Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis. Breast cancer is one of leading causes of cancer death for females worldwide . The morComplex diseases, including breast cancer, are not caused by a single gene mutation. Instead, they are the consequence of multifaceted dysfunctions, including protein/coding genes, non-coding RNAs and their epigenetic modification , 3. DiseDisease risk modules could be detected directly from undirected networks, i.e. gene/protein interaction networks and co-expression networks, by available tools. For example, Cytoscape plugin MCODE has been applied to disease risk module detection from interaction networks for lung adenocarcinoma , non-smaDisease risk modules could also be detected by merging or extending cliques. In graph theory, a clique in a network is a fully connected subgraph where every two nodes are connected by an edge . CliquesMathematical programming has been used to solve network reconstruction problems to achieving globally optimal number of edges and number of quantitative differences . Such kiAfter evaluating differential information of genes with our two measurements , 218 differentially expressed genes (DEGs) and 1209 differential expression variance genes (DEVGs) were obtained. Hence, 1382 differential genes were screened out from the microarray dataset.S > 0.8 , 6 primary modules were discovered , 3 breast cancer risk modules were identified .A literature review was carried out using the online database PubMed (https://www.ncbi.nlm.nih.gov/pubmed) for non-seed genes in 3 breast cancer risk modules. It was showed that 75% (33/44) non-seed genes were associated with breast cancer  using the Enrichr tool, respectively.Breast cancer risk modules were enriched in numerous pathways and functions, especially breast cancer-related ones . It was Other pathways and functions enriched by single breast cancer risk module or all risk modules were also closely associated with breast cancer. “Positive regulation of cell migration” was also a breast cancer-related biological process, which was enriched by only non-seed genes in Module 1. Bisphosphonates, antiresorptive drugs, might be developed as a therapeutic option for breast cancer since it significantly decreased cancer cell migration in a dose-dependent manner . OxidatiFor functions enriched by all risk modules, “PI3K-Akt signaling pathway” is an important signal transduction pathway in cells, which was closely associated with the lymph node metastasis of breast cancer, and could affect breast cancer progression and patient prognosis , 33. “CeMost genes in our breast cancer risk modules, especially non-seed genes, were enriched in breast cancer-related pathways or functions, some of which were also related to cancer hallmark-associated GO terms, such as “HALLMARK_APOPTOSIS” and “HALLMARK_PI3K_AKT_MTOR_SIGNALING”, which indicated the disease association of our risk modules.With genes in breast cancer risk modules as features, breast tumor and normal samples were classified by the SVM classifier. Using LOOCV, all three risk modules achieved an accuracy of approximate 85% for breast tumor and normal samples of GSE15852, although only ~33% genes in each module were differential genes.In order to compare the classification performance of breast cancer risk modules and that of only seed genes in these modules, the classification accuracy was also computed based on SVM classifier with seed genes as features. The classification accuracy was ~83% for seed genes in each risk module, which was inferior to that of breast cancer risk modules .These results exhibited that our risk modules with seed genes and non-seed genes could distinguish between breast tumor and normal samples with higher accuracy than with only seed genes.Additionally, to assess classification significance of risk modules, 100 random gene sets of the same size were selected from the breast cancer-related interaction network. AUC values were calculated utilizing SVM classifier with genes of random gene sets as features. The classification accuracy of breast cancer risk modules outperformed the accuracy of random gene sets of the same size (AUC = ~0.82). This showed that risk modules could classify breast tumor and normal samples effectively with significantly better performance than only seed genes or random selected ones.Then, the classification with genes in breast cancer risk modules as features was conducted on another two datasets, GSE70947 from another platform and samples collected from The Cancer Genome Atlas (TCGA), the accuracy of which was compare with that of seed genes in risk modules . Since tSimilar results that our risk modules with both seed genes and non-seed genes could distinguish between breast tumor and normal samples with higher accuracy than with only seed genes were also obtained for these two datasets.In this paper, an integrated strategy was proposed to identify breast cancer risk modules according to a multi-objective programming model and significance in three scores. A total of 3 breast cancer risk modules were identified. ~70% non-seed genes in these risk modules were confirmed to play vital roles in the development, pathogenesis or signaling of breast cancer and could act as potential breast cancer genes. Most genes in our risk modules, including unconfirmed non-seed genes, were enriched in breast cancer-related pathways or functions. These risk modules could distinguish between breast tumor and normal samples with higher accuracy than seed genes in risk modules. These results indicated the disease association of breast cancer risk modules identified by our integrated strategy.In order to illustrate the robustness of our risk modules, risk modules were re-identified using 90% samples randomly selected from GSE15852. The process was repeated 100 times. Genes of risk modules from random samples were compared with those of breast cancer risk modules from all samples . More thFor the purpose of primary modules detection, cliques of the breast cancer-related interaction network were mined using Cytoscape MClique in our integrated strategy. MCODE and GraphWeb were also taken into consideration for primary module detection. It was showed that the number of genes in cliques/modules mined by MClique, MCODE and GraphWeb had great difference, while classification accuracy varied among the three module sets . Since cA multi-objective programming model based on maximization of two criteria, MI and PCC, was employed for candidate module discovery. Candidate modules based on individual criterion, MI or PCC, were also discovered. Breast tumor and normal samples were classified with these modules as features . CandidaTo further demonstrate the effectiveness of our integrated strategy, genes removed from primary modules were compared to non-seed genes remained in breast cancer risk modules by classification accuracy. The accuracy of non-seed genes remained in breast cancer risk modules was higher than that of genes removed from primary modules . These rResults of breast cancer risk modules and their validation demonstrated the effectiveness of our choice for different steps of our integrated strategy. Nevertheless, there are some potential limitations to our study. First, known disease-associated genes were required for disease-related interaction network construction. Second, expression similarity and difference as well as functions of genes were exploited for disease risk module identification in our integrated strategy, which might be affected by datasets used in the process. The other breast cancer microarray dataset GSE70947 was used to screen differential genes and identify breast cancer risk modules with our integrated strategy. Another two risk modules were identified. With more differential genes in these modules (~55%), they had a high classification accuracy (~0.85), which was still inferior to that of our risk modules identified from GSE15852 (>0.89) for GSE70947. This showed the effectiveness of our integrated strategy and breast cancer risk modules we identified. With other types of information available, they could also be integrated into our integrated strategy to improve its performance or reveal the molecular mechanism of metastasis .Subtype  and Luminal B (LumB)) classification for breast cancer is of great significance for its clinical diagnosis and treatment. To assess the subtype classification ability of breast cancer risk modules, expression values of genes in these risk modules were used to classify different subtypes. All risk modules could distinguish between subtypes with high accuracy .In summary, breast cancer risk modules identified by our integrated strategy were confirmed to play critical roles in breast cancer by literature review, functional enrichment analysis, and classification accuracy. Our integrated breast cancer risk module identification strategy could be extended to other complex diseases for researchers to gain more thorough understanding of their pathogenesis.The microarray dataset GSE15852 (GPL96) was downloaded from Gene Expression Omnibus (GEO) database, which was composed of 43 human breast tumor tissues and their 43 paired normal tissues.Known breast cancer-associated genes were collected from the Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census , the breast cancer gene database of the Tumor Gene Family of Databases , ONGene (http://www.ongene.bioinfo-minzhao.org/) and the Network of Cancer Genes . CGC is an expert-curated and wide-used source of genes driving human cancer . TGDB coA complete gene/protein interaction network is of fundamental importance for the understanding of diseases . Human pAn integrated disease risk module identification strategy was proposed and used to identify breast cancer risk modules. First, differential genes were screened from a breast cancer microarray dataset, and primary modules were detected by merging cliques containing differential genes from a breast cancer-related interaction network. Then, candidate modules were discovered using a multi-objective programming model to maximize two similarity criteria. Finally, breast cancer risk modules were identified according to significance in module score based on MRF, consistency score of functions and difference score for PCC.2fold change (FC) > 1 were selected as the significance threshold for DEG screening. (2) The variance S2 for expression values of gene x could measure how far its expression level in different samples is from the average expression level in all samples:Two measurements were used to evaluate differential information of genes: (1) after preprocessing, the significance analysis of microarrays (SAM) program was used for screening DEGs. The false discovery rate (FDR) < 0.05 and the absolute value of logx (N is the number of samples), V was defined as the absolute value of difference between variance for genes in disease and normal samples:where V of 1000 random genes exceed that of the interested one. Genes with FDR-adjusted p value<0.05 were screened as DEVGs.The p value was evaluated by the number of times DEGs and DEVGs were differential genes for further analysis.Cliques containing 4-8 genes were mined from the breast cancer-related interaction network using Cytoscape MClique. Only those containing differential genes were taken into consideration. These cliques were also centered on seed genes that overlapped with seed genes in other cliques. The extent of overlapping for seed genes in different cliques was evaluated by Simpson index:A and B, A and B, respectively.where S was set to ~0.75. In this case, subgraphs with more than 2000 genes were obtained. Therefore, to identify more rational modules, cliques with 5-8 genes were used for clique merging. That is, each pair of cliques/subgraphs with S>0.8 were merged to form larger subgraphs until S for no subgraph pair was larger than 0.8. Subgraphs obtained at this step were named primary modules.Cliques with 4 genes could be merged if the cutoff of Simpson index Using a multi-objective programming model to maximize two criteria, candidate modules were discovered, under the hypothesis that genes more similar to seed genes may tend to be disease-related. The