Most analyses of spatial clustering of disease have been based on either residence at the time of diagnosis or current residence. An underlying assumption in these analyses is that residence can be used as a proxy for environmental exposure. However, exposures earlier in life and not just those in the most recent period may be of significance. In breast cancer, there is accumulating evidence that early life exposures may contribute to risk. We explored spatio-temporal patterns of risk surfaces using data on lifetime residential history in a case control study of breast cancer, and identified elevated areas of risk and areas potentially having more exposure opportunities, defined as risk surfaces in this study. This approach may be more relevant in understanding the environmental etiology of breast cancer, since lifetime cumulative exposures or exposures at critical times may be more strongly associated with risk for breast cancer than exposures from the recent period.p-values. These surfaces were compared for premenopausal and postmenopausal women, and were obtained for each decade, from the 1940s to 1990s. We found strong evidence of clustering of lifetime residence for premenopausal women (for cases relative to controls), and a less strong suggestion of such clustering for postmenopausal women, and identified a substantial degree of temporal variability of the risk surfaces.A GIS-based exploratory spatial analysis was applied, and spatio-temporal variability of those risk surfaces was evaluated using the standardized difference in density surfaces between cases and controls. The significance of the resulting risk surfaces was tested and reported as We were able to pinpoint geographic areas with higher risk through exploratory spatial analyses, and to assess temporal variability of the risk surfaces, thus providing a working hypothesis on breast cancer and environmental exposures. Geographic areas with higher case densities need further epidemiologic investigation for potential relationships between lifetime environmental exposures and breast cancer risk. Examination of lifetime residential history provided additional information on geographic areas associated with higher risk; limiting exploration of chronic disease clustering to current residence may neglect important relationships between location and disease. In a recent analysis of breast cancer by New York State's Department of Health, a breast cancer cluster in the Western New York area was identified . One of in utero exposures may affect risk [Previous studies using exploratory spatial analyses, however, have been based on either residence at the time of diagnosis or current residence, and only a few recent studies have examined disease risk using information on lifetime residence ,12. For ect risk . To examIn this study, we explored spatio-temporal patterns of risk surfaces using data on lifetime residential history in a case control study of breast cancer. We had previously identified geographic clustering of residence at critical points in early life in relation to breast cancer risk . Here weDescriptive characteristics of study participants by menopausal status are presented in Table We constructed lifetime cumulative risk surfaces to represent exposure opportunities in lifetime because cumulative exposures may be a more accurate indicator of potential environmental exposures related to breast cancer risk. Figure p-values. Those areas with standardized difference greater than 2 SD were quite different between pre- and postmenopausal breast cancer, although ranges of standardized the difference were similar; -6.37 to 4.43 for pre-menopausal, and -6.57 to 3.39 for postmenopausal breast cancer, respectively. There were about 29 rectangular grids in those areas greater than 2 SD for premenopausal, while about 59 grids for postmenopausal breast cancer. Further, the statistical significance of areas must be assessed in light of the fact that multiple areas are tested; these are statistically significant if the difference in density exceeds the critical value of 3.56 at α = 0.05 . There is one small geographic area of special interest for pre-menopausal residences in the central and upper region of the city. When these are compared with the geographic location of clusters of birth and menarche residences identified previously [We evaluated spatial patterns of risk surfaces based on the geographic distribution of lifetime residences in Figure eviously , we founNext, we evaluated effects of other risk factors on the risk surfaces. To create age-adjusted risk surfaces, the standardized difference between case and control densities stratified by menopausal status and age groups was examined. Table Lastly, we were interested in evaluating temporal variability of risk surfaces; the standardized difference was obtained for each decade, for both pre- and post-menopausal residences, from the 1940s to 1990s  that can be used to create risk surfaces, because it is more easily able to handle the difficulties that arise with small densities. Unsmoothed risk surfaces are relatively easy to manipulate, but they are sensitive to geographic scales, while the ratio of case to control density results in unstable risk surfaces due to small number problems. We were able to reduce this problem in creating risk surfaces based on a standardized difference approach. The selection of optimal bandwidths in the application of kernel methods to cluster detection, and comparison of different types of bandwidths, such as adaptive kernel, will be a subject of future study .It is important to note that current approaches to obtaining density surfaces of lifetime breast cancer risk are limited in several ways. First, we are using residential locations of breast cancer cases and controls; we obtained the difference in densities (risk surfaces) based on their residential location to identify areas with relatively higher case density. We do not know actual exposures and breast cancer risk associated with residential locations. However, this study provides evidence of differential exposure opportunities among cases and controls for further epidemiologic assessment, since spatio-temporal clustering of residential locations in a life course may be an indicator of differential exposure opportunities and the subsequent risk of breast cancer. Another limitation of this analysis is that we were unable to incorporate length of residence into the model. We were able to visualize risk surfaces with different weights based on the actual length of residence of each individual, but there were difficulties in incorporating this information into risk surfaces due to a substantial degree of variability in the length of residence. However, we observed spatial patterns consistent with those in Figure There is also need for cautious interpretation of these results due to the potential for selection bias inherent in the study design, including factors such as non-participation, and missing and excluded residential location. Although we had a relatively stable population and about 40% of study participants were lifetime residents in the study area, there may be different geographic patterns among those included and excluded groups. We excluded missing residential information and residential locations outside of the study area. In addition, we had a rate of non-participation of about 30% in the survey. However, in our earlier study , we founThis study has significant implications for further studies on environmental exposure and breast cancer. We had previously found strong evidence of clustering of residence in early life, especially residence at birth and menarche . In thesTo provide more accurate measures of personal cumulative exposures based on complete lifetime residential history, further studies should take into account the different effects of time periods or timing of exposures; construction of lifetime cumulative risk surfaces with different weights for exposure sensitivity at different points in time is a potential improvement and explanation of the methods employed to date. This approach may help to provide an answer to the question of where, when, and what kinds of exposures have influenced individuals' risk for a particular disease. Further, there has been recent development of a GIS-based framework to examine spatio-temporal patterns of lifetime residential history; the geospatial lifeline concept and space-time information system (STIS) approach is a good example of this -27. We aIn summary, we found evidence of clustering of lifetime residence for premenopausal cases relative to controls, and a substantial degree of spatio-temporal variability in the risk surfaces, thus providing a working hypothesis on breast cancer and environmental exposures. Geographic areas with higher case densities need further epidemiologic investigation for potential relationships between lifetime environmental exposures and breast cancer risk. Examination of lifetime residential history provided additional information on geographic areas associated with higher risk; limiting exploration of chronic disease clustering to current residence may neglect important relationships between location and disease. Further studies on the relationship between disease risk and environmental exposures associated with lifetime residential history should be replicated in other settings.Data from a population-based case-control study of breast cancer in western New York (the WEB Study) were used for our analyses. Participants were women, age 35–79 who were residents of Erie and Niagara counties, with no history of cancer other than non-melanoma skin cancer; cases were women with incident, primary, pathologically confirmed breast cancer, diagnosed during the period 1996–2001, and controls were randomly selected and frequency matched to cases on age, race, and county of current residence. Details of the WEB study, including selection, ascertainment, in-depth interview processes, have been described previously . We collx and y coordinates to be used in further spatial analyses. Address geocoding is a process that creates a theme based on the address data in a tabular form (event theme) and a reference feature theme (street map) to add point locations defined by the street address to the map. Matching depends not only on the quality of the reference theme, but also on the quality of the tabular data to be mapped. We used GDT/Dynamap 2000, an enhanced version of Topographically Integrated Geographic Encoding and Referencing system (TIGER), as a reference theme. In a study validating the positional accuracy of TIGER for the use in epidemiologic study [Geocoding of residential locations enables us to record each individual's locational information as ic study , we founThe overall matching rate for the 15,487 Erie and Niagara County residential locations was 82% ; 91% of the matches were matched with complete information (good match), while about 9% of were estimated with partial information (interactive match) as detailed below. Geocoding success rates were lower for earlier residences, mainly due to more missing and partial information of earlier residences and changes in streets names and zip codes. However, it is important to note that there were few changes in street structure for this region during the time period of interest. There was, of course, addition of new streets, but existing streets were unchanged for the most part, and thus we found that the process of geocoding using current information was not inappropriate. We utilized various resources, including historic city directories with address information for residents, historical maps, and commercial address databases, to find missing residence information, and we developed several strategies to improve matching rates . In addik is defined as, , and by averaging over these individual kernel functions, we obtain the kernel density estimator,  ;Kernel density estimation methods have been used for disease mapping and for the detection of geographical location of clusters in epidemiologic settings ,12,29. Ax is the location for density estimation, xi is the observed point location, n is the number of points, and h is a smoothing parameter that regulates the degree of smoothness. Kernel functions are symmetric around zero and integrate to one. Both kernel and kernel density estimator are density functions; thus ∫kx (x)dx = 1 and ∫fh(x)dx = 1. For the two-dimensional kernel density estimator:where  is used, where  and  are estimates of the intensity for cases and controls, respectively. Smoothed risk surfaces using the ratio or log ratio of case to control density can be less effective with small sample sizes; it is possible to have spurious, high risk areas in the application of the ratio of densities when the value of the control density is too small.While the estimation of intensity for one point pattern may show patterns of high and low risk areas, this is of limited utility in epidemiologic applications because it may largely reflect the pattern of population distribution . The ratThe relative difference between two densities provides an alternative way to assess the spatial variability of risk surfaces. Using the square root variance stabilizing transformation and the standardized difference, this measure allows us to identify areas with differences between case and control density exceeding two standard deviations . The stawhere The first step in creating smoothed risk surfaces using kernel methods was to create reference grids and overlay the study area with them. We obtained the smoothed intensity for both cases and controls by calculating the distance between each point on the reference grid and the locations of breast cancer cases and controls. We used the quartic kernel to estimate the intensity of points at each grid point, although the choice of kernel type is not crucial as long as the kernel is symmetrical . We applThe above analyses were repeated with varying bandwidths because the choice of appropriate bandwidth is one of the primary concerns of the kernel method. Although a subjective choice made from a range of values is commonly used , selectiRisk surfaces based on the ratio and difference in density surfaces between cases and controls were implemented in a GIS and S-Plus environment . In addiAll authors have participated fully in the conduct of this study, the acquisition of data, the analysis and/or the preparation of the manuscript in a substantial manner. DH performed the statistical analysis and drafted the manuscript; PAR and JLF provided critical review and input. JLF, JEV, MAB, JN, PM and MT participated in the design of the study, participated in interpretation, as well as in data acquisition efforts. All authors read and approved this manuscript.


