P⩽0.01) were BAIAP2 and CALM2 for overall breast cancer; BAIAP2 and CSNK2A1 for ER+ breast cancer; and BRAF, BAD, and MAPK3 for ER− breast cancer. The association of BAD with ER− breast cancer was explained by a two-SNP risk model; all other associations were best explained by one-SNP risk models. In total, six genes and seven SNPs had suggestive associations with overall breast cancer or ER subtypes in African-American women.The insulin/insulin-like growth factor (IGF) system and related pathways such as growth hormone, and leptin signaling have a key role in cancer development. It is unclear how germline variation in these pathways affects breast cancer risk. We conducted gene-based analyses of 184 genes in the insulin/IGF, growth hormone, and leptin pathways to identify genetic variation associated with risk of breast cancer overall, and for estrogen receptor (ER) subtypes. Tag single-nucleotide polymorphisms (SNPs) for each gene were selected and genotyped on a customized Illumina SNP array. Imputation was carried out using 1000 Genomes haplotypes. The analysis included 91,627 SNPs genotyped or imputed in 3,663 breast cancer cases,  and 4,687 controls from the African American Breast Cancer Epidemiology and Risk consortium, a collaborative project of four large studies of breast cancer in African-American women . We used a multi-locus adaptive joint test to determine the association of each gene with overall breast cancer and ER subtypes. The most significant gene associations ( Overweight women with low insulin levels have no elevated risk of breast cancer compared with normal-weight women with low insulin levels, and women with high insulin levels have elevated risk of breast cancer irrespective of their body weight.5A growing body of evidence shows that the insulin signaling system has a key role in cancer development and progression. Excess body weight, a condition leading to insulin resistance and hyperinsulinemia, is a recognized risk factor for postmenopausal breast cancer.6–9 A pooled analysis of 17 prospective studies found that the association of circulating IGF-1 with breast cancer was not modified by circulating levels of IGF-binding protein 3 (IGFBP-3) , and seems to be specific to estrogen-positive tumors.10Insulin-like growth factor 1 (IGF-1) is a hormone with extensive sequence homology to insulin. In addition, IGF-1 and insulin share downstream signaling pathways. Circulating levels of IGF-1 have been found to be positively associated with breast cancer risk.12 Although there is no evidence linking circulating GH levels with breast cancer risk,14 other lines of research suggest a role of the GH signaling pathway in breast cancer. For example, expression of the GH gene in breast epithelial cells is associated with the presence of proliferative disorders of the mammary gland;15 the GH receptor (GHR) gene has higher expression in breast tumors compared to adjacent normal breast tissue;16 and deficiency of GHR due to splice and nonsense mutations in the GHR gene results in a drastic reduction of risk of any type of cancer.17 Leptin (LEP) signaling also interacts with the insulin/IGF-1 system and may affect risk of breast cancer. Higher circulating levels of LEP have been found associated with breast cancer risk.19 It is noteworthy that although the LEP and LEP receptor (LEPR) genes have a very-low expression in normal breast tissue, both genes are highly expressed in breast tumor 21 in response to high levels of estrogens, insulin, and IGF-1.21Other signaling pathways  interact with the insulin/IGF-1 system to modulate insulin/IGF signaling. Growth hormone (GH) released from the pituitary gland stimulates production and release of IGF-1 from the liver, and elevated levels of circulating GH leads to insulin resistance and hyperinsulinemia.IGF-1 and IGFBP-3 genes.22–24 However, genetic variation in the insulin, IGF, GH, and LEP pathways has not been found to be associated with risk of breast cancer.25 For example, the Breast and Prostate Cancer Cohort Consortium (BPC3) did not find evidence of association of genetic variants in the insulin and IGF pathways with breast cancer after assessing common genetic variation in 24 genes in >6,000 cases of breast cancer and >8,000 controls.25 Associations of genetic variants in the LEP, LEPR, GHs, and GHR genes with breast cancer are inconsistent.26–29 Moreover, most of these previous studies have been conducted in women of European ancestry.Although circulating levels of insulin, IGF-1, GH and LEP in relation to breast cancer have been well studied, less is known about how germline variation in the insulin, IGF, GH, and LEP signaling pathways may affect risk of breast cancer. Several studies have shown that circulating levels of IGF-1 and IGFBP-3 are predicted by genetic variation in the + and ER− breast cancer subtypes.To assess whether genetic variation in the insulin, IGF, GH, and LEP pathways affect risk of breast cancer in African-American women, we conducted gene-based analysis of 184 genes in these pathways in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium in relation to overall risk of breast cancer, and ER+ cases, 1,098 ER− cases, and 582 unknown ER status) and 4,687 controls were included in the present analysis.+, or ER− breast cancer after adjustment for multiple testing , explained the association of BAIAP2 with all breast cancer and ER+ breast cancer. The frequency of the deletion was 4.8% in AMBER controls and ORs were 1.45 (P=6.0×10−7) for overall breast cancer, 1.55 (P=4.6×10−7) for ER+ breast cancer, and 1.39 (P=3.4×10−3) for ER− breast cancer. For CALM2, ORs for SNP rs13032512, with a risk-allele frequency of 5.5% in AMBER controls, were 1.33 (P=1.3×10−4), 1.30 (P=4.1×10−3), and 1.35 (P=8.2×10−3) for overall, ER+, and ER− breast cancer, respectively. The association between CSNK2A1 with ER+ breast cancer was explained by SNP rs434410. The C-allele has 24.3% frequency in AMBER controls, and was associated with higher risk of overall, ER+, and ER− breast cancer.