High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening.Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods  in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors.The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer . Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05).Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.The online version of this article (10.1186/s13058-018-0932-z) contains supplementary material, which is available to authorized users. High mammographic density, the relative proportion of fibroglandular to fatty tissue in the breast, reduces the effectiveness of mammographic screening –4 and inThe most widely used method of assessing mammographic density in the USA is the Breast Imaging Reporting And Data System (BI-RADS) categorisation, where experts assign mammograms to one of four classes, the upper two being considered “dense”’ . The claNow that digital mammography is standard throughout the UK, volumetric measures of mammographic density, made by calibrating pixel values in the raw (“for processing”) FFDM image using a model of x-ray physics and imaging parameters , are nowThe availability of fully automated density assessment paves the way for risk stratification in screening , allowinA previous case-control analysis, carried out in London, compared density measured in the unaffected  breast in 414 women diagnosed with unilateral breast cancer at one hospital with that of 685 unmatched controls attending routine breast screening. Comparing the highest percentage density quintile with the lowest, and adjusting for age, BMI and reproductive variables, the strongest association with risk of developing breast cancer was for Volpara, with an OR of 8.26 (95% CI 4.28–15.96), followed by Quantra, OR 3.94 (2.26–6.86) and Cumulus, OR 3.38 (2.00–5.72) . HoweverWomen invited to the Greater Manchester Breast Screening Service for routine 3-yearly mammographic screening between October 2009 and March 2015 were also invited to participate in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, which aimed to provide women with a personalised risk estimate of their breast cancer risk based on mammographic density and classic breast cancer risk factors obtained via a questionnaire and quantified by the Tyrer-Cuzick risk score . After OIn order to assess density using fully automated methods, the raw FFDM (for processing) image data from GE Senographe Essential mammography systems was obtained. Cancers  were identified through hospital records or through the North West Cancer Intelligence Service; women who moved out the area were considered ineligible. Two case-control datasets were created. In study 1, cases were women with breast cancer detected at the screen on entry to PROCAS and in study 2 cases were women who were breast cancer free at the screen on entry to PROCAS but had breast cancer detected subsequently, either between screening rounds or at a later screen. In these women we analysed the density of the screen on entry to PROCAS.2), hormone replacement therapy (HRT) use (current vs never/ever) and menopausal status . In both studies all controls had a subsequent cancer-free screening mammogram so it was unlikely that early signs of cancer were visible, and in study 2, controls were also matched on year of mammogram at entry.Three controls without cancer were matched to each cancer case based on age (±12 months), BMI category  independently recorded density estimates on a paper form showing four 10-cm horizontal VAS, one for each view, labelled 0% and 100% at the ends of the scale. Forms were read using custom software and visual percentage density calculated. VAS readings were averaged between readers and views, and analysed in quintiles and as Boyd categories  . Due to Cumulus  density assessment was undertaken by a single reader (JS) trained in August 2010 and validated by a member of the PROCAS team (JW) who had herself been trained by the group that developed the software. Reader performance was validated on test sets of data developed for this purpose by the trainers. Processed FFDM images were analysed. Cumulus was undertaken on a single contralateral mediolateral oblique (MLO) view of a subset of the study 1 dataset comprising 311 screen-detected cancers and their matched controls. The reader was blind to case-control status.3), and the dense tissue area as a percentage of breast volume. It also provides a quantized BI-RADS-like score for each view and per breast.Quantra version 2.0  was used to assess density from the raw FFDM images for each view, each breast and each woman, giving breast and fibroglandular tissue volume (cm3) and percentage density by volume. Volpara provided a macro, which produced per-patient results including Volpara Density Grade (VDG 4th and 5th Edition), designed to correlate with BI-RADS 4th and 5th Edition [Volpara Density Algorithm 1.5.0  was also used to assess density from the raw FFDM images for each view, giving breast volume and fibroglandular tissue volume  and percentage density by area for each image and per patient. It also produces per-patient measures of BIRADS 4th and 5th edition [Densitas version 2.0.0  analyses processed FFDM images, giving breast and fibroglandular area . Univariate models were fitted initially, and multivariate models fitted to adjust for the logarithm 10-year Tyrer-Cuzick (v.6) risk score. In study 2 we also adjusted for parity, due to imbalance between cases and controls. We also performed an analysis in a subset of women who had been assessed using all density methods to determine which model performed best and differences between models were compared using the likelihood-ratio chi square. The matched concordance (mC) index, a modification of the concordance index (or area under the receiving operator characteristic curve (AUC)) for matched case-control studies, gives an average concordance index within matched groups  with empirical bootstrap confidence intervals , was calrsion 22  and R 3.rsion 22 .Of the 57,905 women recruited to PROCAS, raw FFDM image data were available for 44,658 women (77%). Unavailability of raw FFDM images was predominantly due to the use of film mammography initially. There were 1004 cases of cancer occurring after consent up to November 2015, of which 704 were included in the analysis. The excluded women comprised 39 women with a pre-existing diagnosis of breast cancer, 13 with synchronous bilateral breast cancer, 118 with film mammograms and 130 with FFDM but for which raw image data was unavailable. Of the 704 women eligible for the analysis, 366 were women with breast cancer detected at the screen on entry to PROCAS (study 1) and 338 were women who were found to be breast cancer free at the screen on entry to PROCAS but had breast cancer detected subsequently, either between screening rounds or at a later screen (study 2). Of the latter, 114 women developed an interval cancer within 5–46 months of entry (IQR 13–31) and 224 women had breast cancer detected at a subsequent screen 17–55 months after entry (IQR 35–38).p = 0.003; study 2: 2.91 vs 2.63, p < 0.001). The reported rate of a previous breast biopsy in cases was 17.8% (study 1) and 22.5% (study 2), and in controls it was 14.5% (study 1) and 15.1% (study 2). The difference in biopsy rate between cases and controls was statistically significant in study 2 (p = 0.005), but was similar (in study 2) to the PROCAS study as a whole. In study 1 significantly fewer cases than controls reported being of “white” ethnic origin , and fewer cases than controls reported having children in study 2 .Matching was satisfactory for both studies Table . There wIn study 1, VAS results were missing for 46 cases of cancer, Quantra failed to produce results for one case and one control, Volpara failed for one case, and Densitas failed for 6 cases and 62 controls. In study 2 there were missing density results for two cases of cancer assessed by VAS, for one case and one control assessed by Quantra and for 7 cases and 34 controls assessed by Densitas.th edition), the OR of VDG4 was 4.39 (95% CI 2.28–8.48) compared with VDG1 . Percent density measured by Densitas and Cumulus was also statistically significant , the OR of VDG4 was 3.00 (95% CI 1.54–5.86) compared with VDG1 . For Densitas and Quantra, those with percentage density in the highest quintile had ORs of 2.34 (95% CI 1.50–3.68) and 1.32 (95% CI 0.85–2.05), respectively  in the highest quintile of density compared with the lowest Table , the OR Visual assessment of breast density recorded on a VAS was the strongest predictor of breast cancer risk, both in the contralateral breast of women with screen-detected cancers and in the average of bilateral mammographic views prior to the detection of cancer. It is unlikely that the presence of cancer influenced visual assessment in study 1, since a blinded re-read of images from the contralateral breast by four readers showed no evidence of bias  and the Volpara and Densitas percent density had the next strongest associations with cancer in both studies, with categorisation into VDG having the largest odds ratio. Volpara, Quantra and Cumulus did not have as strong an association with breast cancer in study 1 as previously reported . This maStudy 2 examined the relationship between mammographic density in mammograms prior to the detection of cancer, and in matched controls that subsequently remained cancer free. This enables us to evaluate which mammographic density methods are most appropriate for stratifying women attending breast screening. Whilst visual assessment was most strongly associated with cancer, it is unlikely to be used widely for population-based stratified screening; we conclude that Volpara or Densitas percentage density provide a pragmatic solution. However, we hypothesise that methods that measure purely the quantity or relative proportion of dense tissue do not fully capture the mammographic risk in the same way as visual assessment by experts, who can see not only the quantity of dense tissue but the location and pattern. The addition of algorithms that automatically quantify mammographic pattern to automated density software could potentially provide a solution that more closely reproduces visual assessment. Recent research in this area has proved promising –36, althThe strengths of this study include the ability to assess the relationship between several measures of mammographic density and risk of breast cancer. As well as examining the association between mammographic density and breast cancer risk, we were able to establish the temporal relationship in study 2. We also gathered detailed information in relation to a number of covariates  via a self-reported questionnaire at entry to PROCAS . Uptake Visual assessment of density, recorded on a VAS and averaged between two independent readers, is a strong predictor of breast cancer risk both in mammograms taken before the detection of cancer and in images of the opposite breast at the time of detection. Percentage density measured by Volpara and Densitas also showed a strong association with breast cancer risk amongst the automated measures evaluated, providing practical automated methods for risk stratification in personalized screening programmes.


Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.This study combines five case–control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH10 and FD_TH15) were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH60 to FD_TH85) were associated with a decreased risk. Increasing the FD_TH75 and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with a decreased risk of breast cancer. For example, 1 standard deviation increase of FD_TH75 was associated with a 13% reduced risk of breast cancer . Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated  contains supplementary material, which is available to authorized users. Women with mammographically dense breasts are at a higher risk of developing breast cancer than women with more fatty breasts. The risk of developing breast cancer can be four- to six-times higher in women with breast density in the top quartile of the population compared to the bottom quartile , 2. Why Breast density texture can be described using numerous statistical descriptors of the distribution and spatial relationship of grayscale values in the image pixels. Texture has been studied as a breast cancer risk factor independent of average breast density –11, but To this end, we amassed a library of imaging features previously reported on in the breast imaging and general imaging literature as candidate descriptors of breast tissue characteristics. In this study, we investigated the association of these descriptors and breast cancer risk using prospectively acquired mammograms from five breast cancer epidemiology studies. We also examined the association of these descriptors to tumor type and ER status.This study is a large, comprehensive pooled analysis of five case–control studies, two of which were nested within cohorts, to examine the association between texture of mammographic density and breast cancer risk and breast cancer subtypes.The studies and populations used in this analysis have been previously described elsewhere . BrieflyP > 0.05). Similarly, matched controls were compared against the whole study population and were found to be comparable (data not shown). Overall, 2830 women were eligible for our case–control set and 6523 (69.7% of population) from MMHS and SFMR were excluded. Of these, mammograms of 1171 breast cancer cases and 1659 controls were analyzed.There were 9353 women with screening visits during the study period from all studies. For MMHS and SFMR only, due to study design, large batches of cases were digitized at one time followed later by batches of matched controls. Thus, to ensure no bias due to potential confounding by digitization we only included those cases and matched controls that were digitized in the same batches, resulting in a substantially reduced sample for these two studies. To ensure that no bias was associated with study exclusions due to digitizer in these two studies, we compared the included cancer cases to the excluded cancer cases. We found that the eligible vs. excluded cases did not differ in terms of their demographic and clinical characteristics  with 16-bit dynamic range and 169-μm pixel size. For NHS and NHSII, film mammograms were digitized at 261 μm per pixel with a Lumisys 85 laser film scanner  or a VIDAR CAD PRO Advantage scanner  and comparable resolution of 150 dots per inch and 12 bit depth. To minimize effects of the film digitization process, we performed a harmonization procedure by rescaling all images to have the same pixel size and dynamic range. The ultimate space resolution was set to 160 μm using a Matlab “imresize” function with default parameters (bicubic interpolation). The dynamic scale of all images was converted into 16-bit grayscale by the proper coefficient multiplication.For this study, the craniocaudal (cc) views of screening examinations of both breasts were digitized at each respective study site. The cc view images were more conducive to being analyzed automatically with our algorithms; also, not all studies had mediolateral oblique views available. The MMHS screen-film mammograms were digitized on the Array 2905 laser digitizer  that has 50-μm (limiting) pixel spacing with 12-bit grayscale bit depth. The MCMAM mammograms were digitized on a Lumiscan 85 scanner with 12-bit grayscale bit depth and 0.100 × 0.100 mmTo quantify PD, two semi-automatic threshold techniques were applied: Cumulus  . The GLCM Energy represents local homogeneity and is a measure opposite to GLCM Entropy. Actually, this texture feature describes the degree of texture uniformity; basically, more homogeneous texture has a higher Energy. For example, the image with only constant grayscale pixels has Energy equal to 0. Other similar texture features from this table are GLCM Homogeneity and Dissimilarity. Homogeneity measures how uniform are the non-zero entries in the GLCM matrix. This feature represents existence of repetitions in texture. The image with irregular texture elements and their spatial positions is characterized by low Homogeneity. An image that contains repetitive structures represents high Homogeneity. Dissimilarity is a measure that defines the variation of gray level pairs in an image. It is very similar to Contrast with a difference in the weight.We automated 46 candidate image texture features into our mammography image analysis program Table . Feature Balance , 24–26.  Balance , 27 and  Balance . The GLCCoarseness, Contrast, Complexity, Strength and Busyness [FT_RMS), first (FT_FMP) and second (FT_SMP) moments of power spectrum, and fractal dimension (FD) from power spectrum exponent (FT_FD) [FD_TH_X, for threshold at X = 5, 10, 15…85%). These features were derived by a box counting method. Further fractal features include FD of the standard deviation (FD_Sigma), intercept of the plot of the standard deviation of the high frequency image as a function of the size the kernel (CD_Yint), slope of the plot of the standard deviation of the high-frequency image as a function of the size the kernel (CD_Slope), standard deviation of the mean value of the breast pixels rows (HZ_PROJ), FD of the surface of the breast considering the gray value represents the height (FD_CALDWELL) [FD_Minkowski) derived from morphological image operations [FD_Minkowski is similar to the box counting fractal dimensions . It is calculated by an image dilation procedure with different scale structure disk element. As a result of edge frequency analysis, the mean gradient parameter was created. We previously demonstrated the utility of this set of features for derivation of volumetric breast density by a statistical model approach [The NGTDM is a column matrix, which was first defined by Amadasun and King . This maBusyness , 28. One (FT_FD) . To defiALDWELL) , 31, anderations . The FD_approach .Tumor type ) and ER status were available using Northern and Southern California Surveillance Epidemiology and End Results programs for SFMR, pathology reports or immunohistochemical analysis of tumor microarrays for NHS and NHSII, and state and clinic cancer registries for MMHS and MCMAM.p < 0.05) in the case–control models were selected for further analysis. Polytomous logistic regression models were fitted to examine associations of features with respect to invasive/DCIS breast cancers and ER status. Contrasts were constructed within the polytomous model framework to test for differences of feature associations between tumor subgroups (p-het). SAS version 9.3 was used for analyses and two-sided p values < 0.05 were considered to be statistically significant. Pearson correlation coefficients were used to examine correlations among features and also correlations of features with PD among control subjects. Dendrograms were created to illustrate clustering among the significant features, age, body mass index (BMI), and PD on data from controls. A hierarchical clustering method using averaged distance was utilized as implemented in “proc cluster” in SAS.Risk factors and PD phenotypes were harmonized on the eligible cases and controls. For all subjects, concordance between features measured on left and right sides were evaluated. Lin’s concordance correlation coefficients were used to summarize the correlation between left and right sides. Values ranged from 0.50 to 0.98 with median of 0.85. Given this, we chose to average sides to reduce noise in the measurements. To avoid issues with outliers and violations of distributional assumptions, the averaged features were normalized within each study using a normal transformation of the ranks. All analyses were performed using the normalized features. Logistic regression models evaluated the overall breast cancer associations with each normalized feature as a continuous variable and results are presented as odds ratio (OR) per 1 standard deviation (SD). All models were adjusted for age (continuous), body mass index (BMI) (continuous), first-degree family history of breast cancer (yes vs. no vs. unknown), PD (continuous), and study. To assess whether there were differences in associations by study, we included and tested an interaction term for texture feature by study. Study-specific results were also examined and summarized. The top 15 of 46 analyzed features that were significant (The baseline case and control characteristics of the eligible population are shown in Table p < 0.05) association with breast cancer after adjustment for age, BMI, family history, PD, and study . Study-specific estimates for SFMR were often not consistent with other studies. In sensitivity analysis, we excluded SFMR to explore the impact of these differences and found similar results (data not shown). Three features with the strongest association were FD_TH_75, Energy, and Entropy. Increasing the FD_TH_75 and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased risk of breast cancer. The fractal dimension features were separated into two groups. The first group described the fractal dimensions in the densest pixels, and contained features FD_TH_60, FD_TH_65, FD_TH_70, FD_TH_75, FD_TH_80, FD_TH_85, and FD_Minkowski. All these features were significant and were associated with a decrease in cancer risk with the most significant association OR ) per 1 SD = 0.87 (0.79–0.95) for FD_TH_75. The second feature group described fractal dimensions in the lower density (less opaque) pixels: FD_TH_10 and FD_TH_15. In contrast to the first group, they were associated with an increase in breast cancer risk. Energy and Entropy demonstrate opposite associations to cancer with OR (95% CI) 0.88 (0.81–0.96) and 1.14 (1.05–1.25), respectively. The GLCM features Homogeneity and Dissimilarity showed opposite trends with OR (95% CI) 1.10 (1.01–1.20) and 0.91 (0.83–0.99), respectively. Table FD_TH_75) it was 0.621, suggesting modest increases in discrimination with the addition of this texture feature.The top 15 of 46 analyzed features had a statistically significant  form a subcluster with Kurtosis and Busyness independent of other features. The second main cluster includes pairs of Entropy/Energy, Dissimilarity/Homogeneity, and FD_TH_10/FD_TH_15. The intercorrelation of each feature and risk factor calculated using control subjects is shown in Table FD_TH_75, FD_Minkowski and Kurtosis from the same primary cluster group. However, the features of the second primary cluster show no or negligible association with PD.Figure FD_TH_75 feature. We selected images with FD_TH_75 values in the top and bottom 20% of values matched by BMI, PD, age, case status, and study. The top