Immune-related etiologic pathways that influence breast cancer risk are incompletely understood and may be confounded by lifestyles or reverse causality. Using a Mendelian randomization (MR) approach, we investigated the potential causal relationship between genetically elevated C-reactive protein (CRP) concentrations and primary invasive breast cancer risk in postmenopausal women.We used individual-level data obtained from 10,179 women, including 537 who developed breast cancer, from the Women’s Health Initiative Database for Genotypes and Phenotypes Study, which consists of five genome-wide association (GWA) studies. We examined 61 GWA single-nucleotide polymorphisms (SNPs) previously associated with CRP. We employed weighted/penalized weighted–medians and MR gene–environment interactions that allow instruments’ invalidity to some extent and attenuate the heterogeneous estimates of outlying SNPs.In lifestyle-stratification analyses, genetically elevated CRP decreased risk for breast cancer in exogenous estrogen-only, estrogen + progestin, and past oral contraceptive (OC) users, but only among relatively short-term users (<5 years). Estrogen-only users for ≥5 years had more profound CRP-decreased breast cancer risk in dose–response fashion, whereas past OC users for ≥5 years had CRP-increased cancer risk. Also, genetically predicted CRP was strongly associated with increased risk for hormone-receptor positive or human epidermal growth factor receptor-2 negative breast cancer.Our findings may provide novel evidence on the immune-related molecular pathways linking to breast cancer risk and suggest potential clinical use of CRP to predict the specific cancer subtypes. Our findings suggest potential interventions targeting CRP–inflammatory markers to reduce breast cancer risk. The pros. 2.0%) . Adiposes. 2.0%) . The inns. 2.0%) . For exas. 2.0%) .For postmenopausal breast cancer, obesity is a well-established risk factor , and CLSe.g., CRP) on an outcome  using genetic variants as an instrumental variable  approach may provide better insight into the immune-related etiologic pathways connected to invasive breast cancer risk. MR evaluates the exposure  which were coordinated to contribute to a joint imputation and harmonization effort for GWASs. Those studies, under dbGaP study accession number (phs000200.v12.p3), consist of five GWASs  Table S12 body mass index (BMI), 0.85 waist-to-hip ratio (WHR), 10·hours/week metabolic equivalent (MET), and 9% calories from SFA on the basis of obesity and related lifestyle guidelines   Table S1LD <0.3) , genome-LD <0.3) , and exoLD <0.3) . Of the i.e., the variant has no pleiotropic pathways other than the exposure)  the variant is robustly predictive of the exposure; (ii) the variant is independent of factors that confound the exposure–outcome association; and (iii) the variant is independent of the outcome, given the exposure and confounding factors of the exposure–outcome association (xposure) . We checxposure) , 21, 22 xposure) , the F-sxposure) , we consxposure) . For thexposure) Table S2xposure) , 52. Forxposure) .i.e., the assumptions violated) and provides a more consistent estimate of the causal effect if the precision of the individual estimates varies considerably, by assigning a weight to the ordered estimate and establishing linearity between neighboring estimates (β1 (intercept), we computed the scaled CRP-GS (= β0 + β1×GS), where two GSs were perfectly correlated (r = 1.0)  method , we emplstimates . When thstimates . The PWMstimates . We creastimates  with ther = 1.0) , 55.vs. ≤3.0 mg/L) in log-odds and the mean change in log-transformed CRP per allele were obtained from our and the three other previous GWASs, respectively. The effect of genetic variants on breast cancer risk was calculated in our study population by using Cox regression with adjustment for (i) age and 10 PCs and (ii) lifestyle covariates in addition to age and 10 PCs. The assumption test was conducted via a Schoenfeld residual plot and ρ evaluation. The Cox results from each of our five GWASs were combined using fixed-effect meta-analysis. The final MR results were reported as risk ratios  and 95% confidence intervals (CIs) for the change in breast cancer risk per unit increase in log-odds or log-transformed CRP.In the MR analysis for the five SNPs from our earlier GWAS, we adjusted a correlation between CRP phenotype and breast cancer in which exposure and outcome were evaluated within the same population. For parameters necessary for the MR analysis, the change in CRP  was used.vs. ≤3.0 mg/L, reflecting low-grade chronic inflammation status), and the other 56 from three other previous GWASs examining continuous natural log-transformed CRP (mg/L). The associations between each of the 61 SNPs and breast cancer risk are shown in 2nd-stage = 0.78, 95% CI: 0.52–1.16; the 56 SNPs’ PWM-HR2nd-stage = 0.91, 95% CI: 0.56–1.49). Removing pleiotropic SNPs did not apparently change the pooled estimates.The total of 61 GWA SNPs identified for their association with CRP concentrations are presented in 1st-stage = 0.17, 95% CI: 0.05–0.63)  was associated with approximately 80% decreased risk of breast cancer among E + P users, particularly in women with <5-years’ use of E + P (PWM-HR05–0.63) Table 1.2nd-stage = 2.14, 95% CI: 1.11–4.10, after exclusion of pleiotropic SNPs). No directional pleiotropy was observed.Similarly, in the MR analysis of the other 56 SNPs, a 1-unit increase in the log-transformed genetically elevated CRP was associated with about 20% reduced risk for breast cancer among women who had used OC for <5 years Figure 31st-stage = 1.79, 95% CI: 1.17–2.76; WM-HR2nd-stage = 2.11, 95% CI: 1.00–4.46)   and human epidermal growth factor receptor 2 (HER2/neu) status]. A 1-unit increase in the log-transformed genetically elevated CRP level was associated with 80% or more increased risk for ER/PR-positive breast cancer (IVW-HR00–4.46) Table 2.We further performed MR G × E analyses to estimate the corrected MR estimates by incorporating the G × E interactions with the selected obesity and sex-hormone lifestyle factors; none of the estimates reached statistical significance and no pleiotropic effect of the estimates was detected Table S6vs. ≤3.0 mg/L, indicating chronic low-grade inflammation or a natural log-transformed 1 mg/L increase) in association with postmenopausal breast cancer risk and showed that genetically determined CRP levels were associated with the risk for breast cancer in women with particular lifestyle factors and breast cancer subtypes. MR findings, if the modeled genetic instruments are not linked to the outcome via any alternative pathway and are not associated with confounders of the exposure–outcome association, may be comparable with those of randomized clinical trials . In particular, longer-term E-only users (≥5 years) had more profound CPR-decreased cancer risk, in dose-response fashion. This finding is not align with our other finding of CRP-increased ER/PR-positive breast cancer risk. It may reflect the different effect of estrogen on cancer risk when it is taken orally, and our finding is supported by those of previous studies , 60 showi.e., different effects from natural progesterone), contrasting with the hepatocellular effect of oral estrogen , and use of WM/PWM estimates. Nevertheless, we cannot completely rule out residual and unmeasured confounding that might have affected our study findings. In addition, potential violation of a linearity assumption in the SNP–exposure and exposure–outcome analyses would result toward a null, rather than generating a spurious association . The genAlthough our MR study was not designed to discover biologic mechanisms, our findings suggest a potential causal relationship between genetically elevated CRP levels and risk of postmenopausal breast cancer, particularly in relatively short-term users of opposed and long-term users of unopposed exogenous estrogen. Also, lifetime exposure to elevated CRP levels is likely to influence the development of hormone receptor–positive and HER2/neu-negative breast cancer. Further biologic mechanistic study may help to elaborate the sex-hormone interactions with CRP on breast cancer carcinogenesis and the molecular pathways connected to CRP and ER/PR/HER2 receptors. Our findings may provide important novel evidence on immune-related etiologic pathways that interact with lifestyle factors, influencing breast cancer risk, and suggest the potential clinical use of CRP to predict specific cancer subtypes and CRP–inflammatory marker-targeting interventions to reduce breast cancer risk in postmenopausal women.helpdesk@WHI.org.Publicly available datasets were analyzed in this study. This data can be found here: The data that support the findings of this study are available in accordance with policies developed by the NHLBI and WHI in order to protect sensitive participant information and approved by the Fred Hutchinson Cancer Research Center, which currently serves as the IRB of record for the WHI. Data requests may be made by emailing All the participant data for the WHI Harmonized and Imputed GWASs under the WHI dbGaP Study used in this study were de-identified by the NHLBI and WHI and consent was obtained from the participants at the source. The study was approved by the institutional review boards of each participating clinical center of the WHI and the University of California, Los Angeles. The patients/participants provided their written informed consent to participate in this study.SJ, JP, ES, MP, HY, and Z-FZ designed the study. SJ performed the genomic data QC. SJ performed the statistical analysis and SJ, JP, ES, MP, and HY interpreted the data. ES and Z-FZ supervised the genomic data QC and analysis and participated in the study coordination and interpreting the data. SJ secured funding for this project. All participated in the paper writing and editing. All authors contributed to the article and approved the submitted version.This study was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number K01NR017852 and a University of California Cancer Research Coordinating Committee grant (CRN-18-522722).