Cadmium is a human lung carcinogen and possesses estrogen-like activity. This combination of carcinogenic and estrogenic activity makes cadmium a contaminant of high concern for hormone-related cancers. Diet and smoking are the main sources of cadmium exposure. The aim of this study was to investigate the association between dietary cadmium intake and risk of breast, endometrial and ovarian cancer in Danish postmenopausal woman.We estimated dietary cadmium intake in the Diet, Cancer and Health cohort at enrolment 1993-97. The estimates were based on food frequency questionnaires and cadmium contents in all foods. Among 23,815 postmenopausal women we identified 1390 breast, 192 endometrial, and 146 ovarian cancer cases from enrolment through December 31, 2010 using the Danish Cancer Registry. Cox regression was used to analyse the association between dietary cadmium intake and cancer risk.p value for interaction  = 0.02.Mean dietary cadmium intake was 14 µg/day. Cadmium was not associated with breast cancer, incidence rate ratio (IRR) = 0.99, 95% confidence interval (CI): 0.87–1.13 per 10 µg higher dietary cadmium intake/day; endometrial cancer, IRR = 1.08, 95% CI: 0.76–1.53; or ovarian cancer, IRR = 1.15, 95% CI: 0.78–1.70. We found a positive association between cadmium and endometrial cancer for the women with BMI<25 , whereas an inverse association was seen for the women with BMI≥25 ; Our study does not indicate that our estimated dietary cadmium intake is associated with hormone-related cancers in women. Breast cancer is the most frequently diagnosed cancer among women in Denmark and endometrial and ovarian cancer are the most commonly diagnosed gynaecological cancers. These three cancers are the most frequent hormone-related cancers among women, collectively accounting for about 6000 new cases of cancer in Denmark each year Established risk factors of these female cancers are related to cumulative exposure to estrogens and reproductive life such as early age at menarche, nulliparity, late age at first pregnancy, short lactation and late menopause in vitro and in vivo studies have demonstrated that cadmium also exerts estrogenic activities, such as proliferation of breast cancer cells Cadmium is an IARC classified group 1 proven human carcinogen of the lung based on mechanistic and epidemiologic evidence from high-exposure occupational settings Diet is a main source of human exposure to cadmium in the non-occupationally exposed population. Cereal products and vegetables are important dietary exposure sources Three prospective cohort studies have assessed the association between dietary cadmium intake and risk of breast cancer, of which one study indicated an association The present study aimed to investigate whether dietary cadmium exposure is associated with hormone-related cancer of the breast, endometrium and ovary assessed in a large population-based prospective cohort in Denmark.This study was approved by the regional research ethic committee for Copenhagen and Frederiksberg. Written informed consent was obtained from all study participants. The study was carried out without contact to the cohort members or their families. Anonymity of participants was retained by strict data management.From December 1, 1993, through May 31, 1997, a total of 57,053 individuals , who were aged 50–65 years, born in Denmark, and had no previous cancer diagnosis, were enrolled in the prospective Diet, Cancer and Health cohort We used the Danish Cancer Registry to identify incident cases of invasive breast cancer, endometrial cancer and ovarian cancer among cohort members. Information on ER status, PGR status and histology type was obtained from The Danish Breast Cancer Cooperative Group (DBCG) We estimated dietary cadmium intake per day for each person in the prospective DCH cohort based on the 192 item semi-quantitative food frequency questionnaire filled in at enrolment. For the calculations we used food monitoring data from The Danish Food Monitoring Programme for Nutrients and Contaminants, 1993–97 We used Cox proportional hazard models with age as the underlying time scale a priori defined potential confounders: Educational level  as a measure of socioeconomic status, smoking status , as smoking is a major source of cadmium and never smokers are at lower risk of endometrial and ovarian cancer, and the following known risk factors: number of births , age at first birth , HRT status , HRT use , age at menarche , BMI (continuous), height , physical activity , and alcohol intake . Linearity was evaluated with use of linear splines with three boundaries for dietary cadmium intake, age at first birth, HRT use, age at menarche, BMI, height, physical activity and alcohol intake and there was no deviation from linearity. Also, we estimated crude and adjusted IRRs for tertiles of daily dietary cadmium intake, based on distribution among all cohort members. Only participants with complete covariate information were included. Only adjusted IRRs are reported here, as crude and adjusted IRRs were similar in all analyses.We estimated crude and adjusted incidence rate ratios (IRRs) using the estimate of dietary cadmium intake as a linear variable. The adjusted models were carried out with a priori specified individual characteristics as potential effect modifiers : Educational level , smoking status , HRT use , BMI , dietary zinc intake , and dietary iron intake .Further, we evaluated In order to minimize the potential effect of exposure to endogenously produced adipose tissue estrogen, obtained hormones from medical treatment and/or to smoking-derived cadmium, we also restricted analyses to participants who: 1) were never smokers and had BMI<25, and 2) were never smokers and never HRT users, 3) had BMI<25 and were never HRT users, and 4) were never smokers, had BMI<25 and were never HRT users. This was only analysed for breast cancer due to the relatively small number of endometrial and ovarian cancer cases.Also, we calculated IRRs for different subgroups of breast cancer classifications: Estrogen receptor status (ER+ and ER-), progesterone receptor status (PGR+ and PGR-) and the two most frequent histology types .The procedure PHREG in SAS version 9.3  was used for the statistical analyses.Among the 29,875 women of the Diet, Cancer and Health cohort, we excluded 338 with a cancer diagnosis before baseline, 1 with unknown month of cancer diagnosis, 25 with no dietary cadmium exposure data, 5,295 not being defined as postmenopausal at baseline, and 401 with incomplete covariate data. This resulted in a study population of 23,815 postmenopausal women with complete covariate data.The mean estimated daily cadmium intake of the cohort was 14 µg/day (5–95% percentiles  = 8–22 µg cadmium/day). No major difference in dietary cadmium intake was observed between cancer cases and cohort. As expected, breast and endometrial cancer cases seemed to be higher educated than the cohort and ovarian and endometrial cancer cases were more likely to be never smokers compared with the cohort. In general, cases were more likely to be current users of hormone replacement therapy and to be nulliparous, compared with the cohort (Data not shown).In our study population, cereal products and vegetables, including potatoes, together contributed to the majority of the estimated dietary cadmium exposure with a mean (SD) of 82% (8%). Specifically, whole grain products contributed a mean (SD) of 33% (12%), potatoes 20% (9%), vegetables, exclusive potatoes, 17% (8%) and refined cereal products 12% (7%). In contrast, meat , fish, fruit and dairy products contributed only with a mean (SD) of 2.2% (1.5%), 1.8% (1.2%), 2.3% (0.2%), and 0.2% (0.2%), respectively, of the mean cadmium intake (data not shown). In general, we did not observe significant associations between dietary cadmium intake and cancer risk for any of the three investigated hormone-related cancers , neitherP value for interactions  = 0.02. However, none of the risk estimates in each individual stratum in the analysis was significant. We did not find any statistically significant interactions for the remaining potential effect modifiers.a priori defined subgroups. We did not establish any significant associations between dietary cadmium intake and breast cancer for any of the subgroups.For breast cancer, risk analyses were also carried out for estrogen receptor classification (ER+ and ER-), progesterone receptor classification (PGR+ and PGR-) and for the two most frequent histology types  . No signIn this study, we did not find significant associations between dietary cadmium intake and risk of hormone-related cancers in postmenopausal women. In line with our results, the large American prospective VITAL cohort study did not find any evidence of an association between dietary cadmium intake and postmenopausal breast cancer risk Two American retrospective breast cancer case-control studies found significant trends for increased odds ratios across quartiles of urinary cadmium levels The clinical, pathologic, and molecular characteristics of breast cancer differ by their ER and/or PGR expression profile and the effects of risk factors of breast cancer, such as reproduction related exposures, also differ by ER/PGR status a priori to obtain a higher estimate for women with lower BMI due to the reduced influence of adipose tissue-derived estrogen exposure. We found an interaction for endometrial cancer and to a lesser extent for breast cancer. This tendency was also seen in other After menopause, women with higher BMI have a slightly increased risk of breast cancer compared with leaner women. This may be explained by the fact that estrogen after menopause is formed mainly in the adipose tissue. Obese postmenopausal women have plasma levels of endogenous estrogens nearly twice as high as lean women Cadmium shares some structural similarities with the mineral zinc and there is some mechanistic evidence that zinc increases the sequestering of cadmium by inducing metal-binding metallothioneins, as well as directly reducing cadmium absorption The major strength of this study is the prospective design that was based on a well-defined cohort with data on potential confounders. Furthermore, virtually complete nationwide registries provided information on vital status and cancer status. Further, disease status could not have biased exposure assessment because questionnaire data was obtained before cancer diagnosis.Non-differential exposure measurement error in the estimation of dietary cadmium intake may be a factor concealing a true association between cadmium and the investigated cancers. That is, a limitation of this study includes our ability to accurately assess dietary cadmium intake, which may have moderated our estimates. Participants of the Diet, Cancer and Health cohort were asked to report their average dietary habits within the year prior to enrolment, and these answers may reflect long-term dietary pattern and long-term exposure to dietary cadmium. However, their dietary pattern may have changed during the follow up period. Also, some deviation in the content of cadmium in specific food items could be another important source of measurement error. Future studies are needed including use of urinary levels as marker for cadmium exposure in relation to hormone-related cancer in large prospective studies.In conclusion, the results of the present study do not support the hypothesis that cadmium contamination of food is a risk factor for postmenopausal hormone-related cancers in women.