CYP1A2*1F and COMT Val58 Met polymorphisms among 332 breast cancer cases and 254 controls in the Hawaii component of the Multiethnic Cohort.Mammographic density is a strong predictor of breast cancer risk and is increased by hormone replacement therapy (HRT). Some associations with genetic polymorphisms in enzymes involved in estrogen metabolism have been described. This cross-sectional analysis examined the relation between mammographic density and the Mammographic density, before diagnosis in cases, was quantified by using a validated computer-assisted method. Blood samples were genotyped by standard PCR/RFLP methods. Adjusted mean percent density was calculated by genotype using mixed models with the unstructured covariance option.CYP1A2*1F gene and percent density, but not the dense area, was suggested (p = 0.11). The relation was limited to controls (p = 0.045), postmenopausal women not using HRT (p = 0.08), and normal weight subjects (p = 0.046). We did not observe any relation between the COMT Val58 Met polymorphism and breast density.A positive association between the C allele in the CYP1A2 genotype and the size of the dense areas suggests an effect on the non-dense, i.e., fatty breast tissue. The discrepancies among studies may be due to differential susceptibility; changes in enzyme activity as a result of the CYP1A2*1F polymorphism may influence breast tissue differently depending on hormonal status. Larger studies with the ability to look at interactions would be useful to elucidate the influence of genetic variation in CYP1A2 and COMT on the risk of developing breast cancer.The lack of an association between the  CYP1A2*1F gene and the Met allele in the COMT gene [CYP1A2*1F was also significantly associated with lower serum estradiol levels during the luteal phase [CYP1A2*1F gene had a lower risk of breast cancer than women with the common alleles [COMT gene, higher breast density was found for postmenopausal HRT users in one report [CYP1A2*1F and the COMT Val58 Met polymorphisms among the overlapping subjects.Mammographic density has been shown to be independently associated with breast cancer risk . AlthougOMT gene . The CC al phase . The paral phase ,17. Alsoal phase . In a ca alleles , but a c alleles . As for e report , lower be report , lower be report , higher e report , and no e report . To clare report  with a me report  and examThe subjects for this analysis were originally recruited for two separate case-control studies ,20 nesteTo assess breast density, the cranio-caudal views for both sides of several mammographic examinations were retrieved . All mamCOMT Val58 Met and CYP1A2*1F (A-154C) polymorphisms as described previously [DNA was extracted from white blood cells and genotyped by PCR/RFLP methods for the eviously .All statistical analyses were performed using SAS version 9  . In ordeDue to the study design, nearly half of the 575 subjects were breast cancer cases Table . On averCYP1A2*1F polymorphism among all women; the difference between the AA and CC genotypes was 5.1% (p for trend = 0.11) . Although we detected no significant interaction effects between the CYP1A2*1F polymorphism and case status, menopausal status, and BMI , we noted a number of differences after stratification by these variables. The association between the CYP1A2*1F allele and percent density was stronger among controls than cases. The difference between subjects with AA and CC genotype was 8.9% (p = 0.045), while there was no significant difference in breast density by genotype among cases (p = 0.86). Given the small number of mammograms among premenopausal women, the 9.6% difference between the two homozygous groups was not significant (p = 0.38). Among postmenopausal women, the 9.1% difference for mammograms taken without HRT use was close to significance (p = 0.08), whereas the difference among HRT users was only 2.5% (p = 0.48). Similarly, the positive association was limited to women with a BMI of less than 25 kg/m2 , whereas percent density did not vary by genotype among overweight and obese women. Stratification by ethnicity showed non-significant higher mean densities for Japanese CC carriers but no relation among Caucasians. The mean densities for the Japanese AA, AC, and CC carriers were 32.9%, 31.7%, and 39.4% (p = 0.44); the respective values for Caucasians were 26.0%, 27.9%, and 25.1% (p = 0.60).Adjusted percent densities were significantly higher with an increasing number of variant alleles in the COMT Val58 Met were not associated with percent density in the entire population or in any subgroup .Genotypes for up Table . The denCYP1A2 and COMT (p = 0.85 and 0.98).When we used the size of the dense areas as dependent variable, we found no difference in mean densities by genotype for CYP1A2 gene were 0.86 (95% CI = 0.60–1.23) and 0.62 (95% CI = 0.30–1.30), respectively. For the COMT Val58 Met polymorphism, the OR was 0.93 (95% CI = 0.64–1.35) for one Met allele and 0.61 (95% CI = 0.37–1.02) for two Met alleles as compared to the women with two Val alleles.Compared to women with the AA genotype, the risk estimates for breast cancer associated with one or two C alleles in the CYP1A2*1F gene, but the relation was limited to controls, postmenopausal women not using HRT, and normal weight women. The fact that the size of the dense areas was not associated with the CYP1A2 genotype suggests that the relation may be due to an effect on the non-dense, i.e., the fatty breast tissue. We did not observe any relation between percent density and the Met polymorphism in the COMT gene.In this analysis of longitudinal mammographic data, we observed a weak association between percent density and the number of C alleles in the p for trend = 0.01) among postmenopausal women not using HRT [CYP1A2*1F [p for trend = 0.03) that also included Latina and African-American women in Los Angeles. The findings with regard to the COMT Val58 Met polymorphism are also inconsistent. Studies in Hawaii [In comparison, a Canadian study reported mean percent densities of 14% for the lowest and 31% for the highest quartile of CYP1A2 activity  declare that they have no competing interests.YT performed the statistical analysis and drafted the manuscript. GM designed the study and helped to perform the statistical analysis and draft the manuscript. LM designed the study, supervised the genetic analysis and critically commented on the drafted manuscript. GM and LM obtained funding for the previous projects. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Many studies have linked higher latitudes with greater breast cancer risk, and seasonal variations are now thought to influence cancer incidence and mortality. The pivotal mediators in these relationships are believed to be sunshine and vitamin D. In clinical studies, poor vitamin D status is associated with a substantial increase in breast cancer incidence and mortality, though until recently, there has been only limited evidence in humans that vitamin D–related factors may reduce breast cancer risk. New studies are now clarifying the role of this nutrient.For most people, sunshine is the main source of vitamin D and specifically of 25(OH)D. Vitamin D is unusual among nutrients because very few foods naturally contain it, and supplementation is intended primarily to compensate for a deficiency of sunshine. Most human photosynthesis occurs in the summer, because the angle of sunlight largely determines the rate of vitamin D synthesis. Among the better-defined mechanisms of vitamin D’s anticancer action are its ability to induce cell differentiation and apoptosis of cancer cells while also inhibiting cell proliferation, angiogenesis, and metastasis.Cancer Epidemiology Biomarkers & Prevention, authors from Mount Sinai Hospital in Toronto described a case–control study involving 972 women with newly diagnosed invasive breast cancer and 1,135 healthy controls. All participants were interviewed to assess vitamin D–related exposures, such as outdoor activities, sun exposure habits , and dietary contributions (cod liver oil and milk consumption). More frequent sun exposure during adolescence was associated with a 35% reduction in breast cancer risk later in life. Lower risk was also linked to cod liver oil intake and drinking at least 10 glasses of milk per week. Milder protection was seen for people aged 20 to 29, but not for people over age 45.In the March 2007 issue of “Our study suggests that vitamin D–related exposures during adolescence and early adulthood, when the breasts are developing, may be more important than later exposure,” says lead author Julia Knight, a senior investigator at Toronto’s Prosserman Centre for Health Research. “A study in postmenopausal women asking about recent diet or sun would miss an effect of earlier exposure.”Breast Cancer Research and Treatment.Knight is also interested in knowing what effect sunlight supplementation may have in women following treatment for breast cancer. An epidemiologic study of different regions of Norway, each with a different annual UV exposure, found that the prognosis was 15–25% better for women diagnosed (and thus treated) in the summer versus the winter. This research was published in the May 2007 issue of Cancer Epidemiology Biomarkers & Prevention.A second study out of Laval University in Quebec sought to assess seasonal variations of breast density, a biomarker of breast cancer risk. The goal was to compare such variation, if any, with blood levels of 25(OH)D. It was assumed that a delay or lag time might be likely between a change in 25(OH)D levels and the resulting change in breast density. The researchers recruited 741 premenopausal women at screening mammography and found a strong inverse correlation between mammographic breast density and circulating vitamin D levels. When a four-month lag time was assumed, seasonal variations of breast density and circulating vitamin D appeared to be highly synchronized, so much so that “variations of breast density appeared to be a mirror image of those of 25(OH)D,” the researchers wrote in the May 2007 issue of “Taking seasonal variations and lag time into account appears to be crucial when studying the relation of circulating vitamin D to breast density,” says lead author Jacques Brisson, a professor in the Department of Social and Preventive Medicine at Laval University. “These