A family history of later-onset breast cancer (FHLBC) may suggest multi-factorial inheritance of breast cancer risk, including unhealthy lifestyle behaviors that may be shared within families. We assessed whether adherence to lifestyle behaviors recommended for breast cancer prevention--including maintaining a healthful body weight, being physically active and limiting alcohol intake--modifies breast cancer risk attributed to FHLBC in postmenopausal women.Breast cancer outcomes through August 2003 were analyzed in relationship to lifestyle and risk factors collected by questionnaire during enrollment (between 1993 and 1998) of 85,644 postmenopausal women into the Women's Health Initiative Observational Study.During a mean follow-up of 5.4 years, 1997 women were diagnosed with invasive breast cancer. The rate of invasive breast cancer among women with an FHLBC who participated in all three behaviors was 5.94 per 1,000 woman-years, compared with 6.97 per 1,000 woman-years among women who participated in none of the behaviors. The rate among women with no FHLBC who participated in all three behavioral conditions was 3.51 per 1,000 woman-years compared to 4.67 per 1,000 woman-years for those who participated in none. We did not observe a clinically important departure from additive effects .Participating in breast healthy behaviors was beneficial to postmenopausal women and the degree of this benefit was the same for women with and without an FHLBC. Nearly 15% of postmenopausal women in the US report breast cancer in a first-degree relative . Most faPhysical inactivity ,7, excesWomen with a first-degree family member who developed breast cancer at the age of 45 years or more are referred to as having a family history of later-onset breast cancer (FHLBC). If the impact of having an FHLBC were modified by preventive behaviors, such findings would have important implications for preventive counseling of these women. This study investigates the degree to which participating in behaviors endorsed by leaders ,13 in brWe performed analyses of the Women's Health Initiative Observational Study (WHI-OS), which began in 1993. Exposure and covariate information was obtained via baseline questionnaires, blood analyses, and anthropomorphic measurements. Each participant was contacted annually to identify hospitalizations or diagnoses of diseases pertinent to the WHI-OS, including breast cancer diagnoses. All diagnoses were confirmed via medical records, diagnostic procedures, and pathology reports.Postmenopausal women were recruited to the WHI-OS from populations of women living near the 40 WHI clinical centers across the US. The most common method of recruitment was mass mailing to targeted groups. Women were eligible for participation in the WHI-OS if they met all of the following conditions: they were between the ages of 50 and 79 years at enrollment (1993 to 1998), they were postmenopausal, they had a life expectancy of greater than 3 years, they had no conditions that made longitudinal participation unlikely , and they were not already participating in another randomized clinical trial. For this study, additional exclusion criteria included the following: personal history of breast cancer, history of unilateral or bilateral mastectomy, or a family history of early-onset breast cancer (diagnosed before the age of 45 years) in a first-degree relative at enrollment. We excluded women in the last of these groups to maintain focus on family histories that reflect shared behaviors, environments, and genetics--and thus that plausibly are influenced by lifestyle behaviors--rather than family histories that are likely to represent genetic influences that are more dominant.Women who reported that a mother or full sister developed breast cancer at the age of 45 years or more were defined as exposed to an FHLBC. All other women were defined as unexposed.Cases are defined as those women who developed invasive breast cancer between enrollment (1993 to 1998) and the end of follow-up (August 2003). Case status was determined via pathology reports by a centralized WHI team of trained adjudicators. One case of invasive breast cancer was first identified at the time of death. The reported cause of death for this case was coronary heart disease. Therefore, we considered this case of breast cancer to be an incidental finding. Since autopsy was not routinely performed on all WHI-OS participants who died, we chose to censor this participant at the time of her death and not consider the event to be a case.Each of the following study variables was obtained by self-report on study questionnaires at baseline: age, household income, educational attainment, menstrual history, reproductive history, personal medical history, exogenous estrogen use, number of sisters, personal history of breast biopsies, usual exercise regimen, usual intake of alcoholic beverages, and smoking history. Alcohol intake was categorized as "breast healthy" if participants drank fewer than seven drinks per week. Participants were asked to report usual frequency, duration, and intensity of exercise. Exercise was categorized as "breast healthy" if participants exercised for at least 20 minutes at moderate/vigorous intensity at least five times per week. Height (without shoes) was measured at baseline. Weight (in light clothing but without shoes) was also measured. Women whose BMI was normal (between 18.5 and 24.9) at baseline measurement and who reported having maintained a BMI of less than 25 during their non-pregnant adult lifetime were categorized as maintaining a "breast healthy" body weight.We described the frequency and distribution of all study variables. We categorized participants into one of four categories on the basis of their participation in the three breast healthy behaviors: exercised moderately/vigorously for at least 20 minutes at least five times per week, maintained a normal body weight, and drank no more than one alcoholic beverage per day. The range of classification was 0 (complete non-participation) to 3 (complete participation). We chose to focus on thee types of activities  because these are fully endorsed as modifiable lifestyle risk factors for breast cancer by the American Cancer Society . Other bWe performed the following primary analyses for both adherence and FHLBC to evaluate whether these factors were associated with the incidence of invasive breast cancer in the WHI-OS. We plotted cumulative hazard curves to visualize the relation, and we fit a Cox proportional hazards model to determine the hazard ratio (HR) and 95% confidence interval (CI). We confirmed proportional hazards by observing similar and non-intersecting hazard curves for each set of exposure groups.For each of the eight strata of adherence by FHLBC, we calculated the rate of invasive breast cancer. Within the four strata of adherence, we calculated the risk attributable to FHLBC as the difference in rate between those with FHLBC and those without FHLBC, divided by the rate in those with FHLBC. To assess whether the relation between FHLBC and invasive breast cancer is modified by adherence, we compared the strata exhibiting the greatest expected contrast: complete participation and complete non-participation. For this contrast, we calculated the following interaction contrast (IC):where FHLBC = first-degree family history of later-onset breast cancer  and BHB = breast healthy behavior .The IC indicates whether the two exposures are synergistic (IC >0), antagonistic (IC <0), or independent of one another on the additive scale (IC = 0). Our judgments about the presence or absence of interaction were based on the degree to which IC differs from 0 and the precision of that estimate. As with our previous work , we chosConfounding of the relation between the outcome and either component cause  can bias the crude IC ,16. NeitA first-degree family history is not, in itself, the active exposure. It is merely a marker for genomic predisposition to breast cancer. Genomic risk is often identified by the occurrence of breast cancer in a sister. It is possible that a participant who has no sister surviving into adulthood has the same genomic predisposition as a woman with a known FHLBC, but this participant has no opportunity to identify this exposure through an affected sister. Therefore, we calculated an IC that was restricted to participants reporting at least one biologic sister who reached adulthood. To establish a participant's eligibility for classification of an FHLBC, we would ideally determine whether her sisters reached the age of 45 years. However, the WHI-OS dataset asks only whether a participant's sister or sisters reached adulthood.The Memorial Hospital Committee for Human Subjects in Research approved this analysis. As required by the WHI protocol, informed consent was obtained from all study subjects.Ninety-three thousand six hundred seventy-six women were enrolled in the WHI-OS. Of these, 5,298 women who reported being told by a physician before enrollment that they had breast cancer, 242 women who had a unilateral or bilateral mastectomy before enrollment, and 2,756 women reporting an early-onset family history of breast cancer (in a mother or sister younger than 45 years old) were excluded.  One point nine percent of participants were lost to follow-up, 2.2% stopped follow-up, and 6.1% were deceased by the end of follow-up.Table complete non-participation versus complete participation = 1.32, 95% CI = 1.03, 1.67) were associated with a greater incidence of invasive breast cancer. The rate attributable to FHLBC did not follow a dose-dependent pattern across strata of breast healthy behavior participation  and absence of breast healthy behavior  did not differ from the crude IC.Age, education, white race, income, mammography adherence before enrollment, hormone therapy use at enrollment, and age at menarche were associated with adherence to the ACS guidelines. However, only the personal history of breast biopsy demonstrated a modest confounding effect on the relation between FHLBC and the incidence of breast cancer Table . The pooAge, white race, mammography adherence before enrollment, history of breast biopsies before enrollment, and hormone therapy use at enrollment were associated with FHLBC, but none of these factors demonstrated a confounding effect on the relation between breast healthy behavior participation and the incidence of breast cancer Table .Fifty-four thousand four hundred seventeen women reported having at least one sister reaching adulthood. When the data were restricted to these participants, the IC differed slightly from the IC among the full sample yet still remained clinically unimportant .We observed that adherence to recommended breast healthy behaviors  did not modify the breast cancer risk attributed to a family history of late-onset breast cancer among postmenopausal women. Therefore, women who participate in breast healthy behaviors appear to derive essentially the same benefits regardless of a family history of late-onset breast cancer. Neither confounding nor exposure misclassification apparently explains our null findings. To our knowledge, no earlier research has evaluated whether adherence to a cluster of behaviors modifies the relation between family history and breast cancer.Some earlier research has addressed the interaction between family history and individual behaviors in their association with breast cancer risk , althougSellers and colleagues  conducte2  versus greater than 27 kg/m2 (overweight), and family history was categorized on the basis of the presence or absence of breast cancer in either a mother or a sister. Compared with the RR estimates of the doubly unexposed group , those of the doubly exposed group, the family history exposed group, and the overweight group are 2.52, 1.54, and 1.27, respectively. This yields an RERI of 0.71, which is greater than the negligible RERI obtained from the cohort of Sellers and colleagues (RERI = -0.15) and the estimate from our study (RERI = 0.02). However, direct comparison with our findings is limited because our exposure classifications  differ substantively from theirs.Using a case-control study design among postmenopausal women from California, Carpenter and colleagues  evaluateOur study has important limitations. Although preventive behaviors are dynamic, this analysis is based on assessment at one time point. It is possible that longitudinal assessment of breast healthy behavior would identify more clearly the distinction between women engaging in sustained participation versus sustained non-participation and, thus, present a stronger contrast. However, lifestyle behaviors are habitual and become relatively stable in adulthood as compared with childhood . Our meaSimilarly, family history is measured only at baseline in the WHI. It is likely that some of our unexposed respondents became aware of a family history during the follow-up period and therefore were misclassified as unexposed. We also expect, however, that the opportunity to influence breast cancer detection is low given the small group of women likely to be misclassified, the short interval during which diagnostic bias might occur, and the thorough diagnostic follow-up on all trial participants. Furthermore, this bias, if present, should occur in both the breast healthy and non-breast healthy strata, thus contributing the same absolute effects on the rate difference estimates and leaving the IC unaffected. We expect, therefore, that any bias due to misclassification of family history is very unlikely to have a substantial impact on our observed IC and null conclusion.despite healthy behavior, thus suggesting greater contribution of genetic and environmental factors among this group. One way to address this concern would be to assess whether a woman's risk attributable to having an FHLBC is influenced by a discordance between her behavior and that of affected relatives. However, the WHI-OS data do not include information about the behavioral norms for women's families or affected relatives and so we could not perform such analyses.Lastly, FHLBC is only a proxy measure for a complex set of breast cancer-relevant exposures shared among families. The exact distribution of shared behaviors, environments, and genes leading to the presence of an FHLBC is likely to differ substantially from one participant to another in this study. Since habitual health behaviors are often shared within families -23, it iGiven the growing societal awareness of, and distress about, the risk of heritable breast cancer ,25, undeBHB: breast healthy behavior; BMI: body mass index; CI: confidence interval; FHLBC: family history of later-onset breast cancer; HR: hazard ratio; IC: interaction contrast; RERI: relative excess risk due to interaction; WHI: Women's Health Initiative; WHI-OS: Women's Health Initiative Observational Study.The authors declare that they have no competing interests.RG, TLL, KJR, HJC, RS, and CBE took part in all analyses and drafts of this manuscript. MR and MLB participated in some analyses and contributed substantially to final drafts and resubmission of this work. MLS and RH took part in data collection as part of the original WHI and contributed substantially to final drafts of this work. All authors read and approved the final manuscript.