Luminal breast cancer constitutes a group of highly heterogeneous diseases with a sustained high risk of late recurrence. We aimed to develop comprehensive and practical nomograms to better estimate the long-term survival of luminal breast cancer.n = 87,867) and validation  cohorts. The cumulative incidence function (CIF) and a competing-risks model were used to estimate the probability of breast cancer-specific survival (BCSS) and death from other causes. We integrated significant prognostic factors to build nomograms and subjected the nomograms to bootstrap internal validation and to external validation.Patients with luminal breast cancer diagnosed between 1990 and 2006 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into the training  were 0.053 and 0.112, respectively. Nine independent prognostic factors for both OS and BCSS were integrated to construct the nomograms. The calibration curves for the probabilities of 5- and 10-year OS and BCSS showed excellent agreement between the nomogram prediction and actual observation. The C-indexes of the nomograms were high in both internal validation (0.732 for OS and 0.800 for BCSS) and external validation (0.731 for OS and 0.794 for BCSS).We established nomograms that accurately predict OS and BCSS for patients with luminal breast cancer. The nomograms can identify patients with higher risk of late overall mortality and BCSM, helping physicians in facilitating individualized treatment. Breast cancer is the most common cancer diagnosed in women,  and apprGiven the potential for a relatively long-term survival of patients with luminal breast cancer, a considerable number of patients might die from other causes. As a result, overall survival (OS) might fail to accurately describe a patient's long-term survival rate attributed to breast cancer. Thus, taking other causes of death into consideration is necessary when estimating breast cancer-specific survival (BCSS).Nomograms have been widely used in clinical oncology as reliable and convenient tools for quantifying risk by incorporating and illustrating important prognostic factors. However, to the best of our knowledge, nomograms for predicting long-term OS and BCSS of patients with luminal breast cancer have not been reported. In this study, we aimed to develop comprehensive and practical nomograms based on a large population with long-term follow-up to better estimate the long-term OS and BCSS of luminal breast cancer patients.The entire population were collected from the Surveillance, Epidemiology, and End Results (SEER) program, containing 176,082 patients with histologically confirmed invasive breast cancer, with 87,867 patients in the training cohort and 88,215 patients in the validation cohort. The flow chart for SEER data selection is shown in Figure P's < 0.001 for all except histology type and P = 0.002 for histology type). These prognostic factors that were identified in univariate analysis were included in the multivariate analysis  and were further confirmed to be independently associated with OS . Black patients had the highest CIDBC (0.100/0.185 for 5/10 years), while white and “other” patients had similar lower CIDBC . There was no significant difference between different lateralities. Additionally, patients with ILC, histologic grade I, negative lymph node or positive ER/PR status had lower CIDBC, and patients with IDC, histologic grade III, more than 3 positive lymph nodes or negative ER/PR status had higher CIDBC . Receiving radiation decreased CIDBC from 0.064/0.128 for 5/10 years to 0.045/0.100 for 5/10 years (P < 0.001). All variables significantly correlated with CIDBC were used to build the nomogram to predict 5- and 10-year BCSS.At 5 and 10 years after diagnosis, the cumulative incidences of death resulting from breast cancer (CIDBC) of the training cohort were 0.053 and 0.112, respectively, while the cumulative incidences of death resulting from other causes were 0.036 and 0.090, respectively. Estimates of CIDBC and other causes by clinicopathological variables are presented in Table Nomograms for predicting 5- and 10 year OS and BCSS were constructed based on the reduced multivariate models in the training cohort Figure . ValidatTo clarify applications of the nomograms, we can take two normal breast cancer patients for examples. Both of them were with IDC, ER positive, PR positive, 3 positive lymph nodes and didn't receive radiation after surgery. The first patients is an Chinese, aged 45 with grade I, 1 cm tumor while the other is black, 70 years old with grade III, 6 cm tumor. According to the nomograms, the Chinese patient gets 5.75 and 6 points in OS and BCSS nomograms, respectively, which meant that the patient's possibilities of 5-year OS, 5-year BCSS and 10-year BCSS are greater than 0.95 while 10-year OS is between 0.90–0.95. In the black patient condition, scores for OS and BCSS nomograms were 22.75 and 25.25, respectively. Thus for this patient, both 5-year OS and BCSS were less than 0.7, and 10-year OS and BCSS were less than 0.4. Based on the different hazard, we could treat these patients accordingly.In light of the heterogeneity and the high risk of late recurrence of luminal breast cancer, a brief nomogram based on long-term follow-up of a population-based cohort that predicts long-term OS and BCSS should be quite useful and practical for clinicians. To date, there is no comprehensive nomogram for luminal breast cancer based on a competing risks model, although several nomograms have been reported to predict prognosis of breast cancer in other subtypes. , 12 UsinThe log-rank test and Cox proportional hazards regression were used for calculating the independent prognostic factors of OS. However, these analyses cannot be used to identify prognostic factors of BCSS due to the possibility of leading to biased results.  ThereforSeveral clinicopathological characteristics were proven to be independent prognostic factors for both OS and BCSS in the present study, including age at diagnosis, race, tumor size, histology type, grade, lymph node status, ER/PR status and radiation, which are in line with previous studies –21. Age Despite these strengths, there are limitations in our study. First, information regarding adjuvant therapy such as chemotherapy, endocrine therapy and targeted therapy is not available in the SEER database. Without the above information, the nomograms might result in bias when used to predict individual outcome even though several pivotal factors had been considered. Second, immunohistochemical markers including human epidermal growth factor 2 (HER-2) and Ki-67 were not recorded in the SEER database between 1990 and 2006, which might also impair the effectiveness of the nomograms. Furthermore, due to the retrospective nature of our study, these nomograms must be further validated in a prospective cohort or a clinical trial before being applied to clinical use.In summary, we developed nomograms to estimate the probability of OS and BCSS of luminal breast cancer based on a large, population-based cohort with long-term follow-up. The nomograms we developed have perfect performance in both the training and validation cohorts, and they are potentially effective tools for predicting the prognosis of luminal breast cancer patients. Our nomograms will help clinicians identify individuals who are at high risk of overall mortality or BCSM within 5 or 10 years, thus providing more individualized treatment strategies.Data were obtained from the SEER program of the National Cancer Institute, which consists of 18 population-based cancer registries . The incn = 87,867) cohort and a validation  cohort.To establish and validate a competing risks nomogram, the eligible patients were randomly divided into a training . Kaplan-Meier plots and log-rank tests were applied to determine univariate prognostic factors. A multivariate Cox proportional hazards model was applied to estimate the independent effects of the univariate prognostic factors on OS. The independent prognostic factors determined by the multivariate analysis were used to construct the nomogram for OS.P values are two-sided, and those less than 0.01 were considered statistically significant on the basis of the large number of patients.BCSS was measured as the time from diagnosis to death attributed to breast cancer, date of last follow-up or December 31, 2011 (if the date of last follow-up was after 2011). Deaths from other causes were considered to be a competing risk. The cumulative incidence function (CIF) was used to assess the probability of breast cancer-specific mortality (BCSM) and death from other causes. CIF was calculated by Gray's test between category groups.  A sub-diTo decrease overfit bias, the nomograms were subjected to 200 bootstrap resamples for internal validation in the training cohort and external validation in the validation cohort, respectively. The marginal estimate versus model average predictive probability was used to create a calibration diagram. The predictions should fall on a 45-degree diagonal line in a perfectly calibrated model. An index of probability of concordance (C-index) between predicted probability and actual outcome was calculated to evaluate the predicting ability and discrimination of the model.  The valuwww.r-project.org). [Identification of independent prognostic factors was conducted using SPSS 18.0 . Construction, validation and calibration of the nomogram were performed on R version 3.1.2 software .  The R pact.org).  and rms ct.org).  and a C-ct.org).  were useOur study was approved by Shanghai Cancer Center Ethical Committee. For cancer is a reportable disease in every state in the U.S, informed patient consent are not required for the data released by the SEER database.