time-related variables may also be crucial when studying the effect of vitamin D supplementation, because the latter is likely to vary with time, typically being more pronounced in fall and winter, for example.”American Journal of Clinical Nutrition found that women who regularly took vitamin D3 and calcium had a 60% reduction in all-cancer incidence compared with a group taking placebos and a 77% reduction when the analysis was confined to cancers diagnosed after the first 12 months. Observational studies such as those spearheaded by Brisson and Knight have suggested a less dramatic reduction in cancer risk (50% or more).Brisson and Knight agree that the next step is to carry out randomized trials of the effect of vitamin D supplementation, as only this type of design would establish causality between high vitamin D intake and reduction in breast cancer risk. A randomized controlled study published in the June 2007 issue of the Public health officials agree that limiting sun exposure will reduce the rate of skin cancers, but some scientists worry that such advice may lead to undesirable tradeoffs on a massive scale. “There are at most one or two extra skin cancer deaths [per hundred thousand people] when you compare the northern to southern United States . . . versus about thirty or forty fewer deaths for the other major cancers,” says Reinhold Vieth, an associate professor of nutrition at the University of Toronto. “So if you tally up the number of deaths likely to be attributable to UV light or vitamin D, it does not look like the most sensible policy to tell people to keep out of the sun just to prevent skin cancer.” Vieth’s advice is for white people to expose more skin for a shorter time, for example by lying in a bathing suit for 10 minutes on each side, twice per week, and over 4 to 8 weeks of summer. Darker-skinned people should stay in the sun longer or take vitamin D supplements.


As part of the Department of Health's National Breast Screening Trial a seven year study is in progress in Huddersfield to assess the effect of an educational programme in Breast Self Examination (BSE) on the mortality due to breast cancer among women aged 45-64. The initial cohort of 22,484 women have completed 3 years in the study and show a higher than expected annual incidence rate of breast cancer. There is no significant difference in the incidence rates between those who attended meetings for BSE instruction and those who did not. Similarly there is no difference in stages of presentation of cancers between attenders and non-attenders at these meetings and also between cancers detected in the first, second and third years. Those who discovered abnormalities during self examination, however, presented with smaller lumps compared to other women. Assessment of prognostic factors do not at this time provide sufficient evidence to show that a community-base BSE campaign will result in a significant improvement in the stage of breast cancer presentation.


The availability of geographic information from cancer and birth defect registries has increased public demands for investigation of perceived disease clusters. Many neighborhood-level cluster investigations are methodologically problematic, while maps made from registry data often ignore latency and many known risk factors. Population-based case-control and cohort studies provide a stronger foundation for spatial epidemiology because potential confounders and disease latency can be addressed.We investigated the association between residence and colorectal, lung, and breast cancer on upper Cape Cod, Massachusetts (USA) using extensive data on covariates and residential history from two case-control studies for 1983–1993. We generated maps using generalized additive models, smoothing on longitude and latitude while adjusting for covariates. The resulting continuous surface estimates disease rates relative to the whole study area. We used permutation tests to examine the overall importance of location in the model and identify areas of increased and decreased risk.Maps of colorectal cancer were relatively flat. Assuming 15 years of latency, lung cancer was significantly elevated just northeast of the Massachusetts Military Reservation, although the result did not hold when we restricted to residences of longest duration. Earlier non-spatial epidemiology had found a weak association between lung cancer and proximity to gun and mortar positions on the reservation. Breast cancer hot spots tended to increase in magnitude as we increased latency and adjusted for covariates, indicating that confounders were partly hiding these areas. Significant breast cancer hot spots were located near known groundwater plumes and the Massachusetts Military Reservation.Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. The analysis suggests further investigation of the potential association between breast cancer and pollution plumes based on detailed exposure modeling. Local disease mapping ("cluster") investigations are often desired by concerned communities, but many epidemiologists resist the pressure to search for environmental causes of clusters. Critics argue that such studies are unproductive and flawed because they often combine unrelated diseases, apply arbitrary or even "gerrymandered" boundaries, contain insufficient numbers of cases, and ignore population density, latency, and known risk factors . Data baIn 1988, an elevated cancer incidence in the Upper Cape Cod region of Massachusetts  using data from population-based case-control studies -12. The Controls were chosen to represent the underlying population that gave rise to the cases, i.e., permanent residents of the same towns during the same time period. Controls were frequency matched to cases on age, gender, and vital status. Because many of the cases were deceased or elderly, three different sources of controls were used: (1) random digit dialing for living controls less than 65 years of age; (2) Centers for Medicare and Medicaid Services  for the living population 65 years of age or older; and (3) death certificates for controls who had died from 1983 onward. There were 842 breast cancer controls, 1205 lung cancer controls, and 1138 colorectal cancer controls.Participants or their next-of-kin completed an extensive interview, providing information on demographics , a forty-year residential history, and potential confounders. "Index years" were randomly assigned to controls in a distribution similar to that of diagnosis years for cases. We used index years to estimate length and time of environmental exposure for controls in a fashion comparable to that of cases. See earlier papers -12 for aAll residential addresses reported by participants in the upper Cape Cod area over the forty-year period prior to the diagnosis or index year were eligible for spatial analysis. We excluded all addresses where residency time began after diagnosis date for cases or index date for controls. The breast cancer data set included 638 cases representing 1061 residential locations and 842 controls representing 1371 locations. The lung cancer data set included 243 cases representing 385 residential locations and 1205 controls representing 1927 residential locations. The colorectal cancer data set included 309 cases representing 469 residential locations and 1138 controls representing 1791 residential locations. Thus, individual participants may have contributed more than one address.Locations of the participant residences were geocoded using the Massachusetts State Plane Coordinate System with North American Datum 1983 (NAD1983) and linked to the participant's interview data. Geocoding, the process where longitude and latitude are determined for each street address, was done without knowledge of case status, and the final data were checked for accuracy. GIS allows us to map the coordinates of participants and link the location to additional individual and environmental information. Figure Statistical methods for mapping area-based epidemiologic data, e.g., disease rates by town or county, are well advanced . MappingAt least two methods provide unified frameworks for mapping point-based epidemiologic data, adjusting for covariates, and hypothesis testing: generalized linear mixed model formulations of kriging ,23,26,271) and longitude (x2)We estimated local disease odds using generalized additive models, a form of non-parametric or semi-parametric regression with the ability to analyze binary outcome data while adjusting for covariates . We mode1,x2)] = S + γ 'z     (1)          logit [p, z is a vector of covariates, and γ is a vector of parameters. The model is semiparametric because it has both nonparametric and parametric components. Without the smooth function, S, the model becomes an ordinary logistic regression on the covariates. Omitting the covariates produces a crude (unadjusted) map. We used a loess smooth which adapts to changes in population density [where the left-hand side is the log of the disease odds at location . Small span sizes produce bumpier surfaces and larger span sizes produce smoother surfaces. As the span size increases, the amount of bias in the fit increases and the variance decreases . We creata alone ,33. We tWe converted from log odds to odds ratios (ORs) using the whole study area as the reference, dividing the odds at each grid point by the odds calculated by the reduced model omitting the location smoothing term. The odds ratio estimates the rate ratio and relative risk. In order to make maps visually comparable, we mapped all results using the same dark blue to dark red continuous (unclassified) color scale and range of odds ratios, 0.25–2.50. This range covers most but not all of the ORs observed in our analyses, preventing maps from being washed out by an area of extremely high or low ORs. We used a linear scale for odds ratios; although a log scale is a good option, it may be more difficult for many people to interpret. As odds ratios near unity appear as a light green, this scale is close to divergent, an effective way to communicate deviations of the map from flatness in both directions. Spectral scales, such as ours, are useful when there is a clear central value – here, an odds ratio of one – from which divergence is important  and the reduced model omitting the smoothing term. The S-Plus software provides an