Familial breast and ovarian cancer prevalence was assessed among 1150 women of Mexican descent enrolled in a case-only, binational breast cancer study. Logistic regression was conducted to compare odds of triple negative breast cancer (TNBC) to non-TNBC according to family history of breast and breast or ovarian cancer among 914 of these women.P-interaction = 0.14) and a first- or second-degree relative (P-interaction = 0.07).Prevalence of breast cancer family history in a first- and first- or second-degree relative was 13.1% and 24.1%, respectively; that for breast or ovarian cancer in a first-degree relative was 14.9%. After adjustment for age and country of residence, women with a first-degree relative with breast cancer were more likely to be diagnosed with TNBC than non-TNBC . The odds of TNBC compared to non-TNBC were 1.93  for women with a first-degree relative with breast or ovarian cancer. There were non-significant stronger associations between family history and TNBC among women diagnosed at age <50 compared to ≥50 years for breast cancer in a first-degree relative  contains supplementary material, which is available to authorized users. BRCA1/2 explain less than 20% of familial cancers   were then asked to report the type of cancer for each family member and his/her age at diagnosis. Women who answered “no” (n = 510) or “don’t know” (n = 28) did not supply any additional information regarding family history of cancer. We also asked participants whether they were adopted and, if so, whether they knew her blood relatives. A woman who reported that she was adopted and did not know her blood relatives was categorized as “don’t know”; adopted women who knew their blood relatives were categorized as positive or negative for family history according to their response.Comprehensive family history data were collected to include first-degree  and second-degree  relatives. Half-siblings were not included. A total of 1150 participants were asked, “Have any of your immediate family members ever been diagnosed with cancer?” Those who answered “yes” . If no FISH results were available, an IHC intensity score of 3/3+ was considered positive, 2/2+ equivocal, and 0/1/1+ negative. If the IHC intensity score was 3/3+, that tumor was classified as HER2+ regardless of FISH value. Tumors with an equivocal HER2 IHC intensity score and no FISH data were excluded (n = 53). Cases were categorized as either TNBC  or non-TNBC, which included luminal A (ER+ and/or PR+ and HER2–), luminal B (ER+ and/or PR+ and HER2+), and HER2+ (ER-/PR- and HER2+) cancers. Of the 1150 women who were asked about their family history, 960 had known TNBC status, of which 914 had known status for family history of breast cancer in a first- or second-degree relative.Age at diagnosis and tumor marker data for estrogen receptor (ER), progesterone receptor (PR), and HER2 were abstracted from medical records. In the abstraction, priority was given to a numeric value for the percent of cells staining, where ER and PR positivity was defined as ≥ 1% cell staining by immunohistochemistry (IHC). Cases were considered HER2+ if amplified as determined by fluorescence Descriptive statistics (mean ± SD and proportions) for family history and risk factor characteristics were calculated. Associations between family history and TNBC status were tested using logistic regression, using non-TNBC as the reference group. Since the primary objective was to quantify the associations between presence of family history of breast cancer and TNBC as a means of understanding tumor heterogeneity rather than building risk models, the models were adjusted only for age at diagnosis (continuous) and recruitment country (U.S. or Mexico). In addition, we found minimal differences in the distributions of reproductive factors by family history, suggesting a lack of confounding by these variables. Analogous models were conducted for family history of breast or ovarian cancer. Each model generated an odds ratio (OR) and 95% confidence interval (CI). Tests for interaction between age and family history on TNBC status were conducted using likelihood ratio tests. All statistical analyses were performed using Stata 13.1 .A family history of breast cancer in a first-degree relative was reported by 13.1% of participants, and 24.1% reported breast cancer in a first- or second-degree relative


The relationship between passive smoking exposure (PSE) and breast cancer risk is of major interest.To evaluate the relationship between PSE from partners and breast cancer risk stratified by hormone-receptor (HR) status in Chinese urban women population.Hospital-based matched case control study.Chinese urban breast cancer patients without current or previous active smoking history in China Medical University 1st Hospital, Liaoning Province, China between Jan 2009 and Nov 2009.Each breast cancer patient was matched 1∶1 with healthy controls by gender and age (±2 years) from the same hospital.The authors used unconditional logistic regression analyses to estimate odds ratio for women with PSE from partners and breast cancer risk.P = 0.002), as were women exposed to passive smoke for 16–25 years , and those exposed to > 4 pack-years . Similar trends were significant for estrogen receptor (ER)/progesterone receptor (PR) double positive subgroup, but not for ER+/PR−, ER−/PR+, or ER−/PR− subgroups.312 pairs were included in the study. Women who endured PSE had significantly increased risk of breast cancer , comparing with unexposed women. Women who exposed to >5 cigarettes/day also had significant increased risk (adjusted OR: 1.99; 95% CI: 1.28–3.10; limitations of the hospital-based retrospective study, lack of information on entire lifetime PSE and low statistical power.Our findings provide further evidence that PSE from partners contributes to increased risk of breast cancer, especially for ER/PR double positive breast cancer, in Chinese urban women. Breast cancer is the most common neoplasm among women, with more than one million newly diagnosed cases and nearly 600,000 cancer death worldwide annually Compared with the western countries, breast cancer in China shows more invasive ductal carcinoma with larger tumor size, later stage, lower ER and PR expression and higher HER2 over-expression. This difference was thought to be associated with the different ethnic background and the modifiable lifestyle risk factors between West and East Unlike most other modifiable lifestyle risk factors, cigarette smoking, including both active smoking and passive smoking exposure (PSE), doesn't actually show consistent association with breast cancer risk in western populations. And the evidence of PSE remains controversial and unclassifiable because of a lack of conclusive mechanistic information Our primary objectives were to determine the relationship between PSE and breast cancer risk, stratified by hormone receptor status in Chinese urban women using data from a hospital-based case-control study.The study's protocol was approved by the hospital human research ethics committee. All the participants provided written informed consent before enrollment.A hospital-based matched case-control study of breast cancer risk was conducted in the First Hospital of China Medical University from Jan 2009 to Nov 2009. All participants were Chinese women residents, living in the Central Liaoning city cluster, aged 18 years or older.Case participants were women histologically diagnosed with primary invasive breast cancer based on the 2003 WHO classification of breast cancer, who received surgery in this hospital and were recruited within 6 months from pathologic diagnosis for their interviews. All information on HR status was verified by pathology reports from the Clinical Pathology Diagnosis Center in this hospital, which classified ER and PR status as negative (IHC −), or positive . Controls were women recruited randomly from Medical Examination Centre in this hospital during the same period according to key inclusion criteria, and anyone who was diagnosed as any benign or neoplastic breast disease, or another malignant disease were excluded. Controls were individually matched to cases as 1∶1 by age (up to 2 years older or younger) and sex.Study participants were interviewed face-to-face by trained personnel using a structured questionnaire. Each interview usually took 40–50 minutes. Participants were asked about a variety of information including: (1) demographic and basic characteristics, such as living area, education, marital status, height and weight; (2) menstrual, contraceptive and reproductive histories; (3) lifestyle factors, such as alcohol consumption, exercise activity and active smoking histories; (4) medical history, such as family history of cancer; (5) for PSE from partner, we asked these questions: (a) How many people living with you smoke cigarettes and/or pipes? (b) How many cigarettes per day were smoked by your partner with whom you live? (c) How long were you exposed to these smokers?All data were checked for completeness and authenticity at the end of each interview by a trained researcher. Those who could not recall their smoking exposure histories or refused to report associated information were excluded. Information bias may influence the association between PSE and breast cancer risk, such as women living with different numbers of family smokers; regular or irregular smokers; or women were active smokers also receiving PSE. We defined the definition of passive smoker as women reported living with a partner, who smoked regularly at home for at least one year, as passive smokers. Women who lived with two or more smokers were excluded from the study. Women who reported smoking 20 