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Young women diagnosed with breast cancer face challenges that interfere with their entire life with psychological and physical effects.We employed a qualitative exploratory descriptive design, and recruited twelve participants through purposive and snowball sampling methods to conduct 12 face to face individual interviews. A reputable review board in Ghana; Noguchi Memorial Institute for Medical Research, gave ethical clearance for data collection. Data were transcribed verbatim and analysed using thematic content analysis.Three themes emerged from the data; physical effects of breast cancer, effects of treatment on body image, and emotional effects of breast cancer diagnosis and treatment. The negative effects of treatment incapacitated most of the women and limited their activities of daily living. Some experienced severe bodily weakness and stayed indoors for days. Most participants felt they looked unattractive because they have had a mastectomy done, and used pieces of rags and handkerchiefs as breast prostheses. Those who had hair loss through chemotherapy used different kinds of wigs to cover their baldness. Almost all the participants cried, felt depressed, and became emotionally unstable when they were told their diagnosis. Some denied their diagnoses and displaced their anger on healthcare personnel. A few of them felt they had brought disgrace to their families because breast cancer is perceived, a disgraceful disease.Young women diagnosed with breast cancer require psychological interventions and physical support from healthcare personnel and their families. Breast cancer is the second most common cancer after cervical cancer in Sub-Saharan Africa , 2. In AIn Ghana, breast cancer is the most prevalent cancer, and one of the commonest causes of cancer deaths in females of all ages with an alarming incidence in young women aged 40 to 49 years . Der MuoThe diagnosis of breast cancer comes as a surprise to every woman no matter the age or social status because women perceive their breasts as something that makes them whole or complete, and therefore feel demoralized and useless when they lose their breast , 10. TheTreatment of breast cancer generally involves chemotherapy, radiotherapy, and surgical intervention , 19. WhiBreast cancer treatment, therefore, affects young women’s physical appearance, sexual and reproductive lives as well as employment –26. EvenThe study employed an exploratory descriptive approach to qualitative research to explore, understand, and document the experiences of young women with breast cancer. The design was considered suitable since little is known about the phenomenon .The study was conducted in the Accra Metropolis, Ghana, and participants were recruited from three hospitals: a regional hospital, a quasi-government hospital, and a tertiary teaching hospital that serves as the United Nations Level Four Medical facility.Women in their fertile age 15–49 years  were theThe sample size was 12 based on the attainment of saturation. Saturation is the point when no new responses or repetitive responses are obtained from study participants . PurposiThe researchers obtained ethical clearance from the Institutional Review Board of the Noguchi Memorial Institute for Medical Research, University of Ghana. A permission letter was attached to copies of the ethical clearance and sent to the three health facilities selected. The first author visited the selected facilities to work with the staff at the breast clinics and the in-patient units briefly before data collection began for participants to be familiar with her. During the interaction, the first author made it known to participants that she is a nurse clinician who was in school pursuing a masters’ programme and has ever taken care of breast cancer patients. The first author gave her contact number to some of the Staff of the selected facilities and asked them to help recruit participants for the study. The authors sought informed consent from every participant and explained to participants that they had the right to withdraw from the study at any time without any consequences. Participants were also given clear unambiguous information regarding the study and were assigned pseudonyms and identification numbers which were used throughout the study to increase confidentiality. Contact numbers of participants were obtained and calls made to schedule an interview with those who consented to participate in the study. The first author conducted the interviews individually at the convenience of participants in English, in their homes, in a room at the premises of the health facilities, and at a place of choice closer to participants’ neighbourhood. A semi-structured interview guide was used during interviews, the interviews lasted 35 to 60 min. During interviews, the researcher was careful not to project her experiences on the participants and wrote down exactly what participants portrayed. Responses of participants were probed further where necessary to allow them to describe their experiences fully. The interviews were tape-recorded, an approach which increased the accuracy of the descriptions of participants’ experiences and consequently increased the credibility of the findings. The researchers considered at all times, stringent attention to details, adhered to procedures, and ensured consistency and accuracy.Data analysis was done alongside data collection. The recorded interviews were transcribed verbatim and read several times to understand exactly what the participants said by two independent coders. First, codes were assigned to meaningful chunks after carefully getting the essence of the data. Similar codes were cautiously grouped to form subthemes and subsequently, themes. The researchers kept logs for reviews to ensure that participants’ views were truly represented. Discussions were done among the researchers on the themes generated, and ensured that the data were free of personal biases. The themes were then put under the various objectives set for the study; physical and psychological effects of breast cancer diagnosis and treatment. The data was finally exported to NVivo version 11 which was used to manage the data. Thick verbatim quotations were also used to back the findings of the study which brought alive the true experiences of the young women.The ages of the participants ranged from 28 to 45 years and among the twelve participants, only one had a family history of breast cancer. The ethnic backgrounds of participants include; Ga, Ewe, Akuapem, Ga-Adangme, and Ashanti which made the data very rich.Table of Sociodemographic Characteristics of participantsThe three themes that emerged include: physical effects of breast cancer, effects on body image, and emotional effects of breast cancer diagnosis and treatment.“ … initially when I went through the chemo I did not take it easy at all, I had a weakness, I vomited continuously for three days and could not eat or drink anything. When I went for the next session, the doctor said I did not have enough blood in me so he suspended the chemo and wrote blood tonic for me to go and buy” (YWP8).“I remained indoors because I was weak and I could not bath, walk or eat so I refused to go for the fifth one as scheduled (YWP10).Young women in this study complained of their inability to carry out their activities of daily living because of the effects of medications given to them. They could not walk, eat, or drink water after chemotherapy sessions. Some had their treatment suspended for a while because of severe anaemia.“I have constant severe headaches, and my immune system is also down. When I started the chemotherapy, it took me about three weeks in the house battling with the effects without being able to go to work. I vomited and became very weak so I was unable to pray” (YWP5).… when I go for the chemo, I am not able to cook and also care for my children. I vomit when I smell food” (YWP9).“The side effects of the treatment also negatively affected participants’ social and spiritual life, comfort, and care of their children due to constant severe headaches.“I have lost my long flamboyant hair, the long hair I decided to hold and twist anyhow, hmm. People use to admire my hair a lot when I go to the salon and now my husband has been complaining about the loss of my long beautiful hair” (YWP11).“I have lost all my hair when I combed it two days after the chemotherapy, my hair came off so I shouted. My auntie became alarmed and reshaped it for me but later, everything came off rendering me bald” (YWP6).Participants who had chemotherapy done, had their hair falling off which they considered a great loss because of how admirable their hair is.“Now this wig is what I have to live with until a miracle occurs. I have bought three types of human hair to appear attractive and be able to go to work” (YWP7).“My husband complains about the wig and said I should not wear the wig but I cannot live without the wig, I just don’t feel comfortable at all seeing my scalp bald” (YWP9).Participants relied on wigs both human and synthetic to make up for their hair loss. Some purchased different types of human hair to change their looks.“Hmm! look at my colour, I have generally darkened including my tongue and gum. Just have a look at my nails, you might think I smoke a cigarette and my breast too is shrinking and discharging, hmm life” (YWP7).Darkening of the tongue, gum, skin, and the nails as side effects of chemotherapy became so obvious in some participants to the extent that, one participant likened herself to a smoker. She further narrated about her breast shrinking and discharging.