It has not been established if dietary factors or nutritional supplements impact on the incidence of cancer in high-risk women. We randomised 1135 women with a BRCA1 mutation to 250 micrograms daily of elemental selenium as sodium selenite, or to placebo, in a double-blind trial. After a median follow-up period of 35 months (range 6 to 62 months), there were 60 incident cases of cancer diagnosed in the selenium-supplemented group, versus 45 cases in the placebo group . Selenium supplementation was not associated with a reduction in the risk of primary breast cancer , of contralateral breast cancer , or of ovarian cancer . The results of this study do not support the recommendation that selenium supplementation should be offered to BRCA1 carriers for chemoprevention.Part II Adnexectomy, genotypes and selenium level as markers of the risk of cancerIn these part we conducted a nested case-control study of 68 women with breast cancer and 17 women with ovarian cancer and 170 controls matched 1 to 2. Cases and controls were matched for age at enrolment, past history of breast cancer, oophorectomy and whether they received selenium supplement or placebo during cancer chemoprevention trial. Combinations of clinical status, genotypes and selenium levels strongly associated with extremely low risk of cancer have been identified.The strongest associations have been found for GPX4 variants:a. all nTT and Se level 60-80µg/l – OR 0.32, p: 0.0009b. all TT and Se level >80µg/l – OR 0.10, p: 0.047c. for carriers without adnexectomy and with TT variant, Se level >80µg/l – OR 0.038, p: 0.014.


However, the role of single nucleotide polymorphisms (SNPs) of miR-101 in breast cancer susceptibility remains unclear. In this study, we genotyped 11 SNPs of the miR-101 genes (including miR-101-1 and miR-101-2) in a case-control study of 1064 breast cancer cases and 1073 cancer-free controls. The results revealed that rs462480 and rs1053872 in the flank regions of pre-miR-101-2 were significantly associated with increased risk of breast cancer . However, the remaining 9 SNPs were not significantly associated with risk of breast cancer. Additionally, combined analysis of the two high-risk SNPs revealed that subjects carrying the variant genotypes of rs462480 and rs1053872 had increased risk of breast cancer in a dose-response manner (Ptrend = 0.002). Compared with individuals with “0–1” risk allele, those carrying “2–4” risk alleles had 1.29-fold risk of breast cancer. In conclusion, these findings suggested that the SNPs rs462480 and rs1053872 residing in miR-101-2 gene may have a solid impact on genetic susceptibility to breast cancer, which may improve our understanding of the potential contribution of miRNA SNPs to cancer pathogenesis.Genetic variants in human microRNA (miRNA) genes may alter mature miRNA processing and/or target selection, and likely contribute to cancer susceptibility and disease progression. Previous studies have suggested that  Breast cancer is the leading cause of cancer-related deaths among women world-wide, with an estimated 1,383,500 new cases and 458,400 deaths in 2008. Although the incidence and mortality rates in developed countries have been decreasing during the past 25 years, both rates have been increasing in many developing countries In recent years, the use of genome-wide association studies (GWAS) to screen disease-associated genetic variants, has led to the successful identification of numerous breast cancer susceptibility regions, supporting a polygenic model of breast cancer susceptibility MicroRNAs (miRNAs) are a class of small (∼22 nucleotides), non-coding single-stranded RNAs, which regulate gene expression by targeting mRNA for deregulation or translational repression et al. first reported that the rs11614913 SNP in the miR-196a2 precursor was associated with survival of patients with non-small lung cancer (NSCLC) and risk of lung and breast cancer miR-196a2, but also influenced the interactions between miR-196a2 and its downstream targets miR-196a2 precursor and breast cancer susceptibility was further confirmed by a research group at Yale University Although the exact mechanisms underlying miRNA deregulation in cancer are not clear, the presence of single nucleotide polymorphisms (SNPs) in miRNA genes, including pri-miRNAs, pre-miRNAs and mature miRNAs, has been shown to influence the processing and/or target selection of miRNAs, thus affecting the risk of cancer MiR-101 belongs to a family of miRNAs involved in various cellular activities, including cell proliferation, invasion and apoptosis miR-101 have been identified on chromosome 1p31.3 (miR-101-1) and chromosome 9p24.1 (miR-101-2). MiR-101 is frequently expressed at low levels in multiple malignancies including breast cancer, hepatocellular carcinoma, glioblastoma, prostate and gastric cancers miR-101 has a tumor-suppressive effect in breast cancer, and miR-101 has been shown to negatively regulate oncogenes including EZH2 and STMN1et al. reported that miR-101 may promote MCF-7 cell growth in an estrogen-independent manner by enhancing AKT activation, suggesting a link between miR-101 and estrogen-independent signaling in estrogen receptor (ER)-positive tumor cells miR-101-associated SNPs in breast cancer risk.miR-101 are associated with the susceptibility of breast cancer in women. To test this notion, we investigated the association of 11 SNPs located in the miR-101 genes with breast cancer risk in a Chinese case-control study.In this study, we hypothesized that polymorphisms of This case-control study was approved by the institutional review board of Nanjing Medical University. The design and performance of current study involving human subjects were clearly described in a research protocol. All participants were voluntary and would complete the informed consent in written before taking part in this research.A total of 1064 breast cancer cases and 1073 cancer-free controls were included in this study, which has been described previously miR-101-1 and miR-101-2 genes (10 kB upstream and downstream) were selected using the International HapMap Project (http://www.hapmap.org), dbSNP (http://www.ncbi.nlm.nih.gov/-projects/SNP/) and UCSC (http://genome.ucsc.edu/) databases. The linkage disequilibrium value (r2<0.8) and minor allele frequency (MAF≥0.05) in the Chinese Han population (CHB) were further applied to screen SNPs. Based on this, 7 tagging SNPs  in the miR-101-1 region and 7 tagging SNPs  in miR-101-2 region were selected. During the process of chip design, three SNPs  were excluded owing to the technical reasons. Therefore, there were totally 11 tagging SNPs were genotyped using the Illumina Infinium® BeadChip (Illumina Inc.) platform in all patient and control samples (n = 2137). Genotype calling was using the GenTrain version 1.0 clustering algorithm in GenomeStudio V2011.1 (Illumina). All SNPs were successfully genotyped with call rates >95%.Tagging SNPs located within the vicinity of mature 2 test and student t test. Associations between the genotypes and breast cancer risk were estimated by computing the odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression analyses. Adjustment factors for the associations included age, age at menarche and menopausal status. The Hardy–Weinberg equilibrium was tested by a goodness-of-fit χ2 test to compare the observed genotype frequencies to expected frequencies among the control subjects. All statistical analyses were performed with Statistical Analysis System software .Differences in the distributions of demographic characteristics, selected variables and genotypes frequencies between breast cancer cases and controls were analyzed by χP>0.05). Compared with control subjects, patients with breast cancer experienced an earlier menarche, later first live birth and a lower proportion of natural menopausal status(P<0.05). Of the 1064 patients with breast cancer, 490 (46.05%) were ER positive, while 506 (47.56%) were PR positive.Demographic information and variables for breast cancer patients (n = 1064) and controls (n = 1073) are presented in P<0.05) and was excluded from subsequent analyses. In multivariate logistic regression models, the rs462480 and rs1053872 residing in the 61 bp and 10 kb downstream of pre-miR-101-2 were significantly associated with an increased risk of breast cancer, but not the remaining nine SNPs . In the combined dataset, compared with subjects with “0–1” risk allele of the two SNPs, subjects carrying “2–4” risk alleles resulted in 1.29-fold  increased risk of breast cancer.Furthermore, we conducted a joint analysis of these two SNPs rs462480 and rs1053872 . There wP>0.05 for heterogeneity tests).We also performed stratification analyses for the combined effect of rs462480 and rs1053872 based on age, age at menarche, first live birth, menopausal status, ER and PR status. As shown in miR-101 gene and predisposition to breast cancer in a case-control study. We found that rs462480 and rs1053872 in the flank region of pre-miR-101-2 were significantly associated with the increased risk of breast cancer in the Chinese population. To our knowledge, this is the first study to evaluate the association of miR-101-related polymorphisms and breast cancer susceptibility.In this study, we evaluated the association of 11 tagging SNPs located in the MiR-101, a miRNA commonly down-regulated in cancer, has been implicated in several key cancer-related processes including cell growth, migration, invasion and apoptosis. Recently, several studies supporting the considerable role of miR-101 in the development of breast cancer have been reported. Sachdeva et al. demonstrated that miR-101 stimulated estrogen-independent growth via upregulation of phosphorylated AKT et al. revealed that miR-101 could act as a key regulator of autophagy, which may sensitize breast cancer cells to 4-hydroxytamoxifen (4-OHT)-mediated cell death et al. revealed that miR-101 was down-regulated in different subtypes of breast cancer, and subsequently showed that miR-101 could inhibit tumor growth and stimulate breast cancer cells to apoptosis by targeting STMN1miR-101 polymorphisms on risk of hepatitis B-related liver disease miR-101-1 was significantly decreased the risk of liver cirrhosis and hepatocellular carcinoma . Furthermore, rs12375841 in miR-101-2 was significantly associated with clearance of hepatitis B viral infection . In this study, we did not observe the significant association between the 5 tagging SNPs  in the vicinity of miR-101-1 gene and the risk of breast cancer. We found that rs462480, a SNP in high LD with rs12375841 (r2 = 1) in miR-101-2, was significantly associated with an increased risk of breast cancer . Meanwhile, the SNP rs1053872 in the flanking region of pre-miR-101-2 was also associated with the susceptibility of breast cancer. In addition, we observed a clear and significant trend toward increased breast cancer risk as the number of variant genotypes of the two SNPs rs462480 and rs1053872.pre-miR-101-2 may influence the processing of mature miRNA by affecting cleavage of Drosha. Further studies are warranted to investigate the underlying biologic mechanisms for the association of SNPs of pre-miR-101 and susceptibility to breast cancer.The pri-miRNA, which is hundreds to thousands of nucleotides in length, is cleaved into pre-miRNA by Drosha in the nucleus, and is subsequently cleaved by Dicer in the cytoplasm to generate the final miRNA duplex pre-miR-101-2 were associated with breast cancer risk in the Chinese population. The rs462480 and rs1053872 SNPs may be considered as candidate genetic markers for the susceptibility to breast cancer in Chinese women. Further studies incorporating diverse populations and functional assays are required to validate and extend these findings.In conclusion, our results indicated that genetic variants in the vicinity of Table S1a T-tests and χ2 tests were used for continuous or categorical variables, respectively; b ER and PR status information was available in 869 breast cancer cases.Demographic and selected variables in breast cancer and control patients. NOTE: (DOC)Click here for additional data file.


Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age .in situ (DCIS) compared to women ages 55–64 and ≥65 years (Page-interaction = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (Page-interaction = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women. Mammographic density (MD) is one of the strongest risk factors for breast cancer: women in the highest quartile of MD have four to six times increased risk of breast cancer compared with those in the lowest quartile . The magTo date, the MD and breast cancer association according to different tumor characteristics is inconsistent -20. SomeGiven these conflicting results and known differences in the tumor biology of younger and older women diagnosed with breast cancer -26, we cParticipating studies included the Mayo Mammography Health Study (MMHS) ,28, the This study was approved by the Institutional Review Boards at the Mayo Clinic, Brigham and Women’s Hospital, the University of California, San Francisco (UCSF), and the Connecticut Department of Public Health Human Investigations Committee. Informed consent was obtained or implied by return of questionnaires .MD was measured using two computer-assisted threshold techniques , and human epidermal growth factor receptor 2 (HER2) status was obtained from state-wide Surveillance Epidemiology and End Results programs (SFMR), pathology reports (NHS and NHSII), state and clinic cancer registries , and medical records . For 66 cases in NHS or NHSII with missing receptor data on pathology reports, receptor status was obtained from immunohistochemical staining performed on paraffin sections of the tumor tissue microarray according to a standard protocol . A largein situ (DCIS)), histologic type , grade , tumor size , involvement of lymph nodes (positive or negative), and receptor status .Standardized percent MD was categorized as 0 to 10%, 11 to 25%, 26 to 50%, and 51%+, with the average MD group (11 to 25%) as the reference group consistent with previous analyses ,37. We fWe pooled data across the six studies and adjusted for study, age (continuous), and body mass index (continuous) in multivariable models. We further considered potential confounding by parity  and first-degree family history of breast cancer  by evaluating the magnitude of change in ORs observed after including each potential confounder individually in the model. Postmenopausal hormone therapy  was also evaluated as a confounder among postmenopausal women in the subset of studies for which this information was available . Addition of these variables to the models did not substantially change risk estimates and they were not included in final models. Results were similar in sensitivity analyses restricted to Caucasians only (data not shown).Pheterogeneity). For subtypes with a natural ordering, including tumor size and grade, tests of trend (Ptrend) across categories were used to assess significance. Formal tests of interaction (Page-interaction) assessed the significance of differential MD associations with each of the breast cancer characteristics and subtypes across the three age groups.Because prior studies suggest differences in the strength of the association of MD with breast cancer by age , and sinP values >0.29).Prior to pooling data across the six studies, study-specific estimates were obtained by fitting separate models for each study and assessing individual associations between MD and each tumor subtype. We assessed the statistical significance of differences in associations by study through testing for interactions between study group and MD category in the pooled analysis, and found no evidence of differences across study . All statistical tests were two-sided and Overall, the mean age at mammogram was 57 years among both cases and controls. The mean time to diagnosis was 4.5 years (range: 0.5 to 17.6). Among both cases and controls, the mean percent MD decreased with age, but in each age group the mean percent MD was higher among cases versus controls Table . DCIS waPage-interaction = 0.02; Table S2 in Additional file A positive association of MD with breast cancer overall, adjusted for age and body mass index, was seen for each study  , the magnitude of the association was similar within each age group  and there was no evidence of significant interaction by age (Page-interaction = 0.15). Also, higher MD was positively associated with invasive tumors of all sizes but the association was stronger for large tumors  compared with small tumors <1.1 cm among all women combined  (Table Page-interaction = 0.24), although there was evidence of a stronger association for node-positive versus node-negative tumors among women ages ≥65 (Pheterogeneity = 0.04).MD was similarly associated with breast cancers of various grades Table . Further) Figure . DespitePage-interaction = 0.04). For women aged <55 years, a stronger association was observed for ER-negative disease  versus ER-positive disease  for MD 51% + versus MD 11 to 25% (Pheterogeneity = 0.09), while associations for women aged ≥55 years were nonstatistically significantly stronger for ER-positive tumors versus ER-negative tumors. MD was similarly associated with PR-negative and PR-positive tumors in all age groups (Page-interaction = 0.10). While there was evidence of a significant interaction between MD and breast cancers by HER2 status and age group (Page-interaction = 0.03), MD was positively associated with both HER2-negative and HER-positive disease in all age groups and there were no clear patterns of differences in associations  had an approximately two-fold higher risk of breast cancer compared with women with MD 11 to 25%, and those with less extensive mammographic density (0 to 10%) had almost one-half the risk as those with 11 to 25% MD. This translates to a relative risk of around four comparing the highest category of MD with the lowest, similar to prior studies. Our findings suggest that MD is a risk factor for all histologic/morphologic subtypes of breast cancer examined, but a stronger risk factor for tumors of large  size and node-positive (vs. node-negative) disease among all women. We found differential associations of MD and breast cancer defined by tumor type (DCIS vs. invasive cancer), ER status and HER2 status, by age group .Most previous studies of the association between MD and breast cancer tumor characteristics have focused on hormone receptor status, with somewhat conflicting results ,37,46,47in situ versus invasive tumors among postmenopausal women in the NHS [Among women aged <55 years who had high MD, we noted similar associations for DCIS and invasive breast cancers. However, we noted a stronger association between MD and breast cancer for invasive versus DCIS cancers among women aged 55 to 64 years and ≥65 years. Three prior studies showed either similar associations of MD with DCIS ,48 and i the NHS . The curOur findings of stronger associations between MD and large compared with small tumors within each age group are consistent with most prior studies -9,15,22.Taken together, our findings suggest that MD is a risk factor for all types of breast cancer. However, some differences in magnitudes of risk associated with MD were noted for specific tumor characteristics primarily related to detection  and for tumor characteristics primarily related to tumor biology (tumor type in older women and ER status in younger women). These observations point to possible heterogeneity by tumor characteristics and, if confirmed and corroborated by molecular-based studies, provide insight into the biological mechanisms by which MD differentially influences cancer risk.Some limitations to this study are worth noting. We pooled data from six different studies, which varied in study design, population characteristics, geography, and calendar year. Our study used clinical pathology information and not centralized pathology review. In addition, there may have been changes in diagnostic criteria over time . For example, some of the earliest diagnoses from the NHS included in this analysis were from the early 1990s before fluorescent in situ hybridization testing was routine. However, we found no evidence of between-study heterogeneity in our results. Further, any differences would be expected to attenuate associations with MD. The majority of women in our study were Caucasian; therefore, our findings may not be generalizable to non-Caucasians. Finally, we lacked data on the detection method , which could have helped to clarify the extent to which our observations reflect delays in diagnosis or tumor biology.Strengths of the study included the sample of over 3,400 breast cancer cases, providing sufficient statistical power to examine associations by age group; cases and controls (except MCBCS) selected from underlying prospective cohort studies or registries with a mean time between mammogram and breast cancer diagnosis of 4.6 years; and comparable estimates of percent MD across readers at the three study sites with demonstrated high intra-reader reliability (intraclass correlation = 0.91 to 0.97) and standardization of percent MD for pooled analyses. The lack of between-study heterogeneity further suggests that pooling these data is appropriate. Also, we had detailed information on tumor characteristics from pathology reports supplemented with information from tissue microarrays. Finally, all mammograms were screening in nature.We observed significant positive associations between MD and breast cancer risk across tumor characteristics and age groups. In addition, we noted stronger associations for tumors of large size and positive lymph nodes across all ages, and ER-negative status among women aged <55 years, suggesting that high MD may play an important role in tumor aggressiveness, especially in younger women. Understanding similarities and differences in etiologic pathways according to breast cancer tumor characteristics and age has important implications for development of risk models and tailored primary prevention. Incorporating MD and traditional breast cancer risk factors into risk prediction models overall