Obesity, a risk factor for the development of postmenopausal breast cancer and certain other cancer types, has also been associated with poorer response to cancer therapy and cancer recurrence. The insulin-like growth factor (IGF) axis also influences cancer risk.In this commentary, we consider the literature on IGF and its binding proteins and the risk of breast cancer, along with effects of obesity, adipokines, and insulin resistance on breast cancer, and the potential for lifestyle interventions to address weight gain and physical inactivity among at-risk women.Greater body fatness is associated with a higher risk of postmenopausal breast cancer. The association may be explained, in part, by hyperinsulinemia and alterations in adipokines and estrogens. Nutrition, energy balance, and levels of physical activity are determinants of IGF bioactivity. Alterations in the IGF axis can increase cancer risk and progression. Results from epidemiologic studies indicate that higher circulating levels of IGF-I are associated with an increased risk of breast cancer.Intervention studies are needed to determine how to sustain the positive effects of exercise over time and to identify the optimal mode, intensity, frequency, duration, and timing of exercise for breast cancer survivors, including important subgroups of survivors such as African American and Hispanic women. Future epidemiologic studies of the relationships between the IGF axis and breast cancer should include adequate numbers of African American women, Hispanic women, and other minority women who have been underrepresented in studies completed to date. Obesity, a risk factor for the development of postmenopausal breast cancer and certain other cancer types, has also been associated with poorer response to cancer therapy and cancer recurrence . The incWeight gain and physical inactivity can occur following breast cancer treatment. For breast cancer survivors, cancer-related fatigue is a common symptom . Among wIn the U.S., breast cancer survival rates are lower among African American women than white women . ModifiaIn this commentary, we consider the literature on IGF, a mitogen in the mammary gland, and its binding proteins and risk of breast cancer. We also examine the roles played by obesity, adipokines secreted by adipose tissue, and insulin resistance on breast cancer carcinogenesis and the potential for lifestyle interventions to address weight gain and physical inactivity among at-risk women and breast cancer survivors, including African American women. We consider the interplay been these factors in influencing risk of breast cancer incidence and recurrence. Finally, we discuss remaining challenges in this area and offer suggestions for additional research.Development and differentiation of the normal mammary gland are dependent on the expression of locally produced growth factors  and systemically acting hormones. Two important factors involved in cellular and tissue function are the estrogen and insulin-like growth factor axes . The intObesity influences the amount of free IGF-I available to cells, but is not a determinant of total IGF-I in the blood . HyerinsThe relationship between pre-diagnostic levels of IGF-I and IGF binding protein-3 (IGFBP-3) and breast cancer risk was examined in a meta-analysis of data from 17 prospective studies conducted in 12 countries . The oveAlthough some studies have suggested that IGF-I is positively associated with risk of breast cancer incidence among premenopausal women, the results of epidemiologic studies have been inconsistent. Prognostic studies have shown that expression of IGF-1R, the receptor for IGF-I, is predictive of improved survival and that its expression is related to hormone receptor status . HoweverPotential mechanisms linking obesity to higher cancer risk include obesity-related insulin resistance, hyperinsulinemia, hyperglycemia, glucose intolerance, and adipocytokine production . AdiposeIncreased body weight and accumulation of central adiposity lead to changes in leptin and adiponectin levels and reduction of insulin sensitivity; weight loss improves insulin sensitivity and decreases risk for many potential complications of obesity . In a stPhysical activity and adopting a healthy diet are beneficial both in the general population and among breast cancer survivors . Among bIn a meta-analysis of studies of breast cancer survivors, In the RENEW trial, which included survivors of breast, colorectal, and prostate cancer aged 65 to 91 years, a tailored intervention consisting of telephone counseling and mailed print materials on diet and exercise delivered over a 12-month period was effective in increasing exercise, improving diet, reducing weight, and increasing quality-of-life . LifestyAfrican American breast cancer survivors are more likely to be obese and less likely to engage in physical activity than white survivors . In a stEpidemiologic studies indicate that greater body fatness is associated with a higher risk of certain cancers, including postmenopausal breast cancer and that the positive association may be partly explained, in part, by hyperinsulinemia and alterations in adipokines and estrogens . ConsideNutrition, energy balance, and physical activity levels are important determinants of IGF-I bioactivity. Alterations in the IGF axis, related to physical activity and nutrition, can increase cancer risk and progression . This axThere is increasing knowledge about the determinants of racial and ethnic disparities in breast cancer outcomes. It is likely that, in addition to a variety of social, structural, and environmental factors, biological factors influence how breast cancer is expressed . Alterat


Breast cancer is the most common cancer in morocco women were it occupies the first place in term of incidence and mortality. The aim of the present paper is to evaluate the risk factors associated with a breast cancer in a population of Moroccan women.A case-control study was conducted with population women whose age is less than 40 years during 2008-2010 at the National Institute of Oncology of Rabat. These women were interviewed for Epidemiological information and risk factor for breast cancer.Included in this study were 124 cases and 148 age matched controls. No statistically significant case-control difference was found for the early age of menarche , and family antecedents of first degree of breast cancer . However physical activity  early maternity age , multiparity  and breastfeeding than 6 months  appear as significant protective factors.This study show the criminalization of only part of the known risk factors of breast cancer in this age group and confirms the probable protective role of physical activity and factors related to life reproductive women in our study . Breast cancer is a clinically heterogeneous and complex pathology  which ocRecruitment of cases and controls: We conducted a case-control study at the National Institute of Oncology Sidi Mohamed Ben Abdallah, Rabat in which we included a population of Moroccan women (collected between December 2008 and December 2010) we have divided into two groups, a group of women with breast cancer, and a group of breast cancer-free controls.Inclusion criteria: The recruitment of breast cancer case has concerned a population of premenopausal women, newly diagnosed and whose age is less 40 years. The diagnosis of breast cancer was confirmed by mammography, biopsy and / or surgery by specialists doctors of the National Oncology Institute. Controls recruited at the same Institute within the framework of the cancer screening campaign organized by government authorities after having undergone a mammography that showed no signs of breast cancer. The age of these women must be less than 40 years.Exemption criteria: For a group of women with breast cancer we excluded from the study: patients who were prescribed a treatment ; patients whose age is superior to 40 years; Postmenopausal women.For a group of breast cancer-free controls: women whose age is superior than 40 years; postmenopausal women.Data collection: The information was gathered from a basic questionnaire that has been evaluated and tested by our research team. The information collected concerned many risk factors implicated in mammary carcinogenesis and having been the subject of several epidemiological studies. The risk factors identified were: (a) age, (b) The body mass index (BMI), (c) Information on physical activity (d) The age at menarche, (e) age at first full-term pregnancy (f) the number of children; (g) the duration of breastfeeding for each child (h) oral contraceptives, (i) The hormone replacement therapy and finally (j) exposure to irradiation.Statistical analysis: Risk factors collected in survey forms were carried out using the Statistical Package for Social Sciences (SPSS) 13.0 software. For bivariate analysis, the comparison of the means for quantitative and qualitative variables was carried out using Student's t-test and Pearson's Chi-square test, respectively. The results were expressed as mean ± standard deviation or in count and percentage. To identify susceptible and protective factors associated with breast cancer, comparisons between groups (women with breast cancer and breast cancer-free controls) were conducted using univariate logistic regression. The first level of significance was 5% and there was a confidence interval of 95%.Ethical considerations: The administration of the National Institute of Oncology Sidi Mohamed Ben Abdallah, Rabat agrees to carry out the study for epidemiological purposes. Respect for the anonymity and confidentiality of information were strictly adhered to. Informed consent was signed before the inclusion of women in the study.Two hundred and seventy two (272) women were included in our study and were divided into two populations, a population of one hundred and twenty four (124) women with breast cancer and a population of one hundred and forty eight (148) women who were breast cancer-free. Analysis of This study realised in Morocco helped to highlight a set of risk factors related to carcinogenesis in a population of Moroccan women with breast cancer and whose age is less than forty years, namely the lifestyle, hormonal factors, factors related to genetics and reproductive life. The first part of this study is oriented towards the study of factors related to lifestyle. Our indicators were corpulence, physical activity and sedentary behavior of patients, and in this context we found no statistically significant association between BMI and the risk of breast cancer . Indeed,In univariate analysis  we foundThis inverse association between breastfeeding and the risk of breast cancer may be explained by the following biological mechanisms: by a reduction in estrogen and an increase in the production of prolactin, which are supposed to reduce cumulative exposure to estrogen in women ; Other hnd degree , family history in particular the first degree. But also secondary factors that influence more or less significantly the risk of a particular breast cancer factors related to gynecologic obstetric history  and factors related to lifestyles such as physical activity.The notion of risk factors for breast cancer is obsolete if we don't consider women's life period of the study;Engaging in regular physical activity is essential to reduce the risk of breast cancer in young women;In the framework of the national program of early detection of breast cancer, screening should be offered to young women with early menarche history.


