BRCA1/2 mutations after Ashkenazi Jews. Compared to non-Hispanic Whites, Latinas have lower GCRA uptake. While some studies have identified barriers for GCRA use in this population, few studies have focused on health care providers’ perspectives. The purpose of the study was to examine providers’ perceptions of barriers and facilitators for at-risk Latina women to participate in GCRA and their experiences providing services to this population. We conducted semi-structured interviews with 20 healthcare providers  recruited nationally through snowballing. Interviews were transcribed. Two coders independently coded each interview and then met to reconcile the codes using Consensual Qualitative Research guidelines. Providers identified several facilitators for GCRA uptake  and barriers . Genetic counselors described important aspects to consider when working with at-risk Latina including language barriers, obtaining accurate family histories, family communication, and testing relatives who live outside the US. Findings from this study can inform future interventions to enhance uptake and quality of GCRA in at-risk Latina women to reduce disparities.The Comprehensive Cancer Network (NCCN) recommends genetic cancer risk assessment (GCRA) referral to women at high risk of hereditary breast and ovarian cancer. Latinas affected by breast cancer have the second highest prevalence of  BRCA1/2 are the most commonly identified genetic mutations responsible for hereditary breast and ovarian cancers (HBOC)  to know whether or not they should have done a bilateral mastectomy”—NavigatorDoctor recommendations (Respect). Several providers noted that at-risk Latinas tend to use the GCRA services when doctors recommend it. Even if the purpose of the visit may not be sufficiently clear, providers shared that often out of respect , Latinas try to follow medical recommendations without questions.“People are sent because their doctors go “you are 35 and you need to go to genetic counseling” and you get people who come here and go “I’m here because my doctor told me I had to, but I don’t know”—Genetic counselorProviders identified barriers for at-risk Latina to participate in GCRA included cost and insurance, referrals, awareness, education, and language, logistic barriers, emotions, spirituality, fatalism, stigma, and systemic barriers . They also noted challenges to obtaining authorization from the insurance companies that often resulted in delays. Navigators and social workers further identified challenges for finding insurance coverage for the needed screening and prophylactic surgical procedures following receipt of a positive test for a deleterious mutation. One provider described how the limited access to testing might lead to suboptimal surgical decisions: “A surgeon may possible recommend to them (Latinas) you might want to think about a bilateral mastectomy just based on your family history and your risk factors, sometimes you don’t need a test… (...) it could really be a workaround but they’re not really getting the testing so we don’t know if we’re doing something that might not be (needed) (...) and it’s not helpful to the family member because there is no test done” (Nurse navigator)In contrast to patient navigators and social workers, genetic counselors appeared to perceive that cost and insurance were no longer significant barriers. They noted that the cost of testing had decreased dramatically over the last 15 years. They also mentioned the increased coverage by insurance companies, noting the implementation of the Affordable Care Act, the availability of assistance programs in some states, and other available funds from hospitals, foundations, or grants. “The other problem is the payment, people think that the insurance will not cover it and they are convinced that they will have to pay for it when in reality, for most people, insurance will cover it”—GeneticistGenetic counselors emphasized the importance of informing at-risk women about the existing resources to cover the costs of testing, since women may be unaware.“Most laboratories have testing hardship program[s], so if someone is low income and we can get them free testing setup, that is actually quite easy, but it is kind of ironic that the genetic counseling part can sometimes be a lot harder to get because there aren’t that many free resources for genetic counseling”—Genetic counselorHowever, some providers mentioned greater challenges with covering the cost of genetic counseling for uninsured women than covering costs of testing. To overcome this barrier, some counselors used grant funds to cover the counseling, while others provided free services.Referrals. Providers described having referral procedures in place at their institutions and also getting some referrals from community-based organizations. However, they also noted gaps and limitations in the referral process. Reasons cited for the suboptimal referral included doctors’ limited knowledge about HBOC risk factors and referral guidelines, primary care doctors’ not assessing or addressing family history of cancer, or patients’ low awareness and knowledge about HBOC and GRCA services. Providers’ limited knowledge about financial assistance programs also resulted in uninsured patients not being referred to GCRA under the assumption that they will not be able to cover the costs.“The providers may not always guarantee that the testing will be covered. A lot of my patients are told that it’s not going to be covered when it actually is, so they are never referred to start”—Genetic counselor“These are all staged breast cancer patients who have already been identified as breast cancer patients and they had been recommended for testing and counseling. Sometimes they get the testing but they won’t get the counseling (because extra cost)”—NavigatorAdditionally, due to the difficulty of covering counseling, some patients get testing without prior counseling.Awareness, Education, and Language. Providers noted that one of the main barriers is the lack of awareness about genetic counseling and testing services in the Latino community. Limited awareness about the services is also exacerbated by language barriers, the limited availability of informational resources in Spanish, and low familiarity with genetics. “Latina women may think ‘how come am I going to go to the doctor if the doctor will not understand what I say or I will not understand what the doctor says’” (Geneticist)Logistic barriers included transportation, childcare, having appointments during business hours, and the subsequent difficulties getting off work. Providers mentioned strategies to address barriers such as offering telephone counseling, covering transportation costs, or trying to schedule multiple visits to the health care team on the same day.“A lot of people work full time and have a lot of problems getting to the appointments during business hours, or have jobs where they can’t leave work or they would have to take time off, they can’t afford the day off or they employer won’t let them take the day off”—Genetic counselorEmotions. Providers mentioned that HBOC can elicit emotions like fear, worry, and guilt which may hinder GCRA participation. Additionally, for newly diagnosed patients, participating in GCRA can feel overwhelming while they are in active treatment and processing emotionally the cancer diagnosis. “A lot of times it is kind of scary for all of them, especially the patients that aren’t breast cancer patients finding out whether or not you have this gene and then having to worry about children or other family members can be scary” (Navigator)Spirituality. Providers noted that spirituality and faith is very important in the Latino community, as many believe that their health outcomes are “up to God”. Faith is often brought up as a source of strength. However, in some cases, spirituality may hinder participation in treatment. “I am not going to do the treatment because God is going to decide if I live or don’t live from this” (Psychologist/counselor)Fatalistic beliefs. Providers also noted that for many women cancer is synonymous to death or that having cancer is inevitable or not under their control. “I have people that come in that are just convinced they are going to get cancer, like yeah, that is just the way it is. Everyone gets cancer; it is not if, it is when” (Genetic counselor)“Some people are not ready in that moment and prefer to wait, especially if they had a negative experience with a relative, so there is people that say I will die of something so I prefer not to know”—NavigatorProviders mentioned that some people are not ready to take action by undergoing testing, some people do not want to know if they are going to have cancer or not, or do not want to hear about management options.Stigma. Providers reported that cancer is often stigmatized in Latino communities, especially in certain subgroups like older generations, women from rural areas, women with lower education, and women recently arrived in the US. “I remember one patient in particular from Bolivia and she felt much safer being here and talking about it with people. She said that if she were to bring this up back home that people would be doing a lot of gossiping and that it would be malicious and hurtful and it would be detrimental to her in various ways”—Psychologist/counselorThe use of the phrase ‘genetic counseling’ can carry stigma, as it can be misunderstood as psychological counseling and the idea of being ‘crazy’. Providers shared that some of their Latino patients believe that just saying the word cancer increases the chances of developing cancer. Providers reported that often people do not want to share the cancer diagnosis with others for fear of being gossiped about or ostracized.Additionally, providers described that breast and ovarian cancers can be stigmatized since they can pose a threat to femininity. Other sources of stigma included the belief that women got cancer because they did something wrong (and that God is punishing them) and thinking that cancer is contagious. Providers noted that this stigma often resulted in the lack of family communication about cancer and not sharing the cancer diagnosis, which could decrease awareness about cancer in the family.Systemic Barriers. A few providers mentioned other potential barriers including fear of discrimination, medical mistrust , difficulties navigating the medical system or not being familiar with a medical system that has specialists , competing priorities, and immigration status.