− breast cancer only. A SNP in BRAF (rs114729114) showed an OR of 2.04 (P=4.9×10−6) for ER− breast cancer. Weaker associations were also observed for overall, and ER+ breast cancer. For BAD, a two-SNP model  was the best fit. Rs2286615  of 4.3%) had an OR of 0.60 (P=5.0×10−4) for ER− breast cancer, and the minor allele of ch11:64038448:I, a deletion, showed an OR of 0.70 (4.4×10−3) for ER− breast cancer. Finally, a SNP in MAPK3 (rs78564187) was associated with ER− breast cancer, OR equal to 1.26 (P=3.7×10−4) per high-risk allele.The other three genes were associated with ERP values<0.01. BAIAP2 was associated with overall and ER+ breast cancer; CALM2 with overall breast cancer; CSNK2A1 with ER+ breast cancer; and BRAF, BAD, and MAPK3 with ER− breast cancer.In this large gene-based analysis of the insulin, IGF, GH, and LEP pathways no genes were associated with breast cancer risk after adjustment for multiple testing, but six genes carried genetic variations showing moderate to strong associations (OR>1.2 or <0.6) for breast cancer overall or an ER-defined subtype, with BAIAP2 codes the adaptor protein IRSp53, which functions as a substrate of the insulin receptor and IGF-1 receptor tyrosine kinases,30 and links membrane bound small GTPases such as Rac1 to trigger re-organization of the cytoskeleton  that encodes the calcium-binding protein calmodulin, involved in cell growth, differentiation, proliferation, and survival.35CSNK2A1 codes a serine/threonine kinase (CK2) that participates in diverse signaling pathways involved in control of the cell cycle, and apoptosis among other cellular processes.36BRAF codes a protein member of the family of Raf serine/threonine kinases that regulate signaling of the MAPK pathway. BAD codes a protein member of the BCL-2 family that regulates programmed cell death and whose proapoptotic activity is regulated by the PI3K/Akt pathway. High levels of phosphorylated BAD (pBAD) have been found associated with development and progression of ovarian, breast, colon, and endometrial cancer.37MAPK3 codes a protein that is member of the mitogen-activated protein (MAP) kinase family that participates in the Ras/Raf/MAPK pathway. Expression of MAPK3 is dysregulated in several cancers including breast.39The insulin, IGF, GH and LEP pathways are well-characterized in the biological literature, and previous literature suggests important functions or potential functions for each of these genes in breast cancer. Namely, CSNK2A1 SNPs have been previously reported associated with breast cancer. A GWAS in German subjects identified rs6038071 (r2=0.001 with rs434410 in African ancestry populations from 1000 Genomes), 40 kb upstream of CSNK2A1, to be associated with familial colorectal cancer.40BRAF is usually amplified in somatic DNA from basal-like breast cancers,41 but to our knowledge, germline variation in BRAF has not been associated with breast cancer. There is also a lack of evidence from previous literature for an association of breast cancer risk with the other SNPs examined.No P<4.7×10−5 to adjust for the total number of tested SNPs.25 Although in BPC3 gene variants in IGF1 and SSTR5 were associated with circulating levels of IGF-1, and SNPs in IGFBP3 and IGFALS were associated with circulating levels of IGFBP-3, these variants only explained a small fraction of the variation of IGF-1 and IGFBP-3 circulating levels.42The present work is a comprehensive assessment of the insulin, IGF, GH, and LEP pathways. Previous studies have partially addressed these pathways, but none have included all of the relevant genes. The Breast and Prostate Cancer Cohort Consortium (BPC3) assessed common variation in 24 genes in the insulin and IGF pathways in European ancestry women and found no single-SNP associated with breast cancer using a threshold of 44 Thus, present findings add to our understanding of the etiology of breast cancer in African-American women. However, we do note some limitations. Most of the SNPs of interest were imputed, although we restricted our analyses to SNPs with high imputation scores and MAFs of at least 2% to minimize imputation errors. Also, we did not examine gene–gene interactions due to limited power even with our large study population.The present study has several strengths, including its large size, information on ER subtypes, and large number of genes and SNPs evaluated in the insulin, IGF, GH, and LEP pathways. Although >90 independent loci have been identified that explain about 16% of the familial risk of breast cancer, most of these variants have been established in Europeans and East Asian populations.In summary, our findings suggest that variation in genes in the insulin, IGF, GH, and LEP pathways contribute to the risk of breast cancer and, in particular, to ER-negative breast cancer in African-American women. Because the strength of these associations was moderate for individual genes, future studies should consider how such genes interact.45 is a collaboration pooling data from four studies, the Carolina Breast Cancer Study (CBCS), the Women’s Circle of Health Study (WCHS), the Black Women’s Health Study (BWHS), and the Multiethnic Cohort (MEC). Briefly, the CBCS is a population-based case–control study of women aged 20 to 74 years that began in North Carolina in 1993.46 Cases were identified through the North Carolina Central Cancer Registry’s rapid case ascertainment system, and controls were enrolled through 2001 using Division of Motor Vehicles lists (age<65 years) and Health Care Financing Administration lists (age⩾65 years). Questionnaire data and samples for DNA analysis were obtained by interviewers in home visits. The WCHS is a case–control study that began in 2002 with ascertainment of cases aged 20 to 75 years from New York City hospitals, later expanding to ten counties in New Jersey, with case identification using the New Jersey State Cancer Registry’s rapid case ascertainment system.48 Controls have been recruited through random digit dialing as well as community-based efforts. In-person interviewers collect risk