packs of cigarettes or more over her lifetime were defined as active smokers and also excluded from this study.Associated PSE variables  were estimated in separate models. Cigarette pack-years were calculated by multiplying the number of cigarette packs per day to which the subject was exposed by the number of years she was exposed. No PSE were taken as the reference; other levels of number of passive cigarettes per day , years of PSE duration  and pack-years  were assessed respectively.2) at five years before interview date was calculated as weight (kg)/height (m2).Women's routine exercise activity was measured in weekly metabolic equivalent task (MET) hours; MET scores 6, 4.5 and 2.5 were categorized as strenuous sports, moderate activity and walking. MET-hour per week for each intensity activity was expressed as MET score multiplied activity time Differences in baseline characteristics were assessed by univariate analysis to select potential confounders, including age at interview, educational level , age (in years) at menarche , parity , oral contraceptive use (no/yes), family history of cancer (no/yes), exercise activity , BMI at five years before interview date, alcohol consumption (no/yes). Confounders were reevaluated in multivariate models to further assess associations between PSE and breast cancer risk. These confounders were selected on the basis of previous established risk factors and potential confounders in this study, including age at interview, age at menarche, menopausal status, oral contraceptive use, family history of cancer, alcohol consumption and BMI.The total samples were then stratified by menopausal status . Associations between PSE and breast cancer risk were further assessed in each subgroup. Additionally, we stratified all cases into ER+ or ER− subgroups, and compared PSE-related risks among these 2 subgroups and controls.t-test for continuous variables and Wald test for trends All statistical analyses were performed with the SPSS 16.0 statistical package . In univariate analyses, statistical comparisons were assessed using two-tailed chi-square test for categorical variables, The study closed on Nov 30, 2009. Out of the 327 eligible case patients, 319 were interviewed, 8 patients refused to participate in the study (non-response rate: 2.4%). Three hundred thirty-nine eligible controls were recruited, and 329 were interviewed finally (non-response rate: 3.0%). The 7(2.2%) cases and 17 (5.2%) controls who could not remember their smoking exposure histories or who had active smoking histories were excluded from our analyses. Finally, data from 312 eligible patients and 312 matched controls as total samples were analyzed. As only 41 ER−/PR+ patients and 20 ER+/PR− patients were included, making them unsuitable for separate analysis, these two kinds of cases were combined to form a single case group in sub-analyses.P = 0.001), to use oral contraceptives , to have first-degree family histories of cancer  and lower BMIs , but fewer were alcohol drinkers . Obvious differences were also seen for PSE. More patients endured PSE at home . However, there were no differences between patients and controls in age at interview, menopausal status, education level, parity, routine exercise activity in terms of MET-hours per week.Comparisons of baseline characteristics and exposure factors inpatients and controls are summarized in P = 0.027) (P = 0.010). Duration of PSE was also correlated with breast cancer risk. Compared with controls, women with PSE less than or equal to 15 years, and those with PSE over 16–25 years had 1.60-fold (95% CI: 0.97–2.64) and 1.87-fold (95% CI: 1.22–2.86) greater risk of breast cancer. However, no association was seen in women with >25 years of PSE history . Cumulative PSE was also significantly associated with breast cancer risk. Compared with controls, adjusted ORs (95% CIs) of women with PSE <4 pack-years, and those with PSE of ≥ 4 pack-years, were 1.12 (0.72–1.76) and 1.71 (1.17–2.50) respectively (P = 0.019). Thus, we found evidence of a dose-response trend in breast cancer risk for amount per day, duration and cumulative amount of PSE.In our study, continuous data of each exposure factor was categorized as mentioned in methods. Category boundaries were shown in = 0.027) . CompareThe association between PSE and breast cancer risk was assessed by menopausal status and further compared in P = 0.009). Relative to controls, adjusted ORs (95% CIs) were 1.91 (1.08–3.38), 1.99 (1.21–3.27), and 1.17 (0.65–2.09) in women having PSE for < 15 years, 16–25 years, and > 25 years, respectively (P = 0.021). Relative to controls, a significant dose-response relationship between cumulative amount of PSE and the risk of HR double positive breast cancer was also shown in women with PSE < 4 pack-years, and those with PSE of ≥ 4 pack-years, whose adjusted ORs (95% CIs) were 1.37 (0.82–2.29) and 1.92 (1.23–3.00) (P = 0.015). On the contrary, there were null associations on all passive smoking factors in ER+/PR−, ER−/PR+, and ER−/PR− patients (P>0.10), but the statistical power was limited by the small sample sizes. Women with PSE ≥25 years had lower risk of breast cancer than did the control group . To sum up, a weak but significant positive relationship between PSE and breast cancer risk was seen for the ER+/PR+ subtype; however, risks to other subtypes are difficult to determine due to limited statistical power.We further explored the relationship between PSE and breast cancer risk by HR status. This trend was only observed in patients with ER+/PR+ group . RelativTo improve the statistical power, we compared PSE-related risks among the ER+ subgroup, the ER−subgroup and controls. In this analysis, PSE remained a significant risk factor when comparing the ER+ subgroup with controls, but was not a significant risk factor when we compared risks in ER– subgroup with its control group, or the ER+ subgroup with the ER− subgroup .Recent studies suggest that PSE is no safer than active smoking These data are consistent with many previous epidemiological studies All the above hinted that there might not existed an identical effect of PSE on breast cancer risk. And we held an opinion that the relationship might depend on ethnicity, breast cancer heterogeneity, and other underlying factors. Therefore future researches should identify high-risk groups among the entire PSE population Moreover, the current data suggest that HR+ breast cancer is particularly associated with PSE, which was consistent with some previous studies of western patients In this study, a weak dose-response trend was seen only in the population who endured ≤25 years of PSE history. Notably those with >25 years of PSE history, the association was null or even slightly inverse  & 3. ThiThe strength of the study was the strict criteria of recruitment and grouping: Participants were all city dwellers, who were presumed to have similar lifestyles. Patients and controls were 1: 1 matched by age (+/− 2 years), which granted intergroup balance as much as possible. We also evaluated PSE quantitatively, which was uncommon compared with the uncertainty of PSE intensity in the previous studies One limitation of the study was its retrospective nature, leading to possible memory bias, especially for recalling partners' behavior among those who were newly diagnosed with cancer. They were more conscious of their disease, and, although they could be more likely to obtain information on their family history and cigarette exposure, were also more prone to exaggerate points that they thought would lead to their disease. This situation may overstate the risk. Additionally, given that each participant reported her husband's smoking behavior, the report of passive smoking, especially as to dose, is a proxy exposure, which might decrease the accuracy. Third, this study was a hospital-based, rather than population-based study, which potentially implies a selection bias. However, recent studies, using the data from this hospital, show that hospital controls are comparable with population controls for most demographic characteristics and lifestyle factors measured To clarify the association between PSE from partners and breast cancer risk in a Chinese urban female population, we performed a case-control study that included 312 breast cancer patients and 312 controls, matched at a 1∶1 ratio by age and residence. We found that PSE was associated with increased risk of breast cancer, with a weak dose-response relationship between PSE and breast cancer, especially in ER+/PR+ cancers. The association between PSE from partners and ER/PR-related breast cancer warrants further study.Table S1Association between passive smoking exposure and breast cancer risk.(DOC)Click here for additional data file.Table S2Relationship between passive smoking exposure and breast cancer risk by ER/PR status.(DOC)Click here for additional data file.


Women with BRCA1 gene mutations have about 30 to 40 times higher risk of ovarian cancer compared to the population risk (the lifetime risk of BRCA1-dependent ovarian cancer is about 40 percent). Because of the high risk BRCA1 mutations carriers at the age of about 40 are recommended prophylactic salpingo-oophorectomy (after completion of reproductive plans). After the surgery there is still a risk of peritoneal cancer which histologically resembles ovarian cancer, but it is much lower than the risk of ovarian cancer before salpingo-oophorectomy: the cumulative risk of developing cancer of the peritoneum is about 4 percent during 20 years from the removal of ovaries and fallopian tubes.We are going to present a case of long lasting diagnosis of pericardial metastases from BRCA1-associated peritoneal cancer.


Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care.We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use.r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02  for PD and 27.63 cm2 (18.22 cm2) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70  for PD, and 1.54 (1.34–1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91–2.45), 2.57 (1.54–4.30), 3.22 (1.91–5.43), 4.88 (2.78–8.55) for PD, and 1.00 (ref.), 1.43 (0.85–2.40), 2.53 (1.53–4.19), 2.85 (1.73–4.69), 3.48 (2.14–5.65) for DA.Median intra-reader reproducibility was high with a Pearson’s PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research. A large body of epidemiologic research indicates that mammographic density (the extent of the breast that appears radiopaque on a mammogram) is strongly associated with breast cancer risk –3. Most Over the last decade, conventional film mammography has largely been replaced with full-field digital mammography (FFDM). Both technologies use X-rays to produce an image of the breast; one image is captured directly on film, while the other is captured as digital data. In FFDM, the raw images are processed for viewing and interpretation by the breast imaging specialist. In addition to improving the image aesthetics and visualization of breast cancer, these processing algorithms, which differ by manufacturer, also reduce the size of the digital file . The rawOnly a few studies of the association of mammographic density with breast cancer risk have been conducted using FFDM images processed for clinical display , 7. ThesThis study is ancillary to a genome-wide association study (GWAS) of mammographic density conducted among approximately 27,000 non-Hispanic white female participants of the Research Program in Genes, Environment and Health (RPGEH), who completed a health survey and provided a saliva sample for genotyping and who had a least one screening FFDM between 2003 and 2013. The RPGEH was developed and is administered by the Division of Research, Kaiser Permanente Northern California (KPNC). Briefly, the RPGEH resource enables research on the genetic and environmental determinants of common, age-related complex health conditions. The resource links together surveys, biospecimens, and derived data, with longitudinal data from electronic health records (EHRs) on a cohort of approximately 200,000 consenting adult KPNC members. Genome-wide genotyping has been performed on DNA extracted from saliva samples of more than 100,000 RPGEH participants enrolled before 2010 (RC2 AG036607).The EHR was used to identify screening mammograms on the study population. Processed FFDMs from 37 different mammography facilities, with one to five machines per facility, were obtained from the KPNC imaging archive. For women with a history of breast cancer, we obtained the closest pre-diagnostic FFDM after the RPGEH survey when available, or prior to the survey date otherwise, and selected the craniocaudal (CC) view of the unaffected breast . For control women, we randomly selected the right CC view for approximately 10 % to blind the reader to case-control status, and used the left CC view otherwise. We excluded women with bilateral breast cancer (n = 15), breast implants (n = 903), whose breasts were too large to be completely imaged on a single exposure (n = 245), or whose images were unreadable (n = 44) or unavailable (n = 625). FFDM images, in Digital Imaging and Communications in Medicine (DICOM) format, were de-identified and downsampled from a pixel size of 70 microns to a pixel size of 200 microns for transfer to the Stanford Radiology 3D and Quantitative Imaging Laboratory. Prior studies of scanned film mammograms have used larger pixel size , which wp < 0.001) improve the reproducibility of readers with little prior experience applying Cumulus to processed FFDM images, and slightly increase the percent density measurements by about two percentage points. As readers gained experience over time, high levels of reproducibility (Pearson’s r >0.90) were attained on processed FFDM images with or without noise reduction. Here, we applied a median filter with a radius of three pixels [FFDMs acquired from Selenia Digital Mammography System machines manufactured by Hologic, Inc. for approximately 21,000 women were randomly assembled into 23 batches of up to 1100 images including 10 % random replicates for quality control. Density measurements were estimated with the Cumulus interactive threshold method . We prevThis case-control study was nested among women between the ages of 40 and 74 years at Hologic FFDM. Breast cancer diagnoses were identified from the KPNC cancer registry, which reports to the California Cancer Registry and to the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) program of cancer registries. The KPNC registry records information on all new primary cancers (except nonmelanoma skin cancer) diagnosed among KPNC members. Data elements and quality assurance measures are similar to SEER. Cases (n = 297) were women with a first primary, unilateral, invasive breast cancer diagnosed after a FFDM. Up to five controls (n = 1149) were selected at random from among women who matched the corresponding case on age at FFDM (exact year), calendar year at FFDM, breast laterality (left or right), and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis in the matched case.Age at mammogram was determined based on date of birth (demographic database) and date of mammogram (mammography database). We used the body mass index (BMI) measured at the patient visit closest to mammogram date when available from the EHR, and computed from self-reported height and weight on the RPGEH survey otherwise. The RPGEH survey provided information on parity and history of breast cancer in a first-degree family member. The KPNC pharmacy database, which records all dispensed outpatient and inpatient prescriptions, was used to determine use of menopausal hormones within the 2 years prior to FFDM.2), it was modeled as both a categorical variable  and as a continuous variable. Total breast area (cm2) was modeled as a continuous variable to facilitate comparison to previous studies [Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with percent density (PD) and with dense area (DA). PD and DA were categorized into quintiles based on their distributions in control women. We applied a square-root transformation to PD and cube-root transformation to DA to obtain normal distributions, and we modeled PD and DA as continuous variables in units of the standard deviation (SD) in controls. To maximize adjustment for BMI  across 23 image batches read over a period of 7 months. The overall mean was 20.02  for PD and 27.63 cm2  for DA.This study included 297 invasive breast cancer cases and 1149 matched controls (Table Odds ratios (ORs) for the association between PD and breast cancer risk are shown in Table Odds ratios (ORs) for the association between DA and breast cancer risk are shown in Table Studies of mammographic density as a risk factor or potential surrogate of breast cancer risk have historically used film-screen mammograms. Now that FFDM has replaced film-screen mammography as the most common breast imaging modality, it is critical to determine whether density measured from digital images, especially processed digital images routinely archived for clinical care, are suitable for research purposes. Our study has shown that breast density measured on processed digital mammograms acquired from multiple Hologic units in general practice settings is reproducible and strongly associated with risk of invasive breast cancer. The associations with breast cancer risk were slightly stronger for PD than for DA, as has been found in studies using film-screen mammograms . Our finThe FDA approved the first clinical digital mammography system for use in the US in early 2000 . The diaTo our knowledge, only one other study has examined breast density measured using Hologic FFDM images in relation to breast cancer risk. Fowler et al.  used botr = 0.92 and 0.87, respectively). Readings from raw and processed images were strongly correlated (r = 0.82), and they had similar means and standard deviations. For raw and processed images, respectively, the mean PD was 21.3 and 22.5 for cases, and 17.7 and 19.8 for controls. PD measured from raw and processed GE digital images also showed similar associations with breast cancer risk (the adjusted OR for quartile 4 vs. 1 was 3.99 for processed and 5.17 for raw images). In another recent study of FFDM images from GE machines [In an early study of FFDM images from General Electric (GE) Senographe mammography machines , Nagata et al.  used an machines , Eng et These initial results from density studies using FFDM images are consistent with results from earlier studies using film-screen mammograms. In a recent meta-analysis of 13 case-control studies of mammographic density and breast cancer risk using density measures from film-screen mammograms, the summary OR for each SD increment of PD was 1.52 for premenopausal women and 1.53 for postmenopausal women [Our study has several strengths. We included FFDMs from multiple different mammography facilities acquired over a 10-year period during 2003–2013, and thus our results are likely to be generalizable to other contemporary multi-institutional studies of Hologic FFDM images. A single radiological technologist conducted all assessments using the operator-assisted Cumulus method, which is the most widely used density measurement method for research studies. We examined both percent density and dense area and were able to adjust for age, BMI, parity, first-degree family history of breast cancer, and use of hormonal therapy – all factors associated with both breast density and breast cancer risk.The study also had some limitations. All study participants are members of the Kaiser Permanente Northern California health plan. While the membership is demographically quite similar to that of the general population in northern California, it does slightly under-represent individuals at the extremes of the socioeconomic spectrum. Nonetheless, members get virtually all their healthcare from the plan, so all mammograms of interest were available to the study.Our study is one of the first to demonstrate that density assessments using Cumulus on processed Hologic FFDM images for clinical display can be highly reproducible and strongly associated with breast cancer risk. These findings add to the growing evidence that FFDM images routinely acquired and stored for clinical care in general practice settings are an appropriate resource that can be leveraged for large-scale research studies of breast density and cancer risk. Our results suggest that the magnitude of associations using FFDM images acquired from Hologic machines, the most common type in the US, are similar to those from GE machines and to film-screen mammography , 2. Furt