We examined the effect of smoking on long-term mortality from breast cancer and other causes among a cohort of women with breast cancer. A total of 975 women diagnosed with breast cancer and aged 40–84 years were followed for a median follow-up of 11 years in the U.S. Health and Functioning in Women (HFW) study. The impact of the individual smoking status and smoking intensity reported in the first few months following breast cancer diagnosis on the risk of mortality from breast cancer and other causes was examined using Cox proportional hazards models. In this study, former smoking was associated with increased risk of other-cause mortality , and the risk doubled with increased intensity . Current smoking  and each additional 10 py smoked  were associated with statistically significant increases in the risk of other-cause mortality. The effect of current smoking on other-cause mortality decreased with advancing stage and increasing body mass index (BMI). Breast cancer-specific mortality was associated with current smoking of ≥50 py , and each additional 10 py smoked . Current smoking, but not former smoking, was associated with increased risk of breast cancer-specific mortality in women with local disease , but not in those with regional and distant disease . Our findings suggest that current smoking at the time of breast cancer diagnosis may be associated with increased risk of breast-cancer specific and other-cause mortality, whereas former smoking is associated with increased risk of other-cause mortality. Smoking cessation at the time of diagnosis may lead to better prognosis among women with breast cancer. The identification of modifiable lifestyle factors that may improve survival is of interest to women diagnosed with breast cancer, as well as their caregivers. A mouse model of mammary cancer-linked cigarette smoke exposure to an increase in the total pulmonary metastatic burden Based on epidemiological and biological evidence that smoking may influence breast cancer progression, we hypothesized that smoking would be associated with both increased risk of death from breast cancer, as well as death from other causes in our prospective study of 975 African–American and white breast cancer survivors from the Health and Functioning in Women (HFW) study The HFW study used in the present analysis has been described previously Smoking status was determined from the baseline questionnaire after breast cancer diagnosis. The questionnaire asked whether the respondent ever smoked cigarettes daily. Never smokers were women who answered “no” to this question. Women were also asked if they smoked at the time of the interview. Respondents that answered “yes” to this question were considered current smokers. Women that answered “yes” to ever having smoked and “no” to the current smoking question were considered former smokers. Smoking status, one of the exposures we considered, was created as a three-level variable indicating whether the respondent was a never/former/or current smoker. Additionally, current and former smokers were asked whether, when they smoked daily, they smoked less than 0.5 packs (1 pack = 20 cigarettes), 0.5 packs, 1 pack, 1.5 packs, or two packs or more a day and the total number of years during which they smoked. The number of py was computed as the product of the packs per day and the number of years smoked. The number of pack-years was used as a continuous predictor in the models. Effect estimates were reported in terms of 10 py increments. The number of “less than 0.5 packs per day” was treated as 0.5 packs, and two packs or more were conservatively counted as two packs. A third exposure combined the number of pack-years and smoking status into a five-level categorical predictor of both longevity and intensity of smoking , because this measure was shown to predict mortality in middle-aged women of the general population 2, from self-reported weight and height at the baseline interview and used as the continuous variable in multivariate models. A comorbidity index was constructed as the number of previously diagnosed conditions reported by the respondents at the baseline interview from a list of 23 conditions that included diabetes, hypertension, stroke, heart disease, gastrointestinal disease, liver conditions, and primary cancers other than breast cancer, which, according to the respondents, currently caused some limitations in their activities The covariates used in this analysis were socio-demographic, lifestyle-related, and clinical prognostic factors that, based on the existing literature and a priori reasoning, could potentially confound associations between smoking and mortality outcomes. Age at diagnosis, breast cancer stage, breast cancer treatment, tumor size, and node involvement were obtained from the MDCSS file, while other variables were obtained from interviews. In analyses, age was used as a continuous variable. Race was coded as either African–American or white. Years of education were recoded into four categories: less than high-school, high-school, college and graduate. The data set included a binary indicator of financial adequacy (0 for adequate and 1 for inadequate) that was based on self-reported current financial resources and whether they met the participant's needs Stage of breast cancer at diagnosis was coded as local, regional, or remote. In addition to information on surgery  and type of adjuvant therapy (radiation or chemotherapy) provided by the MDCSS files, physicians completed a supplementary survey regarding chemotherapy and hormonal therapy administered on an outpatient basis. Data from the two sources were combined to create a two-level treatment variable . The log of the tumor size in millimeters was centered on its mean and used as a continuous variable. The number of positive lymph nodes involved was recoded into a three-level variable .Participants were followed until last contact, death or April, 2012, whichever occurred first. Date and cause of death, classified by the International Classification of Diseases (ICD) codes version 9, were identified through annual vital status surveillance of all patients in the registry, conducted by MDCSS t-test, and between categorical variables were assessed by Pearson χ2 or by Fisher's exact test when counts were small. Differences in sample medians were assessed using the Wilcoxon Rank Sum test. Kaplan–Meier plots were used to examine the association between smoking and mortality. Cox proportional hazards models stratified by age at breast cancer diagnosis with time since diagnosis as the time scale were employed to estimate the association between smoking and other-cause and breast cancer mortality n = 55). The type I error was set at 0.05, and all reported P-values are two-sided. Analyses were conducted in SAS version 9.2  and R version 2.15.Differences between continuous variables were assessed using the Student's P < 0.0001). Overall, the median follow-up time was 11.0 years (interquartile range [IQ]: 4.5–22.4 years). The median follow-up was significantly shorter for current smokers than former and never smokers . There were statistically significant differences in BMI by smoking status. Mean BMI was 26.2 (SD = 4.7), 28.5 (SD = 5.0), and 24.8 (SD = 5.6) kg/m2 among never, former ≥50 py and current smokers ≥50 py, respectively (P = 0.004). Never and former smokers tended to be more educated than current smokers (P = 0.004). Breast cancer staging did not differ by smoking status. The majority of women had localized (53.6%) and regional (40.7%) disease. Only 5.6% of the group had remote disease. Treatment also did not vary significantly by smoking status: 78% of women were treated with modified radical mastectomy; 20% were treated with partial mastectomy; and only 2% received nonsurgical treatment. Of note, among women that died during the follow-up, current and former smokers were proportionately more likely to die from breast cancer compared to never smokers. Approximately 46% of former and current smoker deaths were due to breast cancer compared to 39% of deaths among the never smokers. Tumor size, lymph node involvement, and the number of comorbidities did not vary significantly by smoking status.The distributions of selected demographic and clinical characteristics of the study population, overall and by smoking status, are presented in TableKaplan–Meier plots of breast cancer-specific survival by smoking status indicate markedly shorter survival for current smokers of more than 50 py and similar survival among the remaining groups Fig.. In univKaplan–Meier plots of other-cause survival indicate decreasing survival by increasing smoking intensity Fig., with cuPossible variations in the effect of smoking status on other-cause mortality according to tumor stage and BMI at diagnosis were also evaluated. Current smoking was associated with increased risk of other-cause mortality in women with local and regional/distant disease alike, but the risk was greater among women with local disease. HR for other-cause mortality were 3.18 (95% CI: 2.11–4.81) and 1.87 (95% CI: 1.06–3.30) for women with local and regional/distant disease at diagnosis, respectively (TableIn this study, we evaluated the association of smoking, as reported 2–4 months after diagnosis, with breast cancer-specific and other-cause mortality among breast cancer survivors. We found that former smoking was associated with increased risk of other-cause mortality, and the risk doubled with increasing intensity. Current smoking and every additional 10 py smoked were also associated with statistically significant increases in the risk of other-cause mortality. The effect of current smoking on other-cause mortality decreased with advancing stage and increasing BMI. Breast cancer-specific mortality was associated with current smoking of ≥50 py and each unit increase in 10 py smoked. Current smoking was associated with increased risk of breast cancer-specific mortality in women with local disease, but not in those with regional and distant disease. We found no evidence that smoking reduced the risk of breast cancer mortality by promoting lower body weight.Reports of the association between smoking and breast cancer-specific mortality have been mixed. While some studies were able to establish a statistically significant association versus more aggressive forms of this cancer. In addition, cigarette smoke has been associated with the elevated metastatic potential of tumor cells and stimulation of angiogenesis The impacts of smoking on cardiovascular and respiratory health that lead to premature death are well documented. The biological pathways linking smoking to breast cancer-specific mortality are, on the other hand, less clear. Since breast cancer is a highly heterogeneous disease Limitations of this study include the fact that smoking status, duration, and intensity were self-reported and only once at the baseline examination after diagnosis. Since breast cancer is a disease with constant attrition for up to 30 years, it is possible that additional effects of smoking or more precision may have been observed with a longer follow-up. Another limitation is that smoking was assessed retrospectively, 2–4 months after breast cancer diagnosis. Given the considerable decline in smoking over the past 30 years in the United States The strengths of the study include comprehensive measures of smoking status, as well as a composite measure of duration and intensity, a prospective population-based cohort design, a relatively large set of participants, a long follow-up and our ability to take into account multiple covariates in the tumor-related, lifestyle, and socio-demographic domains. Since women in this study were identified through a large regional, population-based surveillance program, our findings may apply to wider audiences than studies in which subjects were drawn from academic settings. Bias due to loss to follow-up was minimized because mortality status was ascertained annually for all of the patients in the registry.In summary, our findings indicate that cigarette smoking confers a dose-dependent increasing risk of breast cancer-specific and other-cause mortality for women with breast cancer.