“Sometimes, in a few instances, some people are afraid to fill out paperwork for the free testing because they were undocumented. But I would have said this is fairly rare.”—GCThe genetic counselors and the geneticist identified several aspects that are important to consider when providing counseling to at-risk Latina women including language, education and misconceptions, gathering family history, communication with relatives, testing relatives outside the US, logistics, and engagement in decision-making/respect  highlighted how important it was for Latinas to get counseling in Spanish. “(The sessions with interpreters are) about a third longer because you have to repeat everything twice (…) a lot of what we do is try to build rapport with the patient and that’s just an added layer. I have had good sessions with interpreters but it does make it a little more difficult if a patient is more engaged with the interpreter, they [the patients] are looking at the telephone, and it’s hard to make the contact with them. These [are] little things and nuances in a session but overall might really affect the communication.”—Genetic counselorMost monolingual (English) genetic counselors use interpreters who participate by telephone or in person. While counselors valued the availability of interpretation services to provide services to clients with limited English proficiency, they also noted several challenges and shortcomings. For example, using interpreters can hinder building rapport between the counselor and the patient, add time to the length of the session, and, when using telephone interpreters, make it harder for the counselor to get feedback from nonverbal cues.Genetic counselors noted that while in-person interpreters might be preferable, it is still challenging to ensure that the right message is delivered. For instance, interpreters appear to summarize and cut off important information and may translate key terms into Spanish words that have different meanings. In some cases, interpreters might completely mistranslate the information because they become emotionally involved and they do not want to worry patients or because they are unfamiliar with the nuances of genetics: “Some things were more serious than others. For example, a positive test result would occur and the translator would say ‘you have cancer’”. (Genetic counselor)Education and Misconceptions. A number of genetic counselors shared that many of their Latina patients, especially patients who migrated from rural areas in Latin America or those with low levels of formal education, did not have enough knowledge to understand some of the basic genetic concepts. Moreover, some counselors noted the availability of Spanish-language informational brochures, but emphasized that the language was too technical to be useful to the patients. One counselor suggested that providing too much scientific information might not always be necessary. Instead, she recommended providing a brochure that summarizes the key points and after explaining these points in simple terms, spending more time considering and addressing the patient’s actual worries . The counselor suggested that this approach leaves open the possibility of further describing the scientific nuances based on patient preferences. Some of the most frequent misconceptions that counselors reported they encountered during sessions revolved around genetic counseling/testing and biological concepts . Genetic counseling/testing: Genetic counselors noted that many people associate genetic counseling with psychological counseling. Other prevalent misconceptions around GCRA included: (1) Genetic testing will provide a diagnosis for cancer, (2) genetic counseling will inevitably lead to genetic testing, (3) a positive result means patients will have to do prophylactic surgery, (4) a negative result means that patients do not have to worry anymore, and (5) the test results can change.“That’s a question I get a lot. “You know, well, in a few years if you did this test again, could I get back something different?”—Genetic counselorCancer, genes, and heredity concepts: Counselors identified a big gap in patients’ understanding about the different causes of cancer. They mentioned that some Latina patients believe that they caused the cancer  or that all cancers are hereditary . Counselors observed a general lack of knowledge that gene alterations can also be passed by and to male relatives.“One of the major misconceptions is that these breast cancer genes can only come from the mom’s side of the family (…) So that is something that comes as a shock to a lot of people, especially when you’re doing the family history right in front of them (…) and you start to point out things in the dad’s side of the family”—Genetic counselorGenetic counselors identified that patients appear to greatly misunderstand patterns of inheritance: “Sometimes people also think (…) “I look just like my mom and she had cancer, and because I act just like her (…), so I must have it too.” (Genetic counselor)Lastly, providers discussed how different types of cancer such as ovarian, cervical, and uterine cancers are confused or broadly referred to as “female cancers”.Emotions. Counselors highlighted that emotions are an important aspect of genetic counseling. For instance, counselors noted that some Latino patients feel that genetic information is too much to process and fear the results.In contrast, other patients feel relieved by the results (even if test is positive) because it puts an end to uncertainty and patients feel that they finally have a starting point to do something about their cancer risk. Counselors mentioned that some women appear to feel guilty because they think that they did something to cause the cancer or that they could have passed a mutation to their children. For example, counselors commented that grandmothers reflect guilt because they might have “passed it to future generations”, mothers show concern for their children and feel guilty, and fathers feel guilt and sadness as they are protective of their daughters. Additionally, counselors reported that young women who may not yet have children may feel shocked when faced with the possibility of removing their ovaries. “I want my patients to see that information as a gift they provide to their kids. The perception is ‘Oh, I am causing cancer to my kids.’ I try to change that perception to ‘this is random (…) try to see this as a present to your daughter or son, you are giving them the possibility of being proactive instead of reactive in their healthcare’”.—Genetic counselorSeveral counselors emphasized the importance of normalizing people’s beliefs about genetic mutations , emphasizing that people cannot control which genes they pass, acknowledging and legitimizing patients’ feelings of guilt and reframing receipt of a genetic test result as important information they are providing to their family that can allow them to prevent cancer.Family History. Genetic counselors indicated that gathering accurate family history can be challenging since Latina women can lose contact with some relatives due to geographic distance or because they were raised by only one side of the family. Thus, it can be hard to know the vital status of some relatives. Additionally, counselors commented that women may not know the cause of their relatives’ death due to the stigma around cancer, which is common in older generations, or the limited access to doctors, particularly in rural areas. Counselors suggested that sometimes relatives ask doctors not to disclose the diagnosis to the person diagnosed with cancer to avoid worrying them. Additionally, providers noted that due to the violence and political conflicts in several Latin American countries, many relatives die young, so it is impossible to know if they would have developed cancer at a later age.