row of Fig. FD_TH_75. The outer black delineation lines show the delineation lines of the tissue used to describe FD_TH_15. The top left and bottom left images show a top 20th percent tile value of FD_TH_75 while the top right and bottom right images show a bottom 20th percent tile value.Figure FD_TH_10 and FD_TH_15 significantly associated with DCIS risk, but not with invasive cancer. Five features were significantly associated with the ER+ cases  demonstrated modest increases in the discriminatory ability of the model. The patterns of association were found to be similar for the risk of DCIS, invasive breast cancer, and ER+ and ER– breast cancer, although there were differences in magnitude of the associations between invasive/DCIS, ER+/ER– status cancer subtypes, and specific features. We also found that many mammographic features associated with breast cancer were not correlated with PD, a desirable quality for potentially improving the discrimination of risk-prediction models. Specifically, the GLCM FD_TH_5:FD_TH_85), but the association was reversed dependent on the threshold level used to create the line profiles. An example was given of the FD_TH_75 (line profile outlining highly dense tissue) and FD_TH_15 (line profile outlining the edge of the compressed area) in Fig. FD_Minkowski, showed a decreased association with cancer risk similar to FD_TH_75. These measures are closely mathematically related as noted by their clustering in the dendrogram. Unlike other studies, the association of FD_Minkowski feature with breast cancer risk [In previous reports, there have been few examples of texture features that are associated with cancer independent of PD. Torres-Mejia et al.  found nocer risk  remainedEntropy and Energy as well as Homogeneity and Dissimilarity. The Entropy is intuitively assumed to be significant for breast cancer risk because tissue with high entropy is more heterogeneous. Energy value is associated with a reduced risk of breast cancer because it is related to tissue with more homogeneous texture. The features that denoted more coarseness increased risk and those that were less coarse did not increase risk or were protective. The Pearson correlation coefficients show the features in both pairs are highly negatively correlated. The protective character of Dissimilarity (or Contrast) is not intuitive. We can speculate that finer structure has high contrast and has similar behavior to fractal dimension. Other studies provided an important role for mammographic textures such as fractal dimensions, GLCM matrix parameters, and power Fourier spectrum in distinguishing between BRCA1/BRCA2 gene mutations and cancer risks [Entropy associated with an increased risk of breast cancer and represented a measure of spatial disorder likely to show a degree of tissue heterogeneity. It could be associated with processes on the cellular level where increased entropy is stated to be as a metaphor of progressive irreversible loss of initial order  in the cell [FD_TH_75, associated with a decreased risk of breast cancer is also related to tissue heterogeneity but in the opposite direction. As shown in Fig. FD_TH_75 in the bottom 20th percent tile values represents highly heterogeneous tissue.Other associated features include the paired features er risks , 33. Theer risks . Entropythe cell . AnotherFD_TH_10 and FD_TH_15 features look promising to differentiate DCIS from invasive cancer because, even with fewer cases, they showed significance for DCIS and were not significant for invasive cancers. However, the heterogeneity p values to test for differences in effect between DCIS and invasive cancer subgroups were p = 0.09 and p = 0.21 for FD_TH_15 and FD_TH_10, respectively. Finally, film mammography has largely been replaced by full-field digital mammography systems as well as three-dimensional tomosynthesis systems. However, texture features measured using film mammograms have been shown to be in a good agreement with those measures using digital mammography systems [Our study had the following limitations. First, many films, especially from the SFMR, were excluded due to temporal inconsistencies with the digitization of cases and controls. Harmonization procedures were needed to rescale the spatial dimensions and dynamic range. Ideally, all images would have been digitized on one digitizer, or been a native digital format (versus film). We also had few ER– and DCIS cancer subtypes, limiting our power for these subtypes. For example, the  systems . It is aWe conclude that the description of breast density texture from mammograms shows promise as an independent risk factor for breast cancer risk and potentially differentiating between risks of cancer subtypes. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital mammography and tomosynthesis images.


Q4vs.Q1 = 0.74 , P-trend = 0.05), supplemental , P-trend = 0.05), and total , P-trend = 0.01) pyridoxine intakes were inversely associated with breast cancer risk. Total thiamin intake was borderline inversely associated with breast cancer risk , P = 0.05). Statistically significant interactions between alcohol consumption and B-vitamin  supplemental intake were observed, the latter being inversely associated with breast cancer risk in non-to-low alcohol drinkers but not in higher drinkers. This large prospective study, including quantitative assessment of supplemental intake, suggests a potential protective effect of pyridoxine and thiamin on breast cancer risk in middle-aged women.Experimental studies suggest a protective effect of B-vitamins on breast cancer risk, potentially modulated by alcohol intake. However, epidemiological studies are limited, especially regarding non-folate B-vitamins. Furthermore, few studies included quantitative assessment of supplemental intake. This prospective study aimed to investigate the associations between intakes of B-vitamins  and breast cancer risk. 27,853 women aged ≥45 years from the NutriNet-Santé cohort (2009–2016) were included, with a median follow-up time of 4.2 years. Dietary data were collected using repeated 24 h records. A specific questionnaire assessed dietary supplement use over a 12-month period. A composition database of 8000 supplements was developed. Associations were characterized by multivariable Cox models, and 462 incident breast cancers were diagnosed. Dietary (HR Breast cancer is the most common cancer among women in the world, with approximatively 1.67 million new cases of breast cancer diagnosed in 2012; and nearly 522,000 associated deaths [Folate has been by far the most investigated B-vitamin. A limited number of placebo-controlled randomized trials have evaluated the effect of folic acid supplementation on breast cancer risk. In 2013, a meta-analysis of such studies showed null results  . Even ifIn 2010, the World Cancer Research Fund and the American Institute for Cancer Research (WCRF/AICR) conducted a systematic literature review  that incFurthermore, experimental evidence suggested a potential interaction between B-vitamins and alcohol intake  by interfering with their transport, absorption, and metabolism, which may modulate the association with breast cancer risk . HoweverThus, this study aimed at investigating the associations between intakes of B-vitamins  and breast cancer risk in middle-aged women, and their potential modulation by alcohol intake. https://www.etude-nutrinet-sante.fr/). The NutriNet-Santé study is conducted according to the Declaration of Helsinki guidelines. It was approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm n°0000388FWA00005831) and the “Commission Nationale de l’Informatique et des Libertés” (CNIL n°908450/n°909216). Electronic informed consent is obtained from each participant (EudraCT No. 2013-000929-31).The NutriNet-Santé study is a French ongoing web-based cohort launched in 2009 with the objective of studying the associations between nutrition and health as well as the determinants of dietary behaviors and nutritional status. This cohort has been previously described in detail . ParticiAt inclusion, participants completed a set of five questionnaires related to socio-demographic and lifestyle characteristics   ,32,33. MTwo months after their inclusion, participants were invited to fill a questionnaire regarding dietary supplement use . In the Participants self-declared health events through the yearly health status questionnaire, through a specific check-up questionnaire for health events (every three months) or at any time through an interface on the study website. Following this declaration, participants were invited to send their medical records  and, if necessary, the study physicians contacted the participants’ treating physician or the medical structures to collect additional information. Then, data are reviewed by an independent physician expert committee for the validation of major health events. Cancer cases were classified using the International Chronic Diseases Classification, 10th Revision, Clinical Modification (ICD-10) . In thisn = 29,850) because of a very low susceptibility to developing breast cancer. Thus, 27,853 middle-aged women were included in the analyses. For all covariates, there was no missing data except for physical activity (12.7%) and educational level (5%), for which multiple imputation was performed [So far, 57,703 women provided at least three valid 24 h-dietary records during the first year of follow-up and answered the dietary supplements questionnaire. Women aged under 45 years were excluded  using multivariable Cox proportional hazard models with age as the primary time variable. Women contributed person-time to the Cox model until the date of cancer diagnosis, the date of the last completed questionnaire, the date of death or April 2016, whichever occurred first. 