similarity of non-seed genes with seed ones in primary modules was measured by the sum of mutual information (MI) and the average of PCCs:Subject to:where y against each seed gene x in primary modules, candidate modules that satisfied the requirements could be obtained.Through multiple iterations that calculations were performed for each non-seed gene Breast cancer risk modules were identified based on significance of permutation tests for three scores of candidate modules.m genes, a multivariate random variable For a candidate module with f:Gibbs distribution was employed to specify the joint probability of K is a constant that guarantees the probability sum to be 1, T is a temperature parameter controlling the distribution sharpness, andwhere which represents the differential level of seed genes with the similarity between non-seed genes of a candidate module.Therefore, the module score based on MRF was defined ask is the number of interactions in the module M, C1 and C2 are the set of seed genes and non-seed genes in the module, uf and vf are expression differences assessed by the t-test between tumor and normal samples, and ud and vd are the degree of non-seed genes u and v, respectively.where The consistency scores of candidate modules were calculated based on functional consistency between seed genes and non-seed ones:xF and yF are functions annotated by seed gene x and non-seed gene y, respectively. x and y annotated.where PCC between each gene pair in candidate modules for tumor and normal samples were calculated, respectively. PCC difference of a module was defined as the sum of PCC differences for gene pairs in it.Candidate modules with large values for all the three scores indicated their disease association.To obtain the significance of permutation tests for each candidate module, 1,000 random modules with the same number of genes were constructed. All of the three scores were calculated individually for these random modules. Scores significantly greater than the random ones  were considered significant. Breast cancer risk modules were identified as candidate modules significant in all 3 scores.Validation for association of risk modules identified using our integrated strategy with breast cancer was evaluated from three aspects: 1) confirmation rate by literature review, 2) functional enrichment analysis , including Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, performed through a latest enrichment tool Enrichr (http://amp.pharm.mssm.edu/Enrichr/) , and 3) W.L., G.D., J.Z., and L.C. contributed to the study design. G.D. and J.Z. contributed to data collection. W.L., G.D., and J.Z. performed statistical analysis, interpretation, and drafted the manuscript. E.H., Y.H., J.L., X.S., K.W., and L.C. revised the manuscript. All authors contributed to critical revision of the final manuscript and approved the final version of the manuscript. W.L., X.S., K.W., and L.C. provided financial support and study supervision.


The Predicting Risk of Cancer at Screening (PROCAS) study estimated 10-year breast cancer risk for 53,596 women attending NHS Breast Screening Programme. The present study, nested within the PROCAS study, aimed to assess the psychological impact of receiving breast cancer risk estimates, based on: (a) the Tyrer–Cuzick (T-C) algorithm including breast density or (b) T-C including breast density plus single-nucleotide polymorphisms (SNPs), versus (c) comparison women awaiting results.A sample of 2138 women from the PROCAS study was stratified by testing groups: T-C only, T-C(+SNPs) and comparison women; and by 10-year risk estimates received: 'moderate' (5–7.99%), 'average' (2–4.99%) or 'below average' (<1.99%) risk. Postal questionnaires were returned by 765 (36%) women.Overall state anxiety and cancer worry were low, and similar for women in T-C only and T-C(+SNPs) groups. Women in both T-C only and T-C(+SNPs) groups showed lower-state anxiety but slightly higher cancer worry than comparison women awaiting results. Risk information had no consistent effects on intentions to change behaviour. Most women were satisfied with information provided. There was considerable variation in understanding.No major harms of providing women with 10-year breast cancer risk estimates were detected. Research to establish the feasibility of risk-stratified breast screening is warranted. Importantly, the additional cancers identified in higher-risk women were more likely to be stage 2 and above and women in these groups were therefore most likely to benefit from earlier detection.7To make risk stratification possible and for consequent benefits to women to be realised, a formalised approach to identifying and communicating women’s breast cancer risk is required. In line with this, the Predicting Risk of Cancer at Screening (PROCAS) study2 A key set of harms and benefits to consider are the emotional, cognitive and behavioural impacts of receiving personalised breast cancer risk estimates8 that have been considered in depth for breast cancer screening.9 These include undue anxiety and worry about cancer in women informed of high risk. The main potential benefits of breast cancer risk feedback to women would consist of increased knowledge allowing more informed choices regarding prevention options , as well as potential changes in risk reducing behaviours such as increased physical activity, and reduced alcohol consumption.Before a service that provides breast cancer risk estimates could be implemented, or tested in an effectiveness study, it is important to establish the likely harms and benefits of providing women with their personalised breast cancer risk. Potential harms of breast screening include overdiagnosis, invasive testing and incidental findings.The main aim of the present research is to assess the psychological impact of receiving personalised estimates of breast cancer on women who participated in the PROCAS study. Specifically, this manuscript examines the psychological impact of receiving risk estimates based on: (a) the T-C model incorporating breast density information versus, (b) the T-C model including breast density information, and incorporating genetic information from SNPs extracted from saliva, with (c) comparison women also from the larger PROCAS study awaiting personalised risk information. For the T-C and T-C(+SNPs) groups, the impact of levels of risk estimates was also compared for three groups of women at 'moderate-risk' (10-year risk 5–7.99%), 'average risk' (10-year risk 2–4.99%) and 'below average risk' (10-year risk <2%).Women who received risk estimates based on the T-C algorithm, based on self-completed questionnaires and incorporating information on mammographic density.Women who received risk estimates based on the T-C algorithm, based on not only self-completed questionnaires and mammographic density, but also genetic information derived from SNPs.A comparison group of women who would receive their risk estimates in the near future, but were asked to complete questionnaires for the present study before receiving their risk estimates. Study team members were aware of estimated 10-year breast cancer risk of women in this group.The present research was approved by Liverpool East NHS Research Ethics Committee. It was a natural experiment of women from the larger PROCAS study, and employed a 3×3 between-subjects design, with each woman being allocated to one testing group and receiving one of three categorical risk results  'moderate-risk' (between 5 and 7.99% risk over next 10 years), (b) 'average risk' (between 2 and 4.99% risk) and (c) 'below average risk' (less than 2% risk). Women identified as being at high risk, i.e., 8% or higher were excluded, as they had already received offers of individualised risk consultations by telephone or face-to-face with consultants.A total of 131,373 women in Greater Manchester who were invited for routine breast screening during 2009 and 2014 were also asked to participate in the overall PROCAS project, via invitation letter alongside their NHSBSP. In line with usual practice in England, women aged 50–70 years were invited, as well as women invited as part of the AgeX trial, who were 47–59 and 71–73 years (NCT01081288). Of women who attended for screening, a total of 53,596 women (40.8%) consented to the research when they attended their screening appointment, at designated sites within hospitals or in large vans in locations to cover populations that are not within easy reach of the hospital screening sites. Those women who consented then completed a two-page questionnaire to assess their personalised risk of breast cancer. Of these 53,596 women, 51,011 (95.2%) had requested receiving this breast cancer risk estimate in due course. The age of the PROCAS sample was between 47 and 73 years and the majority (90.9%) reported their ethnicity as 'white'.In the overall PROCAS study, there were 3.2% of women identified as being at high risk, 10.3% of women identified as being at moderate risk, 59.3% of women were identified as being at average risk, and 27.2% of women were identified as being at below average risk.Women in the present study were selected from the overall PROCAS study on the basis of being scheduled to receive their estimated breast cancer risk during the study period of November 2015 to August 2016. As the main PROCAS study had the central objective of evaluating the risk model, women who were invited to participate in the present study were expecting a considerable delay between being tested and receiving their results.Women in the T-C, or T-C(+SNPs) groups who were included in the present study were recruited to the study and had risk assessment between May 2012 and July 2013; women in the comparison group were recruited and had risk assessment between September 2013 and March 2015. Women in the (a) T-C and (b) T-C(+SNPs) groups were posted letters containing risk estimates and explanatory leaflets between November 2015 and December 2015. Women in these groups were also posted questionnaires for the present study between January 2016 and April 2016, and women in the (c) comparison group were posted questionnaires for the present study between February 2016 and August 2016. Non-responders were posted reminders after 4 weeks.10 and involved 'think aloud' interviews followed by semi-structured interviews with 37 women. Risk estimates were fed back in categories, following service user input into how best to communicate this information and what labels should be used to best convey personal risk 5. No women in the present study received face-to-face consultations: these were only received by women at 'high risk' who were not included in the present study.The risk estimates were accompanied by explanatory leaflets that provided information on what their result meant, what they could do to reduce their risk and advice on detecting symptoms of breast cancer. The leaflets and letters were co-designed in line with guidance,All participants were sent a participant information sheet for the present study, consent form, questionnaire and pre-paid envelope. Those women who did not respond within 4 weeks were sent a reminder and a further reminder if they still had not replied within a further 4 weeks.All participants were asked to complete the following measures:11 of the Spielberger State-trait Anxiety Inventory,12 with participants indicated for six emotion adjectives  their present feelings by selecting one of the following response options 'not at all', 'somewhat', 'moderately' and 'very much'; α = 0.86.State anxiety was assessed using the six-item short form13 consisting of six statements such as: 'how often have you thought about your chances of getting cancer'. Participants endorsed one of the following response options 'never', 'rarely', 'sometimes' and 'almost all the time'; α = 0.87.Breast cancer worry was assessed using the Lerman Cancer Worry Scale,14Perceived relative risk of developing breast cancer was assessed using a single item that asked women to rate their risk of developing breast cancer in the next 10 years, compared with other women of their age, from 'much lower', 'a bit lower', 'about the same', 'a bit higher' and 'much higher'.15 follow a calorie-controlled diet, be physically activity, attend next mammogram, drink less alcohol and discuss prevention options with their GP . Intention to take up chemoprevention was assessed only for participants in the T-C, or T-C(+SNPs) groups.Intentions to change five health-related behaviours related to breast cancer prevention were assessed using five-point scales adapted from previously published items:Participants in the T-C, or T-C(+SNPs) groups but not the comparison group were also asked to complete the following measures.