“with the big plaster on my wound, I cannot go out. My wound was as big as my palm or yours but though it is getting smaller, I cannot wear a bra because of the pain and also my nipples point out in my dress without a bra and it is embarrassing to me” (YWP1).Participants who went through mastectomy complained about their inability to wear brassieres when dressing up because of their wounds and the pains associated with it. This made it difficult for a participant to go out because she found it embarrassing when her nipples pointed out in her dress when she was not wearing a brassiere.“After the surgery, something like a boil developed in my armpit which is painful and because of that I do not wear sleeveless dresses anymore” (YWP12).“My challenge now is that I like wearing sleeveless but now that I have lost one breast, my only choice is to wear dresses to cover my chest completely. I have small breasts so I wear a bra that pushes up my breasts but now how can I do that again? Hmm!” (YWP4).The loss of the breast through a mastectomy affected participants’ dressing and choice of dresses. Aside, some developed lymphoedema with its associated pain and discomfort which affected their quality of life.“I fold soft rag and put in my bra to replace my breast before dressing up. I have searched for the original breast prosthesis to buy but it is difficult to get. It makes me stand in front of the mirror for a long time.” (YWP2).Oh! it is not easy my sister, after the surgery, I place abdominal pack in my bra and it looks just like my breast“ (YWP10) “… I spend a lot of time dressing up because I have to make sure the breasts are of the same size. I hope to get an artificial breast someday.” (YWP12).Even though my breast has not been removed, it has shrunk and my dresses do not fit me anymore, now when I am dressing, I put a handkerchief on it before I move out of the room. I do it with care to achieve the same breast size.” (YWP11).“In the absence of the breast, some participants folded rags, handkerchiefs, and gauze and put these in their brassier as breast prosthesis. Others emphasized that the non-availability of silicone breast prosthesis made them waste a lot of time when dressing up. Even for participants who have not undergone mastectomy but have had their affected breasts shrunk, used handkerchiefs as a prosthesis to augment the size of their breast.I received a lot of intravenous fluids during my chemotherapy so I have grown fat and my dresses cannot fit me anymore. I also went through axillary clearance which has also resulted in lymphoedema so I dress strategically now due to my health.” (YWP10).“… “This disease has … come to spoil a lot of things. Physically I have changed,I used to be fatter than this but I have grown lean now. I am not able to wear my dresses anymore because they are now loose on me.” (YWP9).There were also changes in body sizes due to the effects of chemotherapy. While some complained of reduced body sizes, others alleged they put on weight due to the treatment received.“I was not given anyone to talk to me no, the doctor told me I have breast cancer and I shouted, Jesus! He looked at me and said go and think of it so that you come for us to take off the affected breast” (YWP 8).“After the tests, the doctor finally said ‘Madam I am afraid you have breast cancer’ and said I have to go through treatment. I was annoyed because when I detected it and reported, they only gave me antibiotics on two occasions so I waited for a year before they saw it, just imagine this” (YWP 12).… I cried, I cried to the extent that all the nurses came to surround me. Oh, I never thought I could get breast cancer one day, not at all, that is the last thing to cross my mind. I never thought of it so I was shocked and sad” (YWP9).““I cried, I really cried that day, and my Aunt tried consoling me but I did not even pay attention to what she was saying. I kept asking why me! Why me?” (YWP6).Most participants in the study did not receive enough counseling during their diagnosis, as most of them were shocked, sad, and cried because they never thought they could develop breast cancer one day. This further intensified with unkind reactions from hospital staff.“Oh I was shocked, I was shocked because we do not have such a disease in our family. There is no diabetes or hypertension in our family and I have not also heard about this kind of disease in our family before so I was shocked” (YWP5).“It was not easy for me at all, and I did not take it lightly … I realised that I was in trouble and I asked myself wherefrom this? For about a week or two I was not myself. I could not sleep, eat or think properly and I saw my world crashing before me” (YWP7).A participant said she could not sleep, eat or think for a week or two and saw her world crashing before her.One day I was sitting on a bus talking to myself. I did not know where I was going until the bus attendant asked me where I was alighting. It was then I realised I neither had a destination in mind nor money to pay for, meanwhile, my baby was at my back. Hmmm I nearly got mad” (YWP4).““When it started, I nearly got mad because I could wear any dress at all without considering where I was going.” (YWP6).“Among my siblings, I am the only one suffering from breast cancer, … so I feel I am the first person to have brought disgrace to our family hmmm!” (YWP9).Some participants lamented they became mentally unstable and started talking to themselves, dressed shabbily, moved out of their houses, and went roaming. Others felt they had brought a disgrace to their families because breast cancer is a disgraceful disease.“ … the doctor asked … if I heard him and I said yes, you said I have breast cancer and I heard you perfectly. I just got up, picked up my bag, and asked my mother to get up so that we could go home. I was in denial but later the reality hit me” (YWP3).A participant narrated she denied the news of her diagnosis and remained indifferent when she was told her diagnosis in the consulting room. She left the consulting room and asked her mother to get up for them to go home.… I woke up with so many tears and sobs, I don’t know, it just came I don’t know why it should happen to me. I don’t know the reason behind it and I don’t know what I have done … I was in real emotional something especially when I saw that the breast wasn’t there (hmmm)” (YWP3).“… when the wound was opened the third day after surgery, oh my God! That was when I realised my breast was gone. … I was annoyed so I stopped talking to all the nurses, I was just crying... In the beginning, it was like a dream but now I have seen that it is real” (YWP4).“One participant refused to eat or talk to people around her while another continued to cry because she did not understand why she suffered breast cancer.“I fear that I may die and leave my husband and children. Though the nurses have talked to me, I’m still afraid I may die for someone to come for my husband and maltreat my children (shedding tears)” (YWP9).Fear of death was also an issue some participants had as they contemplated what would happen to their husbands and children should they die from breast cancer.The study found that most participants after receiving chemotherapy went through the usual side effects of chemotherapy as reported in , 31. TheBody image concerns raised by participants, for example, hair loss, absence of the breast that was complicated by unavailability of breast prosthesis, presence of lymphoedema, and skin changes, affected participants’ quality of life. Similarly, findings from earlier studies confirm that changes in body image among women with breast cancer cause emotional disturbances that permeate their thinking, particularly when looking into the mirror, dressing up, taking a shower, and during sexual intimacy with their partners , 36. MeaIn the absence of breast prosthesis, women in this study used rags, handkerchiefs, and pieces of gauze in their brassieres as breast prostheses after mastectomy. This is in tandem with findings from  where woMiller, Gorcey, and McLellan  indicateInterestingly, the weight gain was not much of a problem to participants who experienced it, just that their old dresses were not fitting them anymore. This was not surprising because typical African women cherish weight gain and body fat as a sign of good living.Emotionally, when participants were told their diagnoses, most of them were stunned, became sad and cried because they had no family history of breast cancer. A study indicates that fear, loss of hope, emotional instability, shock, suffering, and guilt characterise breast cancer diagnosis and treatment . Some ofIt is widely recognised that management of treatment-related complications is a key component of supportive care, which has a well-defined clinical guideline but experts are of the view that this part of the breast cancer treatment is likely to be overlooked in Low, and Middle-Income Countries like Ghana , 52. FroThe findings of this study indicate that all young women did not get adequate support and counseling from their healthcare personnel. The lack of counseling before and after diagnosis worsened the psychological reactions of these young women. As patients’ advocates, nurses should be trained to offer counseling services to women suspected of having breast cancer before and after diagnoses as it is done in the case of Human Immunodeficiency Viral infection in Ghana. Future research could look at developing a Navigation programme for breast cancer care in Ghana and assessing its impact. A quantitative approach could be adopted to look at the impact of psychosocial care in breast cancer patients.Breast cancer diagnosis and treatment create physical and emotional problems that interfere with one’s quality of life. Women who are diagnosed with breast cancer require holistic care from healthcare professionals. Every patient suspected of having breast cancer should be counseled before and after diagnosis to psychologically prepare him or her for treatment. It is also necessary to educate young women with breast cancer on the side effects and expectations of the various treatment modalities before their initiation to enable them to cope better.