COX) is a rate-limiting enzyme in prostaglandins synthesis which exists in two isoforms, COX-1 and COX-2. Over-expression of COX-2 was considered to increase the proliferation and enhance the invasiveness of breast cancer cells. It was suggested that genetic variations in COX-2 could influence its expression. Herein, the present study was aimed to investigate the associations between two mostly studied functional polymorphisms (-765 G > C and 8473 C > T) in COX-2 and breast cancer risk in Chinese Han women.Cyclooxygenase (COX-2 -765 G > C and 8473 C > T polymorphisms using TaqMan assay. We estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) using the logistic regression.In the hospital-based case-control study, 465 breast cancer patients and 799 cancer-free controls were genotyped for the vs. GG: OR = 1.56, 95% CI = 1.11–2.21]. In the stratified analysis, the increased risk was more predominant among the subgroups of younger subjects . Furthermore, the variant genotypes were associated with large tumor size . No significant association was observed for the 8473 C > T polymorphism.Compared with the wild genotype of -765 G > C, we found a statistically significant increased risk of breast cancer associated with the variant genotypes [GC/CC COX-2 may influence the susceptibility and progression of breast cancer in the Chinese Han population.Our results suggest that the functional -765 G > C polymorphism in the promoter of  Breast cancer is the most common malignancy affecting women worldwide and its incidence rate is increasing in both developed and developing countries in recent years . It is tCyclooxygenase is also known as prostaglandin endoperoxide synthetase which plays an important role in the inflammatory process through converting arachidonic acid to prostaglandins . There aCOX-2 have been shown to alter its expression and influence the susceptibility to various carcinomas [COX-2 gene  is located on chromosome 1q25.2-q25.3 and consists of 10 exons spanning 8.3 kb [COX-2 have been identified, but the most extensively studied polymorphisms are the -765 G > C (rs20417) in the promoter and the 8473 C > T (rs5275) in the 3′UTR of COX-2. Previous functional studies have suggested that the -765 G > C polymorphism may eliminate a Sp1-binding site but create an E2F binding site and result in altered COX-2 expression [COX-2 polymorphisms and breast cancer risk and suggested borderline significant increased risk of breast cancer was detected for rs5277 but no significant association was observed for the -765 G > C and 8473 C > T polymorphisms [Genetic polymorphisms in rcinomas -14, inclrcinomas . The humg 8.3 kb . More thpression ,13. The > C rs204 in the porphisms . Howeverorphisms ,19. HereOur study consisted of 465 breast cancer patients which were consecutively recruited between May 2004 and September 2010 at the Second Affiliated Hospital of Xi’an Jiaotong University, China [A consent was obtained from each study subject. The study protocol conformed to the Declaration of Helsinki and was approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University.The whole-genome DNA was isolated and purified from leucocytes of peripheral blood by proteinase K digestion and phenol/chloroform extraction, as described previously . The two2-test . SNP allele frequencies in the controls were tested against departure from the Hardy-Weinberg equilibrium (HWE) using a goodness-of-fit χ2-test before analysis. The associations between the genotypes of the COX-2 -765 G > C and 8473 C > T polymorphisms and risk of breast cancer and patients’ clinical characteristics were estimated by computing odds ratios (ORs) and 95% confidence intervals (CIs) from unconditional logistic regression analysis with the adjustment for age and age at menarche. P value less than 0.05 was considered statistically significant, and all statistical tests were two sided. All of the statistical analyses were performed with the software SAS 9.1.3 .Differences in the distributions of demographic characteristics, selected variables, and frequencies distribution of genotypes the two SNPs between the cases and controls were evaluated using the Student’s t-test (for continuous variables) or χP = 0.257). The frequency of individuals with age at menarche less than 14 was significantly higher in the case group . The percent of patients with tumor size less than 2 cm, 2 to 5 cm and more than 5 cm are 27.1%, 66.7% and 6.2%, respectively.The clinical information of the patients group and the demographic characteristics of both the cases and controls were present in Table COX-2 -765 G > C and 8473 C > T polymorphisms are shown in Table P = 0.203 and 0.117 for -765 G > C and 8473 C > T, respectively). The frequencies of the GG, GC and CC genotype of -765 G > C polymorphism among cases were significantly different from those among controls (P = 0.021). The difference in the frequencies distribution of G and C allele among case and controls were also significant (P = 0.005). Compared with individuals with COX-2 -765 GG genotype, individual with GC or GC/CC genotypes both had a significantly increased susceptibility to breast cancer . These results suggested that the COX-2 -765 G > C polymorphism had effect on breast cancer occurrence. However, we did not observe significant associations between the COX-2 8473 C > T polymorphism and breast cancer risk in any comparison, as shown in Table The genotype and allele frequencies of the COX-2 -765 G > C polymorphism on breast cancer stratified by age. As shown in Table P = 0.049, OR = 1.61, 95% CI = 1.00–2.61) rather than old subjects . The same analyses were also performed for the 8473 C > T polymorphism, however, no positive result was observed (data not shown).We then evaluated the effect of the COX-2 polymorphisms have effect on the clinical features of breast cancer patients, we then analyzed the association between the COX-2 polymorphisms and a series of clinicopathologic parameters, including tumor size, lymph node metastasis, and the statuses of ER, PR and Her-2. As shown in Table COX-2 -765 G > C polymorphism was significant higher in patients with larger tumor size (>2 cm) .To determine whether the COX-2 -765 G > C polymorphism and breast cancer risk. Compared with individuals with -765GG genotype, those with GC or GC/CC genotypes had a significantly increased breast cancer risk of 1.55 and 1.56, respectively. The risk effect was more predominant in younger individuals. Besides, we also found the COX-2 -765GC/CC genotypes were associated with lager tumor size, suggesting the variant genotypes of this polymorphism may participate in the progression of breast cancer.In the present study, we found a significant association between the COX-2 is observed in a variety of human malignancies and in premalignant lesions [COX-2 -765 G > C polymorphism was a functional variant and could alter COX-2 expression, however, with inconclusive results [COX-2 expression compared with the -765G allele and they postulated this effect to be mediated by the loss of Sp1 transcription factor binding to its cognate element [the COX-2 -765 G > C polymorphism could be used as a genetic biomarker to predict cancer susceptibility and progression. In our study, we have observed that variant genotypes of the COX-2 -765 G > C (GC/CC) were associated with increased breast cancer risk, and in patients group that were also associated larger tumor size. Our results are consistent with some studies on other malignancies. For example, Panguluri et al. reported that -765C allele was associated with an increased risk of prostate cancer in African Americans [COX-2 -765 G > C and breast cancer risk; however, the results are inconclusive. Recently, Yu et al. conducted a mete-analysis on this issue and concluded that no significant association was observed for the -765 G > C polymorphisms [COX-2 -765 G > C polymorphism in breast cancer.Over-expression of  lesions -23. It i results ,13. For  element . Howevermericans , and Kohmericans . There aorphisms . Howeverorphisms ,19, whicCOX-2 -765 G > C polymorphism was more prominent in individuals younger than 52 years of age. As it is known, as age increases, individuals are more susceptible to many types of cancer, thus the small effect of the polymorphism may be overwhelmed. However, this observation should be interpreted with caution since we can not rule out the possibility that the number of older women in the older section have been decreased by deaths. We also observed that the COX-2 -765 GC/CC genotypes was associated with larger tumor size. As we mentioned above, over-expression of COX-2 correlated with parameters of aggressive breast cancer, including large tumor size, positive axillary lymph node metastases, and HER2-positive tumor status. A better understanding of the function of the polymorphism in breast cancer may help to reveal the mechanism underlying the associations. However, the results should be interpreted cautiously since there is the possibility that the association may due to a late stage at diagnosis. Nevertheless, if confirmed by additional studies, this polymorphism may help to accurately predict the clinical course of breast cancer.In the subgroup analysis, we found the increased risk associated with the COX-2 polymorphism in our study population were similar to distributions reported in other studies [COX-2 polymorphisms in our study population all conformed to HWE. Therefore, the selection bias in terms of genotype distribution would not be substantial.As our study was hospital-based design, we could not rule out the possible of selection bias of subjects that may have been associated with a particular genotype. However, the genotype distributions of the  studies , and alsCOX-2 -765 G > C polymorphism has a significant influence on the occurrence and progression of breast cancer in the Chinese population. Further functional studies are still required to investigate the role of -765 G > C variation in regulating the gene expression in breast cancer. Since our sample size is moderate and the statistical power of the study is limited, therefore, large population-based prospective studies are warranted to further elucidate the impact of the COX-2 polymorphisms on breast cancer.In conclusion, our case-control study indicates that the The authors declare that they have no competing interests.DZJ, GJ and KHF designed the research. DZJ, GJ, KHF, LD and MXB performed the experiments throughout this research. TW, ZY, ZSQ, GHT and WXJ contributed to the reagents, and participated in its design and coordination. LS and RHT analyzed the data; GJ, KHF and MXB contributed to the writing of the manuscript. Co-first authors: GJ, KHF, MXB and TW. All authors have read and approved the final manuscript.


Direct access to genomic information has the potential to transform cancer risk counseling. We measured the impact of direct-to-consumer genomic risk information on changes to perceived risk (ΔPR) of breast, prostate, colorectal and lung cancer among personal genomic testing (PGT) customers. We hypothesized that ΔPR would reflect directionality of risk estimates, attenuate with time, and be modified by participant characteristics.elevated or average risk. The relationship between risk estimate and ΔPR was evaluated with linear regression; generalized estimating equations modeled this relationship over time.Pathway Genomics and 23andMe customers were surveyed prior to receiving PGT results, and 2 weeks and 6 months post-results. For each cancer, PR was measured on a 5-point ordinal scale from “much lower than average” to “much higher than average.” PGT results, based on genotyping of common genetic variants, were dichotomized as , elevated risk results were significantly associated with positive ΔPR, and average risk results with negative ΔPR  among 1154 participants. Large changes were rare: for each cancer, <4 % of participants overall reported a ΔPR of ±3 or more units. Effect modification by age, cancer family history, and baseline interest was observed for breast, colorectal, and lung cancer, respectively. A pattern of decreasing impact on ΔPR over time was consistently observed, but this trend was significant only in the case of colorectal cancer.With the exception of lung cancer (for which ΔPR was positive regardless of result)We have quantified the effect on consumer risk perception of returning genetic-based cancer risk information directly to consumers without clinician mediation. Provided via PGT, this information has a measurable but modest effect on perceived cancer risk, and one that is in some cases modified by consumers’ non-genetic risk context. Our observations of modest marginal effect sizes, infrequent extreme changes in perceived risk, and a pattern of diminishing impact with time, suggest that the ability of PGT to effect changes to cancer screening and prevention behaviors may be limited by relatively small changes to perceived risk.The online version of this article (doi:10.1186/s12920-015-0140-y) contains supplementary material, which is available to authorized users. Direct-to-consumer personal genomic testing (PGT) is distinct from clinical genetic testing in both its process and goals: whereas clinical testing is typically ordered as part of a medical evaluation to identify highly penetrant mutations associated with rare phenotypes , PGT provides generally healthy consumers with information about common single nucleotide polymorphisms (SNPs) that have been associated with multifactorial diseases . PGT is not diagnostic, but provides risk information to a consumer that he or she may consider in making health-related decisions, such as when to begin cancer screening .The possibility that PGT could impact health services has prompted professional organizations , 2, inclHere, we have analyzed the impact of PGT-derived cancer risk information on consumers’ perceived risk of four cancers for which screening is possible  using data from the Impact of Personal Genomics (PGen) Study . To dateDue to the importance of perceived disease risk in numerous prominent theories of health behavior , we chosThe PGen Study is a collaboration between academic researchers and two PGT companies, and the academic-industry partnership  and