2, continuous), height , physical activity , BMI (kg/mndations ), smokinInteractions between dietary, supplemental, and total intake of each B-vitamin and the following potential modifiers were tested by introducing the product of the two variables into Cox models performed on the whole population study: smoking status (current smokers/non- or former smokers), alcohol intake , and family history of cancer (yes/no). Stratified analyses were performed when p-values for interaction were statistically significant. Analyses were also performed according to menopausal status: women contributed person-time to the “pre-menopause model” until their age of menopause and to the “post-menopause model” from their age of menopause. Models restricted to the ER+ breast cancer subtype were also performed. Statistical power was too limited to perform separate models for the ER− breast cancer subtype. Since the association between baseline dietary exposure and cancer risk may decrease as the follow-up time increases , we havep-value ≤ 0.05 was considered statistically significant. All analyses were carried out with SAS software . A Between May 2009 and April 2016 , 462 incident breast cancer cases were diagnosed: 78 premenopausal and 384 postmenopausal; 72.6% ER+/PR+, 12.4% ER−/PR−, 14.6% ER+/PR−, 0.4% ER−/PR+; 82.2% invasive and 17.8% in situ. The mean age at diagnosis was 59.8 years (SD = 7.2) and mean baseline-to-diagnosis time was 2.5 years (SD = 1.7). The mean number of dietary records per subject over their first year of follow-up was 4.8 (SD = 1.6). The baseline characteristics of participants are described in Q4vs.Q1 = 0.74 , P-trend = 0.05), supplemental , P-trend = 0.05), and total , P-trend = 0.01) pyridoxine intakes were inversely associated with breast cancer risk. Total thiamin intake was inversely associated with breast cancer risk , P = 0.05). No association was observed between the other B-vitamins and breast cancer risk. P = 0.003 for thiamin, P = 0.007 for riboflavin, P = 0.01 for niacin, P = 0.01 for pantothenic acid, P = 0.02 for pyridoxine, P = 0.02 for folate, and P = 0.02 for cobalamin, users vs.non-users = 0.52 , P = 0.01 for thiamin; HRusers vs.non-users = 0.55 , P = 0.02 for riboflavin; HRusers vs.non-users = 0.53 , P = 0.02 for niacin; HRusers vs.non-users = 0.47 , P = 0.01 for pantothenic acid; HRusers vs.non-users = 0.59 , P = 0.01 for pyridoxine; HRusers vs.non-users = 0.60 , P = 0.04 for folate; and HRusers vs.non-users = 0.50 , P = 0.04 for cobalamin), while none of these associations were statistically significant in women with alcohol intake above the population median. No interaction was detected between alcohol intake and dietary or total intake of B-vitamins, nor with smoking status or family history of cancer with any type of intake .Statistically significant interactions between alcohol consumption and supplemental intakes of B-vitamins were observed , P-trend = 0.005 for dietary, HRQ4vs.Q1 = 0.54 , P-trend = 0.06 for supplemental, and HRQ4vs.Q1 = 0.70 , P-trend = 0.04 for total intake of pyridoxine, and HRper 1-unit increment = 0.82 , P = 0.1 for total intake of thiamine). Statistical power was very limited in premenopausal women (n = 118 cases) and no statistically significant association was detected  (data not tabulated). Results of the associations between the different types of pyridoxine intake and breast cancer risk tended to be stronger in the first half of the follow-up period (first 3.5 years) , P-trend = 0.03) for dietary intake, HRQ4vs.Q1 = 0.61 , P-trend = 0.06 for supplemental intake and HRQ4vs.Q1 = 0.66 , P-trend = 0.01 for total intake) compared to the second half of follow-up , P-trend = 0.3) for dietary intake, HRQ4vs.Q1 = 0.25 , P-trend = 0.2 for supplemental intake and HRQ4vs.Q1 = 0.51 , P-trend = 0.06 for total intake). However, results of the associations between thiamine intakes and breast cancer risk were similar between the two halves of the follow-up period. Analyses restricted to the ER+ breast cancer subtype (n = 426) have been carried out, however no significant association was observed. Regarding the ER− breast cancer subtype (n = 36), a lack of power did not allow us to carry out the analyses among this group. Furthermore, similar trends, although less pronounced, were observed when Cox models were only age-adjusted (Compared to the whole population, results of the associations between pyridoxine and thiamine intakes and breast cancer risk tended to be similar when models focused on postmenopausal women (adjusted . ResultsIn this large prospective cohort study, dietary, supplemental, and total pyridoxine intake and total thiamin intake were associated with decreased breast cancer risk. No significant association between other intakes of B-vitamins and breast cancer risk was found overall. A modulation by alcohol intake was suggested by statistically significant interactions: the use of thiamin, riboflavin, niacin, pantothenic acid, folate, and cobalamin supplements was associated with lower breast cancer risk in non-to-low alcohol drinkers, while no association was observed in women with higher alcohol intake. However, no interaction was observed between other types of B-vitamin intakes and alcohol intake, or between any type of B-vitamin intake and smoking status or family history of cancer.While a substantial body of literature is available regarding folate and breast cancer risk ,5, pyridThe epidemiological literature is even scarcer regarding the association between thiamin intake and breast cancer risk. A prospective study including 49,654 Canadian women observed no association between dietary thiamin intake and breast cancer risk .In our study, no overall association was observed between dietary, supplemental, or total folate intake and breast cancer risk, which may partly be explained by the relatively low intakes. This result is consistent with those of a meta-analysis of randomized trials on folic acid supplementation , with thFor instance, an interaction between B-vitamins, and especially folate intake, and alcohol intake has been hypothesized ,23,24,25To our knowledge, very few studies investigated the interactions between smoking status or family history of cancer and intake of B-vitamins on breast cancer risk ,52. In oThe strengths of our study pertained to its prospective design, its large sample size, and the quantitative assessment of nutritional intake from dietary supplements. Nevertheless, some limitations should be acknowledged. First, caution is needed regarding the extrapolation of these results to the entire French population since this study included volunteers involved in a long-term cohort study investigating the association between nutrition and health, with overall more health-conscious behaviors and higher socio-professional and educational levels. Thus, unhealthy dietary behaviors may have been underrepresented in this study, which may have weakened the observed associations. Next, regarding dietary supplement intake, the questionnaire only covered 12 months at the beginning of the follow-up. An update of these data later during follow-up would improve the precision of the exposure assessment. Moreover, despite the careful collection of data on dietary intake and supplement use, the lack of biomarker status measurement may have led to the misclassification of some participants . FinallyIn conclusion, this prospective cohort study suggests that pyridoxine and thiamin could be inversely associated with breast cancer risk, in line with mechanistic hypotheses. Alcohol intake may modulate the associations between B-vitamin supplement use and breast cancer risk. These results need to be confirmed in future large prospective observational and interventional studies before public health recommendations could be derived. In the meantime, reaching an adequate intake of B-vitamins through a balanced diet should be recommended, rather than regular use of dietary supplements, of which the long term effects still need deeper investigation.


CYP1A1, CYP2A6) or in DNA repair  in breast cancer development. Methods: We designed a case control study with 297 patients, 217 histologically verified breast cancers (141 smokers and 76 non-smokers) and 80 healthy smokers in a cohort of Spanish women. Results: We found an association between smoking status and early age at diagnosis of breast cancer. Among smokers, invasive carcinoma subtype incidence increased with intensity and duration of smoking . When smokers were stratified by smoking duration, we only observed differences in long-term smokers, and the CYP1A1 Ile462Ile genotype was associated with increased risk of breast cancer (OR = 7.12 (1.98–25.59)). Conclusions: Our results support the main effect of CYP1A1 in estrogenic metabolism rather than in tobacco carcinogen activation in breast cancer patients and also confirmed the hypothesis that CYP1A1 Ile462Val, in association with long periods of active smoking, could be a breast cancer risk factor.Introduction: Tobacco smoke contains many potentially harmful compounds that may act differently and at different stages in breast cancer development. The focus of this work was to assess the possible role of cigarette smoking  and polymorphisms in genes coding for enzymes involved in tobacco carcinogen metabolism ( N-nitrosamines may induce breast tumours , APEX1 (Asp148Glu) [rs1130409], XRCC3 (Thr241Met) [rs861539], XPD (Asp312Asn) [rs1799793] and (Lys751Gln) [rs13181], CYP1A1  [rs1048943], CYP2A6*2 (Lys160His) [rs1801272], and CYP2A6*9 (−48T > G) [rs28399433] in breast cancer patients and healthy smokers.We analysed the prevalence of the following genetic polymorphisms XRCC1 (Arg399Gln) (p = 0.002) and XPD (Asp312Asn) (p = 0.02).No departure from the Hardy–Weinberg equilibrium was detected for the majority of genes, with the exception of Analyses focused on associations with genotype categorized using a recessive model .XRCC3 Thr241allele and more PYS (p = 0.039). This association was in agreement with previous reports [p = 0.037).Among all smokers, no statistically relationship was observed between smoking habits or the clinical variables and genetic polymorphisms analysed. Only a moderately significant interaction was found between  reports . In addiThe frequencies of genetic polymorphisms studied were similar in both the smoker and non-smoker groups of patients . We did XRCC1, APEX1, XPD, CYP2A6*2 and CYP2A6*9 genetic polymorphisms were similar in both controls and breast cancer patients ). In contrast, the CYP1A1Ile462Ile genotype (most common genotype) was significantly associated with higher risk of breast cancer in smokers ).Comparing only smoker groups, the frequencies of patients . HoweverCYP1A1 Ile462Val was around 5% in breast cancer patients and 21% in healthy smokers, while the frequency of XRCC3 Met241Met was around 29% in breast cancer patients and 12% in healthy smokers, suggesting a possible interaction effect between CYP1A1 Ile462Val, XRCC3Thr241Met and risk of breast cancer in smokers, with regard to the risk for not developing breast cancer in smokers carrying the CYP1A1 Ile462Val genotype after interaction logistic regression analysis ).The frequency of CYP1A1 Ile462Ile was associated with the greater risk of breast cancer (OR = 7.12 (1.98–25.59)).Some studies have reported that duration of smoking is a much stronger risk factor than amount smoked ,18,19,20Smoking represents a potential risk for breast cancer development. Tobacco smoke contains thousands of chemical compounds, many of which are known to be mammary carcinogens . CellulaPtrend < 0.05). Such findings suggest that smoking can affect the incidence as well as the course of cancer. Di Cello and cols. have suggested that cigarette smoke promotes the epithelial to mesenchymal transition, producing a more aggressive breast cancer phenotype in vitro [Among smokers, invasive carcinoma subtype incidence increased with the intensity and duration of smoking . However, the differences were the same independent of tobacco exposure. CYP1A1 metabolizes endogenous molecules and xenobiotics [Among breast cancer group, we found the same genetic polymorphism prevalence to be independent of being a smoker or non-smoker. On the other hand, of the polymorphisms investigated comparing breast cancer smokers and healthy smokers, statistically significant associations between two genetic variants and risk of breast cancer in smokers were observed  allele and smoking was observed in Caucasian women [CYP1A1*2  allele have a significantly reduced risk of breast cancer as compared with carriers of the wild-type allele [CYP1A1 in extra hepatic tissues which are largely exposed to environmental carcinogens. Expressional variation of CYP1A1 has shown down-regulation of CYP1A1 in breast cancer [CYP1A1 Ile462Val substitution situated in the heme-binding region of the enzyme is accompanied by a twofold increase in microsomal enzyme activity [CYP1A1 562Val in controls, and it is tempting to speculate that the CYP1A1*2  allele could even play a protective role in smokers.Kisselev and cols. suggested that the effects of polymorphisms on the function of CYP1A1 may be dependent on the