factor data and obtain samples for DNA analysis.The AMBER Consortium, described in detail elsewhere49 Breast cancer cases are identified by self-report in biennial follow-up questionnaires, and cases are confirmed by medical records or from state cancer registry data and the National Death Index. Approximately 27,000 BWHS participants have given saliva samples for DNA analysis. The MEC is a prospective cohort study in Hawaii and Southern California that began in 1993 with the enrollment of men and women aged 45–75 years.50 Data are collected through questionnaires mailed at 5-year intervals, and breast cancer cases are confirmed by linkage with the California and Hawaii state cancer registries and the National Death Index. Controls for BWHS and MEC were selected from among all non-cases in those studies.The BWHS is a prospective cohort study that began in 1995 when 59,000 African-American women 21–69 years of age from across the United States completed a postal health questionnaire.The CBCS was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill School of Medicine. The WCHS was approved by the Institutional Review Boards at the University of Medicine and Dentistry of New Jersey (presently Rutgers University), Mount Sinai School of Medicine, and Roswell Park Cancer Institute. The BWHS was approved by the Institutional Review Board at the Boston University School of Medicine. The MEC was approved by the Institutional Review Boards of the University of Hawaii and University of Southern California. Written informed consent was obtained from each participant.in situ, with available DNA samples for genotyping. Determination of ER status for cases was based on pathology data obtained from state cancer registry records or directly from hospital records.Eligible cases for analysis were women with a first diagnosis of incident invasive breast cancer or ductal carcinoma 51 (r2⩾0.8) as many SNPs as possible with MAF⩾10%, based on the haplotype structure of the Yoruban population (YRI) in 1000 Genomes (http://www.1000genomes.org/).We selected 184 genes in the insulin, IGF, GH, and LEP pathways from the Molecular Signature Database (MSigDB)51 . Tag SNPGenotyping using the Illumina Human Exome Beadchip v1.1 with custom content was performed by the Center for Inherited Disease Research (CIDR). The variants selected for this analysis were included as part of more than 159,000 custom content SNPs added to the Exome Beadchip to support the scientific goals of the AMBER consortium.P<1×10−4, had call rate<0.98, had >1 Mendelian errors in trios from HapMap (http://hapmap.ncbi.nlm.nih.gov), or had >2 discordant calls in duplicate samples. Genotypes were attempted for 6,936 study subjects from the BWHS, CBCS, and WCHS, and were completed with call rate >98% for 6,828 participants . The University of Washington performed imputation using the IMPUTE2 software52 and the 1000 Genomes Phase I reference panel .Of the 405,555 SNPs attempted for genotyping, 381,212 were released by CIDR and 299,873 of these remained after removing SNPs that were monomorphic, were positional duplicates, were on the Y chromosome, had Hardy–Weinberg Equilibrium Genetic data from 533 cases and 989 controls in the MEC study had been genotyped on the Illumina Human 1M-Duo array and SNPs were imputed from 1000 Genomes. Imputed genotypes from MEC were combined with imputed data from BWHS, CBCS, and WCHS into a final data set after additional quality control. Variants with mismatching alleles or allele frequencies that were different by >0.15 in MEC versus the other three studies were omitted. Also, SNPs with MAFs<0.5% or imputation score INFO<0.5 in either study were removed. After these exclusions, there were 91,627 genotyped or imputed SNPs with MAF⩾2% in the 184 genes of interest.53 Relationship checking using PLINK software54 (http://pngu.mgh.harvard.edu/~purcell/plink/) identified several relatives among and within the individual studies. Related individuals and those with more extreme principal components were flagged so that relationships could be taken into account and sensitivity analyses could be performed. The principal components of genotype were tested for association with case status after accounting for the study covariates: study, age (10-year groupings and matching variable), geographic region (matching variable), and DNA source . No principal components were strongly associated with case status after controlling for the study covariates. For case status and subtype association analyses, we included principal components that were associated with P<0.1 in the full covariate model.Genotype principal components were computed using the smartpca program in the EIGENSOFT package.55 as implemented in the R package AdaJoint. The test identifies the best subset of SNPs that jointly show the strongest evidence for association with disease in a given gene through a variable selection procedure that takes into account the LD structure. The significance level of the gene-based test is evaluated through a direct simulation approach that generates the null distribution of the statistic. Because the score test implemented in AdaJoint is not optimal for rare variants, we excluded SNPs with MAF<2%. To avoid missing independent association signals due to correlations between SNPs, we excluded the SNP with lower MAF from each SNP pair with correlation r2>0.9. These exclusions resulted in a final analytic list of 31,657 SNPs in 184 genes. Our analysis searched up to the best five most significant SNPs within each gene. In order to account for multiple testing, we set the alpha level for statistical significance at 3.0×10−4 (0.05/184 genes).Gene-based association tests were conducted for the 184 selected genes. We used a multi-locus adaptive joint test54 Models were adjusted for the covariates noted above and for genotype principal components 5, 6, and 8.Odds ratios (ORs) and 95% confidence intervals (CIs) for the most significant SNPs in the identified genes were estimated using logistic regression (PLINK version 1.9).