approximate p-value for this statistic assuming a chi square distribution. Because the latter assumption is in general not true for GAMs , we calcIf the deviance global statistic indicated that location was significant at the 0.05 level, we conducted pointwise permutation tests to identify areas with significantly increased or decreased risk. We obtained a distribution of the log odds at every point using the same set of permutations we used for calculating the global statistics. The areas of significantly decreased risk ("cold spots") include all points that rank in the lower 2.5% of the pointwise distributions. Areas of significantly elevated risk ("hot spots") include all points that rank in the upper 2.5% of the pointwise distributions. By drawing the 2.5% and 97.5% contour lines, we mapped areas of significantly decreased and increased risk.a priori based on the current scientific literature or study design, was included in all adjusted analyses of breast cancer: time period of case ascertainment and vital status at interview, age at diagnosis or index year, family history of breast cancer, personal history of breast cancer (before current diagnosis or index year), age at first live birth or stillbirth, and occupational exposure to solvents. A number of other covariates were retained because they changed the appearance of the map: history of benign breast cancer, race, body mass index, history of radiation exposure, and alcohol use. We dropped other covariates from the model because they did not change the appearance of the map, including past use of diethylstilbestrol (DES), oral contraceptives and menopausal hormones, history of cigarette smoking, marital status, religion, education level, exposure to tetrachloroethylene from distribution pipes, and physical activity level. Lung cancer data were adjusted for age at diagnosis or index year, sex, vital status at interview, smoking , living with a smoker, occupational exposure to lung carcinogens , and exposure to radiation. Dropped covariates included alcohol history, use of pesticides/herbicides in the garden, exposure to tetrachloroethylene from distribution pipes, and whether the residence had been treated for termites. For colorectal cancer, we adjusted for age at diagnosis or index year, sex, vital status at interview, history of inflammatory bowel disease, and occupational history associated with colorectal cancer (jobs with asbestos or solvent exposure). History of alcohol use and radiation exposure did not affect the appearance of the maps.A group of core confounders, chosen We restricted analysis to subjects with complete residential histories. In our initial analyses, subjects missing data for other covariates were included in the analyses but those variables were coded as missing using an indicator variable . While tOur initial, no-latency analyses included all eligible residences, i.e., exposures occurring up to diagnosis were assumed to contribute to the risk of disease. However, cancers initiated by exposure to environmental carcinogens typically take more than a decade to develop. We therefore performed a fifteen-year latency analysis by restricting inclusion to the residences occupied by participants at least fifteen years prior to the diagnosis or index year . Between 46% and 48% of residences remained depending on the outcome. In addition, because breast cancer cases were obtained over a ten-year period, there were sufficient cases to perform a twenty-year latency analysis; 37% of residences remained eligible.Some participants lived at more than one location on Cape Cod or more than once at a single location if they moved away and later returned. To determine the effects multiple residences may have had on maps, we also performed analyses that included for each individual only the residence of longest duration that met latency assumptions. Since the resulting data set is smaller, the optimal span chosen by the AIC is often, but not always, larger. As maps can change due to different span sizes, we also analyzed the reduced data set using the optimal span of the original data.Assuming no latency, location was not statistically significant at the 0.05 level Table . AssuminWe next restricted the adjusted 20-year latency analysis to residences of longest duration. One third lived at their residence of longest duration for less than 20 years, 37% for 20–29 years and 30% for 30 or more years. Using the same span size as before (0.15), Figure Compared with the original map produced using indicator variables for missing data (5a), multiple imputation had only minor effects on the appearance of the 20 year latency analysis for all residences Figure ; as all Hot and cold spots became apparent as we increased latency from 0 to 15 years Table . AdjustiFor the adjusted 15-year latency analysis restricted to residences of longest duration, 23% of the subjects lived at their residence for less than 20 years, 18% for 20–29 years and 30% for 30 or more years. Restricting the 15-year latency analysis to residences of longest duration changed the map, eliminating the significant hot spots and the global significance of location Figure . This reThe maps for colorectal cancer showed less variation in odds ratios than those for breast and lung cancer. Location was not statistically significant except at the less plausible assumption of no latency Table . Little As shown in Tables In our analyses, the maps of lung and breast cancer on upper Cape Cod displayed more variation when we controlled for covariates and increased latency. Location also became a statistically more important part of the model. Rather than causing disease clusters as is often assumed, spatial confounding was partially hiding areas of increased risk. If population movement is random with respect to disease status, ignoring latency should cause nondifferential exposure misclassification and tend to make maps flatter. Areas of increased and decreased breast cancer risk became more pronounced, and the maps became more statistically significant, when we increased latency from 0 to 15 to 20 years. The trend towards greater spatial variation in both breast and lung cancer with increased latency is consistent with misclassification of geographically associated risk factors, including environmental exposures. Alternatively, people who lived on the Cape for many years may have personal risk factors that we did not control. A recent non-spatial analysis also found that breast cancer risk was associated with long residence on Cape Cod . In contA number of epidemiology studies have examined cancer and environmental exposures on Cape Cod -15. BrodOur analysis located a significant lung cancer "hot spot" north of the Massachusetts Military Reservation . The resulting map was relatively flat and different in appearance from the breast cancer maps, suggesting no spatial screening bias . Our use of residential history allowed us to take latency into account but produced multiple residences, a potential source of bias. Since we analyzed residences, an apparent cluster may actually be caused by a few people moving within a small area. To examine the effect of multiple residences, we restricted our analyses to residences of longest duration. Although the spatial pattern of risk was similar for breast cancer, there were differences in the location and magnitude of hot and cold spots in the lung cancer analysis. This may indicate that the inclusion of multiple residences biased the lung cancer analyses. Improved methods for analyzing data with multiple residences are needed; weighting by residence time has been suggested . While mSince several areas of elevated risk are near the coast, edge effects must be considered; GAMs may exhibit biased behavior at the edges of the data. However, loess may be less susceptible to this problem than many smoothers  and prelSemiparametric studies of air pollution commonly employ GAMs. The effect of interest is modeled parametrically and several covariates are modeled with smoothers. Dominici et al. ,43 reporChoice of bandwidth is one of the most important issues in smoothing . We usedStatistical methods for mapping adjusted, point-based epidemiologic data are still fairly novel. It would be useful in the future to compare the results of generalized additive models and generalized linear mixed models.Using generalized additive models and GIS, we generated maps of breast, lung and colorectal cancer risk. Our analyses showed little or no association between geographical location and colorectal cancer on upper Cape Cod. We observed an area of significantly elevated lung cancer risk north of the Massachusetts Military Reservation, similar to earlier research linking lung cancer to proximity to the military base. However, this result did not hold when we restricted analysis to residences of longest duration. Our results provide evidence for spatial clustering of breast cancer on upper Cape Cod. Areas of increased and decreased risk of breast cancer were not explained by covariates and became more extreme as we increased latency, findings consistent with geographical exposures. We identified three significant hot spots of breast cancer that coincide with groundwater plumes, an exposure hypothesis that warrants further investigation. We showed that spatial confounding can occur in maps, but in our analyses it tended to obscure rather than create clusters. Spatial epidemiology of population-based case-control studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Generalized additive models provide a relatively straightforward way to perform such analyses using standard software.AIC, Akaike's Information CriterionDES, diethylstilbestrolGAM, generalized additive modelGIS, geographical information systemsMMR, Massachusetts Military ReservationOR, odds ratioThe author(s) declare that they have no competing interests.VV conducted the spatial analyses and drafted the manuscript. TW directed the study, collaborated on all analytical decisions and wrote the second draft. JW provided statistical support and consulted on analytical and editorial issues. AA provided the data and assisted in epidemiologic analysis and editing. DO participated in the design of the study and the editing of the manuscript. The first two authors contributed equally. All authors read and approved the final manuscript.