substrate, and CYP1A1 variants might differ from the wild type in terms of their activity and regioselectivity towards estrogens. From a biochemical point of view, the high estrogen-2-hydroxylase activity of the dy group ,34,35. Fan women , wherease allele . In addit cancer ,39. CYP1activity . Our resIn the literature, declared cigarette consumption often serves as a proxy measure for level of toxin exposure or disease risk. However, for different reasons, consumption may not be a good predictor. The differences in how tobacco is smoked affect real exposure, and it is difficult discern if consumption reported as cigarettes per day correlates with other markers of exposure, the effect of compensation (changes in smoking behaviour to adjust for changes in nicotine levels or in volume of cigarettes smoked), the complex relationship between dose and disease risk for some diseases or whether people maintain the same levels of smoking over time . This isIn addition, based on more recent assessments with the assumption that the effect of the genotype might be amplified at high doses, we studied the effects of long exposure on breast cancer risk in heavy smokers (≥20 YS) . We compCYP1A1 polymorphisms and breast cancer divided smokers into ever or never groups, and did not analyse the effects of intensity, years of smoking or age at initiation. Among the strengths of our study are the prospective design and selection of well-defined groups in terms of smoking. However, our study is subject to certain limitations such as low statistical power (because of low sample size) and passive smoking data not being included. There could be a “partial dose effect” from second hand smoke. We know that a weakness of our study is the low sample size, yet we believe this can be partly overcome by the fact that our population is homogeneous, not stratified and well defined in terms of phenotype assessment. These results suggest that cigarette smoking might play an important role in the development of breast cancer, particularly when women start smoking relatively early in life or when they are exposed for a long time. Our results confirm the hypothesis that CYP1A1Ile462Val, in association with a long period of active smoking, could be a possible breast cancer risk factor and also support the main effect of CYP1A1 in estrogenic metabolism rather than in tobacco carcinogen activation in breast cancer patients.The goal of our research was to combine environment (smoking history) with genetic polymorphisms to improve assessment risk of breast cancer. Moreover, we have included a group of patients who were never smokers to evaluate differences. Most previous studies of ERBB2 gene [On the other hand, breast cancer is a complex disease that is mainly grouped based on its hormone receptor subtype (estrogen receptor or progesterone receptor positivity) and amplification of the BB2 gene . EvidencBB2 gene ,45. SomeBB2 gene ,46,47, wBB2 gene ,49. FurtWe designed a control-based study with 297 female patients in order to detect the possible association between smoking, polymorphisms in carcinogen metabolism and/or DNA repair genes, and risk of breast carcinoma development. Women were randomly recruited from Centro de Patología de la Mama (Fundación Tejerina), Madrid (Spain), from 2010 to 2013. The first group consisted of patients with histologically confirmed breast cancer (smokers and never smokers) and the second group consisted of healthy patients without a previous positive history of breast carcinoma or other malignancies and smokers with more than 1 pack years smoked (PYS). Approval was obtained on 4 November 2010 from the local Ethics Committee (2010/UEM19) and all patients provided written informed consent. The study was in accordance with the Helsinki Declaration.All women completed a questionnaire regarding demographic characteristics, smoking habits, self-reported cigarettes per day (CPD), duration and intensity (PYS). CO expired and cotinine levels in urine samples were analyzed in 27% of smokers to check cigarette consumption. In addition, body mass index, tumour histology, type of treatment or concomitant medication data were collected for each participant.Venous blood samples were collected by venepuncture and processed for leukocyte DNA extraction using a standard phenol chloroform protocol. DNA extraction and genotype analyses were carried out in the Biomedicine laboratory at the Universidad Europea, Madrid (Spain). The study followed recommendations for replicating genotype-phenotype association studies, genotyping was performed specifically for research purposes, and researchers in charge of genotyping were totally blinded to the participants’ identities  . The DNAGenotyping was performed by real-time PCR and Taqman probes with a Step One Real-Time PCR System .CYP1A1*2C allele of 6.4% in Caucasians [Power analysis retrieved a sample size of 94 cases and 94 healthy patients necessary to detect an OR of 4 with a double-tale significant level of a = 0.05 and c2 = 0.80, assuming a prevalence of ucasians .t-test. We used the χ2 test to assess deviations of genotype distributions from the Hardy-Weinberg equilibrium (HWE). We also used logistic regression to determine associations between smoking habits, breast cancer risk and genotypes. All statistical analyses were corrected for multiple comparisons using the Bonferroni method, in which the threshold p-value is obtained by dividing 0.05 by the number of tests. The data were stratified for smoking status, smoking duration or smoking intensity to determine several associations. All analyses were performed with the PASW/SPSS Statistics 20.0  program.We compared smoking phenotypes and clinical characteristics between the different groups with the unpaired Student’s Cigarette smoking might play an important role in the development of breast cancer, particularly when women start smoking relatively early in life or when they are exposed for a long time. Larger studies are needed to confirm our initial findings, however, our results provide evidence that the main effect of CYP1A1 in estrogenic metabolism rather than in tobacco carcinogen activation in breast cancer patients and also confirmed the hypothesis that CYP1A1 Ile462Val, in association with long periods of active smoking, could be a breast cancer risk factor.


Several medical management approaches have been shown to be effective in preventing breast cancer and detecting it early among women at elevated risk: 1) prophylactic mastectomy; 2) prophylactic oophorectomy; 3) chemoprevention; and 4) enhanced screening routines. To varying extents, however, these approaches are substantially underused relative to clinical practice recommendations. This article reviews the existing research on the uptake of these prevention approaches, the characteristics of women who are likely to use various methods, and the decision-making processes that underlie the differing choices of women. It also highlights important areas for future research, detailing the types of studies that are particularly needed in four key areas: documenting women’s perspectives on their own perceptions of risk and prevention decisions; explicit comparisons of available prevention pathways and their likely health effects; the psychological, interpersonal, and social processes of prevention decision making; and the dynamics of subgroup variation. Ultimately, this research could support the development of interventions that more fully empower women to make informed and values-consistent decisions, and to move towards favorable health outcomes. BRCA1, BRCA2, and other mutations, as well as other potential genetic susceptibilities made evident by family histories of the disease. These high-risk women face a lifetime likelihood of breast cancer of between 20% and 80% depending on family history and genetic findings, significantly greater than the average 12% risk for women in the US. A subset of high-risk women face additional stresses related to genetic findings that do not correspond to a very specific risk estimate, or have unclear clinical implications, due to limitations of existing genetic science and risk quantification.Current risk estimation models enable the identification of women who are at elevated risk for breast cancer through genetic testing for BRCA mutation carriers undergo the recommended prophylactic oophorectomy [Several risk management options are available to support women at higher than average risk of breast cancer. Most of these are significantly underused by women who may benefit in terms of reduced cancer risk and cancer-related worry. Only about half of orectomy  and feweorectomy , 3. UndeThis article summarizes what is known and unknown about the various drivers of women’s decisions about breast cancer risk management methods. It begins with a brief overview of breast cancer prevention options for high-risk women, followed by a review of the current literature regarding decision making about these options by specific populations of women, and possible explanations for these patterns. This review also highlights important areas for future research, which could support the development of interventions that more fully empower women to make informed and values-consistent decisions and contribute to favorable health outcomes. It focuses solely on the prevention decision making of women at elevated risk of breast cancer due to identified genetic mutations or familial history. The prevention behavior of average risk women, decisions relevant after a breast cancer diagnosis, and the psychological sequelae of prevention interventions are outside the scope of this discussion.BRCA1/2 mutations [BRCA mutation carriers are unaware of this status [ATM, CDH1, CHEK2, NBN, NF1, PALB2, PTEN, STK11, and TP53) also confer increased breast cancer risk, and testing for these mutations is becoming increasingly available. Positive findings for these mutations are currently associated with recommendations to add magnetic resonance imaging (MRI) screenings and, for a subset of these genes, to consider prophylactic surgery, but other aspects of appropriate clinical management for these patients remain under investigation [Women at elevated risk for breast cancer are those who either have a known predisposing genetic mutation, or have a family history of breast or related cancers sufficient to raise calculated lifetime chance of breast cancer above a certain benchmark—usually 20–25% –13. Studutations , 15, ands status , 17. Curtigation –20. Guidtigation , 22. FouBRCA1/2 mutations, significant family history, or high-risk histology [Bilateral prophylactic mastectomy , the single most effective prevention method, reduces breast cancer risk by about 90% –31 and bistology –38.BRCA mutation carriers between the ages of 35 and 40 years (or when childbearing is complete). For this group, it reduces the risk of ovarian, fallopian tube, or peritoneal cancer by 80% [Prophylactic surgical removal of ovaries and fallopian tubes  is recommended for all r by 80% , likely r by 80% , 40–43  increasing the frequency of clinical breast examinations to biannual checks; 2) initiation of radiologic screening at younger ages, such as 5 to 10 years prior to the youngest age of breast cancer diagnosis in a woman’s family; 3) annual bilateral screening mammograms, combined with targeted ultrasound examinations as indicated; and 4) the addition of breast MRI for women with a lifetime risk of breast cancer of 20% or greater , 