TOX3 gene with breast cancer risk were investigated by some Genome-wide association studies and epidemiological studies, but the study results were contradictory. To derive a more precise estimate of the associations, we conducted a meta-analysis. ORs with 95% CI were used to assess the strength of association between TOX3 polymorphisms and breast cancer risk in fixed or random effect model. A total of 37 publications with 97275 cases and 128686 controls were identified. We observed that the rs3803662 C > T, rs12443621 A > G and rs8051542 C > T were all correlated with increased risk of breast cancer. In the stratified analyses by ethnicity, significantly elevated risk was detected for all genetic models of the three SNPs in Caucasians. In Asian populations, there were significant associations of rs3803662 and rs8051542 with breast cancer risk. Whereas there was no evidence for statistical significant association between the three SNPs and breast cancer risk in Africans. Additionally, we observed different associations of rs3803662 with breast cancer risk based on different ER subtype and BRCA1/BRCA2 mutation carriers. In conclusion, the meta-analysis suggested that three SNPs in TOX3 were significantly associated with breast cancer risk in different populations.The associations of SNPs in  BRCA1, BRCA2 and ATM, were verified to be connected with the increased risk of breast cancer2Breast cancer is the most generally diagnosed cancer and the most common cause of cancer death for females all over the world, particularly in the economically developing countriesTOX3 gene, formerly known as trinucleotide repeat containing 9 (TNRC9), is located in the chromosome 16q 12 and has a tri-nucleotide repeat motive. The gene encoded a protein containing a putative high mobility group (HMG) boxTOX3 region and breast cancer susceptibility have been validated by GWASs and epidemiological studies in European, Asian and African American populations6789101112TOX3, and the rs12443621 and rs8051542 are both lied in an linkage disequilibrium (LD) block containing the 5′ end of TOX3The TOX3 rs3803662 was identified to exhibit association with breast cancer by GWASs67et al.81415et al. reported that rs8051542 was significantly correlated with breast cancer risk in TunisiansThe TOX3 gene and breast cancer risk among diverse ancestry populations and distinct tumor subtypes stratified by estrogen receptor (ER) or BRCA1/BRCA2.Meanwhile, the most recent meta-analysis related to the associations between the above-mentioned 3SNPs with breast cancer risk omitted some important studiesTOX3” or “TNRC9” and “polymorphism” or “genetic variant” or “rs3803662” or “rs12443621” or “rs8051542” and “breast cancer” or “breast carcinoma” or “breast tumor” . First, we retrieved all potentially relevant articles, whose abstracts contained information related to our research purpose. Second, the references from eligible studies were carefully checked for additional relevant literature. Finally, only the comprehensive or the most recent study was brought into this meta-analysis, in the case that the same study population was included in several different articles.We carried out a comprehensive literature search from PubMed and EMBASE databases up to March 2015, using the following search terms “TOX3 polymorphism  and breast cancer risk; (2) odds ratio (OR) and 95% confidence interval (CI) or genotype data of rs3803662, rs12443621 or rs8051542 in breast cancer patients and cancer-free female to calculate OR and 95% CI; (3) studies were confined to human female groups; (4) articles in English.Eligible studies had to fulfill the following criteria: (1) case-control studies or cohort studies evaluating the association between TOX3 rs3803662; GG, AG and AA genotypes for rs12443621; TT, CT and CC genotypes for rs8051542) and P-value for the HWE in control groups. Two investigators independently extracted the above relative data with any disagreement resolved by discussion. If no consensus wasn’t reached, another investigator joined in the discussion. And the final decision was made by the majority of the votes.A standard protocol was applied to extract data. For every eligible study, the following data were extracted: First author’s surname, year of publication, country of origin, population ethnicity, genotyping method, the genotype counts in cases and control  was assessed by using χ2 test to compare expected and actual genotype frequencies among controls of each study. Q-statistic was applied to investigate heterogeneity among studies. P-value greater than 0.1 for Q test suggested a lack of statistically significant heterogeneity, and the fixed-effect model (Mantel-Haenszel method)I2-test was employed to accurately measure the degree of heterogeneity. Furthermore, the I2-value less than 25% was equivalent to mild heterogeneity, and values between 25% and 50% was equivalent to moderate heterogeneity, whereas values greater than 50% was equivalent to large heterogeneity among studies. Potential publication bias was estimated by symmetry of funnel plot of OR versus the standard error of log (OR) and the visual symmetrical plot indicated that there was no publication bias among studies. Sensitivity analyses were conducted to assess the robustness of the results by eliminating each study in turn to show whether the individual data set influenced the pooled OR. Stratified analyses were conducted in terms of ethnicity, estrogen receptor (ER) status, BRCA1 and BRCA2 mutation. All statistical tests in this meta-analysis were two-tailed and P-value ≤ 0.05 was considered statistically significant unless otherwise noted. All statistical analyses were performed with Review Manager 5.2 software recommended by Cochrane Collaboration and Comprehensive Meta Analysis V2 software.The strength of associations between et al. and Garcia-Closas et al.37731111516262630Based on the above selection criteria, a total of 37 eligible studies were included in the pooled analyses, involving 97275 cases and 128686 controls for rs3803662 polymorphism789101112131415161721222324252627282930313233343536373839404142434445812141516172428293141424581215161728293139414245The mixtures of adjusted and crude estimates were used to calculate pooled ORs. The available adjusted variables of included studies were listed in In the subgroup analysis by ethnicity, our results indicated statistically significant associations between the three SNPs and breast cancer susceptibility in Caucasians under all genetic models. Nevertheless, as for Asian populations, T-rs3803662 and T-rs8051542 were shown to be statistically significant correlated with increased risk of breast cancer in all genetic models. In addition, there was no evidence for the statistical significant associations between the three SNPs and increased risk of breast cancer in African population which were almost African-American in our study. The pool ORs and 95%CI for these stratified analyses were detailedly shown in + and ER− tumor  block2) of 0.29 in the HapMap CEU population for European ancestry, but very weak LD was found between these two SNPs (r2 = 0.06) in Chinese2 = 0.08) between rs3803662 and rs8051542 located 52 kb apart from each other in Chinese women2 = 0.15) with each other in European populations41The three SNPs locate in the 5’ end of TOX3 were correlated with elevated breast cancer risk in all genetic models. It is notable that a previous meta-analysis directed by Chen et al.In general, our study proved that the T-rs3803662 and T-rs8051542 in + and ER− subtype of breast cancer, in spite of the slightly weaker association for ER− breast cancer. Additionally, our study demonstrated that T-rs3803662 was statistically significant associated with increased risk in ER+ breast cancer compared with ER− subtype, which was accordance with the researches done by Stacey et al. and Broeks et al.7+ breast cancer in Chinese women8+, Her2−) and Her2+/ER− breast cancer only among whites, respectively25TOX3 gene and pathological subtype of breast tumor among different populations. And in another aspect, these studies provided further support for the hypothesis that different subtypes stem from diverse etiological pathways. Additionally, rs3803662 SNP in BRCA1 and BRCA2 mutation carriers was significantly associated with the increased risk of breast cancer in our analysis, which was in consistent with previous studies26et al. confirmed that the genetic variant was only associated with breast tumor in BRCA2 mutation carriersBRAC1 and BRCA2 mutation.To date, more attention has been paid to the heterogeneity of associations between common genetic variants and breast cancer subtypes. The two large-scale studies37Despite the advantage of large sample size and stratified analyses, the meta-analysis had several limitations that should be taken into account. First, there was extreme heterogeneity for the outcomes of the association between rs3803662 polymorphism and breast cancer risk. Although we reduced the degree of heterogeneity by stratified analyses based on ethnicity, other sources of heterogeneity were not verified, such as different genotyping methods or tumor types. Second, the sample size of African populations (5462 cases and 7155 controls) was relatively small. Therefore there was insufficient statistical power to demonstrate the associations between the 3SNPs and breast cancer risk in Africans. Third, the criterion of control groups was not uniformly defined. The design of eligible studies was based on population or/and hospital patients, thus there were potential risks of breast cancer in control groups. Fourth, the mixtures of crude and adjusted publish estimates, rather than incorporation of adjusted ORs, were used in the meta-analysis. Because of the lack of some individual data, we were unable to adjust effect size with possible confounders related with lifestyle risk factors, such as age, obesity, smoking, alcohol consumption and menopausal status. Furthermore, we were unable to examine the interaction between genetic variables and environment. In recent years, some studies for gene-environment interactions showed that relative risks of breast cancer correlated with low-penetrance susceptibility variants (including rs3803662) didn’t vary significantly with established environmental risk factors, such as reproductive history, menopausal status and body mass index5152TOX3 gene and breast cancer risk in different ethnic groups or subtype tumor. The 3SNPs were associated with the increased risk of breast cancer in Caucasians, while weren’t correlated in Africans. Additionally, rs3803662 and rs8051542 were risk factors for breast cancer in Asians. Furthermore, there were stronger associations between rs3803662 polymorphism and breast cancer risk in ER+ subtype than ER− tumors. Increased risk of breast cancer associated with rs3803662 was confirmed in BRCA1/BRCA2 mutation carriers. However, studies with larger sample size, which use uniform genotyping methods and criterion of control groups, have sufficiently corresponding individual data and consider the interactions of gene-gene and gene-environment will be needed to verify our results for TOX3 rs3803662, rs12443621 and rs8051542 as predisposition markers to breast cancer in clinical application.In conclusion, this meta-analysis indicated that there were different associations between the 3SNPs in How to cite this article: Zhang, L. and Long, X. Association of three SNPs in TOX3 and breast cancer risk: Evidence from 97275 cases and 128686 controls. Sci. Rep.5, 12773; doi: 10.1038/srep12773 (2015).


Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI . In multivariable analysis, absolute dense area  and absolute dense volume  were jointly associated with breast cancer , having a larger discriminatory capacity than models considering the Gail risk factors alone  or the Gail risk factors plus standard area percent density . After BMI was further adjusted for, absolute dense area retained significance  and volume percent density approached significance . This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).All automated density measures had a significant association with breast cancer  contains supplementary material, which is available to authorized users. It is increasingly recommended that breast cancer screening recommendations be personalized on the basis of a woman’s risk for breast cancer , 2. ThisAn opportunity for improving the discriminatory accuracy of risk assessment models is the incorporation of breast density . TypicalTo date, studies evaluating the use of volumetric versus area density measures in breast cancer risk assessment have focused primarily on determining which individual measure is a better predictor of risk and have reported mixed results , 25, 26.P > 0.05).In this Health Insurance Portability and Accountability Act-compliant study approved by the institutional review board at the University of Pennsylvania (protocol #814186), we retrospectively identified women whose unilateral, invasive breast cancer was diagnosed at age 40 years or older from a previously completed multimodality breast imaging trial in our institution . The trial recruited a total of 901 women, each of whom met at least one of the following inclusion criteria: they were presenting for staging with newly diagnosed breast cancer, or they had a mammographically detected suspicious finding (BI-RADS of at least 4) after screening or diagnostic evaluation (or both) and were directed to biopsy, or they had an otherwise-suspicious palpable mass directed to biopsy, or they were evaluated to be at a high risk for developing breast cancer as determined by an estimated high lifetime risk of more than 25 % (using either the Gail or Claus risk models), or they had a recently diagnosed contralateral breast cancer. All women received an array of breast imaging modalities, including digital mammography. Informed written consent was obtained prior to study participation. From these women, 317 were diagnosed with primary breast cancer, of which 231 were invasive. Of the 231 women diagnosed with invasive breast cancer, 146 had raw  digital mammograms available for analysis . From this subset of 146 women, 10 were excluded for having bilateral breast cancer and thus not having a cancer-unaffected mammogram available for our analysis, and eight were excluded for having mammograms with artifacts or insufficient image quality , preventing valid breast density estimates from being obtained accurately with our automated software. An additional 22 women were excluded for being under the age of 40 and therefore not representative of a standard screening population. Thus, a total of 106 women with invasive cancer and no personal history of breast cancer were included in our analysis. No statistically significant differences in risk factor distributions between the included and total excluded women in this study were observed  where digital mammography was available to the general screening population and had raw digital mammograms available. Controls were matched to cases at a 1:3 ratio at 5-year age intervals. This resulted in 318 controls available for analysis, yielding a total cohort size of 424 women (average age of 55.1 ± 8.9 years). For density estimation, we used the mediolateral oblique (MLO) view mammogram of the contralateral, unaffected breast of cases as a surrogate of inherent breast tissue properties; MLO mammograms of controls were subsequently side-matched to cases. For this retrospective analysis, the requirement of informed consent was waived under institutional review board approval.To account for possible confounding of the breast density measures by other known risk factors, we also considered self-reported information regarding standard demographic and reproductive risk factors for breast cancer . For canAs the Gail model  is the c2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), or obese (30 kg/m2 ≤ BMI). In this study, BMI was treated as an ordinal variable.In addition, BMI was abstracted from archived clinical records. Based on available height and weight information, women were classified as normal weight  of similar x-ray attenuation  were estimated by using a publically available software tool , the Labion Fig. . These cion Fig. . The ratm et al.  adapted m et al. . BrieflyLastly, for comparison with the automated density measures, we also obtained standard four-category BI-RADS density estimates via retrospective review of archived clinical reports, in which the density assessment was made at the time of routine clinical evaluation by the interpreting breast radiologist for that individual mammography study.t tests for continuous variables at the standard α = 0.05 significance level. The associations between PD %, VD %, absolute dense area, and absolute dense volume were assessed by using linear regression and Spearman correlation, as were the associations between the four density measures and BMI. Univariate  and multivariable  logistic regression was performed to assess the association between the different log-transformed breast density measures and breast cancer before and after adjusting for the standard risk factors and BMI. Odds ratios (ORs), 95 % confidence intervals (CIs), and significance were estimated for all risk factors. For categorical variables, we used a test-for-trend analysis, except for race and parity which were treated as nominal variables. ORs for continuous density measures are reported per standard deviation increase based on the distribution of the breast density metrics in the controls. Missing risk factors  commonly used in the literature [The area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the discriminatory capacity of each unadjusted and adjusted breast density model . The adjterature  both to terature . These fP = 0.36), first-degree family history (P = 0.89), race (P = 0.22), and age at first live birth (P = 0.08) while being significantly different in terms of their BMI (P < 0.001) and age at menarche (P = 0.02). In terms of mammographic density estimated via automated software, higher absolute density estimates were significantly associated with cancer status (P < 0.001), as were the percent density estimates (P ≤ 0.002), regardless of whether area or volumetric density measures were considered, whereas BI-RADS density estimates were not significant (P = 0.65). Table Women in case and control groups were similar in terms of number of prior benign biopsies ( P < 0.00 and age P < 0.001) correlations were observed between the different quantitative density estimates (Spearman correlation: ρ = 0.24–0.73). Area percent density and absolute dense area had the strongest correlation . Absolute dense volume and area percent density had the weakest correlation . Figure Statistically significant (P < 0.05) correlations were observed between the different quantitative density estimates and BMI (Spearman correlation: ρ = −0.31–0.42). Absolute dense volume and BMI had the strongest correlation , whereas absolute dense area and BMI had the weakest correlation . Figure Statistically significant (P < 0.001) and age at menarche (P = 0.01) were significantly associated with breast cancer for our study population, with later age at menarche being a protective factor  and increasing BMI a risk factor . Parity after age 30 years was also observed to be a risk factor , as was parity prior to age 30 . Before adjustment for the standard Gail risk factors and BMI, all four quantitative breast density measures were statistically significantly associated with breast cancer at the standard level  or the Gail risk factors (Table P < 0.05) but not higher than BMI (AUC = 0.76). In addition, the discriminatory capacity of absolute dense area and absolute dense volume were not found to be significantly different (P = 0.38), nor were area percent density and volume percent density (P = 0.63).Univariate analysis of the standard risk factors Table  shows thP < 0.003) relative to the model including only Gail risk factors. As with the unadjusted models, no difference was observed in the discriminatory capacity between the absolute (P = 0.57) or percent (P = 0.78) area and volume density measures. Finally, when both the standard Gail risk factors and BMI were adjusted for (Table P < 0.001), and most had strengthened associations with breast cancer relative to their unadjusted associations as evidenced by the lower P values, or higher ORs, or both. The discriminatory capacities of the absolute dense area (AUC = 0.85), percent dense area (AUC = 0.85), and percent dense volume (AUC = 0.83) metrics were also significantly (P ≤ 0.005) higher than the baseline standard Gail risk factors and BMI model (AUC = 0.80). Furthermore, after adjustment for both Gail risk factor information and BMI, absolute dense area was found to have a significantly (P = 0.009) larger discriminatory capacity (AUC = 0.85) relative to absolute dense volume (AUC = 0.82), whereas the difference between the area and volume percent density measures only approached significance (P = 0.08). In contrast, BI-RADS density was not found to be significantly associated with breast cancer in our study population either before or after adjustment for other risk factors.After adjustment for the Gail risk factors Table , the asste P = 0.7 or percP = 0.03). A combined, feature-selected model considering all four different breast density measures in combination with the Gail risk factors had an AUC of 0.77 (95 % CI 0.71–0.82), which was significantly higher than both the model including only the Gail risk factors (P < 0.001) and the model including the Gail risk factors plus area percent density (P < 0.001). In this combined model, absolute dense volume and absolute dense tissue area were jointly significant predictors of breast cancer (P ≤ 0.003), while area percent density, volume percent density, and BI-RADS density estimates were not retained (P > 0.1).In multivariable analysis Table , a logisP < 0.001). A combined, feature-selected model considering all four different breast density measures led to a statistically significant improvement in performance (AUC = 0.86) versus the baseline Gail-BMI model (P < 0.001) but not the Gail-BMI plus area percent density model (P = 0.64). In this combined model, absolute dense area was retained as a significant predictor after feature-selection (P < 0.001); volume percent density was also retained by the feature selection process as it approached significance (P = 0.06).Adjusting for BMI in addition to the Gail risk factors Table  also ledAdditional file P < 0.001). Overall, our study serves as a preliminary evaluation that will guide the design of larger prospective studies that will rigorously evaluate the predictive value of fully automated measures in risk assessment. In addition, our study is the first to evaluate the new, publically available LIBRA software [To date, compared with area-based measures of percent density which have been more broadly established as a risk factor, studies evaluating the use of volumetric density measures in breast cancer risk assessment have primarily focused on determining which individual measure is a better predictor of risk and have reported mixed results , 25, 26.software  tool in Previous studies have investigated the potential value of breast density in breast cancer risk prediction, primarily using digitized film mammograms , 40–42. Studies comparing volumetric estimates of breast density to area-based measures have primarily focused on determining which single type of density measure is a better predictor of risk , 25, 26 Although the etiological basis of breast density’s association to risk is not yet fully understood , an addiP < 0.05), which is further enhanced when also adjusting for BMI . The improved performance in our study may be partially due to the use of digital mammography and the use of fully automated measures of density, allowing more precise estimates, potentially alleviating the bias introduced by intra-reader variability in manual assessment [In our study, the model fit using only standard risk factors had an AUC of 0.64, which is in range of the 0.58 to 0.67 commonly reported in the literature for models based only on demographic risk factors , 21, 41.sessment  or the dIt is worth noting that volumetric breast density methods which estimate breast density from two-dimensional (2D) mammography, such as the one used in this work, do not directly measure the “true” volume of the dense fibroglandular tissue in the breast but rather a surrogate estimate based on imaging physics and validated assumptions of the breast anatomy . Recent In our study, we found that BMI has a negative correlation to both area and volume percent density (ρ = −0.32 to −0.16) and positive correlations to absolute dense area (ρ = 0.10) and absolute dense volume ρ = 0.42), which are similar to what has been seen in prior work . Prior s2, which Although there appear to be significant differences between the individual breast density estimates Table , we obsePepe et al. proposed five phases for identifying and validating predictive biomarkers , in whicLimitations of our study include that our analysis is confined to a relatively small sample size and the use of a case–control study design, in which we used contralateral mammograms at the time of diagnosis, rather than prospective follow-up. Of note, certain risk factors known to be associated with breast cancer were not found to be significant in our study, specifically family history, prior biopsy count, and BI-RADS breast density. This is likely because our study was underpowered to reliably detect subtle differences between cases and controls for these measures. Furthermore, it is known that the matching process in case–control studies may introduce biases in assessing biomarker performance  because We demonstrate that novel fully automated area and volumetric-based breast density measures assessed via digital mammography are associated with breast cancer after adjustment for standard risk factors, such as Gail factors and BMI. These quantitative measures, including volumetric and absolute fibroglandular tissue estimates, could ultimately improve breast cancer risk prediction by providing additional information regarding a woman’s risk for breast cancer, compared with standard risk factors alone. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols. As such, our observations will be instrumental in guiding the design of larger prospective studies that will more rigorously validate the predictive value of such new fully automated, quantitative, breast density measures in larger populations.


Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis , SNP panel relative risk  and BMI  were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women  had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women.In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users. Though mammography screening reduces breast cancer mortality, it is imperfect like all screening tests. The high burden of false-positive tests relative to the number of cancers detected has contributed to controversy about the routine use of mammography screening among women ages 40 to 50, as well as about biennial rather than annual screening . After 1Using pretest probability of disease can improve the positive predictive value of a screening test. However, this approach requires the ability to accurately determine an individual’s risk of disease. The Breast Cancer Risk Assessment Tool (BCRAT), or Gail model, uses age, family history of breast cancer, reproductive history and history of breast biopsy or atypical hyperplasia to estimate a woman’s 5-year or lifetime risk of breast cancer . AdditioAlthough many studies have focused on predicting cancer risk in the general population, few have employed risk prediction models to improve decisions about follow-up of abnormal mammograms. Current standards in the United States recommend biopsy of a mammographic abnormality if the radiologist deems the probability of cancer diagnosis to be at least 2% -22. MammIn this study, we assessed the usefulness of the Gail model, BMI and a panel of 12 single-nucleotide polymorphisms (SNPs) to predict cancer diagnosis among women with BI-RADS 4 mammograms. We then evaluated the extent to which these factors could improve decisions about biopsy among this group by reclassifying women without cancer below the biopsy threshold.BRCA1/2 mutation. Women who consented provided a buccal swab for DNA testing prior to their biopsy appointment. Three hundred sixty-three women were enrolled. An additional 119 women with a BI-RADS 4 mammograms from a previous study in which breast imaging modalities were compared at the same institution were also included . Participants in the breast imaging study were enrolled between July 2003 and August 2007. A blood sample from each patient was collected and stored, which was used for genetic analysis. Of the total sample, five patients were missing follow-up information, eleven had data on fewer than nine SNP markers and two had nonbreast malignancies . These participants were excluded, resulting in a total population of 464 for analysis. Both studies were approved by the University of Pennsylvania Institutional Review Board, and written informed consent was obtained from each study participant.Women referred for breast biopsies at the Hospital of the University of Pennsylvania following a BI-RADS 4 mammogram between January 2010 and April 2012 were invited to participate in the study. Women were excluded if they were younger than 20 years old, had a personal history of breast or ovarian cancer, mantle radiation or known Participants completed a health history questionnaire, including information on race, age at menarche, age at first live birth, number of biopsies, presence of atypical hyperplasia and family history of breast and ovarian cancer. Using the BCRAT, we estimated the 5-year absolute risk and relative risk (RRs) of breast cancer using source code version 3.0 from the National Cancer Institute website . BMI wasN = 347) or blood samples (N = 117) were sent to deCODE genetics  for analysis using Illumina Infinium II whole-genome genotyping . The deCODE genetics SNP assay included 12 loci that have consistently been associated with breast cancer risk: 2q35 (rs13387042), MRPS30 (rs4415084), FGFR2 (rs1219648), TNRC9/TOX3 (rs3803662), 8q24 (rs13281615), LSP1 (rs3817198), 5q11 (rs889312), NEK10 (rs4973768), 1p11 (rs11249433), RAD51L1 (rs999737), COX11 (rs6504950) and CASP8 (rs1045485) [Buccal swabs  . The calples N = 7 were seples N = 7 were sein situ (DCIS)). First, each predictor was tested in an age-adjusted model. SNP panel RRs were examined as a log-transformed continuous variables and as categorized RRs <1.00, 1.01 to 1.49 and ≥1.50. The Gail RR was tested as a log-transformed continuous variable. Gail absolute 5-year risk estimate was categorized as <1.67% and ≥1.67%, as these cutoffs have been widely used to denote high risk of breast cancer, as well as for the use of chemopreventive drugs [2, 25 to 29.9 kg/m2, ≥30 kg/m2 and missing. The multivariate logistic regression model included log-transformed SNP panel RR, all Gail risk factors  and BMI. We also examined the predictive ability of the various risk factors. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test to compare observed and predicted outcomes within deciles of predicted risk for each model [The results of the BIRADS 4 biopsies were obtained from pathology records. Logistic regression was used to assess the association of Gail risk factors, BMI and SNP panel RR with cancer diagnosis , and approximately one-half of the study population was over age 50 . The ORs estimated in our model for the categorized SNP panel RRs were comparable to the predefined RR estimates obtained from deCODE genetics. The Gail RR estimate was not significantly associated with cancer diagnosis, nor was Gail absolute 5-year risk ≥1.67%. Among the Gail factors, only age was significantly associated with breast cancer diagnosis, though the ORs for race/ethnicity, age at menarche, age at first live birth and family history of breast cancer were consistent with expected associations. Prior breast biopsy and atypical hyperplasia were inversely associated with breast cancer, though these data were not statistically significant. Few participants (4.3%) reported prior atypical hyperplasia.Table P < 0.001). The SNP panel RR remained strongly associated with breast cancer diagnosis after multivariable adjustment . Higher BMI was also strongly associated with increased odds of breast cancer diagnosis. Obese women  had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m2.In the multivariate model, age, SNP panel RR and BMI were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer  and black (N = 145) women (Table P = 0.053) and multivariate  models, and OR estimates were similar for the SNP panel RR categories and predefined values. There was evidence that the SNP panel RR was associated with breast cancer diagnosis among black women. Among black women, the OR estimate was 4.50 in the age-adjusted model  and 4.21 in the multivariate model adjusted for age, Gail factors and BMI , though these estimates did not reach statistical significance. In addition, the OR estimates for the SNP panel RR categories were similar to the predefined RR values. There was no significant interaction between race and the SNP panel RR (P = 0.880).Next, we evaluated the association of the SNP panel separately for white (en Table . Among wP = 0.0019). All other models exhibited acceptable model fit (P > 0.05). The predictive accuracy was similar for age and the SNP panel RR (0.6848) and age and BMI (0.6845). Age, BMI and the SNP panel RR together yielded an AUC of 0.7007, which was of borderline significance compared to age alone (P = 0.061).We compared the predictive accuracy of the Gail factors, BMI and SNP panel RR Table . First, P = 0.044). Adding BMI to the Gail risk factor model increased the AUC (0.7279), but the difference was not statistically significant (P = 0.341). Subsequently adding the SNP panel RR to the model further increased the AUC . We repeated analyses stratified by age (35 to 49 years and ≥50 years) and found that the addition of BMI and SNP panel RR improved predictive accuracy compared to the Gail factors alone in both age groups, though the AUC values were greater for women ages 50 and older. The addition of the SNP panel had a greater impact on the AUC in the 35 to 49 age group than in women ages 50 and older. When stratified by race, the AUC values were comparable for black women and white women. For the model including Gail factors, BMI and SNP panel RR, the AUC was 0.7518 for white women and 0.7710 for black women. We repeated our analyses excluding women younger than 40, and the results were similar. We performed tenfold cross-validation on the prediction models in the total study population  compared to a model with age alone  or the Gail model including breast density (0.634) . ResearcObese women had more than twice the odds of receiving a cancer diagnosis compared to women of normal weight. One possible explanation for this association is that obese women tend to have less-dense breasts and therefore potentially easier-to-read mammograms, which facilitates a more accurate interpretation of their mammograms by radiologists, such that obese women with a BI-RADS 4 mammogram are more likely to actually have cancer  than nonobese women. The association of BMI with cancer diagnosis may also reflect disease etiology, as BMI is associated with increased risk of postmenopausal breast cancer . AlthougThis study was a first attempt to validate the 12-SNP panel among black women. The SNP panel variants were identified and validated primarily in white/European populations. Several genome-wide association studies and candidate gene studies -60, and N = 75), our study did not have statistical power to fit separate models for DCIS and invasive cancer or to assess interactions between risk factors. We utilized a validated panel of 12 breast cancer–associated SNPs. To date, nearly 70 SNPs have been identified that are associated with breast cancer risk [Several limitations should be considered when interpreting our results. Because we recruited women referred for biopsy at one academic hospital, our study sample may not be representative of all women with abnormal mammograms referred for biopsy. Our sample size was modest, and therefore our results, particularly those of subgroup analyses, should be interpreted cautiously. We performed cross-validation of our prediction models for the entire study sample; however, prospective validation of our results is needed. Given the limited number of cancers (cer risk . TherefoThis study has several strengths. Ours is one of the first studies to develop a cancer prediction model for women with abnormal mammograms. We had rich data on recognized breast cancer risk factors ascertained prior to biopsy. We employed a validated panel of genetic markers associated with breast cancer incidence, with RR estimates independent of traditional breast cancer risk factors. Our study population was diverse in terms of age and race/ethnicity, suggesting that our model could be applied broadly.Our results suggest that pretest breast cancer risk factors could be utilized to individualize biopsy decisions following abnormal mammograms. We found that age, BMI and a 12-SNP panel were significantly associated with breast cancer diagnosis in women with BI-RADS 4 mammograms. The association of obesity with cancer diagnosis was particularly novel and warrants additional investigation. On the basis of results derived from the model using Gail risk factors, BMI and genetic markers, we were able to identify a predicted probability threshold that could be used to identify women who would not benefit from immediate biopsy. Our study, though preliminary, highlights that improved risk modeling for women with abnormal mammograms could reduce the burden of false-positive tests and therefore increase the benefits of mammography. Future studies are needed to validate these results in larger patient populations.