A hospital-based case-control study was conducted over 4 years in Southern France to assess the pattern of established risk factors for breast cancer and to examine its variation according to age at diagnosis. Cases studied (450) were women admitted to the Montpellier Cancer Institute, with histologically confirmed primary breast carcinoma. Controls (576) were patients from a nearby hospital admitted in the early stages of a neurological or mild psychological diseases and from a clinic for general surgery. Any patient with malignant tumours, chronic and cardiovascular diseases were excluded. The total population globally showed the commonly reported pattern for these risk factors. When stratified by age, the reproductive factors occurring early in life  were shown to be significant risk factors only in the youngest group of patients and do not seem to influence risk in older women, for whom risk factors are those occurring later in life . This suggests a complex involvement of the reproductive and sociodemographic features with the various stages of the 'natural history' of breast cancer.


Women younger than 35 years who are diagnosed with breast cancer tend to have more advanced stage tumors and poorer prognoses than do older women. Pregnancy is associated with elevated exposure to estrogen, which may influence the progression of breast cancer in young women. The objective of the present study was to examine the relationship between reproductive events and tumor stage, grade, estrogen receptor and progesterone receptor status, and survival in women diagnosed with early-onset breast cancer.In a population-based, case–case study of 254 women diagnosed with invasive breast cancer at age under 35 years, odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression with tumor characteristics as dependent variables and adjusting for age and education. Survival analyses also examined the relationship between reproductive events and overall survival.Compared with nulliparous women, women with three or more childbirths were more likely to be diagnosed with nonlocalized tumors , and early age (<20 years) at first full-term pregnancy was also associated with a diagnosis of breast cancer that was nonlocalized  and of higher grade . The hazard ratio for death among women with two or more full-term pregnancies, as compared with those with one full-term pregnancy or none, was 2.1 (95% CI = 1.0–4.5), adjusting for stage. Among parous women, those who lactated were at decreased risk for both estrogen receptor and progesterone receptor negative tumors .The results of the present study suggest that pregnancy and lactation may influence tumor presentation and survival in women with early-onset breast cancer. Invasive breast cancer among women younger than 35 years is relatively rare in comparison with breast cancer in older women; only 2% of diagnoses occur in the younger age group . HoweverMalignant breast tumors in women younger than 35 years tend to have features that are characteristic of more aggressive or advanced tumors compared with tumors in older women. The tumors in younger women tend to be larger, have more nodal involvement, are more poorly differentiated, and are more often estrogen receptor (ER) negative than are tumors in older women [Pregnancy and childbirth are generally believed to reduce a woman's long-term risk for breast cancer. However, some studies have documented a transient increase in risk in the years immediately following childbirth -8, and aEarly detection is a key factor in determining whether a woman will survive a breast cancer diagnosis. Advances in screening methods have lead to a significant decrease in the percentage of diagnoses of late-stage disease in the past decade . HoweverThe observation that women with breast cancer under age 35 years are more likely to be diagnosed with advanced stage disease [A population-based, case–case design was used to examine the relationships between host reproductive and other variables, and tumor characteristics at presentation in breast cancer patients diagnosed at an early age.n = 169), in Connecticut (n = 60), or in Massachusetts (n = 69) at the time of diagnosis. The participation rate was 65% for patients from California, 61% for patients from Connecticut, and 55% for patients from Massachusetts.The study included 298 patients with early-onset (diagnosed at age <35 years) breast cancer. Eligible participants were all women aged between 18 and 34 years residing in one of 38 of the 58 California counties  and nonparticipants (mean 30.9 years), participants (73%) were more likely to be non-Hispanic white than were nonparticipants (59%).Before contact with any potential participants, approval was obtained from the local institutional review boards at participating centers. Consent to participate was first obtained from the treating physician. Potential participants were then mailed introductory letters and response forms. Trained interviewers telephoned participants to conduct a structured telephone interview and to schedule an appointment to collect a blood sample. Participants completed a written informed consent and a release of medical information form. The mean time between diagnosis and interview was 11.5 months (range 0–27 months).The interviews obtained information on host characteristics including age, demographic information, reproductive variables , self-reported family history of cancer, and oral contraceptive and other hormone medication use.Data on tumor characteristics were available for 254 (85.2%) participants from the cancer registry databases and pathology reports, and these women comprised the study group. Information on tumor characteristics was collected at the time of diagnosis, including tumor site, histology, laterality, behavior, grade, stage, tumor size, and ER and progesterone receptor (PgR) status.The California Cancer Registry abstracts for all of the California patients were available and were the primary source of tumor characteristic and follow-up data for these patients.The 60 Connecticut patients were linked with the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program's 1973–1999 public use data files  to obtaiThere were 60 patients who participated in the Early Onset Breast Cancer Study and who were Connecticut residents at the time of diagnosis. All of the 60 participants were linked to the SEER records using the variables listed above.Tumor data for the Massachusetts patients were ascertained using pathology reports. Pathology reports were available for 25 of the patients from Massachusetts and were reviewed by a certified tumor registrar and coded for tumor site, histology, laterality, behavior, grade, size, and SEER summary stage. Where available, pathology reports were also used to supplement tumor data for the patients from California and Connecticut.in situ, localized, regional by direct extension, regional by nodes, regional by nodes and direct extension, distant, or unstaged based on the SEER guidelines. Another staging system reported for the patients from California was the TNM staging system, which follows the American Joint Commission on Cancer (AJCC) criteria and consists of assigning appropriate letters or numbers to the following three fields [The SEER summary stage classifies tumors into one of the following categories : in situe fields : T  or nonlocalized .Tumors were classified according to histologic grade or degree of differentiation, as coded in the registry databases or pathology reports. This measures the degree to which the tumor cells have the differentiated or specialized characteristics of the tissue or organ in which they are found. Tumor grade information was available for 213 participants.n = 3 for ER status; n = 3 for PgR status). A total of 182 and 176 records contained information on ER and PgR status, respectively.Tumors classified as borderline for hormone receptor status were considered positive , leaving 296 with complete family history information.Family history of cancer was assessed during the personal interview. A positive family history was considered to be at least one first-degree female relative (mother or sister) diagnosed with breast or ovarian cancer. Adopted participants were excluded from the family history analyses  were associated with tumor characteristics indicative of advanced or aggressive disease , adjusting for age at diagnosis and education (higher of self or spouse). Odds ratios (ORs) were calculated using unconditional logistic regression with tumor characteristics as the dependent variables . Trend tests for select variables were performed by regressing categorical variables as continuous. All analyses were performed using SAS version 8.2 .Data on follow up were obtained for the California cases through 31 December 2001 from the California Cancer Registry and for the Connecticut cases through 31 December 1999 from the SEER 1999 public use data file . Follow-Demographic and tumor characteristics of the 254 women studied are summarized in Table Age- and education-adjusted analyses of reproductive events occurring before diagnosis and tumor characteristics are summarized in Tables P for trend < 0.01). However, when this analysis was restricted to parous women there were no significant associations between age at first full-term pregnancy and stage . Women who had three or more live births were about three times more likely to be diagnosed with nonlocalized disease than were women who did not have any live births. When this analysis was restricted to only gravid women, those with three or more pregnancies were more likely to be diagnosed with more advanced stage breast cancer than were women who reported one to two pregnancies . Number of births and age at first full-term pregnancy were highly correlated, and when terms for both were included in a logistic regression model neither was found to remain statistically significant (data not shown). Age at menarche, total number of miscarriages, total number of induced abortions, time since last full-term pregnancy, breastfeeding history, and oral contraceptive use were not found to be significantly associated with stage.Analyses of breast cancer stage included 247 women for whom tumor stage data were available  for the cases from Connecticut and 54 months (range 3–60 months) for the cases from California, and 52 months overall. Six of the 60 (10.0%) cases from Connecticut and 24 of the 161 cases from California (14.9%) died during the follow-up period.P = 0.19 and P = 0.15, respectively; Fig. P < 0.05). Age at first full-term pregnancy and lactation history were not found to be significantly associated with overall survival . However, gravid women with nonlocalized tumors who had their last pregnancy within 2 years of diagnosis had borderline decreased survival as compared with gravid women with nonlocalized tumors who had their last pregnancy more than 2 years before diagnosis (P = 0.06), but this association was not significant among women with localized tumors , age at first full-term pregnancy , time since last pregnancy , and lactation history . When stratified by stage, women with either localized or nonlocalized disease who had two or more full-term pregnancies did not have significantly decreased survival as compared with women with no or one full-term pregnancies  with survival, adjusting for stage. In these separate analyses, increased parity ≥ 2 