α = 0.86), adapted from a previously published scale,16 which asked women how clear they found the information, how confusing they found it, how well informed they feel about their breast cancer risk information and how satisfied they with the amount of information given. Response options were 'strongly disagree', 'disagree', 'disagree somewhat', 'undecided', 'somewhat agree', 'agree' and 'strongly agree'.Satisfaction with the information was assessed using four items  to 10 (least deprived).To assess demographic and clinical differences in continuous variables between testing groups, ANOVAs were conducted with testing group as an independent fixed effect with three levels. Chi-squared analyses were conducted to assess differences between categorical demographic and clinical variables.Effects on state anxiety, worry, risk perceptions and intentions to change each of five behaviours were examined independently. In each case, ANCOVAs were conducted with testing group and risk result group as independent fixed effects, and BMI, deprivation and age as covariates. For each variable, two analyses were conducted: (1) a comparison of participants in the T-C and T-C(+SNPs) groups, and (2) a comparison of participants in the combined T-C and T-C(+SNPs) groups vs participants in the comparison group. Two sets of sensitivity analyses were conducted, with these analyses repeated with additional covariates: (a) whether the initial mammogram was a first screen or repeat screen and (b) whether women had a subsequent mammogram scheduled before they completed the study questionnaire. These covariates did not make a significant contribution to the outcomes reported in Table Intentions to take chemoprevention and satisfaction with information were also examined using ANCOVAs as above, but only comparing participants in the T-C and T-C(+SNPs) groups.Overall, 2138 women who were scheduled to receive feedback in 2015/2016 were sent questionnaires. Responses were received from 765 women . At least 200 questionnaires were sent to women in each of the nine experimental conditions.The testing groups were not generally well matched Table , with diP < 0.001), although there was a significant moderator effect, where women in the comparison group had higher anxiety levels irrespective of personal risk levels.There were no significant differences in state anxiety between women in the T-C and T-C(+SNPs) groups Table . By cont12 the pattern of results was highly similar. Of 763 women who completed the STAI, 111 (14.5%) exceeded this cutoff. This proportion was higher  in 59/296 women in the comparison group (19.9%) relative to 52/412 women in the T-C and T-C(+SNPs) groups (12.5%). The proportions were not significantly different  between 26/197 women in the T-C only group (13.2%) and 26/270 women in the T-C(+SNPs) group (9.6%). In the T-C and T-C(+SNPs) groups, proportions of women exceeding the cutoff was related to risk estimate received , with differences between women who received moderate (28/159 or 17.6%), average (11/140 or 7.9%) and below average (13/168 or 7.7%) risk estimatesWhen these analyses were repeated using the cutoff for anxiety disorder previously suggested,There were no significant differences in cancer worry between women in the T-C and T-C(+SNPs) groups, although women with higher personal risk were more worried (see Table There were no significant differences in perceived risk between women in the T-C and T-C(+SNPs) groups Table . In thisThere was little evidence of differences between the T-C and T-C(+SNPs) groups in terms of satisfaction with information received Table . SatisfaThere were generally few effects of either testing group or test result group on intentions to change a variety of behaviours Table .Table 5PThere was considerable variation in understanding of test results (see Table There were very few differences on any measure between the T-C only and T-C(+SNPs) groups. Women in both the T-C and T-C(+SNPs) groups had lower-state anxiety levels than women in the comparison group who did not know their risk. Women with higher personal risk were more anxious in all groups, although there was a significant moderator effect, where women in the comparison group had higher anxiety levels irrespective of personal risk levels. Women in the T-C and T-C(+SNPs) groups had slightly higher cancer worry than women in the comparison group. Overall, state anxiety and cancer worry levels were low. There was little evidence that risk communication had effects on intentions to change any behaviour. Women were generally satisfied with information provided, and risk perceptions were in line with the information that had been communicated. When women in the T-C and T-C(+SNPs) groups were asked to select options that they felt best described what their result meant, there was considerable variation in understanding of test results with some misunderstandings apparent.18 Further, studies of the effects of communicating genetic risk information have had an even narrower focus on clinical samples that were selected on the basis of existing high-risk previously detected by phenotypic markers.19 By contrast, in the present research, risk estimates were offered to a population sample of women who attended routine breast cancer screening. This makes the present findings much more informative about the likely population impact of risk communication in the context of risk-stratified screening.8The present research has two features that make it highly innovative. First, the present research reports on the effects of communicating personalised breast cancer risk to women from the general breast screening population for the first time. Previous studies of the effects of communicating personalised risk across a range of conditions have focussed on highly selected samples in research settings.5 The use of a well-validated model incorporating genetic risk information stands in contrast to much research on the effects of communicating of genetic risk that is based on risk conferred by single allele mutations.19The present research compares risk estimates derived from phenotypic sources with risk estimates derived from both phenotypic and genotypic sources, to allow a comparison of how people respond to these two kinds of risk information. An innovative feature of this comparison is that in the present research, risk estimates using genotypic information were based on a large number of genetic markers that allowed more fine-grained estimation of risk.A further strength of the present approach is that the approach taken to risk communication involved considerable user input. First, the decision to communicate risk estimates using categorical descriptors of risk was taken following considerable input from a patient-public involvement (PPI) user panel. Second, the letters and leaflets that were used to communicate risk estimates and the implications of this risk information were developed using several rounds of co-design with women who were drawn from the same population as women in the present research.5A major limitation of the present research is that the three testing groups of women were identified on the basis of convenience in communicating risk estimation and were not well matched on a variety of demographic, clinical and service-level variables. Although this lack of matching is far from ideal, there are good reasons to believe that it has not had a major biasing effect on the results reported. Importantly, for most demographic, clinical and service variables the T-C and comparison groups were similar, with the T-C(+SNPs) group being most different. For instance, women in the T-C group (mean age = 54.3 years) and comparison group (mean age = 52.4 years) were of similar age, but women in the T-C(+SNPs) group were considerably older (mean age = 61.9 years). By contrast, the T-C and T-C(+SNPs) groups showed a similar pattern of responding on the psychological impact variables, which were different from women’s responses in the comparison groups. Thus, it is implausible for the differences in demographic, clinical and service-level variables to be able to account for the results reported. There was no major seasonal variation or major trends in breast cancer screening attendance across England and the North-West of England where the study took place using data routinely reported by Cancer Research UK/ National Screening Committee.5 Nevertheless, the impact of receiving risk estimates three years after consenting to receive these may well have reduced the psychological impact of those estimates. It should be noted however, that questionnaires were completed around three-months following the receipt of risk estimates, so the impact on women receiving risk estimates was comparatively recent.A second limitation of the present research lies in the long duration between consenting to receive risk estimates and receiving risk results, typically around three years Table . This wa20 deprivation scores were substantially lower in women who took up the offer of risk estimation compared to women who declined this offer. This is somewhat inevitable, as around 72% of eligible women currently attend breast screening,21 and of those, women had to consent to participate in the PROCAS study to be eligible for the present research. Based on this, it could be argued that participants were a group that were particularly keen to know their breast cancer risk estimate and this may have influenced the psychological impact of the information. This is possible, although given uptake rates in the NHS BSP and the requirement that people consent to research, there is little that could be done to ameliorate this problem. Further, if risk estimation were to be offered as part of routine NHS BSP, it would be expected that uptake would be socially patterned, so our results may mirror this. Finally, the present analyses controlled for deprivation, to ensure that deprivation did not confound comparisons between groups.A thjrd limitation of the present research relates to the representativeness of the PROCAS sample, with 40% of women attending screening taking up the offer of risk estimation. As previously reported,A final limitation is that there was only a 36% response rate to the questionnaires in the present study. Thus, it is possible that non-responders could have more or less anxiety and cancer worry than responders, with the results obtained concealing a substantial number of women who had higher levels of distress. Although the non-response rate was relatively similar between the groups, underlying differences and bias could exist in non-responders between the groups.16 That is, the levels of state anxiety in women in the comparison group are similar to those of women at moderate-risk in the other two testing groups, whereas women at average or below average risk in these two testing groups have lower anxiety levels. It should be noted however, that the present study found evidence that cancer-related worry was slightly higher in women who received risk estimates than in the comparison group. These findings are consistent with the observation that false-positive breast screening test results can result in slight elevations in cancer-related distress in the long term, even when general state anxiety shows no effects of such test results.9 Overall, mindful of the limitations of the evidence generated by the present study, these results are consistent with the hypothesis that providing women with breast cancer risk estimates does not produce large effects on anxiety or cancer worry.The present results show that women receiving personalised risk information have lower-state