recrParticipants received a genetic risk estimate, based on genotyping of multiple SNPs  or the middle category   23andMe representatives indicated that the company generally considers average risk as within 20 % of general population risk; 2) Pathway representatives agreed that this threshold would in most instances match well the cut-point between their risk categories of “Learn More” and “Be Proactive”; and 3) PGen Study researchers agreed that in the context of DTC-PGT and genetic testing of common, low-penetrance variants, a RR ≥ 1.2 was appropriately indicative of an elevated genetic risk. Results across both companies were therefore dichotomized into two categories: average genetic risk  and elevated genetic risk . Due to the restricted distribution of Pathway results, we were unable to discriminate in our analyses between average and reduced genetic risk results.In order to harmonize genetic risk information across companies, a threshold RR level was selected to distinguish much lower than average (1) to much higher than average (5); [Participants were asked on all surveys to rate their chances of developing each type of cancer “compared to the average [man or woman] of [the same] age.” Responses were recorded on a 5-point risk perception scale ranging from age (5); , 21 alteAge, race/ethnicity , gender,Data for this analysis were obtained from 1155 participants who completed the BL survey plus at least one follow-up survey. Primary analysis samples were restricted to participants with an available genetic risk estimate for the cancer being studied; no missing data for BL-, 2 W-, or 6 M-PR (as necessary) for that cancer; and no reported diagnosis of the cancer being studied at any time during the data collection period.2W) and from BL to 6 M (ΔPR6M) for each cancer. Analyses were adjusted for BL-PR, age, gender, race (White vs. non-White), Hispanic/Latino ethnicity, education (4 categories), smoking status (lung cancer only), and testing company; cancer family history and interest in cancer-specific results were evaluated as possible confounders. From the resulting linear regression models, least squares-adjusted mean ΔPRs were computed, stratified by genetic risk estimate (elevated risk versus average risk).We first performed linear regressions of change in PR from BL to 2 W  to account for the expected correlation between ΔPRWe next investigated effect modification by baseline participant characteristics. In order to maximize power, we conducted interaction analyses in the relevant 2 W follow-up samples, which had the smallest amount of missing data, and used the corresponding linear regression model (described above). Wald tests of significance were used to evaluate, in turn, interaction terms between genetic risk estimate and each of the following: baseline interest, age, gender , cancer family history, and smoking status (lung cancer only). Significant interaction terms were retained, and a final regression model was obtained for each cancer. From these, least squares-adjusted mean ΔPRs, stratified by genetic risk estimate and significant effect modifiers, were computed.6M linear regression models for each cancer, and then evaluated RRq as a categorical variable to observe the pattern of effect across quartiles. Because absolute risk and RR values were not available for Pathway Genomics participants, they were excluded from these analyses.To assess the impact on our results of the decision to use linear regression modeling of ΔPR, which assumes a constant effect of genetic risk information on any 1-unit change in PR, we alternately performed each of the 2 W regression analyses using generalized logistic regression with ΔPR = 0 as the reference category for the outcome. To evaluate our selection of relative risk rather than absolute risk as the main predictor of ΔPR, we evaluated the correlation between RR and absolute risk results for each cancer. We also transformed RR into a four-category variable corresponding to quartiles (RRq), performed a linear trend test of RRq in the ΔPR6M linear regressions, excluding anyone who had received screening for the relevant cancer since receiving their results. To evaluate the impact of informative censoring due to missing PR data at follow-up, we repeated the ΔPR6M linear regressions on a pseudo-population created using inverse probability-weighting (IPW) for missing data [To evaluate the possibility that ΔPR was affected by cancer screening undertaken as a result of PGT, we repeated the ΔPRing data .p < 0.05.All analyses were conducted using SAS software , and models were fitted using PROC GLM (linear regressions) and PROC GENMOD . Statistical significance for all analyses was set at One participant was excluded from all analyses due to missing gender data. For each cancer, between 6.2 % and 14.5 % of participants were excluded from analysis due to missing baseline PR, missing genetic risk data, or a cancer diagnosis prior to or during the data collection period. Sample sizes for each cancer-specific analysis are presented in Table 32 = 8.2, p = 0.040) and income  and greater frequency of any cancer family history  than male participants reporting prostate cancer PR. Baseline PR, interest in cancer-specific PGT results, and reported cancer-specific family history showed considerable variation across samples: for example, only 40.9 % of participants reporting lung cancer PR were ‘very interested’ in learning their genetic risk of lung cancer, while 66.6 % of participants reporting breast cancer PR were ‘very interested’ in learning their genetic risk of breast cancer.Subjects reporting cancer-specific PRs were similar in most cases with respect to age, gender, education, income, race, smoking status, and general cancer family history Table , althougElevated risk results were least frequent for breast cancer  and most frequent for colorectal cancer   was greater in magnitude than mean ΔPR among those receiving an average risk result (range = 0.04–0.22 units). Mean ΔPR for lung cancer was positive, regardless of genetic risk estimate, at both time points. For all other cancers, mean ΔPR was positive among those receiving an elevated risk result and negative among those receiving an average risk result. Effect estimates did not change in models that included adjustment for general cancer family history, specific cancer family history, or baseline interest in PGT cancer risk results.At both follow-up time points, and for all four cancers, multivariate linear regression revealed a significant effect of genetic risk estimate on ΔPR Table . In geneA trend of greater effect sizes at 2 W follow-up compared to 6 M follow-up was apparent for all cancers; however, GEE modeling of the survey by genetic risk estimate interaction found evidence for significant effect modification by follow-up time only in the case of colorectal cancer Table .interaction = 0.0467). Among women in the lowest age group by decile, an elevated risk result was associated with a 1.12 unit greater ΔPR than an average risk result . For each decile increase in age category, this difference decreased by an average of 0.08 units . A similar trend was observed for prostate cancer, but the interaction term between risk result and age was non-significant (p = 0.0591).Increasing age was associated with a significantly attenuated effect of genetic risk estimate on ΔPR of breast cancer at 2-week follow-up (pinteraction = 0.0093). Among participants who reported no history of cancer in first- or second-degree relative, an elevated risk result was associated with a 0.26 unit greater ΔPR than an average risk result ; however, among participants reporting a positive family history of cancer, an elevated risk result was associated with a 0.61 unit greater ΔPR than an average risk result . Adjusted mean ΔPR values, stratified by family history status and genetic risk result are shown in Fig. Family history of cancer was a significant modifier of the effect of genetic risk estimate on ΔPR of colorectal cancer at 2-week follow-up (pp = 0.0108). Among participants who indicated low interest in these results at baseline, there was no significant difference in the impact of an elevated risk result and an average risk result on ΔPR of lung cancer . An elevated risk result was, however, associated with a significantly greater ΔPR than an average risk result among those participants who indicated a moderate or high interest in these results at baseline .  Adjusted mean ΔPR values, stratified by interest and genetic risk result are shown in Fig. 22 = 28.9, p < 0.0001).Baseline interest in lung cancer genetic risk information was a significant modifier of the effect of genetic risk estimate on ΔPR of lung cancer  reported some type of breast cancer screening  since receiving their PGT results; 78 (22.7 % of 343) reported PSA testing; 87 (9.1 % of 947) reported a colonoscopy; and 14 (1.5 % of 945) reported lung cancer screening. Excluding screened participants from the linear regressions presented in Table IV made no substantial difference to the effect estimates or mean ΔPRs  an interest in obtaining this particular information about themselves than among consumers who were “not at all interested” in learning their genetic risk of lung cancer. Although there was no statistical evidence of effect modification by smoking status, we did observe a strong positive correlation between baseline interest in lung cancer genetic risk information and smoking history. Thus, one interpretation of the observed effect modification by interest is that smoking (or other carcinogenic exposure) history drives baseline interest in lung cancer genetic risk information, which in turn modifies consumers’ responses to receiving this information.These findings highlight a complex relationship between genetic information and risk perception, and suggest that consumers incorporate both genetic and non-genetic risk factors into their personal estimation of risk. This interpretation is further supported by the results of effect modification analyses for breast and colorectal cancer: Older women, in whom the impact of genetic risk information was attenuated compared to younger women, have more information on which to base their perceived risk of breast cancer , and may be aware that non-genetic factors  are more important components of their risk than the information provided by PGT. Conversely, in the case of colorectal cancer, for which the impact of genetic risk information was greater among those consumers with a positive family history of cancer than those without, the presence of a family history of cancer may lead consumers to put greater stock in their PGT results, since they already have independent evidence of “genetic risk” of cancer.The relationship between genetic information and risk perception is likely further complicated by the method in which this information is communicated to consumers. Here, each company presented risk information in a different way: for example, 23andMe relied on a visual representation of risk as a proportion of persons, while Pathway presented consumers with a color-coded hierarchy of risk categories. These differences in risk presentation are not expected to lead to confounding after adjustment for PGT company; however, it is possible that the effect of a genetic risk estimate on perceived risk could be attenuated—or magnified—by the way in which the risk is described to the consumer. Similarly, the choice of perceived risk scale may impact the magnitude of effect observed. If, for example, participants were asked to report their perceived risk as a percentage from 0 to 100, an analysis of change in perceived risk might have greater precision compared to our analysis, where perceived risk was measured on a 5-point scale.Elucidating the relationship between genetic risk information and perceived risk may represent one step towards understanding the link between genetic risk information and health behavior changes, and commercial PGT presents an opportunity to study this relationship before SNP-based risk prediction moves into the clinic. PGT for cancer is particularly well-suited to such study because of broad public awareness of cancer risk factors  and prevPerceived risk has been studied extensively in the context of clinical genetic testing for high-penetrance, Mendelian cancer syndromes , 28. In ought to respond to their PGT results remains unresolved: [The question of how consumers esolved:  critics esolved: , and comesolved: . If PGT-esolved: . Of noteesolved: ). This aStrengths of our study include its large sample size, consistency of results across four cancers, and robustness of results in sensitivity analyses. Limitations include those inherent to voluntary survey data, including the potential for selection bias. Analyses of effect modification were limited in power due to sample stratification; thus, response to PGT could be further influenced by baseline participant characteristics in ways that are not evident here. We were also limited in our ability to investigate the impact of receiving a genetic risk estimate indicating lower than average cancer risk, although analysis of the 23andMe data alone suggests that such risk information may be associated with a decrease in perceived cancer risk. Finally, our findings are generalizable to consumers obtaining PGT through a direct-to-consumer model, but not to other forms of genetic testing. PGen Study participants tended to be well-educated, high-earning, and White; interpretation of genetic risk information, and its impact on risk perception, may differ in groups without these qualities.SNP-based genetic risk information for four common types of cancer, when provided directly to consumers via commercial PGT, has a measurable effect on consumers’ perceived risk of these cancers. However, while perceived risk is affected in predictable ways at a population level, effect modification analyses suggest a complex interplay between genetic and non-genetic risk information in consumers’ estimation of personal risk. Our findings further suggest that the effect of PGT on use of cancer screening services and modification of risk behaviors may be mitigated by the small magnitude of its effect on risk perception.