However, this positive association was not detected in the premenopausal and ER-/PR- patients. In conclusion, the present study provides evidence supporting a significantly positive association between plasma prolactin levels and the risk of breast cancer.Breast cancer is the most common cancer among women, and its incidence is on a constant rise. Previous studies suggest that higher levels of plasma prolactin are associated with escalated risk of breast cancer, however, these results are contradictory and inconclusive. PubMed and Medline were used to search and identify published observational studies that assessed the relationship between plasma prolactin levels and the risk of breast cancer. The pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a fixed-effects or random-effects model. A total of 7 studies were included in our analysis. For the highest versus lowest levels of plasma prolactin, the pooled RR (95% CI) of breast cancer were 1.16 . In subgroup analyses, we found a positive association between plasma prolactin levels and the risk of breast cancer among the patients who were postmenopausal, ER As a notorious malignant disease, breast cancer represents the most common type of cancer among women. Unfortunately, the incidence rate of breast cancer has been rising rapidly over the past decades both locally and globally. For instance, breast cancer is currently the second leading cause of cancer-related death in the United States46et al. showed evidence from elegant studies indicating that higher levels of plasma prolactin were associated with an increased risk of breast cancer in both premenopausal and postmenopausal women, which was especially prominent for the estrogen receptor+/progesterone receptor+ cancer type8Notably, Tworoger PubMed and Medline databases were used to identify relevant studies that were published no later than February 2016 with focused on the association between plasma prolactin levels and the risk of breast cancer. The following phrases and terms were used in the searching strategy: “serum prolactin” or “plasma prolactin” in combination with “breast cancer” or “breast carcinoma”. References of the relevant reviews were also scrutinized.Studies were deemed eligible if they met the following inclusion criteria: 1, designed as a case-control or cohort study; 2, assessed the association between serum/ plasma prolactin levels and the risk of breast cancer; 3, all cases of breast cancer diagnosed and verified by pathological biopsies or other standard methods, with controls being females without breast cancer; and 4, reported relative risk (RR) or odds ratio (OR) and the corresponding 95% CI for the highest versus the lowest levels of plasma prolactin.According to the above-described inclusion criteria, the titles and abstracts were reviewed to determine whether a candidate study was potentially eligible. For those that were difficult to determine, a full-text assessment was conducted. The literature selection was conducted in replicates by two independent authors. All disputes, if any, were resolved by discussion.The following information was collected from the included studies: the last name of the first author, publication year, sample size and RR from multivariable models for the highest versus the lowest category of plasma prolactin levels with the corresponding 95% CI.Evaluation of the study quality was conducted using the Newcastle-Ottawa quality assessment scale (NOS)2 statistic. Subgroup analysis was performed to identify various factors that influenced the pooled estimate. Additionally, sensitivity analysis was used to investigate the influence of a single study on the overall risk estimate by excluding one study at a time. Publication bias was detected by Begg’s and Egger’s test. P-value less than 0.05 was considered as statistically significant.STATA version 12.0  was used to analyze the data. The combined RR and the corresponding 95% CI were used to measure the association between plasma prolactin levels and the risk of breast cancer using a fixed-effects model or random-effects model. Homogeneity test was performed with Q statistic and IWe initially identified 572 potentially eligible articles from PubMed and Medline. However, most of them were excluded primarily because they were review articles, reports, animal studies or irrelevant to our analysis. After reviewing the remaining 28 articles, we decided to exclude 15 studies that did not report the highest versus the lowest levels of plasma prolactin and 6 studies that reported duplicate data from the same participants. Ultimately, we selected 7 eligible studies (1 case-control and 6 cohort studies) that involved 12275 participants and 6388 cases101213141516in situ1012131415The baseline characteristics of the included 7 studies were presented in 2 = 7.4%).The adjusted RRs for each study, as well as for all the studies combined, were described in relation to breast cancer risk with the highest versus the lowest levels of plasma prolactin in 1012131415in situ carcinoma, respectively. Lastly, our results also revealed a positive correlation between plasma prolactin levels and risk of breast cancer among the cohort studies (RR (95% CI) as 1.17 ).et al. in 2013 and by Tikk et al. in 2015. However, after removing these two studies, the positive relationship between plasma prolactin levels and risk of breast cancer was still identified, which indicated statistical stability and reliability of our results  as 0.99 ). This may be possibly explained by the great number of postmenopausal women who participate in the blood draw in our studies. New cases that were added to our pool were more likely to be postmenopausal at diagnosis. Combined together, these results indicated that measurement of plasma prolactin levels during the postmenopausal period may be more important and accurate in predicting the risk of breast cancer than during the premenopausal period.in vitro studies demonstrating that long-term prolactin exposure elicits elevated ER expression19In addition, our analysis revealed a positive association between plasma prolactin levels and the risk of breast cancer in patients who were positive for ER+/PR+, but not for the other ER/PR status. This is not a surprising observation because a previous study has demonstrated that prolactin is highly likely linked to the development of ER+ tumors and, importantly, estradiol and prolactin exert a synergistic effect on cell proliferationMeanwhile, our systemic interrogation uncovered a positive association between plasma prolactin levels and the risk of breast cancer in both primary and invasive tumors. Previous literature has demonstrated that prolactin plays an indispensable role in the initiation and development of breast cancer through inducing cell proliferation and inhibiting apoptosisin vitro21It has to be noted that although we identified a significant positive association between plasma prolactin levels and the risk of breast cancer, the mechanisms underlying this relationship remain entirely elusive. Prolactin plays an essential role in the growth of mammary glands and the process of lactation. Notably, a previous study has shown that exogenous administration of prolactin strongly increases the likelihood of mammary tumor formation, and, consistently, suppression of prolactin levels exerts an opposite effect in animal modelsOur results need to be interpreted with precautions for several reasons. Foremost, circulating prolactin in human plasma exists in multiple distinct forms, which may assume different biological functionsDespite the above-mentioned drawbacks of the analysis, the present study has some important merits that deserve special attention. Foremost, our study represents the first attempt to assess the relationship between plasma prolactin levels and the risk of breast cancer by combining all observational studies. It is widely accepted that analysis of individual studies harbors insufficient statistical power. In contrast, we identified and combined all eligible studies in the present study that together constitutes a large sample size, which provided more reliable results with greatly enhanced statistical power. With this strategy, we revealed a positive association between plasma prolactin levels and the risk of breast cancer. Our observations further supported that, in addition to its utility as a biomarker, prolactin may also be a risk factor for breast cancer.Our results demonstrated a positive relationship between plasma prolactin levels and the risk of breast cancer. Particularly, such correlation is markedly prominent for those who are diagnosed as the ER+/PR+ subtype and postmenopausal patients.How to cite this article: Wang, M. et al. Plasma prolactin and breast cancer risk: a meta- analysis. Sci. Rep.6, 25998; doi: 10.1038/srep25998 (2016).