DCC), a netrin-1 dependence receptor, is correlated with cell progression, migration, and adhesion. Evidence indicated that DCC was frequently down-regulated in many cancers. However, the association of DCC with breast cancer remains uncertain. We conducted a case-control study to investigate the impact of three DCC gene variants  on breast cancer susceptibility in Chinese women. This study included 560 breast cancer patients and 583 age-matched healthy controls from Northwest China. The three gene variants were genotyped via Sequenom MassARRAY. Odds ratios (ORs) and 95% confidence intervals (CIs) were utilized to evaluate the associations. We found that individuals with the rs2229080 C/G, C/C, and C/G-CC genotypes had a higher breast cancer risk, and the minor allele C was associated with increased breast cancer risk in an allele model. We observed a significantly decreased breast cancer risk with the rs7504990 C/T, T/T, and C/T-T/T genotypes, and the minor allele T was protective against breast cancer in an allele model. In addition, rs2229080 was associated with the axillary lymph node (LN) metastasis status. An age-stratified analysis revealed an association between rs2229080 and reduced breast cancer risk among older patients (≥ 49 years). Furthermore, the haplotype analysis showed that the Crs2229080Crs7504990Ars4078288 haplotype was associated with a decreased breast cancer risk. However, the results indicated a lack of association between rs4078288 and breast cancer risk. These findings affirmed that rs2229080 and rs7504990 polymorphisms in DCC might be related with breast cancer susceptibility in Chinese women.Deleted in colorectal carcinoma ( Breast cancer (BC) is the most common type of tumors in women worldwide, with 1.8 million incident cases and 464,000 death cases in 2013 [DCC and SAX-3 (Robo) can function individually or in combination with other guidance receptors to control axon growth [DCC is widely expressed by neurons and is enriched at synapses to promote synaptogenesis between mammalian cortical neurons [DCC can induce apoptosis in the absence of its ligand netrin-1 [In the nervous system, axon development is guided by diffusible chemoattractants produced by axonal target cells . Netrinsn growth . Evidenc neurons . In addinetrin-1 .DCC and many cancers. Rs2229080 is located on human chromosome 18, with a C/G single-nucleotide variation, which may alter the DCC expression. Rs7504990 and rs4078288 are located in the intron region and may not directly affect protein function. Rai et al. [DCC polymorphisms and BC susceptibility. In this case-control study, we aimed to examine the association of these three DCC gene polymorphisms  with BC risk in a Northwest Chinese population.Recently researchers are mostly focused on the association between rs2229080, rs7504990 and rs4078288 polymorphisms in i et al.  did not P = 0.612) and menopausal status (P = 0.716). However, the body mass index of BC patients significantly differed from health controls (P = 0.038). Therefore, statistical analysis based on case-control comparisons was adjusted for BMI.The characteristics of the 560 BC cases and 583 controls are presented in Table DCC rs2229080, rs7504990, and rs4078288 variants are shown in Table P = 0.07, 0.50, and 0.99 for rs2229080, rs7504990, and rs4078288, respectively), indicating that community genetic inheritance was balanced in the control samples and that these samples could represent the general population.All three SNPs of DCC gene were successfully genotyped in 560 patients and 583 controls. The genotypes and allele frequencies of the DCC polymorphisms, two were found to be associated with the risk of BC. For the rs2229080 polymorphism, we found that C/G, C/C, and C/G-CC genotype carriers had a significantly increased risk of developing BC ; the same trend was observed for patients with the C allele (P = 0.007). For rs7504990, patients with BC were less likely than controls to carry the C/T, T/T, and C/T-T/T genotypes , and the minor allele T conferred a reduced risk to BC in an allele model . However, the patients and controls did not differ significantly with respect to the frequency of any rs4704853 genotypes (P > 0.05). We also obtained the statistical power of 0.86 and 0.83 for the two significant polymorphisms identified, rs2229080 and rs7504990, respectively. This showed that our sample size of 1143 was adequate and the study was sufficiently able to detect the true association of these two polymorphisms with BC.Among all selected DCC variants were associated with the clinical features of patients with BC, we further analyzed the distributions of DCC variants with respect to a series of clinicopathological parameters including tumor size; axillary lymph node (LN) metastasis; and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki67 index statuses. As shown in Table P = 0.01). However, no significant correlations were detected between the rs7504990 or rs4704853 polymorphisms and clinical features of patients with BC (data not shown).To investigate whether DCC rs7504990 polymorphism distribution were not statistically significant. However, for DCC rs2229080, the GA + AA genotype was significantly expressed lower frequency among older participants (age ≥ 49 years) .The results of a stratified analysis are shown in Table rs2229080Crs7504990Ars4078288, the haplotype Crs2229080Crs7504990Ars4078288 was associated with a decreased BC risk .We then evaluated the relationship between haplotypes and BC risk. Compared with the common haplotype GDCC is a single-pass transmembrane protein that belongs to the immunoglobulin superfamily, and it is a candidate tumor suppressor gene located on chromosome 18q21 [DCC extends more than 1.2 Mb with 29 exons [DCC directs cell invasion through the basement membrane, an essential step in the pathological progression of human cancer [DCC is known to be involved in the following biological processes: guidance of developing axons,4 induction of apoptosis [DCC alterations are apparent in early-stage gastric cancers, emphasizing the importance of this growth regulatory pathway in gastric carcinogenesis [DCC expression might influence the prognosis of patients with breast cancer [DCC alterations has been found in the pathogenesis of meningiomas [me 18q21 . DCC ext29 exons , and is 29 exons . DCC dirn cancer . Currentpoptosis , and angpoptosis . DCC altt cancer . Howeveringiomas .DCC gene variants were found to be correlated with some types of cancer. Rai et al. [DCC haplotype G (rs2229080)-A (rs4078288)-C (rs7504990)-A (rs714) conferred a high risk of gallbladder cancer. The rs714 (A > G) polymorphism contributes to the risk of esophageal and gastric cancers in a Kashmiri population [DCC variants had not been studied in BC. In our study, we have provided the initial observation that the DCC genotype variants rs2229080 and rs7504990, but not rs4078288, contributed differentially to BC susceptibility. The haplotype Crs2229080Crs7504990Ars4078288 