Several studies have shown an increased risk of cancer after non melanoma skin cancers (NMSC) but the individual risk factors underlying this risk have not been elucidated, especially in relation to sun exposure and skin sensitivity to sunlight.The aim of this study was to examine the individual risk factors associated with the development of subsequent cancers after non melanoma skin cancer.Participants in the population-based New Hampshire Skin Cancer Study provided detailed risk factor data, and subsequent cancers were identified via linkage with the state cancer registry. Deaths were identified via state and national death records. A Cox proportional hazard model was used to estimate risk of subsequent malignancies in NMSC patients versus controls and to assess the potential confounding effects of multiple risk factors on this risk.Among 3584 participants, risk of a subsequent cancer (other than NMSC) was higher after basal cell carcinoma (BCC)  than squamous cell carcinoma (SCC)  compared to controls . After SCC, risk was higher among those diagnosed before age 60 . An over 3-fold risk of melanoma after SCC  and BCC  was observed, even after further adjustment for sun exposure-related factors and family history of skin cancer. In men, prostate cancer incidence was higher after BCC compared to controls .Our population-based study indicates an increased cancer risk after NMSC that cannot be fully explained by known cancer risk factors. The non melanoma skin cancers (NMSC) are the most frequently diagnosed malignancies in the United States Several studies have reported that individuals diagnosed with non melanoma skin cancers have higher subsequent or prior diagnoses of second primary malignancies by about 20–60%. By virtue of their high frequency and low mortality, non melanoma skin cancers offer an excellent opportunity to study the factors that put some individuals at increased risk of multiple malignancies. We had the opportunity to elucidate the risk of multiple malignancies using a large population-based series of SCC and BCC cases and controls with individual risk factors collected through a detailed personal interview. In addition, we aimed to assess whether the excess risk of cancer after NMSC could be explained by known cancer risk factors.The New Hampshire Skin Cancer Study (NHSCS) and the additional work described here were approved by the Committee for the Protection of Human Subjects of Dartmouth College. Participants in the NHSCS underwent a written, informed consent process at enrollment. Use of data from the New Hampshire State Cancer Registry was approved by the New Hampshire Department of Public Health Services. Confidentiality was also protected by use of a de-identified dataset in the statistical analyses.The New Hampshire Skin Cancer Study (NHSCS) was first conducted as a population-based case-control study of non melanoma skin cancers (NMSC) diagnosed between July 1, 1993 and June 30, 2002 (New Hampshire residents), obtained through intensive surveillance at dermatologists’ offices and pathology laboratories serving the state. Case selection criteria earlier in the study tended to oversample patients with SCC relative to their incidence in the population, and in later years there was oversampling of cases diagnosed before the age of 51 years (“early-onset”), or with multiple concomitant BCCs. Cases with both incident BCC or SCC during the enrollment phase were eligible based on either diagnosis, as dictated by the sampling methods of the parent study. Controls were selected from NH residents provided by the NH Department of Transportation, frequency matched on age and sex. The New Hampshire State Cancer Registry (NHSCR) is a population-based database of incident, reportable cancers containing, on average, ∼6,500 verified new cases annually among the New Hampshire population of 1.2 million. Incidence data for 1995 onwards meet the standards of the North American Association of Central Cancer Registries for quality and completeness in situ malignancies (excluding cervix and prostate) were included among “prior” cancers.We excluded from the main analyses any individual with a prior history of cancer other than skin cancer (NMSC and melanoma). This exclusion encompassed any non-skin cancer diagnosed before the reference date identified either through NHSCR records or via self-report at the study enrollment interview. Self-reports of major cancers have previously been shown to be fairly reliable rd, 2010, contained 149,523 individuals. We used LinkPlus software To identify cancers that were diagnosed after the reference date, we linked the NHSCS database with the NHSCR incident cancers database for diagnosis through 2009, which, on July 23We linked both the NHSCR and NMSC databases with the New Hampshire death certificate database to identify deaths from 1993 through 2009. We again used LinkPlus combined with manual review to determine deaths among study participants. In addition, we linked with the National Death Index (NDI) through 2009 to identify deaths nationwide. Death data were used to provide censor dates in the survival analysis, and to identify potential cancers that were not identified from cancer registry data.Although the parent study was a case control design, the data were analyzed for this study in a cohort design, using follow-up information from the date of enrollment for participants with BCC or SCC, and controls. The endpoint of our analysis was the time from the reference date to diagnosis of first cancer. Participants who did not develop cancer were censored on the date of death or at the end of follow-up , whichever came first. We used Cox models to determine the hazard ratios (HRs) and 95% confidence intervals (CI) associated with development of a subsequent cancer for BCC and SCC cases (separately) versus controls. We also performed stratified analyses by sex and by age group (<60 or ≥60 years) and restricted to invasive cancers. For all analyses, the HRs were adjusted for known the cancer risk factors, age, sex, and smoking status . We plotted the survival curves in the BCC, SCC and control groups to check the proportional hazards assumption and separately assessed the hazard functions. We examined the contribution of additional variables to the model  by assessing the change in the primary measure of effect; thus, we included variables in the age-, sex-, smoking-adjusted model which led to a 10% or greater change in the hazard ratio for BCC, SCC or NMSC (BCC and SCC combined). The skin cancer study provided interview data from a total of 4,223 individuals followed for a mean of 10.9 years (range 0.8 to 17.0). From the 1,600 individuals enrolled as a result of a diagnosis with BCC, 1,125 with SCC and 1,498 controls , we exclThe mean age of the remaining 3,584 participants included in the analysis was 57.5 (standard deviation 11.7), and 55% were male. During follow-up, 562/3584 (15.7%) eligible participants died from any cause; 203/1,341 (15.1%) of controls, 170/1,363 (12.5%) of those with BCC and 189/880 (21.6%) with SCC. A total of 560 individuals (13.7%) developed 646 subsequent cancers during follow-up; 485 developed 1 type of cancer; 65 developed 2 types of cancer; 10 developed 3 or more types of cancer.After adjusting for age, sex and smoking , the risk of a subsequent cancer (excluding NMSC) was higher following a BCC diagnosis compared to controls . The hazard ratios for BCC were significantly elevated among men  but not women  and among participants aged 60 or more at enrolment  but not among those younger than 60 years . Following SCC, we observed a more modest, not statistically significant increase overall . However, the risk of subsequent cancer after SCC was substantially increased when diagnosed before age 60 , whereas no effect was seen among those diagnosed at age 60 or older  and this difference was statistically significant (p<0.003). In contrast, significant interactions were not seen between age group and BCC, nor between sex and either BCC or SCC . The proFactors other than a history of NMSC that were related to risk of subsequent cancer included increasing age , male sex  and current cigarette smoking  relative to all controls was 1.59 , and 1.39  for only invasive subsequent cancers. When an individual’s referent BCC occurred before age 50, the adjusted HR was similar to that seen in the primary analysis  for all subsequent cancers but lower  for invasive subsequent cancers.An increased risk of prostate cancer was observed after BCC  but not SCC adjusted for age, sex and smoking. These hazard ratios were not appreciably altered by other risk factors, nor by exclusion of prostate cancers diagnosed within 12 months of the referent lesion (data not shown). We examined risk of subsequent cancers in several subgroups. For individuals with multiple BCC (defined as two or more tumors within 30 days), the adjusted HR for all subsequent cancers  SCC patients, 9/1363 (0.7%) BCC patients, and only 1/1341 (<0.1%) controls (data not shown).In sensitivity analyses, we obtained similar results after: (i) excluding cancers diagnosed within the first year, (ii) excluding individuals from the analysis who had a prior melanoma (as well as other non skin cancer); (iii) defining prior cancers by self report only or (iv) by registry report only .In our study, we identified a significantly increased cancer risk after BCC that could not be explained by a variety of environmental, nutritional or behavioral risk factors or by family history of cancer. The increased cancer risk after SCC appeared to be largely confined to those with SCC diagnosed before the age of 60. In our population, the strongest association was for subsequent melanoma of the skin, supporting common susceptibility and exposures such as ultraviolet light in the etiology of skin malignancies. However, after excluding melanoma, there remained a statistically significant increased risk of subsequent malignancy after BCC, especially among men. Any attempt to explain the increased cancer risks after NMSC must consider the environmental, genetic, and personal characteristics that could predispose to both NMSC and other cancers. By adding important explanatory factors to the models, we would expect to see a reduction in the adjusted hazard ratios for risk of cancer after NMSC, but NMSC-related hazard remained in our study, despite the inclusion of many potential risk factors. This suggests the need for studies of more detailed risk factor data including genetic analyses in large populations. A few other published studies have incorporated histological confirmation of NMSC, cancer registry confirmation of subsequent cancer, and multivariable adjustment for individual risk factors. The largest of these was based on 36,102 individuals with NMSC from US prospective cohorts followed by Song et al through postal questionnaires that collected individual level risk factors, although estimates of cumulative sun exposure or family history of NMSC were not available. Other than NMSC, cancers related to UVL exposure, or suspected to be so, include malignant melanoma, cancer of the lip, non-Hodgkin’s lymphoma and leukemias; these are among the cancers that appear to be more common after NMSC in prior studies While our study did not indicate that sun exposure affected non skin cancer risk, it is possible that the sample size may have been too small to do so, especially for specific types of cancers. However, we did detect an excess risk of melanoma following both BCC and SCC with significantly increased melanoma risk in participants reporting a family history of NMSC, but not a family history of melanoma which is a known risk factor for