60. TheLifestyle changes that reduce the risk of breast cancer in the general population are considered wise but insufficient for those with higher, familial risk , 56, 67.BRCA mutation carriers can achieve greater risk reduction by undergoing both BPM and BPSO than either alone [BRCA mutation carriers [It is likely that high-risk women commonly compare the effectiveness and consequences of methods and consider particular combinations of prevention options. There is, however, a sparse evidence base for these comparisons and combinations , 70. It er alone , 71, ander alone . BPSO iscarriers , 73. Procarriers , 75. Givcarriers , 76.BRCA mutation carriers, and less is known about women with apparent hereditary risk who are negative for BRCA mutations, those known to carry other risk-increasing genetic mutations, and those who have not undergone genetic testing. The rate of BPSO in BRCA mutation carriers ranges from 55% to 90% in various populations over periods ranging from 6 months to 10 years after receipt of genetic testing results [BRCA mutation [BRCA mutation carriers to be about 20% and gradually rising [BRCA mutation carriers in some populations may undergo at least one risk-reducing surgery within 5 years of genetic testing [BRCA mutation carriers because ovarian cancer treatment has poorer success rates than breast cancer treatment, because BPSO reduces risk of both ovarian and breast cancer, and because some women find mastectomy more psychologically difficult due to its potential effects on body image and sexuality [BRCA-mutation negative or do not know their genetic status, and who are therefore unlikely to benefit [Most research on uptake of biomedical prevention methods pertains specifically to  results –81. BPSOmutation , 82. Mosy rising , 83, 84,y rising , 81. Ove testing , but thiexuality . CPM rat benefit –89. More benefit , 82, 89.BRCA mutation carriers approached for a chemoprevention trial enrolled [BRCA mutation carriers offered chemoprevention started such a regimen within 4 years of receiving genetic test results [interested in chemoprevention, which one study found to be upwards of 40% [Population studies suggest that only 1–5% of women eligible to use tamoxifen for primary prevention actually do so , 90–92.  results . These us of 40%  of thoses of 40% , 94. Adds of 40% , 93–101.s of 40% .BRCA mutation carriers, and probably more high-risk women without known mutations, engage in surveillance alone, without specific intervention for biomedical risk reduction [Between 20% and 50% of eduction , 77. Deseduction , studieseduction –106. EffThe general relationship between lifestyle factors and breast cancer has been extensively studied, but the specific extent to which high-risk women use lifestyle changes to reduce breast cancer risk has not yet been explored. Future research should examine women’s perceptions and use of dietary and exercise changes as prevention behaviors, and how lifestyle choices relate to women’s other preventive decisions.BRCA1/2 mutation carriers and women who have completed preventive surgeries. This research is largely descriptive and correlational, and does not explore decision-making processes or other factors that affect women’s preferences and choices on a prospective basis.Understanding which women are likely to make particular prevention choices is a key basis for efforts to facilitate informed, values-consistent, health-protective decisions. This involves understanding how uptake rates do or do not fit specific subgroups of women, as well as how psychological and social dynamics may affect the preferences and actions of individuals. Existing knowledge relevant to this area comes primarily from retrospective studies, usually of BRCA mutation carriers choose BPM more frequently than other women at elevated risk, and are more likely to believe that BPM is the best way to reduce both breast cancer risk and worry [BRCA mutation carriers who believe ovarian cancer to be incurable are more likely to undergo BPSO [BRCA1 and BRCA2 mutations may behave differently with respect to BPM and BPSO, but this merits additional investigation [BRCA 1/2 mutations) are in fact significantly more likely to choose CPM [Severity of cancer risk (both diagnosed and perceived) strongly affects prevention behavior. Higher perceived risk of breast cancer is positively associated with considering chemoprevention, BPM, and BPSO among high-risk women in general , 108. Knnd worry . BRCA murgo BPSO . Women wtigation , 111. Amoose CPM . The mosoose CPM , 89, 113BRCA mutation carriers with a first-degree family history of breast or ovarian cancer—particularly in a mother or sister—are more likely to undertake prophylactic surgery, and breast cancer patients with a family history are less likely to choose breast-conserving surgery and are more likely to undergo CPM [Several studies point to strong but complex relationships between family history and surgical choice , 115. BRergo CPM , 111. FuBRCA mutation carriers with poorer self-perceived health may be more likely to choose prophylactic surgery [Some evidence suggests that women’s psychological well-being may also affect their prevention choices. Among women with hereditary risk, BPM is more often chosen by those who experience high anxiety and/or exaggerated perceptions of their risk . BRCA mu surgery . The cho surgery , 117–122BRCA mutation carriers [Existing research indicates that breast cancer morbidity and mortality, access to treatment, and decisions regarding a range of related screening, diagnostic, and treatment questions all differ substantially by race and ethnicity , 123–128carriers , and thacarriers . It woulThe significant body of research that relates socioeconomic status (SES) to healthcare access, general prevention behavior, and health outcomes suggests that cancer prevention decisions may also be systematically related to SES. However, the sparse research on this potential relationship has so far yielded conflicting findings on relationships between prevention-related choices and SES indicators including employment, education, and health insurance status , 130. ThBRCA mutation carriers [BRCA mutation carriers who are often single, childless, and not yet confident making life-altering decisions [Prevention decisions also vary by age. Both BPM and CPM are associated with younger ages among carriers , while tcarriers , 133 (bucarriers  for an eecisions –137.BRCA mutation carriers who have at least one child [Prophylactic surgery is more often chosen by ne child , and thone child , 138. Mane child .Large geographic differences have been observed in the uptake of preventive surgeries, across both nations and subnational regions . Uptake The acquisition and processing of accurate information are necessary conditions for appropriate decision making. Women’s physicians are a trusted but insufficiently studied source of information about cancer risk and prevention , 140. BeFor women with access to them, genetic counselors provide more thorough information about risk and prevention than generalist physicians or oncologists, combined with support to process information and make decisions . AlthougBRCA mutation carriers who choose prophylactic surgery indicate that they may take several years to do so [For many women, prevention decisions are developed through complex processes that can involve explicit deliberation, objective information, intuitive and affective elements, and/or input from others , 156. Peto do so , 110, anto do so , 160.BRCA mutation carriers involves filtering genetic information through a complex framework of diverse psychological, social, and emotional factors [BRCA mutations as pressure to act [Qualitative studies have revealed that women’s conceptualizations of risk and prevention differ substantially from those of healthcare providers –163. The factors . Other r factors ; acting  factors , 167; pr factors , 168; ine to act ; and expe to act , 171. ThThe stress associated with uncertainty is a central part of coping with health risks, and may be triggered at many stages of decision making , 149. CaFinally, the impact of the drivers of decision making discussed above may not be consistent across subgroups of women, and a comprehensive understanding of interactive effects may be critical to the design of effective decision-support interventions. Relevant examples can be found in studies of race and ethnicity in decision making; for instance, subgroups of Asian-American women differ in their use of mammogram screening by ethnicity, health insurance, and SES , 181. MiMany of the most substantial gaps in the research literature have to do not with breast cancer risk reduction options themselves, but instead with how women make sense of and utilize these measures. Key gaps in existing knowledge of women’s prevention decision making are summarized in Table Prior research on women’s use of prevention interventions has been largely quantitative and deductive. This research has provided a powerful foundation for understanding women’s prevention behavior, but has also missed key dynamics such as the potential roles of financial resources, social support, and desire to control one’s health. The ability to support improved decision making will hinge on accurate understanding of women’s own perspectives, which can be illuminated by systematic qualitative research that offers women more space to articulate perspectives on their own experiences, rationales, and challenges.The ability of women to compare their options would be facilitated by research that explicitly documents the effects of various choices on physical, psychological, and social well-being. One potential outcome of such research may be improved decision aids, which have been shown to positively impact knowledge, expectations, distress, and decisions among patients facing multiple medical options with complex pros and cons –189. IniInductive research from women’s own perspectives may illuminate previously unstudied processes important to women’s decisions, and thereby offer new potential approaches for designing prevention-supportive interventions. At a minimum, these dynamics are likely to include: psychological factors from cancer-specific distress and fear for children to the desire to take control of health; social dynamics of support from spouses, family, and friends; exposure to cancer patients, survivors, and advocacy groups; the need to bolster both information acquisition and skills-building to facilitate deliberative decision making; and interactions among the drivers of prevention choice.Existing research in and beyond the area of breast cancer prevention indicate that decision-making processes and prevention choices are likely influenced by the severity of medical risk, geographical context, race-ethnicity, SES, and access to medical information or care. These distinctions may have important implications for tailoring supportive interventions.One key conclusion of this review is that we must broaden our research agenda beyond the medical components of prevention interventions themselves, to focus also on social questions and women’s perspectives relevant to breast cancer prevention. Such research will help resolve crucial mismatches between the biomedical interventions researchers have developed to mitigate cancer risk and women’s real-life prevention behavior, and will ultimately provide critical support for the objective of preventing breast cancer among women at elevated risk.