had a significant relationship with decreased BC risk.In previous studies, i et al.  found thpulation . To dateDCC expression but a lower rate of axillary lymph node metastasis in mucinous carcinoma than in non-mucinous carcinoma in the human female breast [DCC rs2229080 polymorphism was associated with the lymph node metastasis status. Furthermore, rs2229080 conferred a decreased BC risk in older individuals (≥ 49 years). Our results demonstrate that the rs2229080 polymorphism might play a critical role in BC susceptibility. Moreover, DCC rs2229080 polymorphism was reported to occur more frequently in stages C and D in colorectal cancer, and may serve as a prognostic factor for colorectal cancer patients [A previous study found increased e breast . DCC-nege breast . Howeverpatients . Rs22290patients .DCC polymorphism, similar studies with larger sample sizes will be needed for further verification. Second, we did not investigate whether predisposing factors, including high-dose radiation exposure, alcohol consumption, and postmenopausal obesity, were associated with the risk of BC because of a lack of such data from both BC patients and healthy controls. The effect of these factors on BC risk should be assessed in a future study.Our study had some limitations. First, the sample size was inadequate for a stratified analysis and for an analysis of these associations in patients with mix-type BC. As this is the first report of genetic BC susceptibility related to DCC might affect BC susceptibility and progression in Chinese women. Further functional studies and large population-based prospective studies will be needed to provide accurate evidence about the influence of DCC variants on BC.In conclusion, our case-control study indicates that the rs2229080 and rs7504990 polymorphisms in The study was approved by the Institutional Review Board of Xi'an Jiaotong University . Written informed consent was obtained from all participants involved in the study at the time of recruitment.We conducted a hospital-based case-control study of 560 BC patients and 583 cancer-free controls. All participants were recruited without the restrictions of age. All patients met the present pathological criteria of sporadic BC and were diagnosed between January 2013 and October 2014 at the Second Affiliated Hospital of Xi'an Jiaotong University, China. Patients who had other types of cancer were excluded from the study. The controls were randomly selected from among healthy volunteers who underwent annual physical examinations in other hospital departments and who had no prior history of cancer. Controls were frequency matched to patients according to age (±5 years). The methods were performed in accordance with the approved guidelines. All research participants provided written informed consent and were interviewed to obtain detailed information regarding age, sex, ethnicity, religion, place of residence, education, and other potential confounding factors of interest. After the interview, approximately 2 mL of venous blood sample was collected from each healthy control and breast cancer patient before they received the chemotherapy or radiotherapy.DCC SNP genotyping was performed using a Sequenom MassARRAY RS1000, according to the manufacturer's instructions. The corresponding primers used for each SNP in this study are listed in Table Whole blood samples were placed into EDTA-coated tubes and preserved at −80°C until further use. Genomic DNA was isolated from the whole blood samples using a standard phenol-chloroform extraction method. DNA concentrations were measured via spectrometry . For our study, we selected candidate SNPs in TIM according to HapMap data from a Chinese population. To achieve a power of at least 50%, only SNPs with a minor allele frequency (MAF) > 0.01 was included. Sequenom MassARRAY Assay Design 3.0 Software  was used to design a Multiplexed SNP MassEXTEND assay. Finally, three SNPs  were selected according to Chinese population data from HapMap. http://biostats.usc.edu/Quanto.html). HWE was tested by chi-square test for each SNP before the analysis. The Student t-test or the t2 test was used to compare differences in the distributions of demographic characteristics, selected variables, and genotype frequencies between the cases and controls. We conducted a case-control study for all of the subjects, and then the patients were stratified by age. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to assessed the degree of association betweenDCC rs2229080, rs7504990, and rs4078288 polymorphisms and BC. Allele and genotype frequencies were calculated and compared between patients and controls using SPSS 18.0 for Windows . Haplotype analysis was performed with in each gene under the generalized linear models by using the software PHASE. P values < 0.05 were considered to indicate statistical significance, and all statistical tests were two-sided.The statistical power of the case-control study was calculated using QUANTO software 1.2.4 (University of Southern California, Los Angeles, CA, USA;


BRCA1/2 breast cancer families have no identified genetic cause. We used linkage and haplotype analyses in familial and sporadic breast cancer cases to identify a susceptibility locus on chromosome 6q. Two independent genome-wide linkage analysis studies suggested a 3 Mb locus on chromosome 6q and two unrelated Swedish families with a LOD >2 together seemed to share a haplotype in 6q14.1. We hypothesized that this region harbored a rare high-risk founder allele contributing to breast cancer in these two families. Sequencing of DNA and RNA from the two families did not detect any pathogenic mutations. Finally, 29 SNPs in the region were analyzed in 44,214 cases and 43,532 controls from BCAC, and the original haplotypes in the two families were suggested as low-risk alleles for European and Swedish women specifically. There was also some support for one additional independent moderate-risk allele in Swedish familial samples. The results were consistent with our previous findings in familial breast cancer and supported a breast cancer susceptibility locus at 6q14.1 around the PHIP gene.Most non- BRCA1 and BRCA2, account for 10-15% of the familial risk of breast cancer, while mutations in other high-risk genes PTEN, STK11, CDH1, and TP53 or in the moderate-risk genes ATM, CHEK2, PALB2, explain around 5% of familial cases [BRCA1 and BRCA2 [BRCA1/2 families, without identifying any novel putative breast cancer genes [Only 5% of all breast cancer cases are attributed to the segregation of germline mutations in high penetrance genes within families -3. The tal cases . Thus, tnd BRCA2 . Subsequer genes -14. Moster genes , while oer genes , 22. Gener genes . In totaer genes . Over ther genes ). Despiter genes . There ier genes . The remIn an attempt to identify new breast cancer susceptibility loci, a genome-wide linkage analysis was conducted in 96 non-BRCA1/2 families with breast or breast-ovarian cancer. For most families, there