and into models unique to subtype may improve their predictive ability and allow clinicians to identify women at increased risk of breast cancer for targeted prevention efforts. Moreover, because MD itself is a potentially modifiable risk factor , understin situ; ER: Estrogen receptor; HER2: Human epidermal growth factor receptor 2; MCBCS: Mayo Clinic Breast Cancer Study; MCMAM: Mayo Clinic Mammography Study; MD: Mammographic density; MMHS: Mayo Mammography Health Study; NHS: Nurses’ Health Study; OR: Odds ratios; PR: Progesterone receptor; SFMR: San Francisco Bay Area Breast Cancer SPORE and San Francisco Mammography Registry; UCSF: University of California, San Francisco.DCIS: Ductal carcinoma The authors declare that they have no competing interests.CMV, KK, and RMT conceived of and designed the study, directed statistical analyses, interpreted results, substantially revised initial drafts of the paper and provided final review and approval. JS led all aspects pertaining to mammogram acquisition and measurement of mammographic density for UCSF; he also provided substantial contribution to the interpretation of results and final approval of the manuscript. AN was the project manager of the overall study, providing study coordination and data acquisition for all combined information, harmonization of all data elements, as well as providing substantive comments to the drafts of the paper as well as final approval. BF acquired images and estimated mammographic density on UCSF mammograms, reviewed the initial and final drafts and provided final approval. Y-YC, DV, and AHB provided substantive pathology expertise to inform tumor characteristics and combination of information across studies, reviewed the drafts and provided comments and final approval. LM, CGS, MRJ, and VSP performed statistical analyses and interpretation of data, with input from KAB, RMT, KK and CMV. FC is the principal investigator of the MCBCS study and contributed substantive data, interpretation, drafting the manuscript and final approval. SRC is the principal investigator of one of the UCSF SFMR sites and contributed substantive data, interpretation, drafting the manuscript and final approval. KAB contributed to interpretation of analyses, wrote the first draft of the manuscript, which was critically revised by RMT, KK, and CMV, and approved the final draft. All authors read and approved the final manuscript.Additional methods, presenting detailed description of study populations and methods to standardize MD measurements.Click here for fileTable S2. Presenting pooled associations of categorical MD with breast cancer overall and by tumor type and age; and Table S3. Presenting pooled associations of categorical percent MD for morphological subtypes of invasive breast cancer by age.Presenting associations of MD with breast cancer by study ); Click here for file


Breast fibroglandular (dense) tissue is a risk factor for breast cancer. Beyond breast cancer, little is known regarding the prognostic significance of mammographic features.We evaluated relationships between nondense (fatty) breast area and dense area with all-cause mortality in 4,245 initially healthy women from the Breast Cancer Detection Demonstration Project; 1,361 died during a mean follow-up of 28.2 years. Dense area and total breast area were assessed using planimeter measurements from screening mammograms. Percent density reflects dense area relative to breast area and nondense area was calculated as the difference between total breast area and dense area. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards regression.In age-adjusted models, greater nondense and total breast area were associated with increased risk of death  while greater dense area and percent density were associated with lower risk of death . Associations were not attenuated with adjustment for race, education, mammogram type (x-ray or xerogram), smoking status, diabetes and heart disease. With additional adjustment for body mass index, associations were diminished for all features but remained statistically significant for dense area  and percent density .These data indicate that dense area and percent density may relate to survival in healthy women and suggest the potential utility of mammograms beyond prediction of breast cancer risk. Tissue density, a reflection of the physical and biochemical composition of the tissue, can be estimated from the Hounsfield Unit of computed tomography images. Tissue density is particularly useful for capturing a variety of obesity-related health risks as the Hounsfield Unit provides an indication of adipose infiltration into tissue. For example, low liver density reflects fat accumulation, a risk factor for insulin resistance and type 2 diabetes ,2. Low sMammography for breast cancer screening is one of the most widely used forms of imaging but the prognostic value of mammographic images outside of breast cancer risk prediction is largely unknown. In mammography, three metrics which reflect the tissue composition of the breast have been commonly characterized in epidemiologic studies: 1) nondense area which represents breast fat tissue, 2) dense area which refers to fibroglandular tissue and stromal tissue, and 3) percent density, the amount of dense tissue relative to total breast area. Density measures can be estimated quantitatively including planimetry , computeIt is less clear if mammographic features relate to survival among women diagnosed with breast cancer. Several studies have reported no association between breast density and breast cancer mortality -21 or inData was derived from the Breast Cancer Detection Demonstration Project (BCDDP), a study sponsored by the National Cancer Institute and the American Cancer Society. Between 1973 and 1975, 284,780 women aged 35 through 74 years were accrued. The screening phase of the study lasted from 1973 to 1979, during which time women in 29 centers in 27 cities across the United States were screened annually for incident breast cancer . 22 cent2) and 102 underweight women (BMI <18.5kg/m2) were also excluded due to possible confounding effects on mortality resulting in an analytical sample of 4,245 women. Mammographic measurements were completed on 7,251 women. We additionally restricted our analysis to women who were 1) also sampled in the follow-up phase, and 2) had no breast cancer diagnosis at screening or follow-up herein referred to as “healthy women”. Thus, for this analysis data are from 4,424 healthy women which includes women with benign breast disease, women recommended for biopsy and women without abnormality or breast biopsy recommendation. We focused on healthy women because the primary aim of this analysis was to determine whether mammographic measures are associated with mortality risk beyond breast cancer. Of the 4,424 healthy women, 2 women who were deceased but missing date of death were excluded. Because body mass index (BMI) is an adverse prognostic factor for breast cancer  and is sThe BCDDP follow-up cohort study was approved by the Institutional Review Board at the National Cancer Institute. All participants provided written informed consent. Follow-up information included vital status  and date of death obtained from the National Death Index through December 31, 2005. Women who were not identified as deceased were censored at the date through which vital records were complete. Mammographic images from the baseline screening examination of the BCDDP consisted of xerograms  and x-rays (N=771). Measured breast area with dense mammographic appearance was assessed quantitatively with a compensating polar planimeter  on the cranio-caudal view. Using the mammogram image, the reader used a wax pencil to outline the entire breast and the portions of breast containing radio-densities. The reader used the planimeter to trace the outlines of the entire breast and dense breast to compute total breast area and dense breast area, respectively. Nondense area was defined as the difference between total breast area and dense area. Percent density was calculated as dense area divided by the total breast area. Values reported are the average of the left and right breast or the reading of the non-missing breast if only one breast was read. These measures were previously determined and described in detail by Benichou et al.  and ByrnCovariates were chosen a priori and included factors that may potentially confound relationships between mammographic features and mortality including age at mammogram, education, race, type of mammogram (xerogram or x-ray), BMI, smoking status, prevalent self-reported diabetes and heart disease. Education was categorized as less than high school, high school graduate or postsecondary. Race was self-identified and categorized as non-Hispanic Caucasian, non-Hispanic Black, Hispanic, Oriental  or other racial/ethnic group. BMI was calculated from measured height and weight during the screening phase. If multiple measures of weight and height were recorded, the measures closest to the mammogram were used. Information on age started smoking, and date of diagnosis of diabetes and/or heart disease were ascertained from questionnaires during the follow-up period. Summary variables that represent the baseline screening characteristics of women were determined by comparing the study entry date for each participant to the age at smoking onset and date of diagnosis of diabetes and/or heart disease. Smoking status was categorized as never, current, former, or unknown whether current or former. th, 45th, 60th, and 85th). Sensitivity analyses with a ± 5% change in percentile categories yielded similar results. Participant characteristics are presented as medians with interquartile ranges for covariates that were not normally distributed (age and BMI). To meet linearity assumptions, BMI, nondense area and breast area were log-transformed, dense area and percent density were square root transformed. Correlations between age, mammographic features and BMI were examined using the Spearman correlation coefficient (r). The distribution of nondense area, dense area, breast area and percent density were skewed towards the extremes even following transformation and were thus categorized into percentile groups that reflected their distribution in the analytic sample  difference in continuous mammographic measures and with categorized mammographic measures. For categorical analysis of mammographic features, the risk of mortality in each of the upper three percentile groups was compared to the risk for the lowest group. Examination of Kaplan-Meier curves and Schoenfeld residuals indicated that the proportional hazards assumption was not violated. Risk relationships are presented in sequentially adjusted models. Tests for interactions between age and mammographic features were not significant (P>0.05) and thus models were age-adjusted and not age-stratified. We first examined unadjusted associations between mammographic measures and mortality (Model 1). Model 2 is age-adjusted. Model 3 additionally adjusted for baseline variables: education, race, smoking history, type of mammogram, prevalent diabetes and heart disease. Since mammographic features strongly reflect obesity, and obesity is a risk factor for mortality, we adjusted for BMI in a separate model (Model 4) to assess whether mammographic features were independently associated with mortality. The Wald statistic was used to test for an overall effect of categories of mammographic features on risk of mortality. Sensitivity analyses were conducted with additional covariates (menopause at time of mammogram and age at first live birth) that have been implicated in the pathogenesis of breast cancer and may influence breast tissue characteristics. Statistical significance was determined at P<0.05. STATA version 12.1  was used for all analyses. The mean (SD) follow-up period was 28.2 (4.75) years during which time 1361 women died . 