full-term pregnancies as compared with 0–1 full-term pregnancies) was a significant predictor of decreased survival . Age at first full-term pregnancy and time since last pregnancy were not found to be significantly associated with risk for decreased survival .Steroid hormones play an important role in the etiology of breast cancer. Evidence has consistently linked endogenous hormone exposure to breast cancer risk . EstrogeAssessment of reproductive histories of early-onset breast cancer patients indicated that increased parity was associated with diagnosis of nonlocalized disease. However, this association did not extend to pregnancies ending in either spontaneous or induced abortion. This finding is consistent with a recent study of breast cancer patients aged under 45 years  that repHaving a full-term pregnancy within the 2 years preceding diagnosis was not found to be associated with later stage disease, higher grade, or ER or PgR status. These findings differ from a recent study of breast cancer patients aged under 45 years , which fAnother finding of the present study was that early age at first full-term pregnancy was also associated with later stage disease compared with nulliparity. As the age at first full-term pregnancy increased, the risk for having a diagnosis of nonlocalized disease decreased, although risk estimates for any age at first full-term pregnancy were elevated as compared with nulliparity. However, when this analysis was restricted to include only parous women, no significant association between age at first full-term pregnancy and stage was apparent.There is some previous evidence ,19 that Lactation is believed to be associated with decreased circulating levels of estrogen and progesterone, and was hypothesized to be associated with earlier stage tumors, with less aggressive profiles. Lactation among parous women was not found to be associated with tumor stage. However, parous women who reported that they had ever breastfed a child were more likely to have ER-positive and PgR-positive tumors than were parous women who had never breastfed a child. Lactation, therefore, was associated with tumor markers indicative of better prognosis. Another case–control study of women aged 20–74 years  found thPerhaps the difference in results between the present study and the previous one  is in paExogenous hormone exposure, in the form of oral contraceptives, was also examined in relation to tumor characteristics. This source of exogenous hormones was not associated with tumor stage, grade, or ER or PgR status. Oral contraceptive use was further examined by months of use, but no significant associations between months of use and any of the tumor characteristics emerged. Therefore, the present study does not support an association between exogenous hormone use and tumor presentation among early-onset breast cancer patients.Survival analyses stratified by stage suggested that a recent pregnancy (within 2 years of diagnosis) was associated with decreased survival as compared with women who had not had a recent pregnancy among women with nonlocalized tumors. Thirty-eight per cent of the women with nonlocalized tumors who had a recent pregnancy died within 5 years of diagnosis as compared with 15% of the women who had not had a recent pregnancy. This finding corresponds with previous research reporting that a recent pregnancy has an adverse effect on survival following breast cancer diagnosis ,17,22,23The hypotheses and findings presented here have biologic plausibility because of the established relationship between estrogen levels and both breast tissue proliferation and breast cancer risk. Estrogen and other hormone exposures are known to be affected substantially by pregnancy and other reproductive events. Therefore, it is biologically plausible that reproductive events may influence the progression of breast cancer among women in their childbearing years via a hormonal pathway.Including only living cases at the time of the study might have introduced a survival bias among participants. This might have reduced the proportion of women in the study with advanced stage disease. Participants were interviewed within 2 years after diagnosis and, given that breast cancer is generally not a rapidly fatal disease, this may not have introduced much bias. In fact, fewer than 6% of potential participants died before study contact overall.Another limitation is that there was not much information available regarding screening practices. Although we do not expect women in this age group to be receiving screening mammography, it was not possible to adjust for the effect of screening behavior  in the analyses. Therefore, if some of the independent variables such as parity and age at first full-term pregnancy are associated with screening practices, then the effects of these independent variables may have been misconstrued. However, it is likely that reproductive events such as pregnancy and childbirth would be associated with more frequent screening procedures because these events place women in the health care system. If so, then the independent effects of these variables on tumor characteristics may have been underestimated.It is possible that socioeconomic status (SES) confounded the association between reproductive variables and tumor characteristics. Study participants were not well characterized with respect to SES. Education as a surrogate measure of SES was included as a covariate in the analysis, although it was not found to be significantly associated with any of the tumor characteristics under study . Future studies should take care in characterizing participants with respect to SES.Adjuvant therapy information was not available for all participants and was not included in the survival analyses. It should also be noted that a fairly small sample size limited the extent of multivariate analyses that could be performed. Furthermore, follow-up information was not available for the patients diagnosed in Massachusetts, which further limited the sample size available for survival analyses. However, because breast cancer diagnosis at age under 35 years is a rare event, large sample sizes for population-based studies of this subgroup are often not available, and this study made use of a resource of such patients.Reproductive events such as pregnancy play an important role in the etiology of breast cancer. Whether the effect of pregnancy on breast cancer initiation or progression is protective or harmful may depend on the timing of the event in relation to the carcinogenesis process. Women who have had full-term pregnancies, particularly beginning at an early age, have consistently been observed to be at decreased lifetime risk for developing breast cancer as compared with nulliparous women or those who have a first full-term pregnancy at an older age. This may be due in part to the differentiation of breast tissue that occurs during a full-term pregnancy.The findings of the present study suggest that for women diagnosed with breast cancer before age 35 years, increased parity and an early age at first full-term pregnancy are associated with more advanced stage at presentation. Because all of the women in this study were breast cancer patients, it is possible that the pregnancies had an adverse effect on progression of a breast cancer that was already developing. However, this study suggests that the pregnancy need not be particularly recent to have the adverse effect. Therefore, if a woman has a full-term pregnancy and has not already had a prior initiation event, then the pregnancy would be expected to leave the breast tissue more differentiated and less susceptible to carcinogenic influences in the long term. However, the findings of the present study suggest that a full-term pregnancy may have an adverse effect on a breast cancer that is developing. Studies documenting a transient increase in breast cancer risk following pregnancy also support this theory -8.An understanding of the role of reproductive and hormonal exposures in the etiology and progression of breast cancer in young women highlights some of the age-related issues in breast cancer research by emphasizing that well established risk factors in older women may not have similar effects in the disease process in younger women, whose diagnoses occur nearer in time to reproductive events such as pregnancy.CI = confidence interval; ER = estrogen receptor; HR = hazard ratio; OR = odds ratio; PgR = progesterone receptor; SEER = Surveillance, Epidemiology, and End Results; SES = socioeconomic status.The author(s) declare that they have no competing interests.JAL performed the statistical analysis under the supervision of AZ and wrote the manuscript under the supervision of HA-C. All authors read and approved the final manuscript.


A collaborative, hospital-based case-control study was conducted at 12 participating centres in 10 countries. Based on data from personal interviews of 2,116 women with newly diagnosed breast cancer and 12,077 controls, the relative risk of breast cancer in women who ever used oral contraceptives was estimated to be 1.15 . Estimated values of this relative risk based on data from three developed and seven developing countries were 1.07  and 1.24  respectively; these estimates are not significantly different (P = 0.22). Estimates for women under and over age 35 were 1.26  and 1.12 , respectively, and these estimates are also not significantly different (P = 0.38). Risk was highest in recent and current users and declined with time since last use regardless of use. Risk did not increase with duration of use after stratifying on time since last use. Risk did not increase significantly with increasing duration of use before age 25 or before a first live birth. However, a relative risk of 1.5 that was of borderline statistical significance was observed in women who used oral contraceptives for more than 2 years before age 25. No single source of bias or confounding was identified that could explain the small increases in risk that were observed. Chance alone is also an unlikely explanation. The results could be due to a combination of chance and potential sources of bias, or they could represent a weak causal relationship.


A cohort of 11,580 residents of a retirement community initially free from cancer were followed from 1981 to 1989. A total of 1,335 incident cancer cases were diagnosed during the period. Relative risks of cancer were calculated for baseline consumption of vegetables, fruits, beta-carotene, dietary vitamin C, and vitamin supplements. After adjustment for age and smoking, no evidence of a protective effect was found for any of the dietary variables in men. However, an inverse association was observed between vitamin C supplement use and bladder cancer risk. In women, reduced cancer risks of all sites combined and of the colon were noted for combined intake of all vegetables and fruits, fruit intake alone, and dietary vitamin C. Supplemental use of vitamins A and C showed a protective effect on colon cancer risk in women. There was some suggestion that beta-carotene intake and supplemental use of vitamin A, C, and E were associated with reduced risk of lung cancer in women, but none of these results were statistically significant. These inverse associations observed in women seem to warrant further investigation, although there was inconsistency in results between the sexes.