were few individuals available for study and no overall significant positive logarithm of odds (LOD), heterogeneity LOD (HLOD) or nonparametric linkage LOD (NPL LOD) scores were obtained for any chromosome . We assuFine-mapping of the 6q region was conducted using a total of 43 families. Only two unrelated families, 6006 and 6043, shared a unique 2.8 Mb haplotype between the markers D6S1625 and rs2050660 on chromosome 6q14.1  with positive LOD scores for the region revealed no clear pathogenic mutation in any of the six genes within this region. RT-PCR of mRNA from two cases from each of the two families 6006 and 6043 also did not detect any pathogenic mutation. Array comparative genomic hybridization (array-CGH) was performed in the two families, but no copy number alteration was observed in the candidate region. Eleven common variants were identified in the two families (Table p = 0.0005). Although the haplotype was not the same as in the two original families, the existence of a risk haplotype among the Swedish families suggested a Swedish founder mutation at this locus.The 11 variants were used to assess whether the two families’ haplotype was associated with breast cancer risk. We first performed a pilot study using the 31 linked familial cases included in the mutation screen and 95 healthy controls and found one rare risk-associated haplotype  = 26.5, p = 0.03) when the analysis included only the 496 familial breast cancer cases versus all 1,750 controls. This result weakly supported the hypothesis of a risk factor in the region, although the haplotype was still not the same as that seen in the two original families.In the next larger association study, 9 of 11 SNPs were successfully genotyped in 800 breast cancer cases and 1,750 healthy controls. There was no risk haplotype found by comparing all cases to all controls. However, one rare (3%) risk haplotype was suggested (http://bcac.ccge.medschl.cam.ac.uk/). In total 29 SNPs in the region from rs9343798 to rs7768535 were successfully genotyped in 100,165 BCAC samples  as well as in the two original families (6006 and 6043) to search for a confirmation of the hypothesized risk haplotype identified in these two families.To search for additional support for the region and for the two families’ haplotype, we selected SNPs from an extended region around these 11 markers to be included in a custom genotyping array project (iCOGS) of BCAC , early onset , estrogen receptor (ER) positive  and ER negative . Two risk haplotypes were found for all BCAC, one (I) same as in family 6006  recruiting postmenopausal cases, the same haplotype as in family 6043 was associated with breast cancer risk (Table vs. 662 controls) with a predominance of familial breast cancer cases, it was suggested a different susceptibility haplotype, and also a higher associated risk compared to the other risk haplotypes , with consecutive cases from a mammography cohort, suggested yet another risk-associated haplotype .The risk-associated haplotype identified using KARBAC was not studied further since the 29 SNPs were only genotyped in non-related individuals and relatives from the two original families. We hypothesized that the distribution of risk haplotypes of different sizes over a locus could reflect one or several risk alleles. Thus, we performed a haplotype analysis in KARBAC on sliding windows over the 29 SNPs. The result suggested the existence of one risk allele in the HIP gene . SlidingHIP gene . The BCAHIP gene . The alm to PHIP . The samWe had previously tested for mutations in the exonic regions over the whole haplotype in the two families without finding any candidate mutation. A targeted genome sequencing approach was subsequently applied to a 2.8 Mb region in the two original families (from rs9447790 to rs2655685) and the lack of clear pathogenic mutations was confirmed. The two families shared many markers in the region of the haplotype. It was not possible to define the pathogenic mutation in either of the two haplotype regions .PHIP gene and 6043 one or two proximal and distal to the PHIP gene , that they shared a mutation in a rare, high-risk breast cancer gene. In the end, it was clear that they did not share an identical haplotype. Family 6006 had one identical to a risk haplotype in BCAC, in particular for those with early onset and family 6043 was similar to one quite prevalent (frequency 21%) haplotype in Swedish postmenopausal cases (SASBAC) (Table HIP gene .p = 0.0005). However, the evidence was much weaker when sample sizes increased (p = 0.03). All the time the focus was on familial samples, which might explain the difficulties to identify the two original families’ haplotypes since they are not over-represented among familial samples  in one family (6082). This mutation did not segregate in affected members in this family. The other two missense variants in this gene, rs11547228 (p.T874I) and rs7747479 (p.G663V), did not show any increased frequency in the 48 breast cancers subjected to targeted sequencing compared to that in a general population (1000 Genomes Project). Although none of the 29 SNPs shows statistically significant evidence of being an eQTL in breast mammary tissue in GTEx [The target for the risk association is between our original borders D6S1625 - rs2050660 and in this region there are several genes such as gression  and the ll lines . None iset cases . It has igenesis . The PHImelanoma . To our  in GTEx , ENCODE  in GTEx . It is pLinkage analysis typically has been used to find high-penetrance disease , 5 whileThe project has been carried out over a long period and the methods used were appropriate at the time . The firPHIP gene, with low and moderate risk profiles. Further studies of patients with the identified haplotypes and their tumors will be necessary before it is possible to estimate the importance of this locus. Haplotype analysis in populations with a homogenous genetic background will be valuable to further define this locus.Linkage analysis has shown its limitations to define novel loci for a heterogeneous and complex disease, such as breast cancer , 25, 26.Non-BRCA1/2 breast cancer families were recruited through the Cancer Counseling Clinic at the Karolinska University Hospital, Solna, Sweden. The first linkage study involved 272 members from 96 non-BRCA1/2 breast cancer families, including 232 breast cancer patients . These families were selected if two or more first- or second-degree relatives were affected with only female breast cancer, and if the proband had counseled a two to four times increased risk for the disease compared to the normal population, no age limitation required. Twenty of these families have two affected cases, 42 families have three cases, 24 families have four cases, eight families have five cases and two families have six cases, with an average of 3.2 cases per family. Nine of the families were breast-ovarian cancer families. No other types of cancer were presented in this set of families.The second linkage study was based on the cohort of 14 large hereditary non-BRCA1/2 breast cancer families with also other cancers in the family . DNA wasThe association studies used 300 familial cases identified and recruited as described above and 500 consecutive breast cancer cases from a breast cancer clinic at Södersjukhuset in Stockholm . The conThe study was undertaken in accordance with the Swedish legislation of ethical permission (2003:460) and according to the decision in the Stockholm regional ethical committee in Stockholm .Genotyping was carried out using Linkage Mapping Set v2.0- MD10 (Applied Biosystems). 