“There is like this hole certainly in the past, you know, this old taboo that people don’t talk about cancer. And so we did not know what happened to grandma, so and so, or grandpa, so and so, or uncle so and so or whoever was, they just got sick and then they died and we really don’t know what happened”—Genetic counselorEven when women are aware of a cancer diagnosis in the family, they may not have accurate information about which type of cancer it was since they often confuse cervical, uterine, and ovarian, or just refer to them as “female cancers”. Counselors reported difficulty in obtaining medical records to verify the accuracy of self-report. Although having an incomplete family history is a limitation, fortunately it has become less important due to the ability of doing multiple panel testing and the reduction of testing costs. Communication with relatives. Counselors indicated that they try to encourage patients to communicate with relatives, including sharing the test results. Some counselors noted that while some parents embrace testing in the family, other families have a hard time encouraging relatives to do the test for several reasons including feeling guilty, having lost contact with some relatives, not even sharing the cancer diagnosis to avoid worrying others or being a burden to the family, or to avoid showing a sign of weakness to the family. “I usually pass it along to my patient and hope that they pass it on to their relative. But yeah, it can be difficult and frustrating because either from just the location or just the relatives not communicating well, it’s hard to get information to the family”—Genetic counselorSome counselors voiced their challenges tracking whether women shared the information with relatives, especially when relatives were living outside the US.A counselor explained that while writing letters in English and Spanish for relatives was important, it was not always enough if there was no follow-up. Once she had a client diagnosed with breast cancer, who was the sister of one of her clients who had tested positive, yet the newly diagnosed patient was unaware of this information. This case led her to modify her practice to spend more time discussing communication issues and trying to address barriers for testing for the relatives. Another counselor was involved in an intervention that included facilitating communicating results to relatives that participants found extremely useful.Testing Relatives Outside the US. Several counselors mentioned the importance of taking into account the implications of testing for relatives who live outside the US. For instance, women could feel unease if they think that the test information may not benefit their relatives who live in Latin America and may not have access to the services.“Something that is more unique to the Latina population I see, at least compared to other of my patients, a lot of their relatives are not here in the United States. So that brings up questions if I have this information will that benefit my relatives? Will they be able to get tested? Will they be able to get the screening they need or will they be able to get the information they need?”—Genetic counselorAlthough facilitating testing for relatives outside the US was challenging, counselors shared different strategies they use. Strategies included identifying services in other countries , testing relatives when they come to the US, sending testing kits with their clients when they travel, asking the relatives to order the test online, tapping into a research registry with collaborators from Latin America, or posting in the National Society of Genetic Counselors website to find if anyone knows about resources in different countries. Logistics. Difficulty reaching out patients by phone was mentioned as a logistic challenge.“Sometimes it’s just really hard for me to get back in touch with the patient. Sometimes their phone numbers won’t work or they’ll have voicemail and I’ll call with the translator and I’ll call and leave a message but I don’t know if they’ve checked their message, there is just those like logistical things.”—Genetic counselorEngagement in Decision-Making/Respect. Some counselors reported that in general Latinas tend to be less engaged in decision-making and be more compliant with medical recommendations.“I feel like maybe there are fewer questions. And so sometimes I wonder, “is it something getting lost in translation? Do they not feel comfortable?” It’s hard because you want to make sure that everyone has the information to make an informed decision. But there is very little push back (…)”—Genetic counselor“When we ask them ‘would you like to do the test or not?’ the patients are confused and tell me ‘well, you are the health professional, you should decide whether I do the test or not.’ (…). We try to be a little more directive and tell them ‘In my professional opinion I recommend this test (…) but ultimately the decision is yours.’”—Genetic counselorOne counselor explained that the lower engagement in decision-making might be due to the paternalistic approach in medicine that is common in Latin America. For Latina women, the non-directiveness mandate of genetic counseling can be confusing. One recommendation was to use a more directive approach while always stressing that the ultimate decision is theirs.To our knowledge, this study is among the first studies to assess providers’ perspectives about GCRA in at-risk Latina women . ResultsOur results support previously identified cost and insurance barriers to GRCA among high-risk Latinas ,29,45. AThe present findings also suggest that clinical guidelines to refer patients to genetic counseling prior to genetic testing are not always followed, in part due to the difficulty of covering costs of counseling. These observations by providers complement results in a recent study by Katz and colleagues  that ideOur results reinforce emerging evidence and contribute novel information in terms of factors that relate to the quality of genetic counseling for at-risk Latinas. Consistent with the seminal work of Joseph ,38,39, wBeyond language barriers, counselors highlighted other communication issues, including Latinos’ lower engagement in decision-making and potential confusion with the non-directive approach typical in genetic counseling. These findings support prior studies that examined genetic counseling in Latinos ,39,52,53Counselors highlighted several factors that can affect family communication among high-risk Latino families. Our results complement findings from direct observation of counseling sessions with at-risk Latinas and Latina patients’ perceptions ,37,38. FBRCA1/2 mutation carriers who live outside the US are less likely to be tested [Another specific aspect to providing services to at-risk Latina, which is common with other immigrant populations, is the need to explore testing options for relatives outside the US, especially given the evidence that relatives of e tested . This mae tested . As manye tested . Given tThis study had some limitations. We recruited a convenience sample of mostly female providers through list-serves and snowballing, so findings may not be generalizable to other providers who work with Latino communities. Additionally, we did not formally collect information about the percentage of at-risk Latinas that providers see. We had a small number of participants representing each profession and some were under represented  or not represented . Future studies should include a greater number of providers from different professions and specialties. Despite the limitations, the study had several strengths. This study triangulates prior research that focused on at-risk Latinas and offers important insights from the provider’s perspective. The in-depth interviews with different types of providers yielded important insights into various aspects of GCRA services with at-risk Latinas. These findings can inform future interventions to enhance uptake and quality of GCRA in at-risk Latina women to reduce disparities in this underserved population. Finally, this study represents an important contribution to a scarce literature about providers’ experiences working with at-risk underserved populations . This paper examined different provider’s perspectives about the facilitators and barriers for at-risk Latinas to participate in GCRA. Additionally, genetic counselors discussed their experiences providing genetic counseling to at-risk Latinas, including the challenges they face as well as the strategies they use to overcome those challenges. Findings from this study can inform the development of culturally targeted interventions to enhance the uptake and quality of GCRA in at-risk Latina women to reduce disparities.


The prevalence of obesity has been steadily increasing over the past few decades in several developed and developing countries, with resultant hazardous health implications. Substantial epidemiological evidence has shown that excessive adiposity strongly influences risk, prognosis, and progression of various malignancies, including breast cancer. Indeed, it is now well recognized that obesity is a complex physiologic state associated with multiple molecular changes capable of modulating the behavior of breast tumor cells as well of the surrounding microenvironment. Particularly, insulin resistance, hyperactivation of insulin-like growth factor pathways, and increased levels of estrogen due to aromatization by the adipose tissue, inflammatory cytokines, and adipokines contribute to breast cancerogenesis. Among adipokines, leptin, whose circulating levels increase proportionally to total adipose tissue mass, has been identified as a key member of the molecular network in obesity. This review summarizes the current knowledge on the epidemiological link existing between obesity and breast cancer and outlines the molecular mechanisms underlying this connection. The multifaceted role of the obesity adipokine leptin in this respect is also discussed. Overweight and obesity are preventable conditions characterized by an abnormal or an excessive fat tissue accumulation that may significantly impair health status.2)) greater than 25 kg/m2, and to obesity as a BMI greater than 30 kg/m2 . The increase in BMI is typically a result of an imbalance between exceeding energy consumption from dietary intake and energy expenditure from physical and metabolic activity. Unfortunately, fueled by major economic growth, social/technological changes, and a transition in nutrition over the last 30 years, the prevalence of obesity has been on the rise alarmingly , CASP1, and other TLR (Toll-like receptor)-regulated genes  and this adipocyte signature significantly increased in obese versus lean subjects [More recently, a role for inflammasome as an important source of inflammation in the adipose tissue of obese subjects has also been described. Inflammasome, a macromolecular complex composed of Nod (Nucleotide-binding and oligomerization domain)-like