies, sufficiently powered, are needed to discover new regions associated with survival overall or by subtype or treatment subgroups.

Association between a germline OCA2 polymorphism at chromosome 15q13.1 and estrogen receptor-negative breast cancer survival

BACKGROUND

Traditional prognostic factors for survival and treatment response of patients with breast cancer do not fully account for observed survival variation. We used available genotype data from a previously conducted two-stage, breast cancer susceptibility genome-wide association study (ie, Studies of Epidemiology and Risk factors in Cancer Heredity [SEARCH]) to investigate associations between variation in germline DNA and overall survival.

METHODS

We evaluated possible associations between overall survival after a breast cancer diagnosis and 10 621 germline single-nucleotide polymorphisms (SNPs) from up to 3761 patients with invasive breast cancer (including 647 deaths and 26 978 person-years at risk) that were genotyped previously in the SEARCH study with high-density oligonucleotide microarrays (ie, hypothesis-generating set). Associations with all-cause mortality were assessed for each SNP by use of Cox regression analysis, generating a per rare allele hazard ratio (HR). To validate putative associations, we used patient genotype information that had been obtained with 5' nuclease assay or mass spectrometry and overall survival information for up to 14 096 patients with invasive breast cancer (including 2303 deaths and 70 019 person-years at risk) from 15 international case-control studies (ie, validation set). Fixed-effects meta-analysis was used to generate an overall effect estimate in the validation dataset and in combined SEARCH and validation datasets. All statistical tests were two-sided.

RESULTS

In the hypothesis-generating dataset, SNP rs4778137 (C>G) of the OCA2 gene at 15q13.1 was statistically significantly associated with overall survival among patients with estrogen receptor-negative tumors, with the rare G allele being associated with increased overall survival (HR of death per rare allele carried = 0.56, 95% confidence interval [CI] = 0.41 to 0.75, P = 9.2 x 10(-5)). This association was also observed in the validation dataset (HR of death per rare allele carried = 0.88, 95% CI = 0.78 to 0.99, P = .03) and in the combined dataset (HR of death per rare allele carried = 0.82, 95% CI = 0.73 to 0.92, P = 5 x 10(-4)).

CONCLUSION

The rare G allele of the OCA2 polymorphism, rs4778137, may be associated with improved overall survival among patients with estrogen receptor-negative breast cancer.