Background: Early detection of breast cancer alters the prognosis and tools that can predict the risk for breast cancer in women will have a significant impact on healthcare systems in low- and middle-income regions, such as North Cyprus. Objective: In this study, we developed a simple breast cancer risk model for the women of North Cyprus. Methods: Data from 655 women, consisting of 318 breast cancer cases and 337 hospital-based controls, was used to develop and internally validate the model, external validation was carried out using, 653 women consisting of 126 cases and 527 controls. Data were obtained from medical records and interviews after informed consent. Results: A model was derived that consisted of age ≥50 years and <50 years and the presence and absence of >1 first-degree relatives (FDR) with breast cancer. From internal and external validations the model’s AUCs were, 0.66 (95% CI = 0.62–0.70) and 0.69 (95% CI = 0.63–0.74) respectively. Conclusions: A unique model for risk prediction of breast cancer was developed to aid in identifying high-risk women from North Cyprus that can benefit from mammogram screening. Further study on a large scale that includes environmental risk factors is warranted. Breast cancer is a disease that has remained the leading cause of death in women worldwide . In 2018Additionally, the prognosis is always poor for women living in low- and middle-income countries ,8, becauNorthern Cyprus, can be regarded as a middle-income society, small, enclosed, ideal for epidemiological research, has a typical western Mediterranean lifestyle, with living conditions and diets that should be favorable for good health ,10. PrevIn high-income countries, the incidence and mortality associated with breast cancer are decreasing. These may be accounted for because of access to mammogram screening, effective health services, and good execution of breast cancer prevention campaigns, as well as primary prevention policies and guidelines ,15,16. YDespite the fact, mammogram screening has been supported for the early detection of breast cancer, it is not cost-effective and feasible for developing countries, because of the large population of women, underfunding, infrastructure and human expertise needed ,22,23,24Risk prediction models can be used to identify high-risk women that will be eligible for mammogram screening. Thus, reducing the overload on the limited facilities available and narrowing the focus on the appropriate group can also reduce administering unnecessary radiation to women, who are not eligible, while at the same time reducing the economic burden on the government. Currently, several comprehensive breast cancer risk assessment tools exist that incorporate various risk factors for the calculation of breast cancer risk .Many risk factors play a role in the development of breast cancer. Environmental factors that contribute to interethnic variations in risk, age, lifestyle factors, endogenous and exogenous hormonal exposure, childbearing, breastfeeding, reduced physical activity, dietary consumption, certain reproductive factors , body size/obesity contribute to differences in incidence , family Breast cancer risk assessment models are categorized into empirical models or genetic models . A patteThis study was carried out in the hospital, Burhan Nalbantoglu Devlet Hastanesi, Lefkosa, North Cyprus. This hospital treats all breast cancer cases in North Cyprus. Ethical approvals were obtained from Near East University, scientific research evaluation ethics committee, and Burhan Nalbantoglu Devlet Hastanesi’s ethics committee, before the research was carried out (YDU/2018/55-523). All methods were performed following the relevant guidelines and regulations.The retrospective dataset of 655 women, collected from April 2018 to December 2018, was used to derive the model. A total of 318 women had newly confirmed breast cancer, and 337 women were without breast cancer. Women with a history of lobular or ductal carcinoma in-situ were excluded. Only participants between the ages of 30 to 84 years were included in the whole study groups. Informed consent to participate was obtained after the aims of the study were explained by a medical professional.Retrospective medical and demographic information of all participants were collected through interviews.The interview included: age, age at menarche, age at first delivery, menopausal status, presence or absence of benign breast disease, history of breast cancer in first-degree relatives or other relatives, history of hormone replacement therapy including estrogen/progestin and breast density.Sampling size was based on the following calculations:N = 121,257 (Women Population Size);t = t value = 1.96, (at α = 0.05);p =  = 91/100000 = 0.00091 (Expected Frequency);q = 1 − p = 0.99909;d = (Acceptable margin of Error) = 0.001;The required sample size = 317.8 women.The frequency of the risk factors of the study group was analyzed using descriptive statistics. An initial multivariable logistic regression was carried out. The significant variables were considered for further multivariable logistic regression. A forward multivariable logistic regression was used to access the final model. In the multivariable regression analysis, the categories that conferred protection against breast cancer were used as the reference. All statistical analysis was performed with IBM Spss .The whole dataset of 655 women, consisting of 318 breast cancer cases and 337 without breast cancer from the derived phase, was used to internally validate the model using bootstrap with 200 repetitions ,39. For Separate information of 653 women, consisting of 126 cancer cases and 527 women without breast cancer, collected between November 2018 to January 2020, were used to externally validate the model. Total scores for individuals were calculated based on the derived scoring scheme, and the c-statistic was then estimated.A total of 655 women were used to derive the model. Among them, 51.1% were above 50 years; 48.5% (318) of the women had breast cancer, while the rest reported with no breast cancer. A > 1 FDR with breast cancer was reported in 9.9% of the study population .A total of 10 variables were analyzed to access the risk model, After an initial logistic regression of all the variables and two successive forward multivariable logistic regression, the risk factors that were observed to be insignificant were eliminated at each step, then we arrived at two significant risk predictors that comprised the final model, that is >1 FDR with breast cancer OR = 3.0 (95% CI 1.6–5.4) and age above 50 years OR = 3.0 (95% CI 2.2–4.1). The c-statistic of the final model on internal validation was 0.66 (95% CI 0.62–0.70). The estimated coefficients of the two variables served as the basis for the scoring with a range of 0–2. The risk scores were stratified into three groups; low-risk (0) women <50 years and with no >1 FDR with breast cancer, moderate-risk (1) women with >1 FDR with breast cancer or ≥50 years and high-risk (2)—that is, women—≥50 years and with >1 FDR with breast cancer.From the internal validation the average Doriginal and Dboot were (0.328) and (0.350) respectively. The bias or optimism was (0.022).Separate information from 653 women, was used for external validation, consisting of 126 women with breast cancer and 527 women without breast cancer. From In comparison, to a recent validation study carried out on a large cohort, the model showed similar c-statistics, sensitivity, and specificity to the Gail, IBIS and BOADICEA models . The othInherited factors elucidate just about a quarter of breast cancer risk . Meta anAging plays a role in the pathogenesis of breast cancer because of genetic instability, telomere attrition, epigenetic alteration, stem cell exhaustion associated with aging.From 50 years to 70 years and above, the risk of breast cancer increases .The assessment of risk is vital for the management of breast cancer. High-risk women will not automatically have breast cancer, but they are strongly advised to visit cancer clinics. They should have a mammogram at least yearly, for this has been shown to increase detection rates and reduce mortality ,60,61,62We recommend that the women of North Cyprus categorized as high-risk for breast cancer can benefit from regular monitoring using mammograms, early detection, and preventive interventions such as healthy lifestyle and medication.Those women with moderate-risk of breast cancer, should undergo screening of breast cancer risk using mammograms biennially. For women above 50 years with no >1 FDR with breast cancer, while those women below 50 years with >1 FDR with breast cancer can consider the use of medications, such as tamoxifen or raloxifene to reduce the risk of developing breast cancer. Risk and benefits have to be assessed by a medical professional.Low-risk women are required to indulge in primary care since their risk is not different from the general population, but this status is liable to change in the presence of modifiable risk factors. Thus, these women are encouraged to maintain a healthy lifestyle and breast care. Increased awareness of existing and identified risk factors will aid in evaluating their current status and make the appropriate decision for mammogram screening if warranted. These recommendations are summarized in This model can serve as a simple, noninvasive, alternative screening for the identification of high-risk women, thus streamlining the focus of the limited mammogram resources to the right group in low- and middle-income countries such as North Cyprus. Using this model will also reduce unnecessary mammograms needed and radiation exposure to potentially low-risk women. The use of the risk prediction model has additional advantages, as it is not dependent on physical examination, easy to utilize and implement, being cost-effective and will enhance outcome and survival for women categorized as high-risk.The poor funding of health systems in low- and middle-income countries causes problems in the implementation of mammogram screening programs, thereby leaving many of the women out and only a few from the urban centers with insurance policies are privileged to participate.This model can serve as additional screening tools that will aid in identifying women at high-risk that will need an immediate mammogram, thus reducing the burden on the already constrained facilities and hence saving lives.The progress in mobile internet services in low- and middle-income countries has made online materials easily accessible to everyone. Educating and empowering women on how to use the risk prediction tools online will drastically reduce the number of high-risk women that cannot have access to early detection facilities. These will protect more women at the individual and population levels.This initiative is maybe similar to the mobile health  initiative launched by the world health organization in 2012, whereby mobile phones were used to improve the prevention, detection, and management of diseases in low-income countries . The risRisk prediction tools at the moment can serve as cost-saving tools. However, the benefits can only be maximized when all identified high-risk women can receive further confirmation screening and treatment. Therefore, identified high-risk women will still need to visit healthcare centers for counseling and prophylactic treatment.While the models intend to ascertain the risk for an individual, the risk factors utilized to depend on population risk from epidemiological investigations. Therefore, more studies have to be carried out among various populations of women in other to identify new lifestyle/environmental factors, biomarkers, genetic markers and incidence rates that are peculiar to that group, which can be incorporated into prospective risk models because the possibility of identifying those at high-risk would be enhanced by using a comprehensive risk model that integrates all known risk factors . BecauseThe weakness of our study is the fact that it was based on retrospectively collected data. However, the collecting process was done independently, so unlikely to have altered the results and caused bias. The AUC estimates are bound to be biased since our validation was carried out on a case-control group, but this was minimized . The intInformation on environmental risk factors was not collected, and this may have created a gap in the risk factors of the studied population.Despite these biases and limitations, the urgent need for a risk prediction model in providing relevant breast cancer control in developing societies such as North Cyprus outweighs the shortfalls.Our results demonstrated that this newly developed breast cancer risk prediction model is a simple, cost-effective, and noninvasive tool for the identification of high-risk women in North Cyprus that can be eligible for a mammogram. It may serve as a gatekeeper for a mammogram and a radiation saving tool for low-risk women, by reducing unnecessary mammograms and thereby decreasing health costs. This model is suitable for the prediction of late on-set breast cancer risk and further studies on a larger study group, including environmental risk factors will be needed to improve the model.