400 fluorescent-labeled microsatellite markers covered the whole genome with an average resolution of 10 cM. The average heterozygosity of these markers was 0.76 in our sample set. The amplified fragments were separated on an ABI 377 automated sequencer (Applied Biosystems) together with internal size standard. Electrophoretic data were analyzed using GeneScan (v3.1) and Genotyper (v2.0) softwares (Applied Biosystems).Genotyping data were first checked for consistency by using PedCheck . HomozygThe second linkage analysis was carried out in 14 large breast cancer families with other cancer types using a previously published approach  with modhttp://www.ncbi.nlm.nih.gov/projects/dbSTS/) and were labeled with 6-Fam Fluorescein modification at the 5’ end of forward primers. The amplified markers were analyzed on an ABI Prism 377, 3130xL or 3500xL Genetic Analyzer (Applied Biosystems). Softwares GeneScan (v3.1R), GeneMapper (v3.7 or 4.1) and GenoTyper (v2.0) was used to analyze the peaks.Markers were selected throughout the chromosome 6q14 region revealed by the linkage analyses for fine-mapping in additional breast cancer families. Primers for the markers were obtained from dbSTS  for all exons including exon/intron boundaries, 5’ and 3’ UTR sequences and putative promoter regions. All genes were successfully amplified and sequenced. RT-PCR analysis was carried out using mRNA from EBV-transformed lymphocytes from two cases in each of the families 6006 and 6043 to detect aberrant splicing patterns or large insertions / deletions.Genomic DNA from one affected member from each of the 31 families with positive LOD scores for the linked region, including families 6006 and 6043, was PCR-amplified and sequenced for the six genes within this region . Agilent Technologies Suredesign was used to design a targeted 4x180K array . Experiments were performed at the Department of Clinical Genetics at Karolinska University Hospital, Stockholm, Sweden according to the manufacturer’s protocol. Slides were scanned using the Agilent Microarray Scanner . Raw data were normalized using Feature Extraction Software , and log2 ratios were calculated by dividing the normalized intensity in the sample by the mean intensity across the reference sample. The log2 ratios were plotted and segmented by circular binary segmentation in the CytoSure Interpret software . Oligonucleotide probe positions were annotated to Human Genome Assembly hg19.Nine variants (shared sequence variants in family 6006 and 6043) used for the association study in 800 cases and 1750 controls were rs2275291, rs2275290, rs1984195, rs9443632, rs10455356, rs9350797, rs7742431, rs1890229 and rs11752126 genotyped by DeCODE, Iceland.Genotyping within BCAC and iCOGS used a custom Illumina array with 211,155 SNPs. Genotyping, allele calling, quality control and principal components analysis for the iCOGS study are described in detail in Michailidou et al. . In totaCapture sequencing of 48 familial breast cancer patients was performed by Axeq Technologies, USA using a SureSelect target enrichment process followed by 100 bp paired-end sequencing on an Illumina HiSeq2000 sequencer. After sequencing, bioinformatics analysis of the FASTQ files included alignment of sequence reads to the reference human genome (GRCh37/hg19) using BWA and SAMTools, applying GATK -47 base


BRCA1 or BRCA2 have reported strong associations between single-nucleotide polymorphisms (SNPs) and cancer risk. To conduct the first genome-wide association analysis of copy-number variants (CNVs) with breast or ovarian cancer risk in a cohort of 2500 BRCA1 pathogenic variant carriers, CNV discovery was performed using multiple calling algorithms and Illumina 610k SNP array data from a previously published genome-wide association study. Our analysis, which focused on functionally disruptive genomic deletions overlapping gene regions, identified a number of loci associated with risk of breast or ovarian cancer for BRCA1 pathogenic variant carriers. Despite only including putative deletions called by at least two or more algorithms, detection of selected CNVs by ancillary molecular technologies only confirmed 40% of predicted common  variants. These include four loci that were associated (unadjusted P<0.05) with breast cancer , and two loci associated with ovarian cancer (CYP2A7 and OR2A1). An interesting finding from this study was an association of a validated CNV deletion at the CYP2A7 locus (19q13.2) with decreased ovarian cancer risk . Genomic analysis found this deletion coincides with a region displaying strong regulatory potential in ovarian tissue, but not in breast epithelial cells. This study highlighted the need to verify CNVs in vitro, but also provides evidence that experimentally validated CNVs  can modify risk of breast or ovarian cancer in BRCA1 pathogenic variant carriers.Genome-wide studies of patients carrying pathogenic variants (mutations) in  BRCA1 pathogenic variants are at increased risk for developing breast cancer and/or ovarian cancer, but the precise level of these risks is uncertain. Estimates of the cumulative risks of breast and ovarian cancer by age 70 years for BRCA1 pathogenic variant carriers range from 44% to 75% and 43 to 76%, respectively.1 Studies exploring the cause for the range in risk estimates have provided evidence that genetic factors have a key role in modifying cancer risks for carriers.2 The Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA) has facilitated a number of large studies, which have identified variants mapping to >20 loci that are associated with altered risk of breast or ovarian cancer in BRCA1 pathogenic variant carriers.6 The effect size associated with each variant identified to date has been relatively small (hazard ratio<1.5), and together they account for only a fraction of heritable variation in risk in BRCA1 pathogenic variant-positive families.Carriers of 7 of the human genome, which is an order of magnitude greater than the number of base pairs  encompassed by the more commonly studied single-nucleotide polymorphisms (SNPs). Thus, based on base pair coverage, CNVs are responsible for the majority of genetic variability in human populations. CNVs have also been shown to partially overlap or fully encompass genes or regulatory sequences resulting in a range of biological changes, such as altered gene expression.8 