receptors , the adaptor apoptosis-associated speck-like protein containing a caspase-recruitment domain (ASC), and the enzyme caspase-1 (CASP-1), is a novel innate immune pathway required for triggering the maturation of proinflammatory cytokines, including IL-1β and IL-18 . Adipocysubjects . Activatsubjects . Moreovesubjects . The com2 and are now considered as hallmarks of the obese state, leading to coin the term of “diabesity”. Insulin levels were significantly associated with increased breast cancer risk in a large prospective cohort of postmenopausal women [Insulin resistance, hyperinsulinemia, and/or hyperglycemia characterize patients with BMI ≥30 kg/mal women . Elevateal women . In contal women . A pooleal women . Besidesal women ,123,124.al women .The IGF-I signaling cascade has been shown to mediate breast cancer cell proliferation, migration, angiogenesis, survival, and resistance to therapy in different experimental “in vitro” and “in vivo” breast cancer models (reviewed in ). The siIn obese patients, predominantly but not exclusively associated to postmenopausal status, breast cancer risk is also related to elevated circulating sex steroid levels.Few studies have evaluated the relationship of circulating concentrations of sex steroid hormones with breast cancer risk in premenopausal women; thus, data are inconsistent and further evaluations are still required. Indeed, a work published in 2013 in Lancet Oncology reported a strong association of circulating estradiol, calculated free estradiol, estrone, androstenedione, dehydroepiandrosterone, and testosterone with the risk of developing breast cancer in premenopausal women . A case-The pathological expansion of white adipose tissue in obesity leads to the development of a dysfunctional adipose tissue which is associated with an abundant production of several biologically active factors, including hormones, lipid metabolites and inflammatory cytokines, along with an altered adipokine profiles ,149. AdiAdiponectin, whose circulating levels are inversely correlated with adiposity, is a multifunctional protein with pleiotropic effects on several tissues and organs and has been proposed to exert protective roles against the development of obesity-related disorders, such as metabolic syndrome, diabetes, cardiovascular diseases and malignancies . AdiponeIt has been reported that adiponectin negatively influences breast carcinogenesis, although conflicting results have been published. Many clinical investigations suggested that low adiponectin concentrations are associated with an increased risk of breast cancer (OR (Odds Ratio) = 3.63  and OR =Ob) gene. Besides its neuroendocrine function, leptin can affect a wide range of biological activities, including mammary tumorogenesis. Indeed, it has been extensively demonstrated that this adipokine, mainly produced by distant and local adipocytes but also by epithelial tumor cells itself and by other cells within the tumor-stroma , is able to affect different aspects of breast cancer biology in an endocrine, paracrine and autocrine manner [db gene, a member of the class I cytokine receptor family ubiquitously expressed in several tissues. Leptin binding to the long ObR isoform induces activation of several intracellular downstream signaling pathways, such as JAK2/STAT3, MAPK and PI3K/Akt pathways involved in the control of cell proliferation, differentiation, survival, migration, and invasion. Indeed, several data from clinical and experimental studies strongly support the involvement of leptin in mammary tumor development and progression.Leptin, whose synthesis and plasma levels increase in proportion to fat mass, is a 16 kDa multifunctional polypeptidic molecule encoded by the obese The epidemic of obesity is now recognized as one of the most significant public health concerns facing the world nowadays. Pathological expansion of white adipose tissue and high levels of certain cytokines and leptin, as a consequence of the obesity condition, have been implicated in several hallmarks of breast cancer, such as inflammation, sustained proliferative signaling, epithelial-to-mesenchymal transition, angiogenesis, and cellular energetics. However, our understanding of the weight of obesity on breast cancer pathogenesis is only beginning to influence routine clinical practice. Although ongoing studies are focusing on interventions for both primary and secondary prevention of obesity-related breast cancers, key biomarkers of risk are still lacking. Furthermore, whether and how the myriad of local and systemic effects of obesity may impact patient outcome and response to conventional/targeted treatments is an issue that requires additional investigation in experimental models and in humans. As with the development of more personalized oncology approaches, the evaluation of novel therapeutic strategies directed against the mechanisms described above  is also urgently needed in at-risk obese populations.


The relationship between first-degree family history of female breast cancer and prostate cancer risk in the general population remains unclear. We performed a meta-analysis to determine the association between first-degree family history of female breast cancer and prostate cancer risk.Databases, including MEDLINE, Embase, and Web of Science, were searched for all associated studies that evaluated associations between first-degree family history of female breast cancer and prostate cancer risk up to December 31, 2018. Information on study characteristics and outcomes were extracted based on the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. The quality of evidence was assessed using the GRADE approach.Eighteen studies involving 17,004,892 individuals were included in the meta-analysis. Compared with no family history of female breast cancer, history of female breast cancer in first-degree relatives was associated with an increased risk of prostate cancer  with moderate-quality evidence. A history of breast cancer in mothers only  and sisters only  was associated with increased prostate cancer risk with moderate-quality evidence. However, a family history of breast cancer in daughters only was not associated with prostate cancer incidence  with moderate-quality evidence. A family history of female breast cancer in first-degree relatives was associated with an 18% increased risk of lethal prostate cancer (95% CI 1.04–1.34) with low-quality evidence.This review demonstrates that men with a family history of female breast cancer in first-degree relatives had an increased risk of prostate cancer, including risk of lethal prostate cancer. These findings may guide screening, earlier detection, and treatment of men with a family history of female breast cancer in first-degree relatives. Prostate cancer is the second most common cancer and the fifth leading cause of death in men worldwide , 2. CancApproximately 35% of familial prostate cancer risk is explained by known genes , 10. BRCRecently, controversy came from several large-scale, high-quality analyses that attempted to analyse whether there was a correlation between the first-degree family history of female breast cancer and risk of prostate cancer. To better understand this issue, we performed a systematic review with meta-analysis of published literature that investigated the association between first-degree family history of female breast cancer and risk of prostate cancer.A systematic search in MEDLINE, Embase, and Web of Science was performed from the earliest publication date available until December 31, 2018. Additional studies were searched by checking the reference lists of relevant studies. The following search terms were used: ‘(prostate cancer OR prostate carcinoma OR prostate neoplasm) AND (breast cancer OR breast carcinoma OR breast neoplasm) AND (family history)’.Studies were considered eligible if they (1) were published in the English language; (2) had full text available; (3) evaluated the relationship between first-degree family history of female breast cancer and prostate cancer risk; (4) provided risk estimates with confidence intervals (CIs) or available data to calculate these associations; and (5) were cohort, cross-sectional, and case-control studies.Two investigators independently extracted data using a standard data collection form. The data extracted from each study included the following: first author, publication year, study design, country of the study population, sample size, reported primary outcome, follow-up duration, hazard ratio or odds ratio, and relative risk and 95% confidence intervals (CIs) with and without adjustment and adjustment factors.Two independent reviewers evaluated the quality of the included studies according to the Newcastle-Ottawa scale (NOS) . The scahttps://gradepro.org/). The quality of evidence was evaluated according to risk of bias, inconsistency, indirectness, imprecision of the results, and publication bias. The quality of evidence for the main outcome was classified into four grades: very low, low, moderate, and high.The quality of evidence for outcomes was evaluated by two investigators independently using GRADEpro Guideline Development Tool .The primary outcome was relative risks for prostate cancer incidence. Subgroup analyses of the primary outcome were conducted based on the study design, region, and quality (adjustment vs no adjustment). For each study, risk ratio for prostate cancer with the 95% CI was computed. The random effects model was used to compute the pooled risk ratio. Heterogeneity between studies was evaluated using the chi-square-based Q test and IThe systematic search of articles published before December 31, 2018, identified 1554 articles. After screening titles and abstracts, we obtained 61 study reports for full-text review. After a