T3vs.T1=1.67 (1.02-2.71), P-trend=0.04). This association was observed in the placebo group (HRT3vs.T1=2.80 (1.42-5.54), P-trend=0.003), but not in the antioxidant-supplemented group . Besides, in the placebo group, the increased breast cancer risk associated with dietary iron intake was more specifically observed in women with higher lipid intake (P-trend=0.046). These findings suggest that dietary iron intake may be associated with an increased breast cancer risk, especially in women who did not received antioxidants during the trial and who consumed more lipids. This supports the experimental results suggesting that breast cancer risk may be increased by iron-induced lipid peroxidation.Experimental results suggested that iron-induced lipid peroxidation may explain the direct associations observed between red/processed meat intakes and colorectal and breast cancer risk. However, epidemiological evidence is lacking. Thus, we investigated the association between dietary iron intake and breast cancer risk, and its potential modulation by an antioxidant supplementation and lipid intake. This prospective study included 4646 women from the SU.VI.MAX trial (daily low-dose antioxidants vs. placebo). 188 incident breast cancers were diagnosed (median follow-up=12.6y). Dietary iron intake was assessed using repeated 24h dietary records. Multivariable Cox proportional hazards models were computed. Dietary iron intake was associated with an increased breast cancer risk (HR Recently, the International Agency for Research on Cancer (IARC) classified red and processed meat consumption as “(probably) carcinogenic to humans” (Group 2A and 1 respectively) –6. NotabN-nitroso compounds .Table T3vs.T1=2.80 , P-trend=0.003) but not in the group supplemented with antioxidants , P-trend=0.7). A similar modulation was observed when analyses were restricted to post-menopausal women  or to pre-menopausal women .The association between total dietary iron intake and breast cancer risk was modulated by antioxidant intake , P-trend=0.046) while no significant association was detected in women with lower lipid intakes , P-trend=0.1). Similar results were observed with two major long chain n-3 polyunsaturated fatty acids, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA): no association in the antioxidant group, while in the placebo group, positive associations were observed in women with higher intakes (≥ median) of EPA , P-trend=0.004) and DHA , P-trend=0.01), but not in women with intakes < median , P-trend=0.3 for EPA, and HRT3vs.T1= 2.15 , P-trend=0.1 for DHA) (P-interactions=0.04 for EPA and 0.3 for DHA) [data not tabulated].A further exploratory stratification was performed according to the median intake of total lipids Table . No assoin situ breast cancers were excluded (165 cases/4458 non-cases) [data not shown].Results were similar when analyses were restricted to women who provided at least six (156 cases/3586 non-cases) or nine (116 cases/2737 non-cases) 24h-dietary records within the first two years of follow-up, when cases diagnosed within the first two years of follow-up were excluded (163 cases/4458 non-cases) or when In this prospective study, total dietary iron intake was associated with an increased risk of breast cancer. This association was no longer observed in the group supplemented with antioxidants during the SU.VI.MAX trial. In contrast, in the placebo group, a direct association was observed between total dietary iron intake and breast cancer risk, especially in women with higher intakes of total lipids , and notably EPA and DHA, two long chain n-3 polyunsatured fatty acids particularly prone to peroxidation because of their high number of double bonds .To our knowledge, only five prospective studies were performed regarding the association between iron intake and breast cancer risk, with inconsistent results –18. WhilOur results are consistent with the hypothesis that iron intake would increase breast cancer risk, through lipid peroxidation. To our knowledge, this epidemiological study was the first to investigate a potential modulation of the association between dietary iron intake and breast cancer risk by an antioxidant supplementation and by lipid intake. However, in a previous study performed in the SU.VI.MAX cohort , a direcin vitro an inhibition of iron-induced lipid peroxidation by α-tocopherol , socio-demographic variables [age  and educational level ], lifestyle factors , anthropometric factors [height (continuous) and BMI (continuous)], dietary factors  and factors indicating higher individual susceptibility to breast cancer . Interactions were tested between tertiles of dietary iron intake and 1) the antioxidant supplementation of the initial SU.VI.MAX trial and 2) lipid intake. Stratified analyses were performed according to the intervention group of the initial SU.VI.MAX trial (antioxidant/placebo) and further exploratory stratified analyses were also performed according to the median intakes of lipid.For all covariates, less than 5% of values were missing and were replaced by the respective mode value.Since we showed in a previous work  that proin situ breast cancers.Sensitivity analyses were carried out by excluding women who provided less than six or nine 24h-dietary records within the first two years of follow-up, cases diagnosed within the first two years of follow-up, or All tests were two-sided, and P<0.05 was considered statistically significant. SAS version 9.3  was used for the analyses.


BRCA1 and BRCA2 over 20 years ago remains the seminal event in the field and has paved the way for the discovery of other high-penetrance susceptibility genes by linkage analysis. The advent of genome-wide association studies made possible the next wave of discoveries, in which over 80 low-penetrance and moderate-penetrance variants were identified. Although these studies were highly successful at discovering variants associated with both familial and sporadic breast cancer, the variants identified to date explain only 50 % of the heritability of breast cancer. In this review, we look back at the investigative strategies that have led to our current understanding of breast cancer genetics, consider the challenges of performing association studies in heterogeneous complex diseases such as breast cancer, and look ahead toward the types of study designs that may lead to the identification of the genetic variation accounting for the remaining missing heritability.More than 12 % of women will be diagnosed with breast cancer in their lifetime. Although there have been tremendous advances in elucidating genetic risk factors underlying both familial and sporadic breast cancer, much of the genetic contribution to breast cancer etiology remains unknown. The discovery of  Among women, breast cancer accounts for over 25 % of cancer diagnoses and 15 % of cancer-related deaths , demonstBRCA1 and BRCA2 [To identify genetic factors associated with breast cancer predisposition, early studies used linkage analysis and positional cloning in families with multiple affected individuals to discover highly penetrant susceptibility genes such as nd BRCA2 , 5. Althnd BRCA2 , they prMore recently, genome-wide association studies (GWAS) have identified over 80 loci significantly associated with sporadic breast cancer. Collectively, however, these variants explain only 16 % of breast cancer heritability . The inaHere, we will review linkage studies, GWAS, and NGS studies that have led to our current knowledge of the genetics of breast cancer susceptibility. Our emphasis will be on the strengths and limitations of different study designs with the potential to yield clinically translatable discoveries.BRCA1 and BRCA2. The loci where these genes reside were first observed as linkage peaks on chromosomes 17q21 and 13q12, in studies of just 23 and 15 families [BRCA1 and BRCA2 [Clinically, the most important breast cancer susceptibility genes are families , 9. The families , followefamilies , 11 and BRCA1 or BRCA2, respectively, will develop breast cancer by age 70 [BRCA susceptibility variants identified in breast cancer patients with a positive family history are unique to each family. However, founder mutations are observed within certain populations. For example, in Ashkenazi Jews, the BRCA2 c.5946delT (previously 6174delT) mutation is found at an allele frequency (AF) of 0.009–0.015 [The impact of mutations in either gene can be dramatic; 65 % and 45 % of women with deleterious mutations in y age 70 , and they age 70 , 15. Gen09–0.015 .TP53, in which the most common cancer type in women is breast cancer. Malkin et al. [TP53 and LFS because somatic mutations in TP53 were identified in cancer types commonly observed in LFS families. They sequenced TP53 exons 5–8 in five LFS families because this region contains the highly conserved DNA binding domain and harbors most TP53 somatic mutations. Affected members of all families were found to have segregating germline mutations in this region, with inheritance consistent with a dominant model.There are other genes in which germline mutations have been identified that substantially increase the risk of breast cancer. Most of these were initially discovered because they cause a syndrome of which breast cancer is a component. Li–Fraumeni syndrome (LFS), for example, is a cancer predisposition syndrome due to germline mutations in n et al.  initiallCHEK2 was investigated by the same group in 1071 breast cancer patients from 718 families with a positive history of breast cancer and no BRCA mutation, a population-based set of 636 patients, and 1620 controls. They found the CHEK2*1100delC variant at a frequency of 1.1 % in controls, 5.1 % in cases with a family history, and 13.5 % in cases with a family history of male breast cancer [A deletion in PALB2. Biallelic PALB2 mutations cause a Fanconi anemia (FA) phenotype similar to that caused by biallelic BRCA2 mutations. Rahman et al. [PALB2 mutation carriers, like BRCA2 carriers, were at increased breast cancer risk. They sequenced PALB2 in 1084 controls and 923 cases with a family history of breast cancer but no BRCA mutation. They found 10 PALB2 truncating mutations among cases, but none among controls. More recently, Antoniou et al. [PALB2. They found an age-dependent trend in breast cancer risk among PALB2 mutation carriers relative to age-matched controls . Interestingly, women with PALB2 mutations from families with a history of breast cancer had substantially greater breast cancer risk than women with PALB2 mutations but no family history.A similar strategy of examining genes causing syndromes with a high incidence of breast cancer led to the discovery of n et al.  investigu et al.  examinedBRIP1 also causes FA when deleted biallelically. BRIP1 was investigated as a breast cancer susceptibility gene in heterozygous carriers because it interacts with other breast cancer predisposing genes such as BRCA1. Seal et al. [BRIP1 in 1212 breast cancer cases with a family history of disease and no BRCA mutation and in 2081 controls, and found mutations in nine cases (0.74 %) but only in two controls (0.10 %). Intriguingly, no BRIP1-mutated FA family had a family history of breast cancer. More recently, Easton et al. [BRIP1 in more than 13,000 population-based breast cancer cases and 8000 controls, and found no excess of truncating mutations in cases relative to controls . The apparently discrepant results between these two studies may be another example of the importance of family history in determining the penetrance of a risk variant. However, these results also illustrate the challenges inherent in drawing conclusions about rare variants of modest effect, even when analyzing tens of thousands of samples.l et al.  sequencen et al.  sequenceATM, in which biallelic mutations cause ataxia-telangiectasia (AT), was also suspected to be a breast cancer susceptibility gene in carriers because of an increased breast cancer incidence among relatives of AT patients. Renwick et al. [ATM in 443 BRCA-negative cases from families with at least three breast cancer-affected members and in 521 controls. Nine truncating and exon-skipping mutations were identified in cases, while only two were found in controls. All mutations found in cases were predicted to cause AT, and seven