melanoma. In the site-specific analysis, we further found that BCC but not SCC was associated with an increased risk of subsequent prostate cancer. Again, we did not identify any additional risk factors that affect our risk estimates, or could explain this association. However, our approach was potentially limited by lack of statistical power and our method of including site-specific cancers even if intervening cancers occurred. For example, if a BCC were followed by a breast cancer and then a lung cancer, the lung cancer would be included in the site-specific analysis despite the possibility that treatment of the breast cancer may have played a role in the lung cancer etiology.Participants with NMSC in this study were selected from a population-based surveillance program, and are therefore expected to represent reasonably well the general population of individuals diagnosed with NMSC, although it should be noted that the relative proportions of SCC and BCC patients in our study do not reflect population incidence due to oversampling of SCC patients in the parent study. In particular, 3% of cases were diagnosed with both a BCC and SCC within a 30-day period, and these were over-represented in the SCC group. We could not assess the proportions of patients who were diagnosed with both cancers during the course of the study or during their lifetime, although it would have been interesting to analyze this group’s subsequent risks separately. An additional limitation is that controls may have subsequently developed NMSC during a decade of observation, and would therefore be misclassified as controls; this would tend to bias the results of our study towards the null, and reduce our ability to identify true associations between NMSC and the risk of subsequent cancer. Although the expected frequency of subsequent NMSC among controls is relatively high because NMSC are common cancers, fewer than 0.1% of controls developed a major cancer during the first year after the reference date, a figure comparable to the similarly aged US population Another limitation is that we were unable to assess the impact of race because the study population based in New Hampshire lacks racial diversity. Previously, a large cross-sectional study found that self-reported NMSC was more likely to be associated with other self-reported cancers in black than white women In summary, individuals with NMSC and no prior history of non-skin cancer were more likely to develop another cancer following a NMSC diagnosis, particularly melanomas. Detailed individual risk factors other than age, sex and smoking could not explain this increase in cancer risk overall. Sun sensitive skin type and family history of NMSC explained a small fraction of the excess melanoma risk after NMSC, but adjustment for several other known and putative cancer risk factors did not remove the association between NMSC and subsequent melanoma or other cancers. Understanding the shared risk factors that contribute to multiple malignancies may lead to new etiologic insights. In particular, larger studies incorporating detailed genetic data may help to identify NMSC patients at greatest risk for subsequent cancers and who may benefit from more intensive screening.File S1Supplementary data.(DOCX)Click here for additional data file.


Selective Estrogen Receptor Modulators (SERMs) reduce the risk of breast cancer for women at increased risk by 38%. However, uptake is extremely low and the reasons for this are not completely understood. The aims of this study were to utilize time trade-off methods to determine the degree of risk reduction required to make taking SERMs worthwhile to women, and the factors associated with requiring greater risk reduction to take SERMs.N = 107) were recruited from two familial cancer clinics in Australia. Participants completed a questionnaire either online or in pen and paper format. Hierarchical multiple linear regression analysis was used to analyze the data.Women at increased risk of breast cancer  an oophorectomy required a smaller degree of risk reduction to consider taking SERMs worthwhile.Women at increased familial risk appear motivated to consider SERMs for prevention. A tailored approach to communicating about medical prevention is essential. Health professionals could usefully highlight the absolute (rather than relative) probability of side effects and take into account an individual’s perceived (rather than objective) risk of breast cancer. BRCA1 or BRCA2 gene, substantially increases breast cancer risk  , competency in English, aged 18 to 70 years, and no personal history of breast or ovarian cancer or bilateral mastectomy. Participants who had previously undergone bilateral oophorectomy  were not excluded, as their residual risk is still high enough to warrant consideration of SERMs.BRCA1 and BRCA2 mutation status, using Cancer Australia definitions, that is, high risk is greater than three times the population risk and moderate risk is one and a half to three times population risk  were invited to participate by the FCC; interested women were phoned by a researcher to gain verbal consent. Consenting women completed the study online or by paper and pencil. They first completed the consent form, then read a fact sheet that provided information on SERMs, risk-reducing mastectomy and salpingo-oophorectomy to ensure a basic level of knowledge, and then completed the questionnaire. Women were categorized as at moderate or high risk of breast cancer based on family history and sk NBOCC . Ethics BRCA1 BRCA2 mutation status, cigarette smoking status, personal history of blood clots, and family and/or personal history of bilateral mastectomy and/or oophorectomy and/or SERM use.We collected age, ethnicity, relationship status, parity, number of daughters, plans for future children, menopausal status, The primary outcome, patient preferences, was assessed using the TTO method adapted from (Simes & Coates These two scenarios were based on the average lifetime risk of developing breast cancer for a woman at high risk (50%) and for a woman at moderate risk (20%). The “tipping-point”, the risk reduction required to make taking SERMs worthwhile, was computed for each participant for both the 50% and 20% risk scenarios by subtracting the percentage where the woman crossed from not taking SERMs to taking SERMs from the corresponding baseline risk. Thus, a lower tipping point represents a lower degree of risk reduction required to intend to take SERMs.It was emphasized to participants that the choices were hypothetical; there were no right or wrong answers; and the numbers were hypothetical scenarios and did not apply to them personally. Following the TTO, women were asked to list, in order of importance, the three most important factors influencing their decision to take or not take SERMs.r = .70 (p < .001) with another independent variable, parity.Associations between TTO tipping points scores and clinical and demographic characteristics were explored through correlations and adjusted-analysis using multiple linear regression. In order to represent the equally important views of women who did not ‘tip’ , 0.5 was added to all tipping point scores. Age and risk status were included as covariates in all multiple linear regressions due to strong theoretical rationale  completed the questionnaire. Analysis comparing de-identified data on non-responders with that of responders indicated that more women at moderate risk participated (53%) than did not participate (47%) compared to women at high risk (33% versus 67%) or those who were BRCA1 or BRCA2 positive (24% versus 76%). However as few eligible moderate risk women were identified, the absolute numbers were small. There were no other differences identified between responders and non-responders.SD = 10.8). Forty-one women (38% of the sample) were BRCA1 or BRCA2 mutation positive, 56 (52%) were at high risk but without a documented mutation and 10 (9%) were at moderate risk.Participant characteristics are summarized in Table F = 2.48, p = .09, or the 20%-baseline risk scenario, F = 1.63, p = .20.Tipping points are summarized in Figures R2 = .19, p = .01, R2 = .17, p = .02 respectively). Women who intended to undergo or had undergone oophorectomy judged smaller amounts of risk reduction sufficient to take SERMs in both the 50% and 20% base risk scenarios. Additionally, women with higher perceived breast cancer risk required a smaller degree of risk reduction for it to be worthwhile for them to take SERMs in the 20% scenario.Sensitivity analysis showed that having previously taken SERMs did not appreciably change multivariate analysis results, therefore the results shown here include the whole sample. As shown in Table not taking SERMs, followed by low perceived efficacy of SERMs and choosing other methods of risk reduction. Most women (n = 53) did not specify which of the possible side effects were influencing their decision, however, of those who did, menopausal symptoms was the most frequently reported (n = 10), followed by unknown long-term consequences (n = 6), sexual function (n = 4), fertility (n = 4), weight gain (n = 2), osteoporosis (n = 3) and cognition (n = 1).Issues affecting their decision, listed by women, are summarized in Table This study is the first to our knowledge to apply the Time Trade-Off (TTO) method to elucidate how women at increased risk of breast cancer make decisions regarding using SERMs to reduce risk. The substantial minority of women who had no tipping-point was surprising. Six percent and 16% of women, for the 50% and 20% baseline scenarios respectively, consistently chose to take SERMs even when there would not be any reduction in the risk of breast cancer. These women reported being most influenced by: minimizing the stress and worry associated with being at increased risk; family concerns; and lowering their risk of breast cancer. Previous research has found that many women judge negligible benefits sufficient to engage in treatment for breast cancer . Thus general interest in SERMs in this group of moderate and high risk women was high.A small proportion of women consistently chose not to take SERMs, even if SERMs reduced their risk of breast cancer to zero. All such women listed side effects as one of the top three important factors influencing their decision. This is congruent with previous research  an oophorectomy required less risk reduction in order to choose to take SERMs than women who were not considering this procedure. It is perhaps not surprising that women who are motivated enough to consider surgical risk reduction may be more willing to accept SERMs.Subjective but not objective breast cancer risk was predictive of the degree of risk reduction required to take SERMs in the 20% baseline condition, with neither predictive in the 50% baseline condition. This is congruent with previous studies where subjective but not objective risk was associated with either considering tamoxifen  probability of side effects should be highlighted. Our study shows that individual preferences vary widely and thus a tailored approach to medical prevention is essential, perhaps with the assistance of a computerized decision aid that can effectively translate reported relative benefits and risks into absolute benefits and risks individualized to each woman’s circumstances.Future research should endeavor to assess women’s preferences for SERMs in the context of other risk-reduction methods and/or in a sample of women who have declined surgical risk reduction procedures.This study was carried out according to national legislation and was approved by the Human Research Ethics Committee (12/020 [HREC/12/POWH/42]).