full-text review, we finally included 18 published reports comprising 17,004,892 individuals for analysis , 21–37 (2 = 28.70%)  (Table 2 = 48.90%) and pooled case-control studies  (Table 2 = 40.00%) and ≥ 50   with moderate-quality evidence  (Table 2 = 0.00%), but no association between history of breast cancer in mothers only and prostate cancer risk was observed in the five pooled case-control studies  . A family history of breast cancer in mothers only was associated with prostate cancer incidence  with moderate-quality evidence  (Table 2 = 8.40%) and pooled case-control studies  . A family history of breast cancer in sisters was associated with prostate cancer  with moderate-quality evidence  . A family history of breast cancer in daughters only was not associated with prostate cancer . The increased risk of lethal prostate cancer was observed in individuals with family history of female breast cancer in first-degree relatives and in mothers only; however, no association was found between family history of breast cancer in sisters only and risk of lethal prostate cancer, with low-quality evidence  and funnel plots (Table A sensitivity analysis was conducted for prostate cancer risk by excluding individual studies each time, and the results showed no individual study influenced the overall RRs Fig. , indicatts Table , Fig. 6.ts Table , Fig. 6.Eighteen studies involving 17,004,892 participants met the inclusion criteria and were eventually included in our meta-analysis. The findings of this review suggest that prostate cancer risk was increased in individuals with a family history of female breast cancer in first-degree relatives, in mothers only and sisters only. Importantly, we observed increased lethal prostate cancer risks in individuals with family history of female breast cancer in first-degree relatives and mothers only, but not in sisters only. These findings are of great significance because the underlying pathogenesis of prostate cancer is still unknown and may help in screening, earlier diagnosis, and management of prostate cancer.Prostate cancer pathogenesis includes both heritable and environmental causation –40. FamiIn the subgroup meta-analyses based on the study region, a family history of female breast cancer in first-degree relatives was associated with prostate cancer risk in Europe, America, and Asia. A family history of breast cancer in mothers only and sisters only was associated with prostate cancer risk in America, while no significant association was found in Europe and Asia. A family history of breast cancer in daughters only was not associated with prostate cancer risk in Europe, America, and Asia. However, these results need to be interpreted with caution because the number of studies reported in Europe and Asia was relatively small; thus, more studies are warranted to further investigate the potential relationships between family history of female breast cancer and prostate cancer risk in Europe and Asia. In the subgroup meta-analyses based on the study design, a family history of breast cancer in first-degree relatives and sisters only was associated with prostate cancer risk in cohort and case-control studies. A family history of breast cancer in mothers only and daughters only was associated with prostate cancer risk in cohort studies, but not in case-control studies. It is considered that these negative associations were attributed to the limited number of studies included in the meta-analysis.In our analysis, we observed that men with a family history of female breast cancer have a higher risk of prostate cancer, including lethal prostate cancer. The underlying mechanisms of the associations are still unclear. A common gene alteration may be responsible for the clustering of prostate and breast cancer. BRCA1 and BRCA2 gene mutations, confirmed to be linked to breast cancer in families , 45, conAs the number of studies increased, we could perform multiple subgroup analyses to assess heterogeneity and publication bias. To our knowledge, our study was the first systematic literature review with a meta-analysis to evaluate the relationship between family history of female breast cancer and prostate cancer risk. The large sample size is another important strength of this study. The heterogeneity and publication bias in this meta-analysis are small. Moreover, we rigorously used the GRADE approach to assess quality of evidence for the main outcome. However, this study has several limitations. First, there were too few studies to draw a definitive conclusion for the risk of lethal prostate cancer in men with a family history of breast cancer. More prospective cohort studies that evaluate the incidence and prognosis of prostate cancer in men with a family history of female breast cancer are needed. Second, the results showed that the risk of prostate cancer was not significant in individuals with family history of female breast cancer in first-degree relatives diagnosed with breast cancer at the age of < 50 and ≥ 50 years. The results need to be interpreted with caution because only two studies reported these associations in these analyses. Finally, due to the lack of relevant information in the included studies, we did not estimate the risk of early-onset prostate cancer in men with a family history of female breast cancer.Therefore, the results of this meta-analysis indicate that a family history of female breast cancer in first-degree relatives was associated with an increased risk of prostate cancer, including lethal prostate cancer. These findings reinforce the importance of family history of female breast cancer in prostate cancer risk, beyond the roles of family history of prostate cancer. Further detailed work is needed to better investigate the mechanism of these associations and assess the association between family history of female breast cancer and prostate cancer progression and prognosis.


As breast cancer represents a major morbidity and mortality burden in the U.S., with about one in eight women developing invasive breast cancer over her lifetime, accurate low-cost screening is an important public health issue. First-degree family history, often simplified as a dichotomous or three-level categorical variable (0/1/>1) based on number of affected relatives, is an important risk factor for many conditions. However, detailed family structure information such as the total number of first-degree relatives, and for each, their current or death age, and age at diagnosis are also important for risk prediction.We develop a family history score under a Bayesian framework, based on first-degree family structure. We tested performance of the proposed score using data from a large prospective cohort study of women with a first-degree breast cancer family history. We used likelihood ratio tests to evaluate whether the proposed score added additional information to a Cox model with known breast cancer risk factors and the three-level family history variable. We also compared prediction performance through Receiver Operating Characteristic (ROC) curves and goodness-of-fit testing.Our proposed Bayesian family history score improved fit compared to the commonly used three-level family history score, both without and with adjustment for other risk factors . AUCs of ROC curves for the two models were similar, though in all cases were higher after addition of the BFHS.Capturing detailed family history data through the proposed family history score can improve risk assessment and prediction. Such approaches could enable better-targeted personalized screening schedules and prevention strategies. The American Cancer Society recommends routine screening mammography for women above age 45 . HoweverFamily history is well-known as an important risk factor for breast cancer, reflecting joint effects of environment, lifestyle, and genetics. A woman’s breast cancer risk doubles if she has at least one first-degree female relative with breast cancer, with higher relative risk if she is younger and/or if affected relatives were diagnosed at a young age ,9. Studip = 0.0143), a distinction obscured by using a single “>1” category.Although these aspects of family history are known to correlate with breast cancer risk, in many prediction models, detailed family history information is simplified into a single categorical variable based on the number of breast cancer cases among first-degree female relatives. The Gail model uses a three-level categorical variable (0/1/>1) that could lead to loss of predictive power compared to more informative (but harder to ascertain) scores. In Our objective was to develop a risk assessment tool that quantifies such differences by better incorporating details of family structure and history while still being based on easily-collected information that can be used for wider population screening. We focus on first-degree relatives because reporting for them is likely to be more accurate and complete than that for second-degree relatives, even though a full pedigree might contribute additional information.https://sisterstudy.niehs.nih.gov), a prospective study of women with positive family history of breast cancer [in situ (DCIS), were self-reported and confirmed using medical records. We focused on non-Hispanic white women only in this analysis, restricting family history information to first-degree female relatives in order to best demonstrate comparisons to existing, well-validated methods that use the simple 0/1/>1 score. For families with multiple participating women, we randomly selected one to avoid dependencies. About 6.5% of Sister Study participants reported having half-sisters. For the purpose of our analysis, half-sisters were considered second-degree relatives thus omitted from consideration. Our final analysis cohort consisted