had been observed previously in AT cases. Another group performed a meta-analysis using ATM sequence data from 1544 breast cancer cases and 1224 controls [ATM mutation screen in 708 unilateral breast cancer survivors who developed contralateral breast cancer following radiotherapy and 1397 who did not. They found that women with AT-associated ATM mutations treated previously with radiation had significantly greater risk of contralateral breast cancer than unexposed women either with no mutation  or unexposed women with the same mutation . These studies suggest that ATM mutations causing AT but not other ATM variants are associated with increased breast cancer risk in heterozygous carriers and that this risk may be increased by radiation exposure; however, these results await replication, and current guidelines do not recommend that heterozygous ATM mutation carriers should avoid radiation.k et al.  sequencecontrols  performeCDH1, for example, is a tumor suppressor mutated in invasive lobular carcinoma of the breast (ILCB) but not ductal breast cancer [CDH1 cause hereditary diffuse gastric cancer (HDGC) and HDGC patients have a high incidence of ILCB (50 % lifetime risk) [CDH1 germline mutations by performing segregation analysis in 11 families with at least three HDGC cases and a confirmed CDH1 mutation. They estimated the cumulative risk of HDGC and ILCB by age 80 among women in these families to be 83 % and 39 %, respectively.Some genes are uniquely associated with risk for specific breast cancer subtypes. t cancer . Becauseme risk) , Pharoahme risk)  investigIn summary, high-penetrance and moderate-penetrance variants in these genes collectively explain approximately 20 % of the familial risk of breast cancer . UndoubtAlthough rare high-penetrance mutations explain much of the genetic breast cancer risk in a small number of cases, they do not shed light on the role of genetics in nonfamilial breast cancer. There is, however, considerable evidence for a strong genetic contribution to risk even for sporadic breast cancer . Most inFGFR2, TNRC9, MAP3K1, LSP1, and H19. Finally, Hunter et al. [FGFR2 SNPs, thereby independently replicating Easton et al.’s finding.The first three breast cancer GWAS were published concurrently in 2007. In one of these studies, Stacey et al.  used 455r et al.  conducteTo date, more than 60 breast cancer GWAS have been performed . As thisP values [Many variants in GWAS show consistent associations across populations; apparent population-specific associations can often be explained by differences in AF among populations. For example, in 2016 African American (AA) breast cancer patients and 2745 controls, 36 of 47 (67 %) EA breast cancer risk SNPs had ORs in AA in the same direction, and seven (15 %) had nominally significant Typically, a homogeneous disease model is assumed in genetic studies, and cases are lumped together because of the increase in power that comes with increased sample size. Splitting cases by subtype is an alternative study design, with the potential to increase power despite decreasing sample size.There have been GWAS investigating specific breast cancer subtypes based on the presence or absence of estrogen receptor (ER), progesterone receptor (PR), and/or HER2 expression . Broeks 1% = 0.32, OR99% = 3.36). The discriminative accuracy of the PRS as measured by a C-statistic, however, was modest (C = 0.62). The authors estimated that the lifetime risk of cancer for women below the first and above the 99th percentile of the PRS is 3.5 % and 29.0 %, respectively. In the UK, enhanced surveillance is recommended for women with both a family history of breast cancer and a lifetime risk of breast cancer above 17 %. Using the PRS, about 8 % of UK women at this risk level—accounting for about 17 % of breast cancer cases—can be identified. Thus, risk assessment can be marginally improved by incorporating susceptibility variants from GWAS. Although variants in GWAS currently have little impact on public health, this is likely to change in the future.While the 80+ breast cancer-associated loci identified to date have greatly expanded our knowledge of the genetics of the disease, they also have the potential to be of clinical utility. Recent studies have assessed the clinical utility of variants in GWAS using the polygenic risk score (PRS), a crude estimate of a patient’s OR for disease calculated by summing the ORs for each risk allele carried by the patient –45. In oTaken together, these results suggest that lumping and splitting strategies for GWAS are unlikely to identify much of the missing non-high- or non-moderate-penetrance genetic contribution to breast cancer risk. One explanation for this is that GWAS are designed to identify common variants (MAF > 0.01), and to only poorly interrogate rare variants (MAF < 0.01) . Thus, mBRCA1 or BRCA2) are sequenced in stage one, and identified candidate risk variants are genotyped in a much larger set of cases and controls in stage two; perform a staged study in which unrelated individuals sharing critical clinical or other characteristics, such as driver somatic mutations, are sequenced in stage one, and candidate variants are subsequently genotyped in stage two; and sequence multiple related affected individuals from families enriched for disease to identify novel candidate variants and/or genes, and then interrogate these variants and genes in large case–control sets.NGS approaches can be divided into four broad experimental strategies: sequence large numbers of unrelated patients and healthy controls to identify rare variants with AFs differing significantly between cases and controls; perform a staged study in which unrelated individuals from high-risk families meeting certain criteria , while in the French-Canadian set the frequencies were 0.69 % and 0.014 %, respectively (P = 3 × 10–6). Thus, by performing a discovery stage using cases who, based on clinical characteristics and/or family history, were likely to have high-penetrance mutations, the number of hypotheses tested in a subsequent validation stage was limited, thereby minimizing the penalty for multiple testing.To overcome these barriers, the second NGS study design is a staged study in which unrelated cases selected for a presumed high “genetic load” for disease are sequenced in stage one, and only variants with evidence for association are genotyped in stage two in a much larger number of cases and controls. This approach assumes that the genetic complexity in patients with high “genetic load” is considerably reduced as compared with unselected patients because of the high-penetrance mutations. One study using this approach was performed by Cybulski et al. , who seqThe purpose of selecting cases based on their presumed genetic load is to reduce the genetic complexity of the analysis. This concept can be expanded to other clinical or genetic features, which forms the basis of the third NGS study design. For example, in cancer the presence of a tumor genome provides the opportunity to use the mutational landscape of a patient’s somatic genome as supportive evidence to guide discovery of novel candidate germline risk variants, as Kanchi et al.  demonstrEGFR predict both higher somatic mutational burden and also specific somatic exonic microdeletions within EGFR. Additionally, Rausch et al. [TP53 that have been associated with LFS. Similarly, in breast cancer the recognition that some subtypes are enriched for germline deleterious mutations in predisposition genes can help prioritize individuals and families for NGS investigations either to identify known or to discover novel high-penetrance risk variants even in the absence of family history [Other studies have also shown relationships between the tumor genome and specific germline predispositions. Liu et al. , for exah et al.  found thATR, CHEK1, and GEN1 in three separate families. Another 2544 sporadic cases and 7652 controls were sequenced to identify additional rare variants in these three genes. An excess of truncating mutations in all three genes was found in cases, although the total number of cases with deleterious mutations was only 11 [FANCM was significantly more common in a set of more than 3500 breast and ovarian cancer cases as compared with 2000 controls.Finally, building upon the idea of leveraging genetically loaded individuals to improve power, the fourth NGS strategy is to sequence multigenerational families enriched for disease. Sequencing multiple affected family members as opposed to only probands simplifies considerably the analysis by limiting the number of candidate disease-causing mutations to those shared by affected family members and obligate carriers. Spurrell performed whole exome sequencing on 144 affected individuals from 54 breast cancer families with no germline mutations in known breast cancer genes to identify genes with truncating mutations shared by at least two affected family members. The study found germline mutations in  only 11 , who perThese examples illustrate the value of family context for genetic studies. If a gene harbors a mutation segregating in near-Mendelian fashion in one family, then there may be other highly penetrant mutations in the same gene in other families. Furthermore, if mutations identified in a family are also found in the general population, then an extension of this family-based study design is to investigate their contribution to sporadic disease risk. As noted for both the linkage and candidate gene examples discussed earlier, family history can modify the contribution to risk of even highly penetrant variants. This may suggest the existence of genetic modifiers in families that potentiate the effect of risk alleles in these families, or attenuate their affect in unaffected mutation carriers. Thus, these studies may provide the best opportunity to convert insights from rare variants into discoveries of clinical and biological significance.An important caveat to NGS studies is that they are not agnostic; all approaches assume that variants must be filtered based on functional or population characteristics. This limits analysis only to variants with high a-priori likelihood of being functionally important, reducing the number investigated and the burden of multiple testing. Without filtering, all NGS studies would be woefully underpowered. Filters include: minor AF; functional consequence ; and functional annotation, such as predicted importance for protein function  treated with ionizing radiation (IR) are at high risk for IR-induced second primary tumors, particularly breast cancer , 65. BesBRCA1/2 and other high-penetrance and medium-penetrance genes, which are mutated in about a quarter of all familial breast cancer cases. Eighty-four independent loci have been identified by GWAS, which account for a small but meaningful proportion of sporadic breast cancer risk. New technologies such as NGS have catalyzed a new era of discovery by making possible studies unimaginable even a few years ago.The search for germline breast cancer predisposing variants has been highly successful. Linkage analysis and fine-mapping led to the discovery of With new technologies, however, come new challenges, perhaps foremost of which is the urgent need to rethink study design. Complex diseases such as breast cancer lie at the interface of human genetics and epidemiology. Here, we propose that one powerful framework for future investigation may be studying the genetics of breast cancer risk in context; in particular, the context of either a homogeneous exposure or familiality. An important implication of epidemiology-guided study design is that the contribution of variants to disease is not monolithic; variants that are highly penetrant in one context may not be associated with disease in another.Finally, genetics is predicated upon collaborative research. The sine qua non is shared access to large numbers of samples with well-annotated clinical and exposure data. However, genetics is only the starting point. Moreover, breakthroughs in breast cancer prevention and treatment can only come through functional follow-up of genetic discoveries. This is an exciting time for genetics, but only through concerted multidisciplinary efforts will the clinical promise of genetics will be realized.