anxiety levels and only slightly higher cancer worry than women who were awaiting their risk estimates. It is important to note that overall, the levels of state anxiety and cancer-specific worry were low. The higher levels of state anxiety observed in the comparison group in the present study are consistent with the observation that uncertainty may provoke more anxiety than definitive bad news.18 This review found that there was little effect of personalised risk on behaviour, especially when risk information is communicated in numerical form, as opposed to using visual or other vivid means of feedback. These findings are also in line with a systematic review of the effects of communicating genetic risk information on behaviour, which found even clearer evidence that genetic risk information has little effects on behaviour.19 It is also notable that women who were told that they were at lower risk than other women did not show intentions to engage in unhealthier behaviour. This finding is in line with systematic reviews which show a general absence of effects on behaviour due to false reassurance from negative screening results.22The present study found no consistent effects of communicating personalised breast cancer risk on intentions to change lifestyle behaviour. Such a finding is consistent with a recent systematic review of systematic reviews of the effects of communicating personalised disease risk on lifestyle behaviour.The major implication of the present research is that there is no compelling evidence that communicating personalised breast cancer risk estimates causes any major harms. There may be effects on cancer-related worry, but these effects were small. Further, there may be room to ameliorate such effects by improved communication of risk information. The ratings of perceived risk in the present study were consistent with the idea that women in the T-C and T-C(+SNPs) groups understood the risk information they had received, and satisfaction with information was generally high. However, there was evidence that satisfaction was lower for women who were told they were at higher risk. Further, understanding of risk results was variable, and examination of cancer worry ratings according to understanding of results suggests that cancer worry may be particularly high for women who (erroneously) thought their result indicated that they were likely to develop breast cancer.18 Nevertheless, the provision of risk information is a necessary part of identifying women at higher risk who can then be offered additional screening or chemotherapy.The general absence of effects on intentions to change any behaviour suggest that providing risk information alone is unlikely to result in changes in behaviour without additional support being provided.23). Increasing frequency of screening of women at high-risk and decreasing frequency of screening of women at low-risk is likely to lead to a better balance of benefits and harms of screening, and may be cost-neutral.Given these results, progressing to further consider the feasibility of risk-stratified screening appears warranted (see also further evidenceThe present research is currently the best available evidence on the likely harms and benefits of risk-stratified breast screening as part of the NHS Breast Screening Programme. Future studies of the feasibility of risk-stratified screening appear warranted, but could benefit from several improvements on the present research. First, future research should compare women who are offered personalised testing with women offered routine breast cancer screening, which would be a better comparator than women awaiting personalised test results. Allocation of women to these conditions should be done via randomisation or other procedures to ensure well-matched samples.Future research should consider effects in both the short-term and longer-term. Such research should make greater use of theories of risk communication to better understand reactions to the information provided. It would be useful to examine effects on behaviour using validated tools, rather than intentions to change behaviour as in the present research. This could include objectively assessed behaviours such as attendance at future screening rounds and uptake of chemoprevention as well as changes in behaviour such as physical activity. Such research could examine how effects of risk communication on positive health behaviour could be facilitated. Future research could consider aspects of informed decision making other than just knowledge/ understanding of test results.Should such work show there to be no major harms of stratified screening in terms of emotional and behavioural outcomes, and if uptake of the offer of stratified screening is sufficiently high, a full evaluation of the effectiveness and cost-effectiveness of such a programme would be warranted.The present research indicates that there would be no major emotional or behavioural harms of providing women with 10-year breast cancer risk estimates. Given this, further work of better quality to establish the feasibility of risk-stratified screening is warranted. As part of such work, there is a need to improve the risk communication materials to increase understanding and satisfaction with information, particularly for women at higher risk.


Breast cancer is the most common cancer type in women not only in world but also in Malays ethnicity between Malaysia and Indonesia. Breast cancer has varying incidence in every country, but genetic factor by family history influence the incidence of breast cancer. This systematic review and meta-analysis was performed to determine family history of breast cancer and breast cancer risk between Malays ethnicity in Malaysia and Indonesia.This meta-analysis was conducted on published research articles on family history of breast cancer and breast cancer risk between Malays ethnicity in Malaysia and Indonesia published between Jan 1999 and Jul 2018 in the online article databases of PubMed, ProQuest and EBSCO. Pooled odds ratios (OR) were calculated with fixed and random-effect models. Publication bias was visually evaluated by using funnel plots and statistically assessed through Egger’s and Begg’s tests. Data were processed using Review Manager 5.3 (RevMan 5.3) and Stata version 14.2 (Stata Corporation).P<0.00001]). There was not significant publication bias for studies included in family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia.We reviewed 1123 articles. There are 10 studies with number of samples 4511 conducted a systematic review and continued with Meta-analysis of relevant data. The results showed significant association between family history of breast cancer with breast cancer risk in Malays ethnicity in Malaysia and Indonesia (OR = 3.34 [95% CI 2.68–4.15, This analysis confirmed the association of family history of breast cancer and breast cancer risk between Malays ethnicity in Malaysia and Indonesia. Breast cancer ranks first of all cancer disease in women encountered worldwide . An estiBreast cancer is a disease caused by multifactorial: age, genetic and inherited, estrogen replacement, oral contraceptive, carcinogen exposure, alcohol consumption, high fat consumption, and smoking. Breast cancer incidence differs in every country –5. NorthThe degree of risk was a function of the type of relative affected (first or second degree), the age at which the relative developed cancer, and the number of relatives affected. Compared to individuals with no family history of breast cancer .Subjects with a family history of first-degree relatives including sisters, mothers or children with breast cancer or distant relatives, grandmothers, grandchildren, aunts or nephews, who had breast cancer showed higher risk (OR=2.95 and OR=2.84) compared with the results of the meta-analysis of OR = 1.5–2.1 . Family family history of breast cancer and awareness of breast cancer risk could facilitate its early detection. It can be more effectively treated in earlier stage than when clinical signs and symptoms present, justifying early detection efforts. Through measurement of breast cancer risk, it can be seen whether a person has a safety risk to breast cancer, adequate for breast cancer prevention or harmful to the occurrence of breast cancer  statement . Meta-anThe variables of this study included independent variable is family history of breast cancer, and dependent variable is breast cancer in Malays ethnicity in Malaysia and Indonesia.This study was conducted by collecting data through the identification of published research articles on family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia in online article databases of PubMed, ProQuest and EBSCO . IdentifThe data collection was done through online search. The research was limited to English language articles. The article type was limited to journal articles. The research subject was limited to research with human subject. The abstract of articles with potentially relevant titles was reviewed, while the irrelevant articles were excluded.Furthermore, articles that have potentially relevant abstracts will be reviewed in full-text, while the irrelevant articles were excluded. The inclusion criteria of this study sample were research on family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia with case-control and cohort study. Exclusion criteria were: the research was not available in full text form and when these criteria were not satisfied or if the provided information was insufficient for data extraction. The following data were obtained from each article: first author’s name and year of publication, region, type of study and number of sample.Two independent investigators carefully extracted information from all studies that satisfied the inclusion criteria in accordance with a standardized protocol. Disagreements were resolved by three other investigators. Quality assessment was conducted using Newcastle–Ottawa Quality Assessment Scale (NOS). The papers with a total score of 0–3, 4–6, and 7–9 points were specified as the poor, moderate, and high quality .I2>50% because these tests presented minimal statistical power in cases with few studies and small sample sizes. A random effect model was used when significant heterogeneity was observed; otherwise, a fixed effect model was utilized. Data were analyzed by using Review Manager 5.3 (RevMan 5.3).The analysis held to get the value of pooled odds ratio which is the combined odds ratio value from the research. Results were pooled using odds ratio with corresponding 95% confidence intervals (CIs). Significant heterogeneity was indicated by P-value of <0.05 was considered statistically significant.Publication bias was visually evaluated by using funnel plots and statistically assessed through Egger’s and Begg’s tests. Meta-analysis was carried out in Stata version 14.2 (Stata Corporation). A two-tailed P<0.00001]). Heterogeneity among studies for family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia  had a variation of homogeneous research for the occurrence of breast cancer. Funnel plots for identify publication bias among studies family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia (P=0.877) and Begg’s test (P=0.901).The selection of studies was conducted to obtain 10 studies with number of samples 4511 related to family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia . Figure ndonesia . There wP<0.00001]). There was no significant publication bias for studies included in family history of breast cancer and breast cancer risk in Malays ethnicity in Malaysia and Indonesia. This study revealed the similarity that subjects with a family history of breast cancer had a significantly higher risk of breast cancer than those not have a family history, consistent with other studies  that breast cancer can be detected early because of breast cancer is discovered in stage early can still be cured, so that the incidence of cancer is no longer increasing.This analysis confirmed the association of family history of breast cancer and breast cancer risk between Malays ethnicity in Malaysia and IndonesiaEthical issues  have been completely observed by the authors.