45 women who were not known to have breast cancer during the BCDDP follow-up study died due to breast cancer. Associations between continuous mammographic features and risk of mortality per SD difference are shown in th percentile, the upper three groups of nondense area and breast area had increased risk of mortality (Model 1). Associations were reduced with adjustment for age (Model 2) and but remained significant for the upper 2 groups of nondense area and the upper 3 groups of breast area (Model 3). Inverse relationships were observed for dense area and percent density; compared with the 15th percentile, women in the upper three groups had a lower risk of mortality (Model 1) that persisted after adjustment for age and additional covariates (Models 2 and 3). Adjustment for BMI (Model 4) attenuated associations except for marginal associations with the upper group of dense area , and the upper groups of percent density: 40th percentile , 65th percentile  and 85th percentile . Results were similar when models of percentiles and continuous mammographic features were adjusted for menopause status and age at first birth. Associations between percentiles of mammographic features and risk of death yielded similar results . CompareThis study contributes to our understanding of the prognostic value of mammographic features by examining associations with mortality risk in healthy women, a seldom studied area. Greater nondense area and total breast area were associated with increased mortality risk but associations were not independent of BMI. In contrast, greater dense area and percent density were both associated with lower mortality risk independent of risk factors and BMI. For every SD increase of dense area, there was a 6% reduction in mortality risk and for every SD increase of percent density, there was a 7% reduction in mortality risk. These results suggest that mammographic images may capture features that are independently related to survival. Thus, when evaluating mammograms in relation to breast cancer risk, the characteristics of breast tissue and specifically, dense tissue and percent density may provide novel prognostic information for healthy women. In this population the association between mammographic features and risk of death appears to largely reflect risk carried by overweight or obesity. All mammographic features were correlated with BMI . Our risk models also seem to reflect the risk for mortality attributable to excess body weight -31. The The basis for the observed associations between mammographic density characteristics and mortality risk are unclear and likely complex. Relationships between adipose tissue, disease and disease-related mortality are possibly mediated through carbohydrate metabolizing characteristics of adipose tissue, secretion of cytokines  and/or aDirect comparison of our results is limited by the lack of studies on mammographic features and health outcomes in healthy women but parallels can be drawn to studies of breast cancer mortality. The association between greater dense area and lower mortality we observed is consistent with a study of breast cancer cases from a Danish screening study that reported lower risk of all-cause mortality in women with “mixed/dense” breasts . The posIt is likely that the associations we observed with mammographic features and mortality risk are not specific to a body compartment or tissue, but rather represent a systemic effect. For example, obesity manifests as fatty infiltration of the liver  and skel2 [Strengths of this study include a long follow-up period of more than 30 years and the quantitative continuous measures of breast nondense and dense areas. Continuous measures overcome the limitations of BI-RADS classification which can have substantial interobserver variability , potenti2  and it iIt is also important to acknowledge potential bias within our participant population when considering the generalizability of our results. Our sample of healthy women included women with benign breast disease and women recommended for biopsy at baseline screening, who may have had a higher risk of breast cancer than the general population. However, we excluded women with incident breast cancer during follow-up screening and only a small number of women in our population N=45) died from breast cancer. It is likely that our analytical sample is healthier than the general population. The prevalence of smoking is low, and the prevalence of diabetes is lower than women in the Second National Health and Nutrition Examination Survey which was conducted during a similar time period as the BCDDP [ died froMammographic images are often not exploited beyond assessment of breast abnormalities and breast cancer risk but as we demonstrate here tissue characteristics captured with mammography may carry broader health risks. There are potential public health implications from our results given the availability of mammographic images from widespread annual mammographic screening. Further studies are needed to replicate these results and explore potential mechanisms. Although these questions require clarification, the results of this study suggest a secondary role for analyses of mammographic images that may impact estimation of mortality risk in healthy women.


Previous studies have shown that woman’s risk of breast cancer in later life is associated with her infants birth weights. The objective of this study was to determine if this association is independent of breast cancer risk factors, mother’s own birth weight and to evaluate association between infants birth weight and hormonal environment during pregnancy. Independent association would have implications for understanding the mechanism, but also for prediction and prevention of breast cancer.Risk of breast cancer in relation to a first infant’s birth weight, mother’s own birth weight and breast cancer risk factors were evaluated in a prospective cohort of 410 women in the Framingham Study. Serum concentrations of estriol (E3), anti-estrogen alpha-fetoprotein (AFP), and pregnancy-associated plasma protein-A (PAPP-A) were measured in 23,824 pregnant women from a separate prospective cohort, the FASTER trial. During follow-up  31 women (7.6 %) were diagnosed with breast cancer. Women with large birth weight infants (in the top quintile) had a higher breast cancer risk compared to other women (hazard ratio (HR), 2.5; 95% confidence interval (CI), 1.2–5.2; P = 0.012). The finding was not affected by adjustment for birth weight of the mother and traditional breast cancer risk factors . An infant’s birth weight had a strong positive relationship with the mother’s serum E3/AFP ratio and PAPP-A concentration during pregnancy. Adjustment for breast cancer risk factors did not have a material effect on these relationships.Giving birth to an infant with high birth weight was associated with increased breast cancer risk in later life, independently of mother’s own birth weight and breast cancer risk factors and was also associated with a hormonal environment during pregnancy favoring future breast cancer development and progression. Prediction of breast cancer plays a pivotal role in its prevention and screening During pregnancy a woman is exposed to high concentrations of placenta–derived factors including estriol (E3), anti-estrogen alpha-fetoprotein (AFP) and pregnancy-associated plasma protein-A (PAPP-A)th and 6th examination cycle (1991–1998) and who also participated in the Framingham Offspring Birth History Study were eligible for inclusion in the current analysis. Among 509 women participants, 426 had a live birth with infant birth weight available in 419. Maternal birth weight was also known in 235 of those women. All participants provided written informed consent and the study was approved by the Institutional Review Board.Associations between maternal and infant birth weight and subsequent maternal risk of breast cancer were studied in a prospective cohort of women participating in the Framingham Offspring Study of the original participants’ adult children and their spouses. The study design and implementation details were previously described Associations between infants birth weight and maternal serum concentrations of E3, AFP and PAPP-A were studied in a prospective cohort of women participating in the First and Second Trimester Evaluation of Risk for Aneuploidy (FASTER) trial. Study design and implementation details have been previously described Participants in the Framingham Offspring Birth History Study provided information on their own birth weight and the birth weight of their children, along with other factors related to those births. For this analysis we evaluated the birth weight of the first live born infant. Collected was also information on breast cancer risk factors including age, age at menarche, age at first live birth, age at menopause, race/ethnicity, parity, body mass index, diabetes and use of hormone replacement therapy. Maternal history of breast cancer was determined among women whose mothers participated in the Framingham cohort.In the FASTER trial infants birth weight was recorded at birth and study analyses were conducted using birth weight itself and using growth potential norms. Growth potential norms account for physiologic and pathologic factors affecting fetal growth including gestational age, maternal weight, height, race/ethnicity, parity, education, marital status, smoking status and reproductive technologies assisted conception, characteristics associated with both birth weight and risk of breast cancer In the Framingham Offspring Birth History Study breast cancer definition included invasive and non-invasive breast cancer. At each study visits, information on clinical outcomes including cancer was collected. All cancer reports were verified by central medical record review and review of submitted pathology specimens. All cancer cases had microscopic confirmation.In the FASTER trial analyses serum concentrations of E3, AFP and PAPP-A were measured using standardized immunoassays . Results were converted to multiples of the gestational age medians (MOM) and corrected for maternal weight.The associations between maternal and infant birth weights and maternal risk of breast cancer in the Framingham Offspring Birth