of 37,720 women, of whom 814 (2.2%) reported only a half-sister with breast cancer and thus had no affected first-degree female relatives.We used data from The Sister Study , and mimics the real-world setting. On the other hand, pure risk is defined to be the probability of developing the disease of interest in the absence of all other competing risks . For a detailed explanation and illustrative example, consult Gail .pure family-specific breast cancer risk p follows a Beta prior distribution, with parameters satisfying two constraints. First, the expected value of the population pure lifetime breast cancer risk (comprising both invasive and in situ cancers), p0 = α/(α+β), was set to 0.197 based on a calculation from Surveillance, Epidemiology, and End Results (SEER) (April 2017 Data Release) cancer registry data [absolute lifetime risk of invasive and in situ breast cancer of 0.149 based on the same SEER data. Second, the pure lifetime risk of breast cancer given an affected first-degree female relative was assumed to be double compared to a cancer-free family [α and β as 1.184 and 4.828, respectively. We further assume the family-specific hazard function is λp(t) = f(p)λ0(t), where t denotes age, λ0(t) is the population baseline hazard, and the effect of family history is multiplicative through f(p). With this information, we can fully specify the likelihood.We assume that for each family, the lifetime try data  whereby e family ,9. Thesen first-degree female family members, we observe  for the ith female relative, i = 1, 2,…,n, where Xi = min is the minimum of her censoring time (Ci) (age time at which we ascertain her status) and age time of breast cancer onset (Ti), and di = I(Xi = Ti) is an indicator of whether she was diagnosed with breast cancer. Through the likelihood, we incorporate breast cancer history of family members up to their current age, death age, or diagnosis age, with their breast cancer experience being weighted by their aggregated risks up to their censoring or diagnosis ages.For a specific family with We use the posterior expectation of the family-specific lifetime risk Here, λp(t). Our score can be used as a covariate , together with other variables, to represent the family structure and history. Finally, this score is easily updated as time passes and further breast cancer cases occur, enabling improved future prediction.One advantage of our score is that additionally includes the BFHS. A likelihood ratio test is performed on these nested Cox models in order to determine whether inclusion of the BFHS significantly improves the fit of the model in the presence of the particular covariates. The covariates used in these three sets of models are as follows:To compare the three-level family history score (0/1/>1) and our proposed Bayesian family history scores (BFHS), we focus on three sets of paired Cox models with time-to-breast cancer as the outcomes. In each pair, model (a) uses the three-level 0/1/>1 score as the family history covariate (in conjunction with specified other known breast-cancer covariates), and model (b) Model 1: family history variable(s) onlyModel 2: family history variable(s), Gail model-identified covariates and interactionsModel 3: family history variable(s), Gail model-identified covariates and interactions, and baseline menopause status and BMI categoryThe detailed covariate definition in Models 2 and 3 are slightly different from the original Gail model as follows. Model 2 adjusts for known breast cancer risk factors as in the Gail model: baseline age (<50 vs. ≥50), age at menarche , number of prior biopsies , age at first live birth , or ≥30), an interaction between baseline age and number of biopsies, and an interaction between age at first live birth and three-level family history. In the original Gail model, all categorical risk factors were treated linearly, with risk ratios assessed proportionally according to ordering. However, given the large sample size of the Sister Study, we were able to include these covariates and interactions categorically. A likelihood ratio test suggested that using them categorically provided a better fit for our data (p = 0.003). Accordingly, all models considered in this analysis used categorical covariates and interactions to best utilize available data. Model 3 included the additional covariates of baseline menopause status and BMI category, which have been shown in prior studies to be significantly associated with breast cancer risk , and werAs our cause-specific Cox models estimate pure risks and are thus subject to the competing risk of death, to make a fair comparison to the Gail model, we must estimate absolute risks based on our data. We first estimated age-specific mortality rates by using a LOESS smoothed curve on Sister Study data. As the Sister Study population is healthier than the population at large, the estimated mortality rates are generally lower than equivalent national mortality rates; 363 (1.0%) participants died within five years without having developed breast cancer. We then calculate the absolute risk with an approach similar to the one by Gail et al. in prior analysis  using th2 statistic was calculated ask = 1, 2, …, 10 indexes the risk bins, Ok denotes the observed number of breast cancer cases, and nk the total number of women assigned to bin k by the model . There were 853 (2.3%) women who were lost to follow-up prior to five years without dying or developing breast cancer in the five year interval. For these women, we weight their contribution to the denominator by the ratio of the absolute risk they experienced during follow up to their five-year absolute risk. For our purposes, a less significant p-value indicates better fit. As a base comparator, we examined the performance of the Gail model itself (Model 0) as implemented by the Breast Cancer Risk Assessment Tool, available online from NCI (https://www.cancer.gov/bcrisktool/).We further compared the prediction performance of the Bayesian score with the three-level family history score by using these calculated five-year absolute risks for Models 1, 2, and 3, comparing models in which the family history variable used was the three-level 0/1/>1 variable to those in which we additionally included the BFHS. We binned women into 1% risk categories ranging from 1% (or lower) to 10% (or higher) according to the calculated five-year absolute risks, and plotted bar graphs of the observed number of women in each category who developed breast cancer within five years against the expected number as calculated by the candidate models. We additionally provided 95% exact binomial confidence intervals for each bin. To assess overall goodness-of-fit, a χFor the above goodness-of-fit calculations we directly estimated the baseline hazard from our data given that our cohort of women with positive family history is likely at a higher risk of breast cancer; however, in the absence of this contextual information, one can estimate the baseline hazard using SEER data. To avoid overfitting on our dataset during this comparison, we utilized ten-fold cross-validation to fit these models using the same training and testing datasets for all sets of models.To evaluate the predictive performance of each model, receiver operating characteristic (ROC) analysis was also performed on the calculated five-year absolute risks. As these tests can potentially be used as screening tools for the general population, we focused on thresholds that correspond to 90% or greater specificity, to limit false positives. We calculated area under the curve (AUC), partial area under the curve (pAUC) and sensitivities. We did not have sufficient numbers of low risk women to evaluate low-risk thresholds due to the nature of our study participants.Among the 37,720 participants used for analysis, 2,181 (5.8%) developed breast cancer through July 2017 (Data Release 6.0), including both invasive breast cancer and DCIS. 1,352 of these cases were diagnosed within 5 years of enrollment. On average, women had 3.9 (range: 0–16) first-degree female relatives, including 1.3 (range: 0–5) breast cancer cases at baseline; the median follow-up time was 7.89 years (range: 0.01–11.62). Full baseline characteristics are summarized in 814 (2.2%) women had only half-sisters with breast cancer , 26,883 (71.3%) had exactly one first degree female relative (full sister or mother) with breast cancer, and 10,023 (26.6%) had at least two first degree female relatives with breast cancer. Among these three groups, the mean (SD) BFHS were 0.177 (0.01), 0.288 (0.02), and 0.401 (0.04), respectively . When restricting to participants with exactly three first-degree female relatives, as in Adding the BFHS to a univariable model that used only the 0/1/>1 family history score significantly improved the fit of the Cox model . This conclusion also held in multivariable models that adjusted for Gail-identified risk factors , and in the model that included additional covariates and interactions . By contrast, in the “reverse” analysis in which we instead considered adding the three-level categorical family history to Cox models that already included the BFHS, the fit was not significantly improved .Figs AUCs, pAUCs (specificity 90%-100%), and sensitivities at 90% specificity for the candidate models and the original Gail model are summarized in Finally, the BFHS itself has a natural interpretation as the posterior family-specific lifetime pure breast cancer risk. Our proposed risk score captures detailed information about family structure and history vis-à-vis breast cancer. When applied to a large cohort of women with a first-degree family history of breast cancer, likelihood ratio tests showed that our score provided additional predictive information to a model that used the three-level family