Reproductive history has been associated with breast cancer risk, but more knowledge of the underlying biological mechanisms is needed. Because of limited data on normal breast tissue from healthy women, we examined associations of reproductive history and established breast cancer risk factors with breast tissue composition and markers of hormone receptors and proliferation in a nested study within the Karolinska Mammography project for risk prediction for breast cancer (Karma).Tissues from 153 women were obtained by ultrasound-guided core needle biopsy as part of the Karma project. Immunohistochemical staining was used to assessed histological composition of epithelial, stromal and adipose tissue, epithelial and stromal oestrogen receptor (ER) and progesterone receptor (PR) status, and Ki-67 proliferation status. An individualised reproductive score including parity, number of pregnancies without birth, number of births, age at first birth, and duration of breastfeeding, was calculated based on self-reported reproductive history at the time of the Karma study entry. All analyses were adjusted for age and BMI.Cumulated reproductive score was associated with increased total epithelial content and greater expression of epithelial ER. Parity was associated with greater epithelial area, increased epithelial–stromal ratio, greater epithelial ER expression and a lower extent of stromal proliferation. Increasing numbers of pregnancies and births were associated with a greater epithelial area in the entire study set, which remained significant among postmenopausal women. Increasing numbers of pregnancies and births were also associated with a greater expression of epithelial ER among postmenopausal women. Longer duration of breastfeeding was associated with greater epithelial area and greater expression of epithelial PR both in the entire study set and among postmenopausal women. Breastfeeding was also positively associated with greater epithelial ER expression among postmenopausal women. Prior use of oral contraceptives was associated with lower epithelial–stromal ratio amongst all participants and among pre- and postmenopausal women separately.Reproductive risk factors significantly influence the epithelial tissue compartment and expression of hormone receptors in later life. These changes remain after menopause. This study provides deeper insights of the biological mechanisms by which reproductive history influences epithelial area and expression of hormone receptors, and as a consequence the risk of breast cancer.The online version of this article (10.1007/s10549-018-4768-0) contains supplementary material, which is available to authorized users. Reproductive history, like parity, age at first birth and number of births, has consistently been shown to be associated with breast cancer risk . Women wDuring puberty, pregnancy and postmenopausal involution, important shifts occur in normal breast tissue composition in response to changing hormone levels –12. EpitA few studies, with conflicting results, have investigated how reproductive behaviour influences oestrogen receptor (ER) and progesterone receptor (PR) expression in normal mammary tissue –22. ChanTo understand the mechanisms by which reproductive history influences breast cancer risk, better knowledge of how these factors influence the breast tissue composition is needed. In this study, we examined associations of reproductive history and established breast cancer risk factors with breast tissue composition and markers of hormone receptors and proliferation in a nested study within the Karolinska Mammography project for risk prediction for breast cancer (Karma) .Karma (Karolinska Mammography project for risk prediction for breast cancer) is a population-based prospective cohort study initiated in January 2011 and comprises 70,877 women attending mammography screening or clinical mammography at four hospitals in Sweden , 28. WomWhole, 4-µm-thick, paraffin embedded breast core needle biopsy sections were analysed using conventional immunohistochemical (IHC) staining. Staining of the slides has been described elsewhere . BrieflyPreparation of slides and image analysis has been described previously . In brieBreast cancer risk factors and lifestyle factors included in the study were age at biopsy, BMI, use of oral menopausal hormone therapy, menopausal status , previous benign breast disease history , age at menarche, parity, number of pregnancies, and number of births. Age at first birth and minimum duration of breastfeeding was assessed among parous women. Biopsy-based outcomes were total percentage epithelial and stromal content in the biopsy, total percentage epithelial immunohistological expressions of ER, PR and Ki-67, and positive or negative stromal expression of ER, PR and Ki-67. Linear regression was used to examine the association between established breast cancer risk factors and reproductive history with epithelial, stromal and adipose tissue measures expressed as percentages of the total area of the section, and percentage expressions of epithelial ER, PR and Ki-67 made from the histological sections. To study the balance in tissue composition between the epithelial and the stromal compartment, the ratios of epithelial-to-stromal proportions were evaluated by linear regression.The assumption of normal distribution was inspected in all analyses and log transformations were made when necessary. All linear regression analyses of epithelial and stromal tissue compartment distributions and epithelial protein expressions were adjusted for age and BMI unless otherwise indicated.Logistic regression was used to examine the association between established breast cancer risk factors and reproductive history with stromal protein expression. The analyses of stromal protein expressions were adjusted for age.We created a reproductive score reflecting the reproductive activity of each study participant. The individual score was calculated by summarising the number of reproductive events  and it was defined by including the variables parous , number of pregnancies without births (range 0–4), number of births (range 0–5), age at first birth  and minimum duration of breastfeeding . Analyses of proportional growth by quartile groups of epithelial area (%) and epithelial ER (%) with reproductive score were assessed using univariate analysis of variance. Linear associations between percentage epithelial area or epithelial ER and reproductive score (continuous) were analysed by linear regression. Odds ratios (ORs) were evaluated using multinomial logistic regression comparing quartile groups of epithelial area or epithelial ER  with reproductive score for all women and stratified by menopausal status.In secondary analyses, we stratified by menopausal status at biopsy. In total, 55 women were premenopausal at biopsy collection, and 95 were postmenopausal. Menopausal status was missing for three participants.p values were used for all analyses with a p value of less than 0.05 considered to be statistically significant. Statistical analyses were performed using SPSS IBM, version 23.Two-tailed Distributions of demographics, menstrual and reproductive history, and selected risk factors for breast cancer are described in Table p = 0.049). Greater BMI was associated with a 5.3% smaller epithelial area per increasing BMI unit (p = 0.026), which remained significant among postmenopausal women (p = 0.038). Postmenopausal parous women had a 70.5% greater epithelial area compared to nulliparous postmenopausal women (p = 0.037)  Table .Fig. 1Mup < 0.001) per quartile increase in reproductive score when adjusted for age and BMI in the total data set, which remained similar among postmenopausal women  . To better capture the combined effects of reproductive factors on the epithelial tissue compartment, we created a reproductive score reflecting the reproductive activity of each study participant. Total per cent epithelial area increased by 43.4% (p for trend = 0.001) (Table p for trend = 0.002)  Table , with th2) Table , Fig. 1,p = 0.033) (Table p = 0.049) . Furthermore, higher BMI was associated with a significantly higher expression of PR . Increasing number of births was significantly associated with greater expression of ER in the total data set , which remained significant among postmenopausal women  (Table p = 0.026), and greater ER by increasing number of pregnancies . Minimum duration of breastfeeding was positively associated with a 3.2% higher expression of PR in all women per additional month of breastfeeding (p = 0.003), with similar results among postmenopausal women . Among postmenopausal women, breastfeeding was also associated with greater ER .In the total data set, higher age was significantly associated with a 1.0% higher expression of ER per year increase at biopsy (3) Table  and a 2.p for trend = 0.043), with the OR in the highest quartile of reproductive score (median ER 43.0%) compared with the lowest quartile (median ER 16.1%) being 1.29 (95% CI 1.04–1.61), when adjusting for age and BMI. When stratifying be menopausal status, a higher reproductive score was positively associated with 4.5% greater epithelial ER expression per unit increase (p for trend = 0.017)  when adjusting for age and BMI  . This finding remained significant after stratifying into premenopausal and postmenopausal women (p = 0.002) and (p = 0.035), respectively. We also investigated the epithelial–stromal ratio in our cohort. In the total dataset, increasing age was associated with a reduction of the epithelial–stromal ratio (p = 0.008) . Higher age at first birth was associated with a greater epithelial–stromal ratio in the total cohort (p = 0.004) and among premenopausal women (p = 0.012). Among postmenopausal women, parous women had a greater epithelial–stromal ratio (p = 0.036); furthermore, greater epithelial–stromal ratio was associated with higher number of births (p = 0.021). Prior use of oral contraceptives was associated with lower epithelial–stromal ratio amongst all participants and after stratification by menopausal status .The only risk factor significantly associated with stromal area in our study was BMI, which was associated with a smaller stromal area among all women (p = 0.040). Likewise, higher BMI was associated with expression of stromal Ki-67 among postmenopausal women (p = 0.030). Lack of stromal PR was associated with higher age at first birth among all women (p = 0.012), and in postmenopausal women (p = 0.042).In the total dataset, nulliparous women more frequently exhibited positive stromal expression of Ki-67 Below is the link to the electronic supplementary material.