History Study cohort were analyzed using multivariable proportional-hazards models. Age was used as the time scale, age at examination as time of study entry and breast cancer diagnosis was taken as the event. The loss to follow-up or death unrelated to breast cancer were censored. Cumulative probability of breast cancer curves were constructed with the use of the Kaplan–Meier method for subjects with infants birth weights in top and in lower quintiles.Hazard ratios for breast cancer were calculated for the infant birth weight in the top quintile compared to the lower quintiles using proportional hazard regression. The hazard ratios were adjusted for the mothers own birth weight and propensity score. The propensity score was calculated using logistic regression to account for the influence of breast cancer risk factors which could also be associated with infant’s birth weight and potentially confound the association. The factors included in the propensity score were age at entry into the study, age at menarche, age at first live birth, age at menopause, race/ethnicity, parity, body mass index, diabetes at entry into the study, family (participants mothers) history of breast cancer and use of hormone replacement therapy The associations between infant birth weight and growth potential percentile and maternal serum E3/AFP ratio and concentrations of PAPP-A were evaluated using multivariable fractional polynomial and logistic regressions In the Framingham Offspring Birth History Study participants of 419 women who had a live birth, four did not attend subsequent examinations and five had a prior or concomitant breast cancer diagnosis. Characteristics of the remaining 410 women included in study analyses are shown in During up to 17 years of observation  31 of 410 women (7.6 %) were diagnosed with breast cancer. The median time interval from delivery of the first child to diagnosis of breast cancer was 38 years (interquartile range 29–44 years). The median age at the diagnosis was 61 years (interquartile range 55–67 years). Women with large birth weight infants (in the top quintile) had significantly higher risk of breast cancer than women with infants whose birth weights were in lower quintiles (Log-rank test p = 0.009). The cumulative probability of breast cancer for women whose infant’s birth weights were in the top compared to lower quintiles are depicted by the Kaplan-Meier curves .In the time-to-event analysis the risk of breast cancer was 2.5 fold higher in women whose infant’s birth weight was in the top quintile compared to women with infant birth weight in the lower quintiles  . The risThe characteristics of the study participants from the FASTER trial cohort are presented in Women whose infant’s birth weight was in the top quintile had a 25% increased risk of having an E3/AFP ratio in the top quintile  and a 25% increased risk of having a PAPP-A concentration in the top quintile , in comparison to other women. Women with infant growth potential percentile in the top quintile had a 30% increased risk of having an E3/AFP ratio in the top quintile  and a 35% increased risk of having a PAPP-A concentration in the top quintile , in comparison to other women. Goodness of fit test did not show evidence of poor fit .We investigated the relationship between a woman’s risk of breast cancer and her own birth weight and the birth weight of her first child. In a separate cohort we also evaluated the relationship between an infant’s birth weight and the hormonal environment during pregnancy. We observed that women giving birth to large birth weight infants were at increased risk of subsequent breast cancer. This risk was independent of the mother’s own birth weight and traditional breast cancer risk factors. In a separate analysis, mothers whose infants had large birth weights had during pregnancy, elevated E3/AFP ratios and higher PAPP-A concentrations, hormonal conditions favoring breast cancer development and progression.2 = 0.20, P = 0.002), a finding nearly identical to one reported in a large population of over 100,000 families A recent re-analysis of individual participant data from 32 studies and meta-analyses found that a woman’s birth size correlated with subsequent breast cancer in adulthood Since after the first pregnancy the effect of infant birth weight on the maternal risk of breast cancer is likely to be confounded by the subsequent pregnancies, we evaluated the effect of the first infant’s birth weight In this study we have shown, that women delivering large babies have elevated E3/AFP ratio and PAPP-A concentrations, thus likely high net estrogen activity and free insulin-like growth factors concentrations, both favoring breast cancer development and growth. The association with the percentile of growth potential was stronger than with the infants birth weight, likely due to lesser degree of confounding by maternal characteristics associated both with the birth weight and with the risk of breast cancer.It is biologically plausible that the relationship between delivery of a large infant and increased risk of breast cancer is mediated by the hormonal environment during pregnancy of high maternal concentrations of E3, low concentrations of AFP and high concentrations of PAPP-A. The circulating concentrations of those hormones during pregnancy are massive in relation to other times in woman’s life A limitation of this study is that the association between large infants birth weight and risk of breast cancer and the association between large infants birth weight and a hormonal environment during pregnancy were tested in different populations. Thus, we were not able to definitely demonstrate that hormonal environment during pregnancy mediates the relationship between large infants birth weight and the risk of breast cancer. However, observation of complementary findings in two different populations would be unlikely if the findings were spurious. The birth weights in the Framingham Birth History Study were self-reported but maternal recall was shown to be highly reliable for first births In summary, delivery of a large birth weight infant is a strong and independent risk factor for later breast cancer in women, which precedes the cancer diagnosis by decades. These results suggest that a pregnancy that produces a large birth weight infant is associated with a hormonal environment in the mother favoring breast cancer development and progression and results in increased risk of breast cancer following this pregnancy.


PRL) or prolactin receptor (PRLR) genes also play a role. Thus, we examined the relationship between single nucleotide polymorphisms (SNPs) in PRL and PRLR, serum prolactin levels and breast cancer risk in a population-based case-control study.Studies suggest that high circulating levels of prolactin increase breast cancer risk. It is unclear if genetic variations in prolactin  and 95% confidence intervals (CIs) for genotype associations with breast cancer risk were estimated using unconditional logistic regression, adjusted for age and study site. Geometric mean prolactin levels were estimated using linear regression models adjusted for age, study site, blood collection time, and menstrual cycle day .We genotyped 8 PRLR rs249537 ; and in postmenopausal women, PRLR rs7718468  and PRLR rs13436213 . However, mean serum prolactin levels for these SNPs did not vary by genotype (P-trend > 0.05). Other SNPs were associated with serum prolactin levels: PRLR rs62355518 (P-trend = 0.01), PRLR rs10941235 (P-trend = 0.01), PRLR rs1610218 (P-trend = 0.01), PRLR rs34024951 (P-trend = 0.02), and PRLR rs9292575 (P-trend = 0.03) in premenopausal controls and PRL rs849872 (P-trend = 0.01) in postmenopausal controls.Three SNPs were associated with breast cancer risk: in premenopausal women, PRLR and breast cancer risk. Altered serum prolactin levels were not associated with breast cancer risk-associated variants, suggesting that common genetic variation is not a strong predictor of prolactin-associated breast cancer risk in this population.Our data provide limited support for an association between common variations in  The reproductive hormone prolactin is produced primarily by the pituitary gland and in lesser amounts by several other tissues, including breast tissue. Prolactin plays a central role in breast development, differentiation, and lactation (reviewed in ), but exThere is a growing body of epidemiologic evidence supporting an association between circulating prolactin levels and breast cancer risk, although data are not conclusive. Large prospective studies have reported a positive association between prolactin levels and breast cancer risk -11. No aPRL) and prolactin receptor (PRLR) genes may be associated with increased prolactin levels and breast cancer risk. Exploration of genetic variants in PRL and PRLR has identified single-nucleotide polymorphisms (SNPs) that alter transcription factor binding [PRL and PRLR and breast cancer risk in the Polish Breast Cancer Study. Additionally, we examined the association between PRL and PRLR SNPs and serum prolactin levels among controls, hypothesizing that altered serum prolactin levels may be an intermediate marker between genetic variation in PRL and PRLR and breast cancer risk.In addition to reproductive and environmental factors, genetic variation in the prolactin  and study site. All participants provided informed consent, and the study protocol was approved by ethics committees in Poland and the US. A total of 2,386 cases (79% of eligible) and 2,502 controls (69% of eligible) were enrolled in the study.The design of the Polish Breast Cancer Study has been reported ,21. BrieStudy participants provided information on demographics, reproductive and medical history, oral contraceptive and postmenopausal hormone use, and other potential breast cancer risk factors during a personal interview. Height, weight, and waist and hip circumferences were measured by a trained nurse. A blood sample was provided by 84% of cases and 92% of controls.PRL and PRLR were genotyped by using the Fluidigm dynamic array at the National Cancer Institute (NCI) Core Genotyping Facility  [2r) of 0.80. Tag SNP selection was performed by using Tagger in Haploview . SNPs previously reported by Lee and colleagues [Tag SNPs in MD, USA) . SNPs welleagues  to be asTwo SNPs (rs2244502 and rs4425481) failed the Fluidigm genotyping and were repeated by using Taqman assays. Four thousand two hundred twenty-nine participants had adequate DNA for Taqman genotyping, and genotype calls were made for 4,047 (96%).PRL and 20 PRLR - were genotyped in 1,965 cases and 2,229 controls, whose characteristics are displayed in Table PRL SNP rs9466314 was monomorphic and excluded from further analysis. Genotype frequencies for two PRLR SNPs were not consistent with Hardy-Weinberg equilibrium (HWE) in controls (defined as P < 0.001: rs7731880 and rs12153280) and these SNPs were also excluded from the analysis. Associations with serum prolactin and breast cancer risk were examined for the remaining 25 SNPs. Linkage disequilibrium (LD) among control subjects was limited; the