history score, both with and without inclusion of known risk factors. Hence, we emphasize the importance of collecting this family history information for population screening purposes and urge the research community to do so in future studies.ad hoc in their formulation and do not have the benefit of being likelihood-based to lead to a directly interpretable equivalence to family-specific lifetime risk. Rieger and Mansmann have also taken a Bayesian approach in the context of risk modeling in colon cancer in order by treating family history as a Bayesian network modeled using factor graphs [An advantage of the Bayesian score is its foundation in likelihood-based methods that take into consideration observed and expected cases among families. Other scores based on observed and expected cases have been proposed: Yang et al. proposed a standardized statistic that aimed to characterize deviations from expected risk for each family; Brewer et al. proposed a standardized incidence ratio as a family history score ,18. Hower graphs . SimilarAlthough one could use the Bayesian score as a multiplier to the estimated SEER population hazard to directly estimate age-specific risk, use of the Bayesian score as a covariate in a Cox model allows incorporation of additional covariate effects. Such covariates may vary through time—due to the nature of the BFHS, it may be constructed as a time-varying covariate itself, in addition to other potentially time-varying covariates such as menopause status. An advantage of the Cox model is the flexible way in which such time-varying covariates can be used to inform risk prediction.In our analysis, we focused on non-Hispanic white women with a family history of breast cancer, and hence estimated hazard ratios from our model are not immediately generalizable to the wider population. Our analysis results suggested that BFHS is useful in improving accuracy of breast cancer screening in women with family history of breast cancer. However, the proposed score and methodology is generalizable to wide populations: all that is needed is an estimate of the baseline hazard for the population of interest. This can be either estimated using empirical data at hand , or by using population estimates . Although estimated hazard ratios may differ, the underlying methodology remains the same. Our approach may also be applied to any disease where family history is an important risk factor and for which necessary surveillance data exist, such as in the previously mentioned application of colon cancer .status of first-degree female relatives, we believe that the practical impact of misreporting error will be minor .One limitation is that our proposed score only uses family history of first-degree female relatives, even though information from second-degree relatives may be informative as well. On the other hand, cancer in more distant relatives may be both biologically less informative and less well-reported, detracting from the usefulness of this addition. Though self-reporting error is an issue inherent in any family history-based risk assessment score, we feel that use of information only from first degree female relatives mitigates the recall error problem. Additionally, as long as the misreporting error is only in the ages and not in the status of family members, the impact to the calculated BFHS and therefore risk prediction is relatively minor. To provide a concrete example, suppose a woman aged 51 has three first-degree female relatives, two of them breast cancer free at ages 78 and 53, and one who was diagnosed with cancer at age 55. Suppose the participant misremembers her sister’s diagnosis age and reports 52. This misreporting results in BFHS changing from 0.2844 to 0.2861, which corresponds to the hazard changing to 1.013 times that for the correct family structure when using our best fitting model (3b). Suppose she additionally misremembers one relative’s age as 76 instead of 78. The BFHS changes to 0.2863, which corresponds to hazard approximately 1.015 times that for the correct family structure when using our best fitting model (3b). Since it is unlikely that a woman will misreport the prima facie, we believe that achieving such an AUC using only family history and a few additional covariates–in the absence of detailed genetic typing–is impressive for a disease with as varied an etiology and risk factors as breast cancer. It is our intention that out score, which only uses information that is relatively easy and accurate to obtain, may be useful to calculate as a simple “first line calculation” that is more accurate than is currently available. Use of a predictive model with our Bayesian score may provide a woman with a general sense of her breast cancer risk and so she can make a more informed decision regarding whether to go for more personalized genetic testing or screening by an expert.From a practical standpoint, although the AUC in Model 3b of 0.70 may seem to be relatively low Although women in high risk bins comprise a relatively small portion of the population, the total number of women in the U.S. population who could potentially be assigned to these bins may be large. Thus, improved identification of high risk women is important in minimizing the total health care burden. Still, our proposed score and prediction model apply to women regardless of family history. Unfortunately, our ability to evaluate the performance of our risk prediction approach in very low risk women was limited, because most Sister Study participants are at elevated risk by design. However, our proposed score does have the ability to differentially categorize women at low risk based on family structure. For example, compared to a woman from a larger family with older female relatives without breast cancer, a woman from a small family with younger and as yet cancer-free relatives will have a higher Bayesian score, a subtlety missed in conventional family history scores.The biggest improvements in risk prediction were in the highest and lowest risk groups. Since the group of women at relatively low risk is a much larger group of women in the general population, use of our proposed score may lead to reduced screening costs, reduced false positive rates, and lower healthcare burden due to unnecessary preventative screening and over-diagnosis. Because we could not directly evaluate performance in low risk women, we explored using existing cohorts focusing on breast cancer as a primary outcome. Unfortunately, most studies have not collected the family history data required . Due to potential cost-benefits in the low risk population, validation in other population-based cohorts is recommended.In summary, detailed first-degree family history data may be considered as a combined surrogate for genetics, lifestyle, and shared environment, and can be easily assessed and updated through questionnaires. Compared to models based on genetics and full pedigree, information needed for calculating our score is easy and inexpensive to obtain. Currently used categorical family history variables lose valuable information that is captured by the Bayesian score. We recommend that future studies collect more detailed first-degree family history data and make use of that information in risk prediction models.S1 Fig(PDF)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.


Limited evidence, mostly from studies in Western populations, suggests that the prognostic effects of lifestyle-related risk factors may be molecular subtype-dependent. Here, we examined whether pre-diagnostic lifestyle-related risk factors for breast cancer are associated with clinical outcomes by molecular subtype among patients from an understudied Asian population.In this population-based case series, we evaluated breast cancer risk factors in relation to 10-year all-cause mortality (ACM) and 5-year recurrence by molecular subtype among 3012 women with invasive breast cancer in Sarawak, Malaysia. A total of 579 deaths and 314 recurrence events occurred during a median follow-up period of ~ 24 months. Subtypes  were defined using immunohistochemical markers for hormone receptors and human epidermal growth factor receptor 2 (HER2) in conjunction with histologic grade. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between risk factors and ACM/recurrence were estimated in subtype-specific Cox regression models.p value < 0.05). Among luminal A-like patients only, older age at menarche [HR (95% CI) ≥15 vs ≤ 12 years = 2.28 ] and being underweight [HRBMI < 18.5kg/m2vs. 18.5–24.9kg/m2 = 3.46 ] or overweight [HR25–29.9kg/m2vs. 18.5–24.9kg/m2= 3.14 ] were associated with adverse prognosis, while parity/breastfeeding [HRbreastfeeding vs nulliparity = 0.48 ] and older age at FFP [HR > 30 vs < 21 years = 0.20 ] were associated with good prognosis. For these women, the addition of age at menarche, parity/breastfeeding, and BMI, provided significantly better fit to a prognostic model containing standard clinicopathological factors alone . Overall, the results were similar in relation to recurrence.We observed heterogeneity in the relationships between parity/breastfeeding, age at first full-term pregnancy (FFP), family history, body mass index (BMI), and tumor subtype (Our finding that breastfeeding and BMI were associated with prognosis only among women with luminal A-like breast cancer is consistent with those from previously published data in Western populations. Further prospective studies will be needed to clarify the role of lifestyle modification, especially changes in BMI, in improving clinical outcomes for women with luminal A-like breast cancer. In addition to impacting incidence, lifestyle and environmental risk factors for breast cancer may influence disease progression. Several studies have previously evaluated this