Weak negative associations were seen for diabetes OR 0.61 (95% CI 0.40–0.92) and cigarette smoking OR 0.86 (95% CI 0.75–0.99), and a positive association was found with hormone therapy OR 1.24 (95% 1.14–1.35). Conclusion: Our data indicate that breast tissue in middle-aged women is highly dependent on childhood body constitution while adult life-style plays a modest role, underlying the need for a long-term perspective in primary prevention of breast cancer.Background: Attention in the 2000s on the importance of mammographic density led us to study screening sensitivity, breast cancer incidence, and associations with risk factors by mammographic density in Danish breast cancer screening programs. Here, we summarise our approaches and findings. Methods: Dichotomized density codes: fatty, equal to BI-RADS density code 1 and part of 2, and other mixed/dense data from the 1990s—were available from two counties, and BI-RADS density codes from one region were available from 2012/13. Density data were linked with data on vital status, incident breast cancer, and potential risk factors. We calculated screening sensitivity by combining data on screen-detected and interval cancers. We used cohorts to study high density as a predictor of breast cancer risk; cross-sectional data to study the association between life style factors and density, adjusting for age and body mass index (BMI); and time trends to study the prevalence of high density across birth cohorts. Results: Sensitivity decreased with increasing density from 78% in women with BI-RADS 1 to 47% in those with BI-RADS 4. For women with mixed/dense compared with those with fatty breasts, the rate ratio of incident breast cancer was 2.45 (95% CI 2.14–2.81). The percentage of women with mixed/dense breasts decreased with age, but at a higher rate the later the women were born. Among users of postmenopausal hormone therapy, the percentage of women with mixed/dense breasts was higher than in non-users, but the patterns across birth cohorts were similar. The occurrence of mixed/dense breast at screening age decreased by a z-score unit of BMI at age 13—odds ratio (OR) 0.56 (95% CI 0.53–0.58)—and so did breast cancer risk and hazard ratio (HR) 0.92 (95% CI 0.84–1.00), but it changed to HR 1.01 (95% CI 0.93–1.11) when controlled for density. Age and BMI adjusted associations between life style factors and density were largely close to unity; physical activity OR 1.06 (95% CI 0.93–1.21); alcohol consumption OR 1.01 (95% CI 0.81–1.27); air pollution OR 0.96 (95% 0.93–1.01) per 20 μg/m In population-based breast cancer screening programs, all women in the target age-group are invited to screening, but not all of them attend. In these settings, breast cancer screening has been found to reduce breast cancer mortality by 20–25% for the entire population of women invited, and by 35% for women who attended and were actually screened . HoweverDue to the use of personal identification numbers in nationwide population and health registers, Denmark offers exceptional possibilities for epidemiological research. Denmark, furthermore, established two pioneer breast cancer screening programs in the early 1990s and used a dichotomized code for mammographic density to schedule women for one or two-view mammography at the next screening. On this basis, using our population-based cohort, we first determined the association of mammographic density on screening sensitivity and breast cancer incidence, and second, searched for determinants of mammographic density with other data from these women. In Denmark, breast cancer screening started in the municipality of Copenhagen in 1991 and in the county of Funen in 1993. Together, the two areas covered about 20% of the Danish population. Women aged 50–69 years were invited to screening every second year. At the first screening, all women had two-view mammography, which takes a craniocaudal and an oblique view ,5. All mBreast cancer screening became nationwide in Denmark from 2007–2008, organized into five regional programs all using digital mammography. Since November 2012, mammograms in the Capital Region have been coded according to version 4 of the BI-RADS density code [The mammography density data used for the studies reported in the following sections came from (1), the dichotomously coded mammograms from the two pioneer screening programs during 1991/93 to 2001; and from (2) the BI-RADS density coded mammograms from the Capital Region part of the national program during 2012–2013.The cohorts of screened and density-coded women were followed up for death and emigration via linkage with the Central Population Register, and for incident cases of breast cancer via linkage with the Danish Cancer Register, The dichotomously coded data from Copenhagen were further linked with the Copenhagen School Health Register, including weight and height measurements for women attending school in Copenhagen. Linkage was made also with the Copenhagen part of the Diet, Cancer and Health prospective cohort study. The dichotomously coded data from Funen were linked with the Odense University Pharmacoepidemiological Database . The perFour types of statistical analyses were made. First, sensitivity and specificity of screening by density was estimated from the screen-detected cancer cases and from the interval cancers. A screen-detected cancer was defined as an incident breast cancer diagnosed within 6 months of a positive screening mammogram. Interval cancers included two groups; first, a breast cancer diagnosed in a screen-negative woman within 2 years of the screen; second, a breast cancer diagnosed in a screen-positive woman 7 months to 2 years after a positive screen. Sensitivity was defined as (screen-detected cancers)/. Specificity was defined as /(breast cancer free women).Second, breast cancer incidence by density was calculated for a long follow-up time based on the dichotomized data as the incident cases divided by the accumulated person years for screened women. The rate ratio of women with mixed/dense breasts compared with women with fatty breasts was calculated with Poisson regression controlling for age and calendar period. For a short follow-up time based on the BI-RADS density coded data, we calculated the risk of breast cancer for women with a given BI-RADS as the number of incident cases within 2 years divided by the number of women screened. Age-adjusted relative risks were calculated by using BI-RADS1 as baseline.Third, the proportion of screened women with mixed/dense breasts was tabulated against variables of age, birth cohort, and use of postmenopausal hormone therapy. Fourth, cross-sectional associations between potential risk factors and the occurrence of mixed/dense breasts were estimated as odds ratios (OR) using multivariate logistic regression with controls for, among other factors, age and body mass index (BMI). Fifth, breast cancer incidence by use of postmenopausal hormone therapy or childhood body constitution combined with mammographic density at the first screen was calculated as the number of incident cases divided by the accumulated person years. Cox regression analysis was used, with age as the underlying time scale. The impact of childhood constitution was calculated as per z-score of BMI at a given age . VariousApproval of data handling by the Danish Data Protection Agency serves as ethical approval for register-based research in Denmark.During the first ten years of the Copenhagen pioneer program, 134,640 screening examinations were made, and 46% of them were coded as fatty . The 61,In 2012/13, in total 54,808 women were screened in the Capital Region, and of these, 28% had BI-RADS density code 1; 40% code 2; 27% code 3; and 5% code 4 [In the Copenhagen pioneer program, the 61,741 women with fatty breasts accumulated 158,017 person years, during which 315 breast cancer cases were diagnosed , and the 71,823 women with mixed/dense breasts accumulated 155,110 person years, during which 694 