Rates of contralateral risk-reducing mastectomy have increased substantially over the last decade. Surgical oncologists are often in the frontline, dealing with requests for this procedure. This paper reviews the current evidence base regarding contralateral breast cancer, assesses the various risk-reducing strategies, and evaluates the cost-effectiveness of contralateral risk-reducing mastectomy. Breast cancer is the most common cancer in women worldwide, with 1.7 million new cases diagnosed in 2012  during tBreast cancer survivorship has improved as a result of early detection and advancing treatment modalities . As suchOnce diagnosed with breast cancer, these women will have an increased risk of developing a contralateral, metachronous breast cancer . The levThere is ongoing interest in contralateral breast cancer (CBC). Surveillance, Epidemiology, and End Results Program (SEER) data from the US  has confWe examine the multiple risk factors known to contribute to developing CBC. Survival data are analysed and the contribution of the following risk factors is discussed: gene mutation and family history, histology, ER status, and HER2 status. We evaluate the different risk-reducing strategies (surgery and chemoprevention) and their efficacy and cost and finally consider the patient's perspective. We have included chemoprevention using antiendocrine treatment in the section of ER status as this is relevant to this section.This review aims to serve as an aide-memoire for clinicians to refer to when counselling women on CBC.BRCA1/2, TP53) and a significant family history. BRCA1/2 mutation carriers have a CBC risk of 2-3% per annum [ TP53 patients, though there is limited data on this group of patients [High-risk patients include breast cancer sufferers with known genetic mutations  quote actuarial incidence rates of CBC in this group of women at 5, 10, 15, and 20 years were 3%, 6.1%, 9.1%, and 12%, respectively, amounting to 0.6% per annum . This le BRCA1 and BRCA2. Mutations in these tumour suppressor genes confer an up to 80–90% lifetime risk of developing breast cancer [The two most commonly studied breast cancer susceptibility genes aret cancer –14.These women have a significant CBC risk once diagnosed with breast cancer. BRCA1 mutation carriers has been estimated to be above 60% [ BRCA2 mutation carriers slightly lower at around 50% [ BRCA1 where the majority of tumours would not receive endocrine therapy.The risk, up to age 70 years, of a CBC inbove 60%  and in Bound 50% . Howeveround 50%  has show BRCA1/2 mutation carriers [Several studies have evaluated further CBC risk factors withincarriers , 10, 11.carriers , 17 and  TP53 mutation in Li-Fraumeni syndrome. These mutation carriers will have a nearly 100% lifetime risk of developing cancer with the vast majority developing breast cancer by the age of 46 [Breast cancer remains the most common malignancy in women harbouring age of 46 .TP53 and BRCA1/2), although only 11 TP53 mutation carriers were included in their extended analysis. It is possible that adjuvant endocrine and anti-HER2 treatment will influence CBC risk in TP53 mutation carriers as the majority of these patients are ER and HER2 positive [Given the rarity of this condition, there is very limited data on CBC risk. Evans et al.  studied positive . EffortsA positive family history of breast cancer increases the risk of CBC although this risk pattern is complex. Vichapat et al.  studied  BRCA1/2 mutation with a family history was highest in women diagnosed at an earlier age with their index breast cancer (<45 years), those with a young first-degree relative, particularly with bilateral disease. The 10-year cumulative CBC risk stratified by age was 6.7% (50–54 years), 9.0% (40–44 years), and 14.7% (30–34 years).The WECARE study (Women's Environment Cancer and Radiation Epidemiology Study) conducted a population-based case control study comparing asynchronous bilateral breast cancer patients (case) against unilateral breast cancer (control) patients . They coA study from the Mayo Clinic  followedThe Vichapat et al. study  assessedHigh grade of primary tumours (RR 1.3 for Grade 3 cancer compared to Grade 1), increasing size , and number of positive lymph nodes  were all shown to be important risk factors .  Table Up to 30% of breast cancers  express Saltzman et al.  performeSeveral studies  have confirmed that antihormonal agents (SERM and aromatase inhibitors) given to high-risk women for up to 10 years can reduce the incidence of CBC and primary breast cancer . Women w BRCA1/2 mutation carriers who took Tamoxifen following their index breast cancer. Of interest was the similar risk-reduction of a short period of Tamoxifen (<1 year) compared to longer use (>4 years). This has implications on women who have concerns over the side effect profile of long term Tamoxifen use and may rationalise the short-term use of this drug.Recently, Gronwald et al.  were ablCytotoxic chemotherapy agent recommended as adjuvant or neoadjuvant treatment of primary breast cancer has been shown to reduce the risk of CBC. The Early Breast Cancer Trialists Group (EBCTG) showed a marginal reduction in the incidence of CBC over a 15-year follow-up period , which wThe WECARE group carried out a case-control study  and foun BRCA1/2 mutation related breast cancers are the subject of much interest [PARP inhibitors and their effect on triple negative andinterest . Initialinterest  have conBRCA1/2 and FH) undergoing BRRM and CRRM [ BRCA1/2 mutation carriers with unilateral breast cancer to matched mutation carriers who did not undergo CRRM. The overall 10-year survival was 89% in the CRRM group compared to 71% in the non-CRRM group (P < 0.001), which was independent of RRBSO. This is in contrast to van Sprundel et al. [ BRCA1/2 mutation carriers and found that at 20 years the survival rate of those who underwent CRRM was 88% (CI 83–93%) compared to 65% (CI 59–73%) who did not undergo CRRM.The aftermath of Angelina Jolie's announcement of her bilateral risk-reducing mastectomy (BRRM) has raised public awareness of risk-reducing surgery , 35. Sevand CRRM –39. Our l et al.  who founl et al.  followedBRCA mutation patients is less clear. Younger women under the age of 49 [BRCA patients. They estimated a less than 1% absolute 20-year survival benefit from CRRM amongst all age groups, ER status, and cancer stage groups.The survival benefit in non-ge of 49 , 42 withge of 49 . Portschge of 49  used a MP < 0.001). Similar proportions of patients underwent implant-based reconstruction (36%) and tissue-based reconstruction (37%). There was a trend of increasing numbers of reconstructions during this time period, and Caucasian women, under the age of 45 years with a diagnosis of a node-negative lobular carcinoma or DCIS, were more likely to choose reconstruction.Several studies have shown that access to immediate breast reconstruction positively affects the decision for CRRM , 45. RecWomen undergoing reconstruction following CRRM are 1.5 times more likely to have a major complication requiring hospitalisation or reoperation  comparedn = 21) of women undergoing RRM harboured occult invasive cancer in the mastectomy specimen. Of these 21 patients, the SLN was positive in only 4/21 patients and negative in the remainder (17/21). This was higher in women with advanced cancer in the contralateral breast. Overall, 2.8% (n = 36) of women benefited from SLNB that included 19 cases of a positive SLNB result requiring completion axillary surgery and 17 women who had invasive disease in the mastectomy specimen but a negative SLNB, thus avoiding further axillary surgery. This is offset against the 5% lymphoedema rate associated with SLNB [SLNB at the time of risk-reducing surgery remains controversial. A recent meta-analysis of 1251 patients  showed tith SLNB . Kuwajerith SLNB  retrospe BRCA1/2 mutations [Health care economics contribute to the decision making process. Cost-effectiveness with life expectancy gains is well established in the setting of bilateral RRM in women harbouringutations , 52. Fewutations –55. Deshutations  analysedIn contrast, Roberts et al.  found thZendejas et al.  used a MCurrently in the UK, there are no funding restrictions within the National Health Service on CRRM. Breast cancer patients can choose between delayed and immediate (performed at the same time as the therapeutic mastectomy) without financial scrutiny provided that there is backing from the relevant clinicians.One of the main driving forces for CRRM is patients' worry and anxiety about developing another breast cancer and having to undergo further treatment including chemotherapy. This is often the most difficult component to assess, as the psychology behind it is multifactorial. A recent US study reported that 68.9% of patients undergoing CRRM did not have genetic or familial risk factors for CBC  and thatA recent study  assessedRates of CRRM have increased in the US, a trend that in the future other countries may follow. This is of concern given that the actual incidence on CBC is on the decrease as a result of successful adjuvant treatments. BRCA1/2 mutation have an approximate 2-3% annual incidence on developing CBC. In nonmutation carriers with a family history, young age at first diagnosis and first-degree relative are particularly strong risk factors.CBC risk assessment is multifactorial and may be assessed in a multidisciplinary setting. The most significant risk factors are gene mutation status and significant family history, which can result in at least a fourfold increase in CBC risk. Patients harbouring aTumour biology is important. The ER status is of particular importance given that approximately 70% of all breast cancers are hormone sensitive. Risk reduction with antiendocrine treatment is approximately 50% with tamoxifen and 70% with aromatase inhibitors. As predicted, women with ER negative breast cancer have an increased risk of CBC. The use of cytotoxic agents and targeted treatments  marginally reduces CBC, with the greatest benefit for young women having chemotherapy. BRCA1/2 mutation. There is no survival benefit in the non-high-risk group.CRRM offers the greatest risk reduction of CBC, up to 95% in women with a family history. Survival benefits are conferred on those high-risk patients with aAccess to immediate breast reconstruction positively affects a woman's decision to opt for CRRM, with a significant number experiencing operative complications. Occult disease in the CRRM specimen occurs in less than 2% of women with no clear evidence to support sentinel lymph node biopsy.Survival benefits and cost-effectiveness are seen in those at the highest risk of CBC (gene mutation carriers) and these patients are likely to benefit most from CRRM.The assessment of CBC risk is multifactorial and may be assessed in a multidisciplinary setting. Arrington et al.  showed tAll clinicians treating breast cancer patients should be familiar with CBC risk and have the opportunity to discuss the various options including CRRM, chemoprevention, and routine surveillance. The multidisciplinary team is invaluable in guiding women based on objective assessment of genetic and family history, tumour biology, and psychological support.It is becoming apparent that women seeking CRRM are categorised into different risk groups  and that