mean 2rvalues were 0.21 (range of 0 to 0.84) among sequential PRL SNPs and 0.13 (range of 0 to 0.45) among PRLR SNPs. Additional positional information and allele frequencies are presented in Table A total of 28 tag SNPs - 8 SNP genotyping completion proportions ranged from 88% to 98%. To confirm the accuracy of genotype calls, 152 samples were genotyped in duplicate. Concordance between duplicate samples was less than 98% for three SNPs - rs62355518 (97%), rs7734558 (96%), and rs7735260 (82%) - and at least 98% for the remaining SNPs.Serum prolactin concentration was measured in a subset of cases and controls, whose selection was described in detail by Faupel-Badger and colleagues . ControlSerum prolactin levels were measured by Quest Diagnostics  by using the Bayer ADVIA Centaur immunoassay  and were calculated after calibration with known prolactin concentrations. One control subject was excluded from further serum prolactin analyses because of a concentration outside the assay limits of detection (0.3 to 200 ng/mL). The within-batch coefficient of variation (CV) was 3.84% and the between-batch CV was 2.31%, as reported previously .Age was defined as age at diagnosis for cases or as age at interview for controls and was included in models using 5-year categories. Women were classified as premenopausal if they reported that they were still having natural menstrual periods; medical records were used to determine menopausal status for women who did not know whether they were still experiencing natural menstrual periods.Among parous women, breastfeeding duration was the sum of the number of months of reported breastfeeding for each child. Among postmenopausal women, oral HT use was based on self-reported use of estrogen and progesterone pills for purposes other than birth control and classified as never-use (<1 month use), current use (≥1 month use and last use up to 6 months ago), recent use (≥1 month use and last use from 6 months up to 2 years ago), and past use (≥1 month use and last use at least 2 years ago). Family history of breast cancer was based on female first-degree relatives with breast cancer. Age at menarche, highest level of education, and number of alcoholic drinks per week were also based on self-report.2, 25 to <30 kg/m2, and ≥30 kg/m2). Time of blood collection was included in models by using 2-hour categories. Time since last menstrual period was the number of days between blood draw and the participant's most recent menstrual period and was used as a continuous variable.BMI (in kilograms per square meter) was calculated from height and weight measurements and included in models as a categorical variable  statistical model. Genotype-specific ORs were estimated using the co-dominant (two degrees of freedom) model.Odds ratios (ORs), 95% confidence intervals (CIs), and Wald P value for the overall association between haplotypes and breast cancer by using the sliding window approach with a three-SNP window. ORs, 95% CIs, and P values were estimated for individual haplotypes with a frequency of 5% or greater within regions where haplotypes were globally associated with breast cancer . Haplotype effects were modeled as log-additive by using logistic regression and were adjusted for age and study site.Haplotype associations for breast cancer risk were explored by using Haplostats ,24. For Serum prolactin levels were log-transformed, and linear regression was used to estimate geometric mean prolactin levels by genotype in controls and to estimate the association between genotypes or haplotypes and serum prolactin levels. Previous studies have shown that prolactin levels are higher in premenopausal compared with postmenopausal women and that prolactin levels vary with the day of menstrual cycle in premenopausal women ,26. OwinP values are presented in all tables. We also evaluated the false discovery rate (FDR) by using the Benjamini and Hochberg method [Statistical tests were two-sided . Unadjusted g method  to assesThe mean age of participants was 56 years, and 73% of cases and 69% of controls were postmenopausal. The majority of cases and controls were parous , reported having breastfed , and never drank alcohol  . For both SNPs, the association was strongest for women carrying the minor allele homozygous genotype .We examined the association between variant alleles in 25 SNPs - 7 PRLR were significantly associated with prolactin levels in premenopausal controls: rs62355518 (P = 0.01), rs10941235 (P = 0.01), rs1610218 (P = 0.01), rs34024951 (P = 0.02), and rs9292575 (P = 0.03)  (Table P = 0.09). In addition, visual examination of the data shows that the distribution of prolactin concentrations was similar by genotype   and this association requires replication.We examined the association between SNPs in PRL and PRLR and breast cancer risk. Lee and colleagues [PRL and PRLR tag SNPs and breast cancer risk in a case-control analysis of approximately 3,500 multiethnic premenopausal and postmenopausal women and reported that PRL rs9466314 was associated with increased breast cancer risk and that PRLR rs34024951 was associated with decreased risk. rs34024951 was not associated with premenopausal or postmenopausal breast cancer in our study population but, along with rs7718468, was a member of a four-SNP haplotype that was associated with postmenopausal breast cancer risk. This suggests that the region marked by rs34024951 and rs7718468 may be in LD with a breast cancer risk-associated locus. Neither rs34024951 nor rs7718468 or the PRLR haplotype was associated with serum prolactin levels in postmenopausal controls in our study; thus, our data do not support the hypothesis that the risk mechanism is linked to circulating prolactin levels.Few studies have investigated the association between SNPs in lleagues  evaluatePRL and PRLR SNPs related to breast cancer risk in African-American or African populations.Replication of the association between rs9466314 and breast cancer risk was limited by population differences in risk allele frequencies. The population studied by Lee and colleagues  includedComparison with other studies was also limited by a lack of overlapping SNPs between the studies. None of the SNPs we found to be associated with breast cancer  was included in the analysis by Lee and colleagues . SNPs inPRL promoter SNPs rs1341239 and rs12210179 were positively associated with familial breast cancer. SNP rs12202764, which is in LD with rs12210179, was not associated with breast cancer in our study. SNP rs1341239 was initially selected for genotyping but failed assay design; SNPs in LD with rs1341239 were not genotyped in our study. A positive nonsignificant association between PRL rs2244502 and familial breast cancer was also reported in the same study [In another study, Vaclavicek and colleagues  reportedIn vitro studies have described polymorphisms in PRLR - I76V (rs16871473) and I146L (rs72478580) - [PRLR genetic variation and breast cancer risk.78580) - ,18,28 th78580) - ,28. The PRL and PRLR genotypes associated with breast cancer; prolactin levels did not vary by genotype for any of the breast cancer-associated SNPs in this study . To our knowledge, only Lee and colleagues [PRL and PRLR genotypes and breast cancer risk, but examination of serum prolactin levels was restricted to a sample of postmenopausal women only. Nonetheless, in that study, prolactin levels varied by genotype for risk-associated SNP rs9466314, but not rs34024951 [Our data did not support the hypothesis that serum prolactin levels differ for women with lleagues  have exa34024951 .PRL extrapituitary promoter in breast cancer cell lines [It is likely that SNP-related variations in prolactin do not translate directly into altered disease risk and that other biological factors are at play. Several breast cancer risk factors have been reported to be associated with prolactin levels  (reviewell lines . Furtherll lines ). RefinePRL and PRLR SNP studies. Thus, confirmation in other populations is necessary. The results from Bogorad and colleagues [Interpretation of our results was limited by the lack of a validation population and by lack of overlap between SNPs genotyped in our and previous lleagues  and Lee lleagues  suggest PRL and PRLR genetics and breast cancer risk.A major strength of this study was the measurement of serum prolactin levels. This allowed us to examine relationships between circulating prolactin levels and risk alleles, addressing the hypothesis that risk alleles have a measurable effect on circulating prolactin levels. Additionally, we conducted this analysis within a large, population-based case-control study. The range of prolactin levels in controls is representative of prolactin concentrations among women in the general Polish population. As discussed above, prolactin concentrations in the breast may differ from circulating concentrations. Measurement of prolactin in breast tissue may provide additional information about the link between PRL or PRLR and breast cancer. Future studies should be conducted in ethnically diverse populations and should have sufficient sample size to study rare variants.We found limited evidence for a relationship between common genetic variants in PRL: prolactin (gene); PRLR: prolactin receptor (gene); SNP: single-nucleotide polymorphism.BMI: body mass index; CEU: Utah residents with Northern and Western European ancestry from the CEPH (Centre d'Etude du Polymorphisme Humain) collection; CI: confidence interval; FDR: false discovery rate; HT: hormone therapy; HWE: Hardy-Weinberg equilibrium; LD: linkage disequilibrium; MAF: minor allele frequency; OR: odds ratio; The authors declare that they have no competing interests.SJN carried out the analysis and drafted the manuscript. JMF-B and MES contributed to the organization and design of the study and contributed to manuscript preparation. MMG, JL, LAB, BP, BKV, and MG-C contributed to the organization and design of the study. RTF contributed to the data analysis and study design. AAA contributed to the data analysis. RMP contributed to the data analysis and manuscript preparation. SC supervised genotyping. JDF directed the organization and design of the study, data analysis, and manuscript preparation. All authors read and approved the final manuscript.Supplementary tables S1-S3. Supplementary table S1. PRLR haplotypes associated with postmenopausal breast cancer in the Polish Breast Cancer Study. Supplementary table S2. Association between PRLR and PRL SNPs and breast cancer risk in the Polish Breast Cancer Study. Supplementary table S3a. Association between PRLR and PRL SNPs and serum prolactin levels among premenopausal controls in the Polish Breast Cancer Study. Supplementary table S3b. Association between PRLR and PRL SNPs and serum prolactin levels among postmenopausal controls in the Polish Breast Cancer Study.Click here for fileSupplementary figure S1. Log-prolactin distribution in postmenopausal controls, by rs849872 genotype.Click here for file