cases were diagnosed. Comparing the latter group with the first, gave an age-adjusted rate ratio of 2.45 (95% CI 2.14–2.81) [The 54,808 women screened in the Capital Region in 2012/13 had in total, 578 breast cancers diagnosed within 2 years of their last screens . Compared with women with BI-RADS density code 1, women with successively higher density codes had an increased risk of breast cancer, with relative risks of 1.6 (95% CI 1.2–2.0), 1.9 (95% CI 1.4–2.3) and 2.0 (95% CI 1.3–2.8) for codes 2, 3 and 4, respectively [The density data from the two pioneer programs from Copenhagen and Funen showed the well-known decrease in density with increasing age; this was seen both in the cross-sectional data and by age within each birth cohort. We noted, however, that there was also variation across birth cohorts; for a given age, a larger proportion of women from younger birth cohorts had mixed/dense breasts . As hormThe Diet, Cancer and Health study took place in Copenhagen in 1993–1997, recruiting persons aged 50–64. About 5700 women from this study were screened in Copenhagen in 1993–2001. This allowed for cross-sectional studies of associations between risk factors reported in the health study and mammographic density registered at screening, and all estimates were adjusted for age and BMI, among other factors. The presence of mixed/dense breasts was statistically significantly more common among current hormone users compared with never-users, OR 1.24 95% CI 1.14–1.35)  Table 2Table 2. –1.35  TaUsing a longitudinal perspective study, we investigated the association between childhood body constitution and mammographic density at screening adjusting for age. Denmark, back in time had annual school health examinations, including measurements of weight and height from age 7 to age 13. Data for children from Copenhagen born 1930–1983 have been computerized. A previous follow-up study found that lean girls had a high risk of breast cancer in adulthood: relative risk 0.94 (95% CI 0.91–0.97) per 1-unit increase of BMI at age < 8 years, and 0.96 (95% CI 0.93–0.99) at age 8–14 years  Figure .At present, about one in three women in the screening age of 50–69 years had dense breasts (BI-RADS density codes 3 or 4). The Danish data confirmed the previously reported association of poorer screening sensitivity and greater breast cancer risk with greater mammographic density. Childhood body constitution has a strong association with breast tissue and breast cancer risk in mid-life age. Lean girls developed mixed/dense breasts, and they had an increased risk of breast cancer that was statistically explained by, and therefore, could be caused by, their mixed/dense breasts.As reported previously, the proportion of women with mixed/dense breasts was lower for older women, but our data indicated that this decrease occurred at a later age in recently born cohorts compared with cohorts born longer back in time. The proportion of women with mixed dense breasts decreased with increasing age both for current and never users of hormone therapy. However, at a given age, more hormone users than non-users had mixed/dense breasts, indicating that the age and menopause-related decline in breast density on average occurred later in hormone users than in non-users. On this basis, both density and hormone use are expected to affect the risk of breast cancer at a given age. This was confirmed by our cohort data, as the risk of breast cancer was increased in hormone users when controlled for density; and increased in women with mixed/dense breasts when controlled for hormone use. Mammographic density was largely independent of other lifestyle factors in adults, bringing attention to the importance of the early-life exposures.The 28% of women with BI-RADS density code 1 found in the Danish data is high from an international perspective. In the US Breast Cancer Surveillance Consortium data, only 9% of women screened had BI-RADS density code 1, but it should be taken into account that these data included women from the age of 35 years and above . In bothFrom our data, we found that women with mixed/dense breasts are 2.5 times more likely to be diagnosed with breast cancer than women with fatty breasts. This corresponds rather well with the observed 5-year rate ratio of 2.20 and 2.41 for women with BI-RADS density codes 3 and 4 compared with women with BI-RADS density code 1 in the US Breast Cancer Surveillance Consortium data [3 in BMI at age 15 was found [Data linking measured body composition in childhood with mammographic density in mid-life age are rarely available. It is, therefore, very interesting that from studying 989 women similarly in the MRC 1946 British cohort, an OR of a greater Wolfe grade of 0.56 (95% CI 0.49–0.64) per 2.8 kg/mas found . This isas found . Similaras found , and in as found . Our stuThe likely decrease in mammographic density with increasing age has been proposed to reflect changes in the breast tissue related to menopause. Our finding of a later decrease in mammographic density across birth cohorts might, therefore, reflect changes in biological aging. However, the density codes in the Danish pioneer screening programs were given for pragmatic reasons only, and we could not exclude the possibility that the cohort pattern was derived from a change of coding practice. It was, therefore, interesting that a tendency for higher density in younger birth cohorts was observed also by a large Dutch study that used automated density coding . The DanOur studies were all based on population-based data from organized screening programs, and the use of a comprehensive register data ensured full follow-up of all cohort members. In the risk factor analysis, we took advantage of the presence of a number of other databases, and it is a strength that these exposure data were all collected independently of the screening activity.The risk factor analyses had, however, to be restricted to women present in the common part of screening and risk factor data. The Odense University Pharmaco-epidemiological Database covered all prescribed and purchased drugs from women from 1992 onwards, and the Funen screening program started in 1993, so hormone therapy data could be retrieved from all screened women. The Diet, Cancer and Health study recruited persons age 50–64 in 1993–1997, about one third of invited persons participated, and data were not available on all variables from all participants. Out of roughly 30,000 women screened at least once in Copenhagen at age 50–64 in 1993–1997, about 5700 could be included in the risk factor analysis . The CopThe dichotomized code for mammographic density from 1991–2001 was used for administrative reasons only: to decide on whether a given woman should have one or two-view mammography at next screen. Fatty breasts were intended to correspond to BI-RADS code 1 and part of code 2 . A laterThe fact that the risk of breast cancer is higher and the sensitivity of screening lower in women with dense breasts than in women with fatty breasts should be taken into account in the planning of personalized screening. Our data, furthermore, indicated that the proportion of women with dense breasts at a given age was higher in recently than in earlier born cohorts, a pattern expected to affect the need for resources in personalized screening.In conclusion, the Danish data confirmed the patterns seen elsewhere, that screening sensitivity decreased with increasing mammographic density, and that women with dense breasts had a higher incidence breast cancer than women with fatty breasts. Furthermore, our Danish data showed that childhood body constitution had a strong association with the composition of breast tissue at middle-age. Breast density was lower for older women, and this aging process was associated both with hormone use and birth cohort. The mammographic density data accumulated in the Danish breast cancer screening program provided valuable insight into breast cancer aetiology.