Currently, tamoxifen is the only drug approved for reduction of breast cancer risk in premenopausal women. The significant cardiovascular side effects of tamoxifen, coupled with lack of a survival benefit, potential for genotoxicity, and failure to provide a significant risk-reduction for estrogen receptor-negative breast cancer, all contribute to the low acceptance of tamoxifen chemoprevention in premenopausal women at high-risk for breast cancer. While other prevention options exist for postmenopausal women, there is a search for well-tolerated prevention agents that can simultaneously reduce risk of breast cancers, cardiovascular disease, and type-2 diabetes. Metformin is a well-tolerated oral biguanide hypoglycemic agent that is prescribed worldwide to over 120 million individuals with type-2 diabetes. Metformin is inexpensive, safe during pregnancy, and the combination of metformin, healthy lifestyle, and exercise has been shown to be effective in preventing diabetes. There is a growing awareness that prevention drugs and interventions should make the “whole woman healthy.” To this end, current efforts have focused on finding low toxicity alternatives, particularly repurposed drugs for chemoprevention of breast cancer, including metformin. Metformin's mechanisms of actions are complex but clearly involve secondary lowering of circulating insulin. Signaling pathways activated by insulin also drive biologically aggressive breast cancer and predict poor survival in women with breast cancer. The mechanistic rationale for metformin chemoprevention is well-supported by the scientific literature. Metformin is cheap, safe during pregnancy, and has the potential to provide heart-healthy breast cancer prevention. On-going primary and secondary prevention trials will provide evidence whether metformin is effective in preventing breast cancer. Currently, tamoxifen is the only drug approved for reducing risk of breast cancer in premenopausal women. The approval of tamoxifen was based on the first National Surgical Adjuvant Breast and Bowel Project (NSABP) Breast Cancer Prevention Trial P1) . . 2). Thep < 0.0001) in all breast cancers (p < 0.0001) and DCIS (p = 0.009). Although tamoxifen-prevention was given for 5-years, follow-up evaluation of the high-risk subjects provide evidence that the long-term risk-reduction in subjects who took tamoxifen may persist up to 10 years   cancers , 11 in hp = 0.8)”  a significant risk reduction for ER- breast cancer and (2) a survival benefit. An extended analysis (median 16 years) of IBIS-I study participants, continues to shows in the tamoxifen vs. placebo arms “no difference in the number of breast cancer deaths ( = 0.8)” .Despite initial recommendations by the FDA and American Society for Clinical Oncology, very few women take tamoxifen ; it is eCYP2D2 gene; this gene variant has low activity to convert tamoxifen to its more active metabolite, 4-hydroxytamoxifen. Lacking in the analysis was a consideration of the concentration of 4-hydroxytamoxifen required to saturate ER; consequently, prospective clinical studies did not demonstrate a reduction in tamoxifen efficacy in individuals with the CYP2D2 variant  trial , raloxifene was shown to reduce the incidence of breast cancer in postmenopausal women . RaloxifWomen are not just at risk for breast cancer but also face the risk of developing heart disease, obesity, and type-2 diabetes. Furthermore, with the risk of currently available chemoprevention agents potentiating cardiovascular disease, there is a need to identify agents that can effectively target both conditions: breast cancer and cardiovascular disease. To this end, current efforts have focused on finding alternative prevention strategies that have the potential to reduce not just breast cancer but also reduce the risk for cardiometabolic diseases. Potential strategies have included exercise, aspirin, and metformin.Metformin  is a well-tolerated oral agent that is prescribed for first-line treatment of type-2 diabetes , 22 and Until recently, most clinical care has focused on treatment of type-2 diabetes rather than its prevention. However, several well-controlled studies have shown that it is possible to prevent type-2 diabetes through a combination of diet, exercise, and metformin. The Diabetes Prevention Program/Diabetes Prevention Program Outcomes Study (DPP/DPPOS) is the largest and longest clinical trial of metformin for the prevention of type-2 diabetes , 28. Stup = 0.007;  or obese.Epidemiology studies investigating the impact of metformin on breast cancer incidence are limited by several factors. These factors include: (1) racial and ethnic differences in body mass index (BMI), (2) inability of BMI to precisely identify individuals who are metabolically unhealthy, and (3) the heterogeneity of breast cancer as a disease. A BMI ≥30 kg/mdiposity . BMI is 2 are metabolically healthy, “metabolically healthy obese” (2) who have abnormal metabolic profiles and are at increased risk for cardiovascular disease and type-2 diabetes. Current definition of metabolically unhealthy individuals with a normal BMI includes (1) BMI <25 kg/m2, (2) insulin-resistance, hypertriglyceridemia, (3) abdominal fat distribution, and (4) elevated blood pressure  ; this stThe most complete and well-designed epidemiologic study was a retrospective multi-center population-based case-case study of 4,557 women with breast cancer ages 20–69 years old performed by Chen et al.; 1,446 women had TNBC . The invOR = 1.54; 95% CI: 1.07–2.22 and OR = 1.80; 95% CI:1.13–2.85, respectively)  within 2 years of diagnosis, increased the odds of a woman having TNBC (ctively) . These lEpidemiologic studies are powerful tools for generating associations but do not test mechanisms. First off, as pointed out by Chen et al., it may be that the women who had the most poorly controlled diabetes , were thAfter oral administration, the oral bioavailability is 55 ± 16% (mean ± standard deviation); metformin is predominantly absorbed in the small intestine . MetformDespite metformin being one of our oldest medications, the precise molecular mechanism(s) underlying metformin's insulin-lowering effects, as well as its potential anti-neoplastic potential, are not completely understood. It is well-accepted that metformin inhibits hepatic gluconeogenesis and secondarily lowers circulating insulin. However, the precise mechanism(s) of metformin-action remains a work in progress. Two major pathways are thought to account for the main actions of metformin and metformin's proposed anti-cancer effects ; both paMetformin signals via an AMPK-independent pathway; in this pathway metformin secondarily lowers circulating insulin levels and inhibits insulin/insulin-like growth factor-1 (IGF-1)-signaling. Under nutrient-rich circumstances, IGF-1 binds to the IGF-1 receptor (IGF-1R) leading to activation of (1) PI3K (phosphatidylinositol-3-kinase)/AKT/mTOR-network signaling and (2) RAS/RAF/mitogen activated protein kinase (MAPK) . This sp = 0.041) and in the metformin arm (p = 0.027) but was unchanged in women who did not take metformin ; women with Luminal B breast cancer had the greatest benefit . Furthe= 0.005; . A third= 0.005; . There h= 0.005; ]. Still,Currently many ongoing prospective clinical studies are testing the metformin for primary and secondary prevention of breast cancer . TogetheMetformin is cheap, safe during pregnancy, and has shown to prevent type-2 diabetes. There is a need for prevention drugs that target both ER+ and ER- breast cancer as well as providing prevention for cardiometabolic disease. Metformin clearly lowers insulin-signaling; signaling pathways activated by insulin are known to drive biologically aggressive breast cancer and predict poor survival in women with breast cancer. Despite the fact that metformin targets many key breast cancer pathways, there is much to be learned about whether metformin can prevent breast cancer and/or breast cancer recurrence. Window-of-opportunity trials provide important clues to metformin's impact on normal and malignant breast tissue, but results have not been entirely consistent. Currently, it is unclear which breast cancer subtypes may benefit the most from metformin. It is likely that MA-32 will provide answers to many of these questions. There is also much to be learned about metformin, insulin resistance, and BMI; specifically, whether metformin's impact is only in women who are metabolically unhealthy and/or have high BMI, or whether metformin can benefit all women. Biomarker studies that define key signaling pathways impacted by metformin will be critical to design and inform future clinical trials. Over the next 5 years on-going primary and secondary prevention trials will show (or not show) the ability of metformin to prevent breast cancer. Hopefully, these studies will not just provide a yes/no answer also provide the biomarkers to determine which women will maximally benefit from metformin. In the words of several of my patients “Please do not quote statistics at me; these statistics are about other women. If I take a prevention agent, I want to know if the prevention agent is working in my breasts.”All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions.Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions.int = 0.85 (0.78-0.93), Pint = 2.8 x 10–4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10–5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth.Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors. The association between common breast cancer susceptibility loci and breast cancer risk is not strongly modified by established breast cancer risk factors.The combined effect of susceptibility loci and established risk factors is thus well described by a multiplicative model.We found one noteworthy gene-environment (G x E) interaction with overall and with estrogen-receptor-positive breast cancer risk, which was replicated, and two novel noteworthy G x E interactions with ER-negative breast cancer risk.In an independent dataset, we replicated two previously reported G x E interactions.BRCA1, BRCA2, TP53, ATM and CHEK2 increase the risk of breast cancer,,Breast cancer is a complex disease with both environmental and genetic factors contributing to risk. Well-established modifiable and non-modifiable environmental factors include age at menarche, parity, age at first birth, breastfeeding, body mass index (BMI), use of menopausal hormonal therapy (MHT) and alcohol consumption.Estimation of any combined effect of genetic and environmental factors, including gene-environment (G x E) interactions, is considered to possibly improve breast cancer risk prediction, and hence identification of women at high risk for targeted prevention. However, development of these risk models depends on knowledge of the joint effects of genetic and environmental risk factors, in particular departures from a multiplicative model .,Previous G x E interaction studies of breast cancer have reported nearly 30 potential G x E interactions, with little evidence of departures from the multiplicative model.IJE online). Participants were excluded if they were male, were of non-European descent, had breast tumours of unknown invasiveness, or had in situ disease or prevalent disease at the time of assessment. Women with unknown age at reference date (defined as date of diagnosis for cases and of interview for controls) were also excluded. For each risk factor, only studies with risk factor information for at least 150 cases and 150 controls were included. All participating studies were approved by the relevant ethics committees and informed consent was obtained from study participants.We analysed data from 46 studies  participating in BCAC (2), adult height (per 5 cm), lifetime alcohol consumption (per 10 g/day), current smoking (yes or no) and current use of combined estrogen-progesterone menopausal hormonal therapy (MHT) (yes or no) as well as current use of estrogen-only MHT (yes or no) for postmenopausal women.Data for risk factors from different studies were harmonized according to a common data dictionary and were centrally quality controlled. For both case-control and cohort studies, epidemiological risk factor data were derived with reference to reference date (described above). We used reference age as surrogate to categorize women as probably premenopausal (<54 years) or postmenopausal (≥54 years) status. The environmental variables available for analysis were: age at menarche (per 2 years); ever parous (yes or no); for parous women, number of full-term pregnancies , age at first full-term pregnancy (per 5 years), ever breastfed (yes or no), duration of breastfeeding (per 12 months); and for all women, ever use of oral contraceptives (yes or no), adult body mass index (BMI) separately for pre- and postmenopausal women . These variants have been associated with breast cancer risk either through GWAS,A total of 205 common breast cancer susceptibility variants were selected for evaluation of G x E interactions (IJE online).For replication of the previously reported interactions, we analysed a subset of 30 544 cases and 37 616 controls genotyped using the OncoArray array, which had not been included in previous G x E studies. We evaluated 33 potential G x E interactions that had been previously reported , along with the main effect for study design.2 index.Analyses were conducted separately for overall breast cancer risk and for ER subtype-specific breast cancer risk. The analyses were performed separately for women genotyped by iCOGS or OncoArray, and the results were meta-analysed using a fixed-effects inverse-variance weighted model. Between-study heterogeneity in the G x E interaction effect estimates was assessed by Cochrane’s Q test and IMHT was classified into estrogen-progesterone therapy (EPT) and estrogen-only therapy (ET). Models assessing the association with current MHT use by type were adjusted for former use of MHT and use of any MHT preparation other than the one of interest. All analyses of MHT use were restricted to postmenopausal women. Models evaluating the association with current smoking were adjusted for former smoking.et al.To assess the noteworthiness of the observed G x E interactions, we calculated Bayesian false discovery probability (BFDP) at five different prior probabilities for a true association . G x E interactions with BFDP <80% were considered as noteworthy. This was based on the assumption of a 4-fold cost of a false non-discovery compared with the cost of a false discovery and that the probability of observing a true interaction odds ratio (OR) inside the range of 0.66-1.50 was 95%, as proposed by Wakefield IJE online. The number of cases and controls with data for each risk factor varied, ranging from 23 755 cases and 30 153 controls with data for parity to 5078 cases and 6867 controls with data for cumulative lifetime intake of alcohol in the iCOGS dataset, and from 37 863 cases and 44 533 controls with data for parity to 12 213 cases and 13 232 controls with data for lifetime alcohol intake in the OncoArray dataset .The studies included in this analysis are listed in ,IJE online). The associations of the environmental risk factors with breast cancer risk were as expected in the population-based studies; in brief, age at menarche, being parous, number of full-term pregnancies, ever breastfeeding, cumulative duration of breastfeeding and premenopausal BMI were negatively associated with breast cancer risk, whereas age at first full-term pregnancy, ever use of oral contraceptives, postmenopausal BMI, current use of EPT, adult height, current smoking and cumulative alcohol consumption were all positively associated with breast cancer risk (IJE online).The SNP associations with risk of overall as well as ER subtype breast cancer were consistent with those reported in literaturemeta-int = 0.85, 95% confidence interval (CI) = 0.78-0.92, Pmeta-int = 7.4 x 10–5, BFDP = 0.73] with overall breast cancer at 0.1% prior probability. The minor allele of SNP rs4442975 was associated with a stronger reduced risk of breast cancer for current users of EPT  than for never users of MHT  . The association of rs4442975 with reduced risk of ER-positive breast cancer was stronger for current users of EPT  than for never MHT users  (We identified three SNP-risk factor interactions as noteworthy (BFDP <0.8) at ≤1% prior probability . The str84-0.90) . This inmeta-int = 0.91, 95% CI = 0.85-0.96, Pmeta-int = 8.2 x 10–4, BFDP = 0.74). The minor allele of the rs6596100 variant was associated with a reduced risk of overall breast cancer  and ER-positive breast cancer , respectively, but not ER-negative breast cancer . The rs6596100 associated risk of ER-negative breast cancer appears to decrease with number of full-term pregnancies for parous women, with the estimated per-allele ORmeta being 1.06 (95% CI = 0.95-1.17) for women who had had one full-term pregnancy and 0.92 (95% CI = 0.82-1.04) for women who had had four or more full-term pregnancies . The risk conferred by rs6596100 on ER-negative breast cancer was decreased for women with age at first full-term pregnancy below 20 years  but increased for women with age at first full-term pregnancy ≥30 years  (IJE online). Several other interactions were found to be noteworthy (BFDP <0.8) at 5% prior probability (IJE online). Meta-analysed results of all the G x E interactions for overall and ER subtype risk are shown in IJE online.For parous women, we observed noteworthy evidence that the ER-negative breast cancer risk associated with rs6596100 was also modified by age at first full-term pregnancy (OR97-1.24) . HoweverIJE online). We estimated an interaction OR for overall breast cancer of 0.80  for current EPT use and rs13387042, a SNP for which we had previously reported an interaction OR of 0.83 .int = 0.64, 95% CI = 0.45-0.92, Pint = 0.01), compared with ORint of 0.53  in Rudolph et al.Pint = 0.03). For the G x E interaction between SNP rs3817198 and number of children for parous women, which had the strongest evidence for overall risk of breast cancer in previous analyses ,int = 0.94, 95% CI = 0.94-1.00, Pint = 0.03).In replication analyses, we found evidence for two previously reported associations in the independent subset of OncoArray data  G x E interactions related to breast cancer risk based on prior probabilities ≤1%. The strongest evidence was found for effect modification between rs4442975 and current use of EPT with overall and ER-positive breast cancer risk. Moreover, we found evidence of interactions between the SNP rs6596100 and number of full-term pregnancies and age at first full-term pregnancy, respectively, for ER-negative breast cancer risk.2 = 0.93) and conditional analysis yielded a significant association only for rs4442975, so that these results reflect the same interaction. Fine-mapping and functional analyses have identified rs4442975 to be the most likely causal variant in this region.IGFBP5 gene and estrogen pathway in breast cancer.The SNP rs4442975 is located in an intergenic region on the long arm of chromosome 2 (2q35). Another SNP within the same genomic region, rs13387042, was previously reported to show an interaction also with current use of EPT.IGFBP5 promoter, suggesting that the T-allele of rs4442975 decreases susceptibility to breast cancer via increased expression of insulin-like growth factor binding protein 5 (IGFBP5).,IGFPB5 variant-related susceptibility to breast cancer. Note however that two other independent breast cancer risk variants in this region  and AF4/FMR2 family member 4 (AFF4). INQUISIT predicts HSPA4 as the most likely target, due to overlap of multiple correlated SNPs lying in HSPA4 promoter region, distal regulatory elements and coding sequence. HSPA4 gene is responsible for production of heat shock proteins (Hsps), particularly those belonging to the family HSP70. The underlying mechanisms regarding the relationship between rs6596100 and these pregnancy-related risk factors are unknown at present. It is plausible that a lower estrogenic milieu due to reproductive factors may affect the formation of multicomplexes between steroid receptors like ER and heat shock proteins (HSPs), therefore affecting signalling pathways such as Wnt, ErbB, serine/threonine and tyrosine protein kinase, which are known to be involved in breast carcinogenesis. Whereas there is some biological plausibility regarding the observed interactions with rs6596100, the findings nevertheless could be by chance, and thus require independent replication.Women of young age at first pregnancy are known to have increased circulating sex hormone-binding globulin and prolactin but decreased total estrogen levels.CCDC88C gene.,et al.The SNP rs941764 is located on chromosome 14 in intron of A role of alcohol has been well recognized in initiation and progression of breast cancer, presumably via multiple cellular and molecular mechanisms, including the EGFR/ErbB2 pathways. Downstream to EGFR/ErbB2 pathways lie multiple pathways such as the MAPK, Wnt/GSK3β/β-catenin pathways.IJE online).Our study provides the most comprehensive evaluation to date of potential effect modification of all known common genetic susceptibility variants by environmental risk factors for breast cancer. Our findings are based on the largest available dataset on breast cancer. Despite its large sample size, the study may remain statistically underpowered, considering the rather modest effect sizes of most of the common variants associated with breast cancer risk, and particularly for risk factors for which we have fewer data  and by the European Communitýs Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009–223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report.Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065 and Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009–223175) (COGS), Cancer Research UK , the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative , the Department of Defence (W81XWH-10–1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065.The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation  and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is an NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society . The Australian Breast Cancer Tissue Bank (ABCTB) is generously supported by the National Health and Medical Research Council of Australia, the Cancer Institute NSW and the National Breast Cancer Foundation. The AHS study is supported by the intramural research programme of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119) and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur, Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11-192, Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev and Gentofte Hospital. The CNIO-BCS was supported by the Instituto de Salud Carlos III, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070). The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The CTS was supported by the California Breast Cancer Act of 1993, the California Breast Cancer Research Fund (contract 97–10500) and the National Institutes of Health . Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting programme mandated by California Health and Safety Code Section 103885. H.A.C .receives support from the Lon V Smith Foundation (LVS39420). The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council ; Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK , Medical Research Council  (United Kingdom). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01–1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia . G.C.T. and P.W. are supported by the NHMRC. R.B. was a Cancer Institute NSW Clinical Research Fellow. L.M.B.C. is supported by the ‘Stichting tegen Kanker’. D.L. is supported by the FWO. The MARIE study was supported by the Deutsche Krebshilfe e.V. , the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201] and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973. The MISS study is supported by funding from ERC-2011–294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286 and CA177150. M.S.K.C.C. is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted by the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute  and the North Carolina University Cancer Research Fund. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107 and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. PROCAS is funded from NIHR grant PGfAR 0707–10031. D.G.E. is supported by the NIHR Biomedical Research Centre in Manchester (IS-BRC-1215–20007). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by Cancer Research UK  and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for P.D.P.P. from the NHS in the East of England through the Clinical Academic Reserve. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The SMC is funded by the Swedish Cancer Foundation. The UCIBCS component of this research was supported by the NIH  and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WHI programme is funded by the National Heart, Lung, and Blood Institute, the US National Institutes of Health and the US Department of Health and Human Services . This work was also funded by NCI U19 CA148065-01.dyz193_Supplementary_DataClick here for additional data file.


Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables . C-statistics were used to evaluate discriminative ability.Since 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was significantly associated with BC . Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653.In total, 320 women developed BC. The PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population.The effect size of the PRS In ten prospective studies, this PRS showed an association with breast cancer with an odds ratio (OR) of 1.61 per standard deviation (SD) of the PRS distribution,11 and an area under receiver–operator curve of 0.630. It has been shown that the greatest breast cancer risk stratification in the general population and in women with a family history of breast cancer can be obtained by using the combined effects of the PRS and lifestyle/hormonal risk factors in the BOADICEA model.10Many risk prediction algorithms have been developed to quantify the combined effect of various risk factors to predict the risk of developing breast cancer.12 Women start at age 50 years with biannual mammograms until the age of 75. Before considering risk-stratified approaches based on BOADICEA, it is important to assess its clinical validity in the Dutch population. In this study we validated the association between the PRS313 and breast cancer in a Dutch prospective cohort, its effect on predicting in situ breast cancer, and explore the discriminative ability of an individualized 10-year breast cancer risk score based on the PRS313 and several known risk factors using the BOADICEA version 5 model. We also assessed how a risk-based approach of population-based screening could have impacted breast cancer detection rates in our study cohort.Currently, breast cancer screening in the Dutch population is age-based.13 Briefly, in the year 1989, individuals aged 55 or older were recruited into the RS-I cohort, which was extended in 2000 under similar criteria (RS-II cohort) and in 2006 by the inclusion of individuals with an age between 45 and 55 (RS-III cohort). The overall response rate was 72%. In 2008 the Rotterdam Study comprised 14,926 subjects aged 45 years or older, including 8823 women. For our study, we included all 6670 women for whom genotype data were available. Genotyping was not performed for the excluded 2153 women because of a low-quality DNA sample or because they declined blood donation for DNA at study entry.The Rotterdam Study (RS) is a prospective population-based cohort study of elderly Dutch individuals living in the Ommoord district of Rotterdam in the Netherlands.The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus Medical Center and by the Dutch Ministry of Health, Welfare, and Sports. All participants provided written informed consent to participate in the study and to have their medical information obtained from treating physicians.13 In total, 468 women had a breast cancer (invasive or in situ) diagnosis of whom 148 had been diagnosed prior to entry into the Rotterdam Study, and were excluded from further analyses. All participants were interviewed at home at inclusion, underwent extensive examinations every ~5 years in the Rotterdam Study research facility, and received follow-up questionnaires  and through linkage with Dutch Hospital Data, Netherlands Comprehensive Cancer Organisation, and histology and cytopathology registries in the region.res Fig. , as desc13 Standard quality control was completed, including selection on European ancestry, and imputation was performed using the Haplotype Reference Consortium (HRC) 1.1 and 1000 G phase 3 reference panels.15 Of the 313 variants used to calculate the PRS, 28 were directly genotyped by the arrays. Two variants were imputed with a quality below 0.3 and the remaining 283 variants were imputed with an average imputation quality of 0.95 (Table Genotyping was performed with the Illumina 550 K (RS-I and RS-II cohorts) and 610 K (RS-III cohort) arrays.95 Table .nij is the number of risk alleles  for variant i carried by individual j and ORi is the per-allele OR for breast cancer associated with variant i. The ORs were obtained from the Breast Cancer Association Consortium (BCAC) study11  age, (2) age and PRS313, (3) age and risk factors, and (4) age, PRS313, and risk factors. Risk factors included are age at menarche, age at menopause, number of children, age at first live birth, use of oral contraception, use of hormone replacement therapy, body mass index (BMI), height, and alcohol use. For the variables that could vary over time, we used fixed variables. As BOADICEA ignores any risk factors for which the value is missing,10 no imputation was performed, and missing variables were kept missing.Cumulative 10-year breast cancer risks were calculated with BOADICEA version 5,Because BOADICEA calculates cumulative breast cancer risks up to age 80, 10-year breast cancer risks were only calculated for 4377 women with an age of inclusion up to 70 years. Women were considered affected if they developed breast cancer (invasive or in situ) within 10 years after inclusion in the Rotterdam Study.Cumulative incidences were calculated using the Kaplan–Meier method.313 and breast cancer risk in the Rotterdam Study cohort, Cox regression analyses were performed. Relatedness among individuals of the same family was accounted for by correcting standard errors using a sandwich estimator. All models were adjusted by the age at inclusion in the Rotterdam Study. Incident breast cancer, in situ or invasive, was the event of interest. The time at risk was defined as the time elapsed between the inclusion date and the date of occurrence of the event of interest or right censoring. Right censoring could be due to (1) end of follow-up in January 2014 or (2) death. The proportional hazard assumption for the model was tested. Sensitivity analyses were performed (1) for invasive breast cancer only by censoring the in situ breast cancer cases, (2) for in situ breast cancer only by censoring the invasive breast cancer cases, (3) by censoring at the age of diagnosis of another type of cancer, and (4) by stratifying on Rotterdam Study cohort. To define the association between the PRS313 and other tumors than breast cancer, similar Cox regression analysis was performed by censoring the breast cancer cases if they did not develop another tumor before the breast cancer diagnosis.To estimate the association between the PRS313 holds, we ran the model considering the categorical covariate given by the percentile groups of the PRS313  based on the distribution in the unaffected women in this cohort. The discrimination ability of the PRS313 in our sample was evaluated using the C-statistic,16 by groups based on quantiles of the age of inclusion in the Rotterdam Study . Differences in the C-statistics were tested by computing bootstrap CIs for the differences among groups.To investigate if the linearity assumption for the effect of PRS313 with breast cancer was investigated using age as time scale and considering three age-dependent coefficients in the Cox model, corresponding to three different age intervals: (1) younger than 50 years, (2) between 50 and 75 years old, and (3) above 75 years old. These cut-offs were chosen based on their clinical relevance since women between 50 and 75 years are eligible for population screening according to the Dutch guideline.12The possible time-varying association of the PRSTo validate the BOADICEA 10-year cumulative risk scores, model calibration and discrimination ability in our sample were assessed. Calibration was investigated by