question, with mixed results. While some studies have documented older age at menarche –3, earlyFindings from expression profiling studies have been used to classify breast cancers into intrinsic subtypes -enriched, basal-like, and normal-like subtypes), which were associated with different prognoses  and can Three previous studies have evaluated the relationship between breast cancer risk factors and survival according to molecular subtype, one among women in Seoul, South Korea  and the Despite racial and geographic variations in the incidence, presentation, and outcome of breast cancer; so far, most investigations on risk factors in relation to tumor subtypes and survival have been conducted in European populations. This analysis, therefore, aims to evaluate the association between breast cancer risk factors and tumor molecular subtypes, defined by hormone receptors and HER2 in conjunction with histologic grade; and to examine the relationship between risk factors and survival by molecular subtype among women in Sarawak, Malaysia.Sarawak is a Malaysian state on Borneo with a multiethnic composition, comprising of native Borneo populations (51%), Chinese (25%) and Malays (24%) . Overall+/PR− or ER−/PR+) and/or those that homogeneously or heterogeneously express hormone receptors (i.e. ER+ and/or PR+) but are also high proliferating (high KI67 or grade 3) and/or HER2+ are classified as luminal B. In keeping with this definition, we utilized ER, PR, and HER2 in addition to histologic grade . To test for associations between risk factors and molecular subtypes, we constructed a polytomous logistic regression model with tumor subtype as the outcome  and risk factors , 13, 14, and ≥ 15 years (late menarche)], parity and breastfeeding , age at FFP , family history [yes and no (reference category)], and BMI  as explanatory variables, with adjustment for age at diagnosis  and ethnicity .We categorized risk factors based on what is the convention for each variable and in accordance with what has been published in large-scale studies of breast cancer , 26. We P <  0.1 in the basic model were to be mutually adjusted for in a multivariable model that included the standard prognostic factors mentioned above. Using likelihood ratio (LR) test, we compared this model with one containing only the clinicopathological factors. For sensitivity analysis, we conducted survival analysis for women stratified into two age groups (< 50 yrs and ≥ 50 yrs). Also, we performed additional sensitivity analysis by excluding women with stage IV disease from our multivariate analyses for both all-cause mortality and recurrence. Results were very similar from these sensitivity analyses as compared to analyses including all women and we therefore presented results from all patients. All analyses were two-sided and performed using Stata statistical software version 14.0 .The association between breast cancer subtypes and all-cause mortality/recurrence was determined using Kaplan-Meier survival curves and Cox-proportional hazards regression models, which included adjustments for standard prognostic parameters including age at diagnosis, ethnicity, BMI, histologic grade, TNM stage I–IV , systemic therapy (endocrine (tamoxifen or aromatase inhibitor (AI)) and chemotherapy), radiotherapy and surgery. Follow-up started at diagnosis of breast cancer and ended at time of event (recurrence/death) or censoring . For all-cause mortality, we censored at 10 years because this is the threshold at which most breast cancers are, by convention, considered cured in the absence of recurrence or death. We adopted a two-step approach in our survival analyses. In the first step, each of the above risk factors was modeled separately in basic models adjusted for standard prognostic factors separately for each tumor subtype. To test for heterogeneity in risk factor and survival relationships by subtype, we included an interaction term between each risk factor and tumor subtype. Violation of the proportionality assumption of the hazard model was tested by modeling each risk factor as a time-varying covariate. In the second step, it was decided, a priori, that factors that were associated with survival with 2. The majority of the patients were Chinese (48%) and had early-stage  and HR-positive (66%) tumors , age at FFP , breastfeeding practices , family history  and BMI  differed by subtype. No differences were observed in the distributions of age at menarche, age at menopause and parity according to subtype. The frequencies of all clinicopathological parameters differed by subtype, with low-grade, small, early-stage and node-negative tumors being more frequent for the luminal A-like subtype ; P value = 0.02] and triple-negative  tumors were significantly less likely to be obese than those with the luminal A-like subtype.Table P trend = 0.08) but not the other subtypes. Results were similar in basic models for recurrence  = 0.06]; parity/breastfeeding ; older age at FFP ; and being underweight  or overweight  remained significantly associated with 10-year all-cause mortality in women with the luminal A-like subtype (Table 2 (8df) = 21.78; P value = 0.005]. In general, the results were consistent in relation to recurrence   ≥15 years vs ≤ 12 years = 2.28 ; N = 719), we observed an inverse association between each breastfeeding duration category and all-cause mortality  and recurrence . Among women who breastfed, all-cause mortality did not significantly vary by breastfeeding durations  but women who breastfed for > 10 months tended to have better recurrence outcomes .When we examined the association between duration of breastfeeding and all-cause-mortality/recurrence for luminal A-like cases with complete information on breastfeeding duration  seem to show higher frequencies among women with the luminal A-like subtype compared with women with other subtypes. Further, we found that age at menarche, breastfeeding, and BMI were independent prognostic factors for both overall mortality and breast cancer recurrence but only for women with the luminal A-like subtype, which had better survival and recurrence outcomes than the other subtypes.− (HER2-enriched and triple-negative) than luminal A-like tumors, which may be reflective of population/ethnic differences. In line with this hypothesis, a previous study conducted in South Korea [Our findings that nulliparity and older age at FFP were more prevalent in luminal A-like patients are consistent with those reported in studies in Western countries , 27. Howth Korea  also shoth Korea  reportedth Korea .The prevalence of obesity is still much lower in most Asian populations compared with other race/ethnicity groups. In contrast to the reduced breast cancer risk associated with higher BMI among premenopausal Western women, obesity is associated with increased risk for both premenopausal and postmenopausal Asian women –35. The + tumors which have better prognosis than HR− tumors [+, expression of hormone receptors in tumors occurs in a spectrum. Whereas some tumors have very high expression levels, others have lower levels despite crossing the threshold for consideration as HR+. Due to differences in cumulative lifetime exposure to endogenous estrogens, luminal A-like tumors occurring among women with late menarche may have lower levels of hormone receptor expression, hence worse survival/recurrence outcomes, than those occurring among women with early menarche. Indeed, this is consistent with the finding by Song et al. that longer duration of endogenous estrogen exposure was associated with better survival [Most epidemiological studies considering the prognostic significance of age at menarche in breast cancer have treated the disease as a homogeneous entity, and results from these studies are largely conflicting –3, 8, 38− tumors , 41. Altsurvival . On the Our findings of breastfeeding being associated with better outcomes are generally consistent with previous reports , 12, 42.Results from several studies, summarized in two comprehensive reviews and meta-analyses , 47, areStrengths of this study include: a population-based breast cancer case series in an understudied Asian population and the collection of detailed questionnaire information, which allowed us to account for various confounding variables including sociodemographic factors. Several limitations of this study should be noted. First, we did not include controls in our study and therefore our case-case comparisons could not be translated into relative risk estimates. Second, relatively small sample sizes for some of the subtypes may have affected our power to detect significant associations. Third, data on the specific cause of death were not available and, therefore, we only evaluated risk factors in relation to all-cause, but not breast cancer-specific, mortality even though some of the risk factors, such as BMI, are important predictors of death from other causes . NonetheIn conclusion, our data indicate that risk factors for breast cancer are differentially associated with tumor subtypes and exert subtype-specific influence on survival/recurrence from the disease. Specifically, we observed that menarche after the age of 15 years, FFP after 30 years, underweight or overweight BMI, and breastfeeding practices were associated with survival/recurrence only among women with luminal A-like tumors. These findings are supportive of the prognostic value of reproductive and lifestyle-related factors in tumors with biologically favorable profiles, and could have implications for clinical counseling and for the development of subtype-specific prognostic tools. Future prospective studies are needed to delineate the role of lifestyle modification, especially changes in BMI, in improving clinical outcomes for women with luminal A-like breast cancer.