Vitamin D receptor (VDR) polymorphisms have been inconsistently associated with breast cancer risk. Whether risk is influenced by polymorphisms in other vitamin D metabolism genes and whether calcium or vitamin D intake modifies risk by genotype have not been evaluated.Bsm1,Apa1,Taq1, and Fok1, a poly(A) microsatellite, and associated haplotypes (baTL and BAtS). We also examined one SNP in the 24-hydroxylase gene (CYP24A1) and two in the vitamin D-binding protein (group-specific component [GC]) gene. Participants completed a questionnaire on diet and medical history at baseline in 1992. This study includes 500 postmenopausal breast cancer cases and 500 controls matched by age, race/ethnicity, and date of blood collection.We conducted a nested case-control study within the Cancer Prevention Study II Nutrition Cohort of associations between breast cancer and four VDR single-nucleotide polymorphisms (SNPs), Bsm1 bb SNP who consumed greater than the median intake of total calcium (≥902 mg/day) had lower odds of breast cancer compared to women with the Bb or BB genotype and less than the median calcium intake . Similar interactions were observed for Taq1  and the poly(A) (LL) repeat.Incident breast cancer was not associated with any genotype examined. However, women with the We found no overall association between selected vitamin D pathway genes and postmenopausal breast cancer risk. However, certain VDR gene polymorphisms were associated with lower risk in women consuming high levels of calcium, suggesting that dietary factors may modify associations by VDR genotype. Vitamin D has been associated with a lower risk of several types of cancer, including breast cancer ,2. Its aTaq1,Bsm1, and Apa1), include an intronic G→A replacement , an intronic C→A replacement , and a T→C replacement in exon 9 , which result in a synonymous change at codon 352. Although not functional, these SNPs are strongly linked with a poly(A) microsatellite repeat in the 3' untranslated region ) protein and 1,25(OH)2D degradation enzyme (CYP24A1) genes in relation to breast cancer risk in a nested case-control study of postmenopausal women enrolled in a large prospective cohort. Of particular a priori interest was whether calcium and vitamin D intakes modified associations by genotype.We examined several polymorphisms in the VDR gene, their associated haplotypes, and polymorphisms in the vitamin D-binding  II Nutrition Cohort, a prospective study of cancer incidence among men and women from 21 states in the US, established by the American Cancer Society in 1992 . At enroFrom June 1998 through June 2001, blood specimens were collected from 21,965 Nutrition Cohort postmenopausal women. Using blood samples, we identified 502 breast cancer cases who were cancer-free in 1992. For each case, we randomly selected one postmenopausal female control who had provided a blood sample and was cancer-free (except non-melanoma skin cancer) at the time of the case's diagnosis [Fok1, Bsm1, Apa1, and Taq1. A variable-length poly(A) sequence in the 3' untranslated region was assessed using methods previously described [14-A17) or long .Genotyping was performed at Applied Biosystems  by laboratory personnel blinded to case-control status; blinded quality control samples for 10% of participants were inserted to validate genotyping procedures. TaqMan  was used to distinguish alleles of the VDR gene at restriction enzyme sites escribed  and was GC gene, which codes for the vitamin D-binding protein (DBP). We also measured a synonymous A→G SNP in exon 4 of the CYP24A1 gene (rs2296241), which encodes 24(OH)ase, which initiates degradation of 1,25(OH)2D.We also genotyped selected SNPs in vitamin D pathway genes that were relatively common and validated at study inception. Using TaqMan, we genotyped a T432G aspartate to glutamate SNP (rs7041) and an A436C threonine to lysine SNP (rs4588), both in exon 11 of the Bsm1 (93%). All SNPs were in Hardy-Weinberg equilibrium (p >0.05).Concordance for quality control samples was 100%. The overall success rate for genotyping assays was at least 95%, except for Calcium, vitamin D, and dairy product intakes were measured in 1992 to 1993  using a r = 0.66, 95% CI 0.51 to 0.76) and for dairy products .The FFQ was validated using four random 24-hour recalls collected throughout a 1-year period as the comparison measure among 441 Nutrition Cohort participants . Median 2 test to assess whether VDR, GC, and CYP24A1 genotype distributions were in Hardy-Weinberg equilibrium. Haplotypes of the VDR SNPs Bsm1,Apa1, and Taq1 and the poly(A) repeat were estimated using the expectation-maximization algorithm implemented in the TAGSNPS program [n = 393) and in situ (n = 107) breast tumors; sensitivity analyses excluding in situ cases showed similar findings; thus, all cases are presented.We used a χ program . To accoCovariates included education, age at menopause and menarche, parity, age at first live birth, personal history of benign breast disease, family history of breast cancer in mother or sisters, postmenopausal hormone replacement therapy use, and body mass index. Adjustment for alcohol consumption did not change the effect estimates and was not included.Fok1, GC SNPs and the CYP24A1 SNP were examined. Statistical interaction was evaluated using the Wald test; a p value of less than 0.05 was considered statistically significant.We examined whether dietary or total calcium or vitamin D modified associations by genotype; interaction terms between genotypes and each dietary variable in continuous form were included in the conditional logistic regression models. Total energy intake was included in the models examining dietary interactions. Two-way interactions of the VDR haplotypes, GC) or CYP24A1 gene was significantly associated with overall postmenopausal breast cancer risk (Table baTL (45.2%), BAtS (38.7%), and bATL (12.3%). Haplotype associations were similarly null: baTL OR = 0.89 (95% CI 0.66 to 1.20), BAtS OR = 1.24 (95% CI 0.92 to 1.66), and bATL OR = 0.96 (95% CI 0.69 to 1.32) for one or two copies versus none.Descriptive characteristics of participants in this study are presented in Table BB or Bb Bsm1 genotype (reference group), women with calcium intakes of more than 902 mg/day and the bb genotype had a 39% lower risk of breast cancer (pinteraction = 0.01). Similar findings were observed for those with the Taq1 TT and the poly(A) LL tail. These interactions were similar and statistically significant for dietary calcium, and no effect modification was observed with dietary vitamin D or dairy product consumption. Diet did not modify the associations between GC or CYP24A1 gene polymorphisms and breast cancer risk (not shown). We also observed no statistically significant effect modification in analyses stratified by median UV index for the state of residence as a proxy for cutaneous production of vitamin D. In addition, we observed no significant interactions between any two-way combination of VDR haplotypes, Fok1, or CYP24A1 or GC genotypes or in specific combinations of Fok1 and poly(A) genotypes.In analyses stratified by dietary factors, total calcium intake (but not vitamin D intake) modified the associations between polymorphisms near the 3' end of the VDR and breast cancer risk  long repeat (LL) genotype. Among women with a calcium intake lower than the median, these VDR genotypes were not associated with lower breast cancer risk.None of the genotypes or haplotypes examined from the vitamin D pathway was associated with postmenopausal breast cancer risk in all women. However, women with a calcium intake higher than the median were at lower breast cancer risk if they had the VDR Bsm1 Bsm1 bb genotype or associated polymorphisms in the 3' region and breast cancer risk is in contrast to findings in five studies that reported increased risk of breast cancer, mostly among Caucasian women with the bb genotype [baT haplotype allele likewise have been inconsistently related to breast cancer risk [BsmI BB versus the bb allele were similar for premenopausal (OR = 0.92) and postmenopausal (OR = 0.94) women.The lack of association between the genotype -11. Fourgenotype -15; two genotype  and Taiwgenotype  women. Ocer risk . It is uin vitro [2D [BsmI bb VDR genotype had a 6.8-fold higher risk of breast cancer than individuals with higher 25(OH)D levels and the BB or Bb genotype [In our study, dietary calcium intake, but not vitamin D intake, significantly modified the association between VDR SNPs near the 3' end of the gene and breast cancer risk. Both nutrients have direct effects on cell proliferation and differentiation of several cancer cell lines in vitro ,29. In ain vitro . It is pin vitro , potentiFok1 ff SNP and breast cancer risk do not replicate the positive association observed in the Nurses' Health Study [eagues [Our null findings for the th Study  but do ath Study ,13,17, nth Study ,15. Guy  [eagues  reportedGC gene (codes for DBP) or one in CYP24A1 and postmenopausal breast cancer. The polymorphic DBP facilitates vitamin D actions, and DBP alleles differ in their affinity for 1,25(OH)2 D [CYP24A1 gene codes for 24(OH)ase, which degrades 1,25(OH)2D. The potential for the SNPs we examined to alter DBP or CYP24A1 function is possible but has not been studied. Some studies have suggested a relationship of these SNPs with other outcomes, including asthma, glucose tolerance, and bone mineral density [We observed no association with two non-synonymous polymorphisms in the 5(OH)2 D . The CYP density -35. We aLimitations of this analysis include the use of a single assessment of diet, on average 6 years before diagnosis of breast cancer, which may have led to misclassification of dietary intake. We did not measure blood levels of 25(OH)D to assess vitamin D status. However, our FFQ was validated  and dietGC, or CYP24A1 genes and postmenopausal breast cancer risk among US Caucasian women. However, calcium intake appeared to modify the association of VDR SNPs with breast cancer risk. More research is needed to determine whether environmental factors, particularly diet, influence cancer risk by vitamin D pathway genotype.Our research does not support an independent association between selected polymorphisms in the VDR, CI = confidence interval; CPS = Cancer Prevention Study; DBP = vitamin D-binding protein; FFQ = food frequency questionnaire; GC = group-specific component; OR = odds ratio; SNP = single-nucleotide polymorphism; VDR = vitamin D receptor.The authors declare that they have no competing interests.MLM conceived the study and drafted the manuscript. VLS, HSF, EEC, and RMB made substantial contributions to the conception and design of the study. MJT, CR and EEC made substantial contributions to the acquisition and interpretation of data. WRD conducted the statistical analyses. All authors were involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.