comparing overall observed versus expected cumulative risks and by visually inspecting the calibration plots based on risk deciles. Because of the presence of right censoring, empirical risks at 10 years were estimated using the Kaplan–Meier method. As in the association analyses, discrimination was evaluated using C-statistics.p value of <0.05. All analyses were performed with R version 3.5.3.17Statistical significance was defined as a two-sided 313 in groups of affected  and unaffected women (including women who developed another tumor than breast cancer) are shown in Fig. We included 6522 women in the main analyses with an average age at study entry of 66 years. Of these, 320 developed either invasive or in situ breast cancer during follow-up and 744 developed another type of tumor; the overlap between these two groups was 16, all of whom developed another type of tumor first Table . The med313, after 20 years of follow-up, the incidence in the highest quintile was 10.8%, 95% CI [8.5%–13.1%] and 4.4%, 95% CI [2.8%–6.0%] for the lowest quintile , indicating that the HR remained constant over time. The discriminative ability of the PRS313, as measured by the C-statistic, was 0.632, 95% CI [0.58–0.69], 0.673, 95% CI [0.61–0.73], and 0.562, 95% CI [0.48–0.62] for women included before age 60, between age 60 and 70, and above age 70 respectively  invasive breast cancer only, (2) censoring at another tumor if applicable, or (3) stratifying by the Rotterdam Study subcohort all showed similar results .During follow-up, 744 women developed a tumor other than breast cancer without evidence for association with the PRS313 decreased with increasing inclusion age, with the HRs per SD declining from 2.74, 95% CI [1.72–4.37] for women included before age 50, to 1.74, 95% CI [1.52–2.00] for women included between 50 and 75 (pdiff = 0.066). The HR for women included after age 75 was 1.29, 95% CI [1.08–1.55], and the p value of the difference with respect to the youngest group was 0.003 , henceforth the “full” model. The PRS313 was the strongest factor contributing to discrimination, relative to age and other risk factors . The median follow-up in this subcohort was 10 years (range 0.03–10 years), and the cumulative incidence of breast cancer was 4.4% (95% CI [3.7–5.1%]). The distributions of 10-year cumulative risk scores under different models are shown in Figs.  invasiveion Fig. , it unde313 and age only, 130 cases (80% of incident cases) occurred in a screening group of 2863 women ; in 1481 women who would not be screened, 33 breast cancers occurred. In Fig. Using the full model and a threshold of 2.5% 10-year breast cancer risk, which approximates the risk of women entering the age-based population screening program in the Netherlands, 101 cases (62% of incident cases) occurred in a screening group of 1956 women  and 62 breast cancers occurred in 2421 women who would not be screened Fig. . Using t19 Increasingly, these are being integrated into computational models that allow personalized breast cancer risk assessment, which has potential application beyond current practice of genetic testing in family cancer clinics.20 The BOADICEA algorithm is among the most comprehensive risk models presently available for breast cancer risk assessment.10 Here, we validated the most recent version of this model in a large prospective population-based Dutch cohort of women above 45 years, which has not been part of the previously published BCAC study.11 Unsurprisingly, the best discrimination was achieved after inclusion of all available risk factors, with the largest contribution deriving from the PRS313. The PRS313 was significantly associated with breast cancer, with a similar effect size as in other prospective series of different geographic origin,11 demonstrating its robustness and potential application to the Dutch population.Many risk factors for breast cancer, both genetic and nongenetic, have been identified in the past decades.313 improved the discriminatory ability from 0.531 to 0.636, compared with a model using age only, which could only be marginally improved further (to 0.653) by adding lifestyle, reproductive factors, and anthropometric data. This is in line with previous research, showing that the variance explained by the risk factors is modest compared with the PRS313 risk stratification.21 Results of the calibration showed that BOADICEA underestimated the observed risks, especially in the higher categories of risk. One possible explanation is that BOADICEA v5 uses the population breast cancer incidences of the United Kingdom as baseline risk, which are slightly lower than those in the Netherlands.1 But more importantly, data on family history, mammographic density, and rare high-risk variants in BRCA1 and BRCA2 were lacking in our cohort. In another prospective validation study of a previous version of BOADICEA in two cohorts of women from Australia, Canada, and the United States, information on family history and BRCA1/2 carrier status, but not the PRS313, was available, and here, BOADICEA overestimated 10-year cumulative risks in the highest risk quantile.9 Possibly, the missing data on family history and BRCA1/2 status in the Rotterdam Study were in fact more prevalent than modeled by BOADICEA. Our calibration results indicate that for proper use in the general population, information on family history may be important.The PRS313 alone would have detected more cases than the full BOADICEA model, but would also have identified a larger screening group. Apparently, women in the Rotterdam Study have on average fewer nongenetic risk factors compared with the total population, which on average slightly modifies their risk in a downward direction. The PROCAS study used the Tyrer–Cuzick model with mammographic density and risk factors, combined with a PRS based on 18 single-nucleotide polymorphisms (SNPs);22 they found 82% of the cases to occur in 68% of women with a 10-year breast cancer risk above 2%, i.e., very similar to what we found with the PRS313 alone.We illustrated the potential impact of the model in detecting breast cancer in a population screening setting in which women would participate based on their individual risk. In this illustration, the PRS23 Our cohort data did not include information on whether incident breast cancers were screen-detected or not, hence we cannot exclude that high-risk women disproportionally self-selected for mammographic screening, which could explain this bias. In contrast, for the in situ carcinomas, more high grade tumors were found in the >2.5% 10-year risk group compared with those with lower risks. Histological grade of ductal carcinoma in situ (DCIS) has been suggested to be one of six factors associated with subsequent development of invasive disease,24 albeit not very strongly so. It remains possible that the PRS313 is more strongly associated with low grade invasive breast cancer than with higher grades, as observed for some individual variants,26 and inversely so for DCIS. It will be important to replicate this in larger studies to inform the evaluation of the cost-effectiveness of a risk-based versus age-based entry of the population screening.7Remarkably, we found the proportion of low grade invasive tumors to be higher in those with a 10-year risk >2.5%, compared with those with lower risks. Screen-detected invasive cancers are more likely of lower grade and stage.27 in our cohort the PRS313 is associated with in situ breast cancer as well, with a nonsignificantly lower effect size than for invasive breast cancer. This corresponds well with a previously reported association of an 18-SNP-based PRS22 and with previous results showing that the association of 51 of the 76 investigated breast cancer loci with DCIS is in the same direction as for invasive breast cancer.28 Although BOADICEA is presented as a model that predicts invasive breast cancer,10 these results suggest it might also predict in situ breast cancer. Larger studies are needed to confirm this and provide more accurate risk estimates, specifically in the setting of population screening programs.Although PRS development studies have included only invasive breast cancer,27 we found that the effect size of the PRS for breast cancer declined with increasing age. While this is not yet modeled in BOADICEA, this could be important to consider for women under the age of 50 who are at this moment not eligible for population breast cancer screening in the Netherlands, because our results suggest that using the overall HR would be underestimating risk in this age group.As in previous studies,313 with these cancers, suggesting it specifically predicts breast cancer. Another prospective study also reported no association between other types of cancer and a sum of breast cancer risk alleles at 72 loci.29 Because we only analyzed all other tumors combined, we cannot exclude that the PRS313 has an association with one specific type of other cancer.In the Rotterdam Study, malignancies other than breast cancer are also recorded. We found no evidence for association of the PRS13 Furthermore, for a large group of women, there is extensive follow-up of up to 25 years.A strength of our study is the prospective population-based study design, including all women in a specified locale near Rotterdam. Because of the high response rate (>70%) it is a good representation of the Dutch population in that age category.Besides that information on mammographic density and family history was lacking, another limitation of our study is the unknown ER status of the breast tumors, precluding the analysis of ER-positive and ER-negative disease separately. Furthermore, to evaluate the introduction of risk-based entry into population screening, establishing the detection rate of breast cancers below the age of 50 would have been relevant, which was not possible in our older cohort of women. Finally, we excluded nearly 25% of all women in the Rotterdam Study because no genotyping data were available. Declining blood donation for DNA extraction did not lead to differences in the basic characteristics between the genotyped and nongenotyped groups. Therefore, if a selection bias was present, we believe this bias would be small.313 replicates robustly in the Dutch population and the discriminative power of the BOADICEA model seems appropriate for implementation into breast cancer prevention programs, such as those currently ongoing in cancer family clinics in many countries worldwide. However, application to the general population would require recalibration of BOADICEA to address underestimation in the higher risk categories. Although the Rotterdam Study design precluded analysis of breast cancer–specific mortality, our evaluation of clinical validity provides first insights into how a risk-based entry could impact the efficacy of the breast cancer population screening program in the Netherlands.In summary, the PRSSupplementary Information