Mutations in the p53 tumor suppressor gene and accumulation of its protein in breast tissue are thought to play a role in breast carcinogenesis. However, few studies have prospectively investigated the association of p53 immunopositivity and/or p53 alterations in women with benign breast disease in relation to the subsequent risk of invasive breast cancer. We carried out a case-control study nested within a large cohort of women biopsied for benign breast disease in order to address this question. After exclusions, 491 breast cancer cases and 471 controls were available for analysis. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI). Neither p53 immunopositivity nor genetic alterations in p53 (either missense mutations or polymorphisms) was associated with altered risk of subsequent breast cancer. However, the combination of both p53 immunopositivity and any p53 nucleotide change was associated with an approximate 5-fold nonsignificant increase in risk  but the confidence intervals were extremely wide. Our findings raise the possibility that the combination of p53 protein accumulation and the presence of genetic alterations may identify a group at increased risk of breast cancer. Mounting evidence suggests that cancer results from a series of mutations in genes involved in cell growth and differentiation, DNA repair, and cell cycle control , 2. In tMutations in the p53 gene are among the most common genetic alterations found in breast cancer, occurring in 30–50% of cases of sporadic breast cancer , 5. Thern = 104) of breast cancer cases. In the present study, conducted in a different study population comprising a large cohort of women biopsied for benign breast disease, we investigated the association between p53 protein accumulation and p53 mutations in exons 5 to 10 and subsequent risk of breast cancer.Most previous studies of p53 in benign breast tissue have examined the prevalence of p53 immunopositivity and/or p53 mutations in case series, often involving only a limited number of cases –29. Few  We carried out a case-control study nested within a cohort of 15,809 women who received a breast biopsy either at Guy's Hospital  or within the Kaiser Permanente Northwest (KPNW) health care system . At Guy's Hospital, women were enrolled from 1946 to 1984 and at KPNW from 1970–1994. Women were eligible to participate if they had a histopathologic diagnosis of benign breast disease (BBD) on their index biopsy and were at least 21 years old at the time of the index biopsy. A detailed description of the study has been published previously . Information on sociodemographic factors, reproductive and menstrual history, medical history, family history of breast cancer, and exogenous hormone use was obtained from medical records . For the London cohort, the National Health Service Central Register provided information on breast cancer diagnoses and deaths of cohort members. In Portland, cases were ascertained through linkage to the KPNW cancer registry. Followup in London continued until 12/31/2003 and in Portland until 12/31/2001.Cases were women who had a biopsy for BBD with a subsequent diagnosis of in situ or invasive breast cancer. In both cohorts, controls were women who had a biopsy for BBD and who were alive but had not developed breast cancer during the same follow-up period as that for their corresponding case and they were individually matched to cases (1 : 1) on age and on age at diagnosis of BBD . The controls were selected with replacement and were eligible to be selected again as controls or to become cases subsequently. For this reason, although 1,065 women were selected from the two cohorts, there were a total of 1,092 records . After eFor the present study, formalin-fixed paraffin-embedded blocks of benign breast tissue were retrieved from tissue archives. Hematoxylin and eosin-stained sections from the tissue blocks were reviewed histologically and classified according to the criteria developed by Page and Anderson  and withμm) were cut from the paraffin blocks, mounted on aminopropyltriethoxysilane coated slides , and deparaffinized. The sections underwent antigen retrieval (microwaved in 10 mmol/L citrate buffer (pH 6.0) for 15 minutes at a medium-high setting), and immunostaining was performed as described previously using antibody reactive with p53  [κ = 0.64) on the presence or absence of p53 immunostaining in the benign specimens reread by the same reviewer without knowledge of the result of the first reading [Sections  . Immunor reading .μm-thick sections were cut from the paraffin blocks, dewaxed, and stained briefly in hematoxylin. The breast epithelium was microdissected from the stroma , collected in a microfuge tube, and digested with proteinase K , 10 mmol/L EDTA, 0.5% Tween 20) for at least 24 hours at 55°C. Proteinase K was inactivated by heating to 95°C for 15 minutes. The DNA was amplified by PCR using the primers for exons 5 to 10 and conditions listed in Two 10 Of the 937 women (962 records) available for analysis, blocks of paraffin-embedded benign breast tissue were obtained and immunohistochemical/molecular analysis was completed for 884 women: 453 (92.3%) of the 491 cases and 431 (91.5%) of the 471 controls. Seventy-eight women (38 cases and 40 controls) were missing immunohistochemical results either because tissue samples were not obtained or because there was no breast epithelium in the tissue sections, rendering them unsuitable for immunohistochemical analysis. p53 sequencing results were available for 780 records (404 cases/376 controls) for exon 5, 788 records (399 cases/389 controls) for exon 6, 792 records  for exon 8, 797 records (407 cases/390 controls) for exon 9, and 719 records (366 cases/353 controls) for exon 10. Sequencing results for all exons were available for 651 records (329 cases/322 controls).P values <0.05 were considered to be statistically significant. The design of the original study, laboratory assays, and statistical analysis all conform to the REMARK guidelines [In order to make use of all available data we carried out an unmatched analysis and estimated odds ratios (OR) and 95% confidence intervals (95% CI) for the association between immunohistochemically detected p53 protein accumulation and p53 changes detected by sequencing using unconditional logistic regression models . p53 chaidelines .Compared to controls, cases were more likely to have proliferative disease with atypia and to have a family history of breast cancer in a first-degree relative . Cases wp53 immunopositivity was not associated with altered risk of subsequent breast cancer . FurtherP < 0.0001). The proportions of women with hyperplasia (present/absent) by percent cell staining positive were 57.8%/86.1%, 21.1%/6.5%, 14.9%/5.9%, and 6.2%/1.5%. Overall histological category  was also associated with the presence of p53 staining, P = 0.03. Presence versus absence of any genetic alteration was not associated with histological category—P = 0.88 (data not shown). Presence of p53 staining  was strongly associated with presence of hyperplasia  with those of the unmatched analyses. When cases diagnosed within the first 3 years of followup were excluded from the analysis, the results were unchanged (data not shown).In this large case-control study nested within a cohort of women biopsied for benign breast disease, p53 immunopositivity was not independently associated with increased risk of breast cancer. Mutation analysis revealed a low frequency of p53 changes, and these were not associated with increased risk. However, there was a suggestion that the combination of immunopositivity and the presence of a p53 mutation was associated with substantially increased risk, but the number of women in this group was small and the confidence intervals were very wide.  In an earlier case-control study nested within a cohort of 4,888 women diagnosed with benign breast disease , p53 proThe most common p53 mutations occurring in sporadic breast cancer cases are missense point mutations or inframe deletion/insertion mutations, which account for 53–73% of all p53 mutations in these subjects . BecauseMost benign breast disease is associated with only a moderately increased risk of progressing to cancer, whereas the small subgroup with atypical hyperplasia is at substantially increased risk of progression , 32. We It has been proposed that mutations in the p53 gene observed in benign breast tissue may lead to genetic instability and defective DNA repair and, ultimately to clonal expansion of transformed cells and progression to invasive breast cancer . Point mStrengths of the current study include the large cohort of women who underwent a breast biopsy at two collaborating centers and were followed for an average of 14 years, the availability of immunohistochemical and p53 mutation results for >90% of cases and controls, and the ability to adjust for major breast cancer risk factors. Adjustment for these risk factors appeared to attenuate the OR for missense mutations. An additional strength is the fact that p53 analyses were performed without knowledge of case-control status, making differential bias in the assessment of p53 status unlikely.Sequencing was used to identify gene alterations, but this method is influenced by the amount and quality of DNA. Furthermore, the number of samples suitable for analysis was limited by the age of the samples and by variation in the types of fixatives. Ninety-nine percent of samples from Portland were formalin fixed, and 99% of those from London were fixed with Formal saline. We repeated the analysis of immunopositivity (present/absent) and of any genetic alteration (present/absent) by center, and the results did not differ from the overall results, although the statistical power was limited.N = 78) were compared to those with results (N = 884) with respect to baseline characteristics, apart from a difference in mean age (55.7 for the former and 48.4 for the latter), few differences were seen. When women lacking sequencing results (N = 318) were compared to those with sequencing results (N = 644), again, aside from a difference in mean age (52.1 for the former versus 47.4 for the latter), other background characteristics were similar. Finally, the small number of women who showed both immunopositivity and genetic alterations precluded firm conclusions about the effect of these factors in combination and, also, precluded examination of specific mutations.Several additional limitations of the study should be mentioned. Paraffin-embedded blocks of benign breast tissue were not obtained for 8% of cases and controls. When women lacking immunohistochemistry results (In conclusion, the findings from this study suggest that the combined assessment of p53 overexpression and mutations in women with normal or benign breast tissue may identify a subgroup at increased risk of developing invasive breast cancer. However, the functional significance of different genetic alterations and their possible role in the progression culminating in invasive breast cancer needs to be elucidated. It also remains to be determined whether p53 immunohistochemical and mutation analysis can improve on the use of histology in the clinical assessment of a woman's risk of breast cancer.