Full-term pregnancies reduce a woman’s long-term breast cancer risk, while abortions have been shown to have no effect. The precise minimal duration of pregnancy necessary to lower a woman’s breast cancer risk is, however, unknown. Here we provide evidence which point to the protective effect of pregnancy on breast cancer risk arising precisely at the 34th pregnancy week. Using a cohort of 2.3 million Danish women, we found the reduction in breast cancer risk was not observed for pregnancies lasting 33 weeks or less, but restricted to those pregnancies lasting 34 weeks or longer. We further found that parity, socioeconomic status, and vital status of the child at birth did not explain the association, and also replicated our finding in data from 1.6 million women in Norway. We suggest that a distinct biological effect introduced around week 34 of pregnancy holds the key to understand pregnancy-associated breast cancer protection. It is known that full-term pregnancies can reduce a woman’s breast cancer risk. Here, the authors interrogate data from 2.3 million Danish women, showing that this protective effect arises at precisely the 34th week of the pregnancy, and replicated this finding in 1.6 million women from Norway. Both the number and timing of a woman’s childbirths have long been known to influence her breast cancer risk2, but how these factors influence breast cancer etiology is not well understood. Previously, full-term pregnancies in early life (<30 years) have consistently been associated with a long-term reduced risk of breast cancer4. Conversely, a transient increased breast cancer risk immediately following full-term pregnancies have been observed5. Induced abortions and other pregnancies of short duration have, on the other hand, been shown not to influence breast cancer risk7. We hypothesized that by investigating pregnancies of intermediate to long duration in early life  we could determine the minimal duration of pregnancy associated with a reduced risk of breast cancer and thereby potentially point to underlying mechanisms of the protective effect.Breast cancer is the most common malignant cancer in women and a major cause of disease burden worldwideTaking advantage of the Danish national registries on childbirths and cancer, we established a nationwide cohort including 2.3 million women with detailed information on reproductive history from 1978 to 2014, and assessed the association between the duration of a pregnancy and the long-term risk of breast cancer. We replicated this analysis in an equivalent Norwegian cohort of 1.6 million women.The Danish cohort consisted of 2,311,332 women, with altogether 3,275,559 childbirths. The women were followed for 46,128,328 person-years (average 20.0 years follow-up per woman) and 61,349 (2.7%) developed breast cancer. We focused on follow-up from 10 years or more after pregnancy, to highlight the long-term effect of pregnancy on breast cancer risk. Table Figure We speculated whether the observed reduced long-term breast cancer risk following any birth at an early age varied by pregnancy duration. To study this, we included information on pregnancy duration. Figure We replicated the analyses in a similar cohort of 1,635,839 Norwegian women with altogether 2,420,518 pregnancies identified in the National Registry and the Norwegian Medical Birth Registry. The women were followed for 35,171,205 person-years, in which 24,095 developed invasive breast cancer.Figure When we combined the Danish and Norwegian cohorts Fig. , the redWe performed additional sensitivity analyses to evaluate the association between a specific duration of a pregnancy and a woman’s long-term breast cancer risk  with data from the Medical Birth Registry and the Danish Cancer Registry. The CRS contains detailed demographic information on all Danish residents, including linkage of women to their children’s dates of birth. Since April 1, 1968, all Danish residents who were alive or born thereafter have been assigned a unique identification number in the CRS. This number permits information from different national registries to be linked together. All live and stillbirths in Denmark, with dates of birth, have been registered since 1973 in the Medical Birth Registry. Since 1978, gestational week at time of birth has been recorded. For sensitivity analyses, we obtained information on induced abortions in Denmark from the National Registry of Induced Abortions, where induced abortions have been mandatory reported to since 1939.35. From Statistics Denmark we acquired time-varying, individual-level socioeconomic data to address covariates potentially associated with reproduction and breast cancer36; educational attainment (since 1970), employment status (since 1976), and disposable household income (since 1990).Information on breast cancer diagnoses was retrieved from the Danish Cancer Registry, which contains information on all cancers diagnosed in Denmark since 1943 and is considered close to complete37 and the Cancer Registry is considered accurate and close to complete with regard to cancer diagnoses from 195338.In Norway, we linked data from the National Registry, the Medical Birth Registry of Norway, and the Cancer Registry of Norway. The Medical Birth Registry has registered all births  since 1967The research project was approved by institutional review for inclusion on Statens Serum Institutes permit for research projects given by the Danish Data Protection Agency (permit No. 2015-57-0102) and approved by the Regional Ethics Committee of Western Norway (permit 252.06).We established a cohort of all Danish women born between January 1, 1935 and December 31, 2002. Using the CRS number, we linked information on each woman’s childbirths with the corresponding pregnancy duration , and information on whether she developed invasive breast cancer. We furthermore established a cohort of all Norwegian women born between January 1, 1935 and December 31, 1994, with equivalent information on reproductive history and breast cancer.Incidence rate ratios (in the following termed RR) of breast cancer by pregnancy history were estimated by log-linear Poisson regression in the Danish cohort, the Norwegian cohort, and the combined cohort. In Denmark, each woman was followed from January 1, 1978, or from her 12th birthday, whichever came later, until breast cancer, death, emigration or December 31, 2014, whichever came first. In Norway, each woman was followed from January 1, 1967, or from her 12th birthday, whichever came later, until breast cancer, death, emigration or December 31, 2006, whichever came first. All analyses were adjusted for effects of current age and time period in 5-year categories.4. Thus, instead of describing history by the total number of childbirths , pregnancy history was evaluated by the RR for women with n births compared with women with n−1 births . This reparameterization allows for a focus on the effect of each additional birth on cancer risk. The RRs were assumed to be the same regardless of birth number, and the presented RRs are therefore RRs for each additional birth. To allow for a different short-term and long-term effect of pregnancy, RRs were allowed to vary according to time since birth  for parous women. In the presentation of the model we focused on the parameters related to the long-term effect of pregnancy. We furthermore allowed RRs to be different for childbirths at younger (<30 years) and older maternal age (≥30 years) to focus on early age pregnancies which have previously been associated with long-term reduced risk of breast cancer4. The previously used method (4) was extended to include pregnancy duration. In the previous approach the effect of each birth was stratified according to time since birth and age at childbirth, but in this extended approach it was further stratified by pregnancy duration. Thus, RRs were allowed to vary by duration of the pregnancy in weeks, by the following categories: 20–27, 28–29, 30, 31, …, 41, 42–45 weeks, missing duration of pregnancy, duration of pregnancy not reported, extremely early births (<20 weeks), and extremely late births (>45 weeks). The four last categories are further described in Table Pregnancy history was modeled by time-dependent variables as described previouslyIn the analysis of pregnancy duration, all parameters described above were included simultaneously. For example, for biparous women whose first birth occurred in early age at week 38 and whose second birth occurred in late age at week 40, their pregnancy history was modeled by four parameters: the short-term and long-term effect of an early age birth at week 38, and the short-term and long-term effect of a late age birth at week 40. Thus, when estimating the long-term effect of the early age pregnancy at week 38, the model also included the short-term effect of an early age pregnancy at week 38, the short-term effect of an late age pregnancy at week 40, and the long-term of an late age pregnancy at week 40.The analysis of pregnancy duration was based on follow-up time from January 1, 1978 in Denmark, and from January 1, 1967 in Norway, when the respective Medical Birth Registers began recording gestational week of birth. Childbirths registered in civil registrations systems were incorporated in the analyses to adjust for the effects of pregnancies before start of the Medical Birth Registers.In analysis of the effect of age at childbirth on breast cancer risk, RRs were allowed to vary according to age at delivery in the categories <20, 20–21, 22–23, 24–25, 26–27, 28–29 and ≥30 years. In analyses of the adjustment effect of socioeconomic status, each socioeconomic variable was added as an additional variable.All analyses were performed using SAS version 9.4 and procedure GENMOD.Using nationwide registries from Statistics Denmark on educational attainment, employment and disposable household income starting from respectively 1970, 1976, and 1990, we were able to create a time-varying, three factor adjustment for socioeconomic status. The following categories of the three variables for socioeconomic status were used:Educational attainment: primary schooling; high school; high school with technical or mercantile focus; short basic education; higher education of short duration; higher education of intermediate duration; academic bachelor degree; academic master’s degree; and academic doctoral degree or equivalent educational degree.Employment status: business owner, ten or more employees; business owner, five to nine employees; business owner, one to four employees; business owner, no employees; business owner, unknown number of employees; co-working spouse; executive officer in business, organization or public office; employee in job which necessitates advanced skills; employee in job which necessitates intermediate skills; employee in job which necessitates basic skills; employee, other; employee, unknown position; unemployed for more than 6 months; social security recipient because of disability; in educational program; disability pensioner; pensioner; early retirement pensioner; social security recipient; other; children under the age of 15 years; housewife (only categorized 1976–1990).Disposable household income: groups of 10%-percentiles according to the 5-year disposable household income distribution.In order to investigate the effect of birthweight relative to gestational age in pregnancies of different duration, we combined data on gestational age and birthweight from the Danish Medical Birth Registry compiled from 1978 and onwards. We defined a birth small for gestational age (SGA) if the birthweight was below the 10th percentiles of births at the given gestational week, in the corresponding 5-year period. We then stratified by weight category (SGA vs. 10–100th percentile of birthweights at same week) and assessed risk of breast cancer after a pregnancy of given duration, grouped into the following lengths of pregnancy: week 20–33, week 34–36, week 37, 38, 39, 40, 41, and week 42 or longer. To estimate RR of breast cancer by both relative birthweight and gestational period, we extended the model so that the pregnancy effect also varied by relative birthweight.Our estimates of the effect of an early age pregnancy stratified by the duration of pregnancy Fig.  suggest fig2C. In this model, the long-term effect of each early age pregnancy with duration of the pregnancy w, is modeled as βw, with w noting the gestational week categories described in the paper. We compared Mfig2C with simple week-specific threshold models (Mthreshold(w0)) by which a certain threshold of pregnancy duration is associated with a decreased risk of breast cancer. In that model the long-term effect of each early-age pregnancy according to the duration of the pregnancy w is modeled as β∙I(w ≥ w0), i.e., by one parameter. With regard to the other parameters in the model, the two models are similar, i.e., a total difference of 14 parameters.The model used in Supplementary Figure We furthermore compared models that allowed for difference in the pregnancy effect according to parity  and country . All models were compared by the deviance .fig2C and the week-specific MThreshold(w0) models for different threshold values , w0, when using the simple week-specific threshold model (Supplementary Fig. Supplementary Figure Supplementary InformationPeer Review File