AR gene and risk of breast cancer in the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3).Androgens have been hypothesised to influence risk of breast cancer through several possible mechanisms, including their conversion to estradiol or their binding to the oestrogen receptor and/or androgen receptor (AR) in the breast. Here, we report on the results of a large and comprehensive study of the association between genetic variation in the AR gene in a panel of 95 advanced breast cancer cases. Second, a dense set of markers from the public database was genotyped in a panel of 349 healthy women. The linkage disequilibrium relationships (blocks) across the gene were then identified, and haplotype-tagging single nucleotide polymorphisms (htSNPs) were selected to capture the common genetic variation across the locus. The htSNPs were then genotyped in the nested breast cancer cases and controls from the Cancer Prevention Study II, European Prospective Investigation into Cancer and Nutrition, Multiethnic Cohort, Nurses' Health Study, and Women's Health Study cohorts .The underlying genetic variation was determined by first sequencing the coding regions of the AR gene and risk of breast cancer, nor were any statistical interactions with known breast cancer risk factors observed.We found no association between any genetic variation  in the AR gene are not associated with risk of breast cancer.Among postmenopausal Caucasian women, common variants of the  The covariates included to account for breast cancer risk factors were age at menarche , menopausal status , parity (ever/never full-term pregnancy), body mass index (BMI)  and family history with the model containing these main effects, and an additional interaction term, with homozyotes of the ≥22 allele with no family history as the reference.The exon 1 CAG repeat was analysed as previously reported . InteracAR. One very common (approximately 70%) haplotype exists, with six lower-prevalence haplotypes being defined each by the htSNPs. The minimum R2H for these six SNPs was 0.77 in the Japanese, white, and Latina samples from the SNP selection panel. However, these SNPs do not describe haplotype diversity among African-Americans (minimum R2H = 0.03).Figure +  and ER- (n = 590) cases , PR+  and PR- (n = 860) cases , or localised  and metastatic  cases . No statistically significant interactions were observed between haplotypes and common breast cancer risk factors such as family history (yes/no), BMI , age at first full-term pregnancy , or alcohol consumption  . These results were not materially different after excluding African-American women from the MEC (344 cases and 426 controls).A total of 5,603 cases and 7,480 controls were available for genotyping. Table AR CAG repeat and family history on breast cancer risk in the NHS with follow-up to 1996 [AR CAG repeat length and positive family history were observed in either the further NHS follow-up or the WHS. The decrease in risk associated with shorter repeats among family history positive cases as previously reported [Data from further follow-up of the NHS and the WHS did not support the previous findings of interaction between the  to 1996 . No statreported  was not AR gene is a likely candidate gene, no association between polymorphisms in the AR gene and breast cancer risk was observed, despite the large sample size  and systematic approach of this study.One of the main aims of the BPC3 was to overcome limitations of prior studies by increasing sample size and, therefore, power of the study. By choosing genes involved in the synthesis, metabolism, and signaling of sex hormones, we aimed to maximise the possibility of finding alleles that predispose to breast cancer. Although the AR gene were associated with decreased risk of disease in women with a family history of breast cancer in the NHS. Adding samples from further follow-up cycles of the NHS, as well as samples from the WHS, we were unable to confirm this initial finding.In a previous study , shorterBRCA1  and BRCA2, although highly penetrant, are of low prevalence in the general population. Very few common polymorphisms have been shown to be associated with breast cancer risk. Using a candidate gene approach to select genes of possible interest in breast cancer etiology has also yielded very few breast cancer-susceptibility loci. One possible explanation for the lack of consistent association between common polymorphisms and breast cancer risk in individual studies is that the change in risk associated with common variants is too low to detect in individual studies and results that are reported may reflect publication bias.Mutations in genes such as AR gene with breast cancer risk. A concern that is more specific to the AR gene is that, due to the gene's location on the X chromosome, X chromosome inactivation could bias risk estimates associated with a causal allele toward the null. Such bias would be especially likely if the same X chromosome  were inactivated in all breast tissue within each woman. However, X chromosome inactivation occurs very early in embryonic development and differs between lobes within the same breast [The 95% confidence intervals in our study were narrow and exclude a substantial association between common variants in the e breast . AssuminAR gene as a susceptibility locus for breast cancer occurring before menopause. Additionally, although the MEC does provide information from non-Caucasian individuals, there are not a sufficient number of samples, and htSNPs selected to describe genetic variation in Caucasians is not sufficient among African-Americans to definitively exclude polymorphisms in the AR gene as breast cancer-susceptibility alleles except in Caucasians. Among the latter, neither common variants nor the CAG repeat in exon 1 of the AR gene is associated with risk of postmenopausal breast cancer.Due to the low numbers of premenopausal women in our study, we cannot exclude the AR gene are not associated with breast cancer risk among postmenopausal Caucasian women.Common polymorphisms in the AR = androgen receptor; BMI = body mass index; BPC3 = Breast and Prostate Cancer Cohort Consortium; EPIC = European Prospective Investigation into Cancer and Nutrition; ER = oestrogen receptor; htSNP = haplotype-tagging single nucleotide polymorphism; MEC = Multiethnic Cohort; NHS = Nurses' Health Study; OR = odds ratio; PR = progesterone receptor; SNP = single nucleotide polymorphism; WHS = Women's Health Study.The authors declare that they have no competing interests.DGC, H Blanché, CLP, EEC, GAC, and MCP made up the writing committee for this work and were responsible for data analyses, manuscript preparation, and editing. NB and MF performed the htSNP selection and contributed substantially to manuscript editing. SC, FC, CAH, PK, DOS, and MY provided expertise in genotyping and results analyses, as well as manuscript editing. DA, NEA, PA, GB, H Boeing, JB, FC-C, HSF, SEH, BEH, RH, DJH, RK, LK, LL, EL, DP, PP, ER, MT, AT, and DT contributed substantially to sample collection and manuscript editing. All authors read and approved the final manuscript.


Terry MB, Gammon MD, Zhang FF, Tawfik H, Teitelbaum SL, Britton JA, Subbaramaiah K, Dannenberg AJ, Neugut AI. 2004. Association of frequency and duration of aspirin use and hormone receptor status with breast cancer risk. JAMA 291:2433–2440.Aspirin has been used as a nonprescription pain reliever for more than 100 years, with more than 80 million tablets currently consumed in the United States every day. However, it was not until the 1970s that the mechanism of action was discovered; aspirin was found to inhibit the production of proinflammatory prostaglandins. In the past 20 years, regular aspirin use has been shown to protect against heart disease, stroke, and colorectal cancer. Now NIEHS grantee Marilie Gammon of the University of North Carolina School of Public Health and colleagues report that regular aspirin use may also protect against breast cancer.2, inhibition of prostaglandin production will also decrease the production of estrogen. Given the importance of estrogen in the development of breast cancer, Gammon and colleagues undertook an epidemiologic study to determine whether there was any association between regular NSAID use and reduced risk of breast cancer.Research suggests that inhibition of prostaglandin synthesis may prevent cancer. Cyclooxygenase (COX) is involved in the synthesis of prostaglandins. Aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) are known to block the active site of COX and, therefore, inhibit prostaglandin production. Because the final reaction in the synthesis of estrogen depends upon a cytochrome P450 enzyme that is stimulated by prostaglandin EThe team conducted a population-based study of 1,442 women with breast cancer and 1,420 controls. The women were interviewed and asked to report their intake of aspirin, ibuprofen, and acetaminophen. Dose was not considered; instead, the team looked at duration and frequency of use. Regular use was defined as women who took aspirin at least 4 times per week for at least 3 months and initiated use at least 1 year prior to the reference age (age at diagnosis of breast cancer or corresponding age for controls). All exposure information was truncated to 12 months prior to the reference age.Regular aspirin use was inversely associated with hormone-responsive breast tumors, with the strongest results for women who took 7 or more tablets per week. The results of ibuprofen use were generally weaker. There was no association with use of acetaminophen, which does not inhibit prostaglandin synthesis.–Jerry PhelpsThis study adds to the growing body of data that supports the regular use of aspirin as an effective chemopreventive agent for hormone-responsive breast cancer tumors. This effect most likely occurs through the inhibition of prostaglandin and subsequent inhibition of estrogen biosynthesis. However, the reduced risk must be confirmed before clinicians can make definite recommendations to women at risk for breast cancer.