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Objective: To assess the impact of infertility treatment with causes of infertility on incidence of breast cancer., Study Design: Historical prospective cohort study of 1135 women attending major university clinics for treatment of infertility in Sweden, 1961-1976. Women were classified as users of clomiphene citrate or gonadotropins, or a combination of both therapies. Standardized incidence ratios were calculated to estimate relative risk of breast cancer., Results: We observed 54 cases of breast cancer during 1961-2004, which did not significantly exceed those expected. Users of high-dose clomiphene citrate had an almost 2-fold increased risk (standardized incidence ratio, 1.90; 95% confidence interval, 1.08-3.35). This association was more pronounced among women referred for nonovulatory factors, with 3-fold increased risk (standardized incidence ratio, 3.00; 95% confidence interval, 1.35-6.67)., Conclusion: No overall increased risk for breast cancer was shown with infertility treatment. Women with nonovulatory causes treated with high-dose clomiphene citrate therapy may have an elevated risk for breast cancer.

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2 > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10(-7)). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.

The association between the cytochrome P-450 1B1 (CYP1B1) Val432Leu polymorphism and breast cancer was assessed through a meta-analysis of all published case-control studies and a pooled analysis of both published and unpublished case-control studies from the Genetic Susceptibility to Environmental Carcinogens (GSEC) database ( www.upci.upmc.edu/research/ccps/ccontrol/g&#95;intro.html ). GSEC is a collaborative project that gathers information on studies of metabolic gene polymorphisms and cancer. Thirteen articles were included in the meta-analysis (14,331 subjects; 7,514 cases, 6,817 controls); nine data sets were included in the pooled analysis (6,842 subjects; 3,391 cases, 3,451 controls). A summary meta- or pooled estimate of the association between the CYP1B1 Val432Leu polymorphism and breast cancer could not be calculated because of statistically significant heterogeneity in the point estimates among studies. No association between the CYP1B1 Val432Leu polymorphism and breast cancer was observed in Asians (for Val/Val and Val/Leu combined, odds ratio (OR) = 1.0, 95% confidence interval (CI): 0.8, 1.2). An inverse association was observed in populations of mixed/African origin (OR = 0.8, 95% CI: 0.7, 0.9). The pooled analysis suggested a possible association in Caucasians (for Val/Val and Val/Leu combined, OR = 1.5, 95% CI: 1.1, 2.1), with effect modification across age categories. The observed effect of age on the association in Caucasians indicates that further studies are needed on the role of CYP1B1 Val432Leu in estrogen metabolism according to age, ethnicity, and menopausal status.

Background: From 1968 to 2002, Singapore experienced an almost three-fold increase in breast cancer incidence. This increase appeared to be different across the three main ethnic groups: Chinese, Malays and Indians. This paper used age-period-cohort (APC) modelling, to determine the effects of age at diagnosis, calendar period, and birth cohort on breast cancer incidence for each ethnic group., Methods: This study included all breast cancer cases (n = 15,269) in the three ethnic groups, reported to the Singapore Cancer Registry from 1968 to 2002 between the ages 25 to 79. Age-specific fertility rates from the Department of Statistics were used to explore the role of fertility., Results: In the 1970s, Indian women had the highest age-standardized breast cancer but by the mid-1980s the highest rates were seen among the Chinese. Remarkable differences were seen in the age-specific incidence rates by ethnic groups. After age 49, the incidence rates for the Chinese and Malays leveled off whereas it continued to rise in the Indians. While our analyses provided some evidence that an age-drift model described the trend seen in the Indians, age-period-cohort model and age-cohort model had the best fit for the Chinese and Malays aged 25 to 79 respectively. Overall, Chinese and Malay women born in later cohorts were at increased risk of developing breast cancer relative to their counterparts in the earlier cohorts. The three ethnic groups experienced similar changes in their fertility in the 1970s, which likely explained much of the increase in their breast cancer incidence but not the ethnic differences. There was a stronger inverse association between total fertility rate and pre-menopausal breast cancer incidence in the Chinese and Malays than the Indians., Conclusion: The observed dissimilarity among ethnic groups suggests ethnic differences in exposure or response to certain risk factors. It is likely that longer and subtler differences in childbearing trends and other risk factors may further explain these ethnic differences.

Cytochrome P450 1B1 (CYP1B1) is active in the metabolism of estrogens to reactive catechols and of different procarcinogens. Several studies have investigated the relationship between genetic polymorphisms of CYP1B1 and breast cancer risk, however, with inconsistent results. We investigated such an association in postmenopausal Swedish women, with special emphasis on long-term menopausal hormone users, in a large population-based case-control study. We genotyped 1521 cases and 1498 controls for the CYP1B1 single nucleotide polymorphisms (SNPs) m2, m3 and m4 and reconstructed haplotypes. The frequencies of CYP1B1*1, CYP1B1*2, CYP1B1*3 and CYP1B1*4 alleles among controls were estimated to be 0.087, 0.293, 0.444 and 0.175, respectively. It thus appeared that very few haplotypes contained combinations of SNPs at two or three loci and that single SNP genotype data effectively represented haplotypes. Odds ratios (OR) and 95% confidence intervals (CI) were calculated from logistic regression models. We found no overall association between any CYP1B1 genotype and breast cancer risk. The data indicated, however, that women who had used menopausal hormones for 4 years or longer, and carried the CYP1B1*3/*3 genotype may be at increased risk of breast cancer, OR 2.0 (95% CI 1.1-3.5), compared with long-term users without this genotype. We explored the effect of CYP1B1 genotype on breast cancer risk in subgroups defined by body mass index, family history, smoking and catechol-O-methyl transferase genotype, but found no convincing evidence for interaction. In summary, our results strongly indicate that the studied CYP1B1 gene polymorphisms do not influence breast cancer risk overall but may modify the risk after long-term menopausal hormone use.

It has become increasingly clear that cancer can be considered neither purely genetic nor purely environmental. A relatively new area of cancer research has focused on the interaction between genes and environment in the same causal mechanism. Primary candidates for gene-environment interaction studies have been genes that encode enzymes involved in the metabolism of established cancer risk factors. There are common variant forms of these genes (polymorphisms), which may alter metabolism and increase or decrease exposure to carcinogens, thus impacting the risk of cancer. We present an overview of enzymes involved in carcinogen metabolism, present epidemiological tools to evaluate gene-environment interactions, and provide examples from cancers of the breast, lung and large bowel.