Importantly, these inherited structural variants have a role in many complex diseases,9 and comprise a proportion of the mutation spectrum for known cancer syndromes, such as hereditary breast–ovarian cancer syndrome, Lynch syndrome and Li–Fraumeni syndrome.10 Moreover, recent genome-wide CNV studies have reported associations between a common deletion polymorphism overlapping APOBEC3 and risk of both breast and ovarian cancer.13 Thus, other common and rare CNVs may similarly affect genes involved in cancer-related pathways. The contribution of germline CNVs to variable risk in individuals with deleterious BRCA1 pathogenic variants is unknown.Copy-number variants (CNVs) are estimated to cover 5–10%BRCA1 pathogenic variant carriers, with or without breast and/or ovarian cancer, using a previously published SNP-based genome-wide association study.14 To maximize the sensitivity for CNV discovery, multiple CNV calling algorithms were applied to the data set. Analyses identified several putative CNVs overlapping gene regions associated with risk of breast or ovarian cancer for BRCA1 pathogenic variant carriers and a requirement for validation in larger studies.In this paper, we conducted a large genome-wide CNV analysis of 2500 BRCA1 pathogenic variant carriers was drawn from 20 centers from North America, Europe and Australia as reported previously.14 Eligibility criteria for study participants included the following: (1) female carriers of pathogenic BRCA1 variants; (2) at least 18 years of age at recruitment; and (3) Caucasian self-reported ancestry. BRCA1 pathogenic variant carriers selected for the study were stratified into two groups consisting of women diagnosed with invasive breast cancer when younger than 40-years old (n=1250) and women who had not developed breast cancer or who had developed a first ovarian cancer when 35 years of age or older (n=1250). All BRCA1 pathogenic variants are listed in http://www.ncbi.nlm.nih.gov/clinvar/). All carriers were recruited for research studies using ethically approved protocols at host institutions.A total of 2500 All DNA samples were genotyped with the Human610-Quad BeadChip  with ~610 000 markers (including ~20 000 non-polymorphic markers) for SNP and CNV analysis. Data for each array were normalized using GenomeStudio 2011.1 software (Illumina). Probe information including, genomic location, signal intensity (Norm R), allele frequency (Norm theta), Log R Ratios (LRRs), B allele frequencies (BAFs) for each sample was calculated and exported from GenomeStudio.15 QuantiSNP (v2.1),16 CNVPartition  and GNOSIS . Quality control procedures were performed to remove poor quality array data ,ray data . SamplesBRCA1 pathogenic variant carriers, common and rare deletions that overlapped gene regions were assessed using a genome-wide approach. Our study focused primarily on genomic deletions that overlapped gene regions for several reasons: (1) inter-individual analysis of CNVs is not straightforward as these variants do not typically occur in discrete genomic regions. The start and end coordinates of gene sequences were therefore used as a non-redundant approach to define CNV regions across the genome. (2) In contrast to duplications or copy-number gains, the genomic location of a deletion can be predicted from the array data. These data were not able to show the genomic location of a duplicated region, thus gene(s) or other functional genomic regions that are potentially disrupted by these structural events remain undetermined. (3) Whole or partial gene deletions are known to be potentially disruptive by causing haploinsufficiency or truncation of the expressed protein, and (4) common and rare CNVs that have previously been reported to be associated with breast and ovarian cancer risk have typically been deletion events.18To identify new genomic loci contributing to breast or ovarian cancer risk in 19 and defined the genomic intervals for a total of 18 791 unique genes (22 All CNVs used for this study are deposited in the dbVar database (https://www.ncbi.nlm.nih.gov/dbvar) with the accession number nstd132.We annotated 39 544 UCSC RefSeq (NCBI36/Hg18) transcripts using the SOURCE databaseue genes . Thus, eGTF2H2) also analysed by with a Nanostring TagSet. Custom primer and probe sequences are presented in Accessible DNA samples from the study cohort were used to validate 29 putative deletion regions. All predicted common (>1% frequency) deletions found associated with breast or ovarian cancer risk were chosen for validation. Copy-number assessment was carried out using Nanostring nCounter Elements TagSets  and Taqman assays. Target-specific Nanostring probes for 10 CNV and 10 invariant genomic regions are listed in n=1202 affected, n=1117 non-affected). Pathogenic variant carriers censored at their ovarian cancer diagnosis were considered to be unaffected in the breast cancer risk analyses. For ovarian cancer risk , study participants were classified at the age of ovarian cancer diagnosis or censored at bilateral prophylactic oophorectomy, or age at last observation. Pathogenic variant carriers diagnosed with breast cancer were treated as unaffected at the age at breast cancer diagnosis. Analyses were carried out within a survival analysis framework. As BRCA1 pathogenic variant carriers were not randomly sampled with respect to their disease status, analyses were based on the modeling the retrospective likelihood of observing the CNV conditional on the observed phenotype.23 Two separate models were fitted to evaluate associations between CNVs with breast cancer and ovarian cancer risk, and were assessed using the 1 d.f. score test statistic.23 Q-values for the discrete test statistics were calculated by filtering the P-values using the T-method24 with a critical threshold of 0.05, such that genes with total number of deletions of four or more were retained.For the breast cancer risk association analysis, study participants  were claBRCA1 pathogenic variants, including 1202 breast cancer cases (1117 non-breast cancer affected) and 357 ovarian cancer cases (1962 non-ovarian cancer affected), using published genotype data from Illumina 610K SNP arrays.14 A total of 60 893 CNVs were called across the study participants using four different algorithms  that passed the data quality threshold . Of these, 89% and 94% CNVs were predicted by PennCNV and QuantiSNP, respectively, compared with a lower prediction rate from GNOSIS (35%) and CNVPartition . Interrogating the CNV calls from at least two algorithms revealed a deletion overlapping the BRCA1 gene in 14 study participants  dominated the CNV calling in this region, with PennCNV alone detecting a deletion in 14 cases .A total of 21 013 CNVs were predicted to overlap at least one of 5848 different RefSeq genes across the study cohort. The average number of CNVs overlapping genes per individual was 9.1 (range 1–107). Deletions overlapping genes were detected approximately three times as often than duplications (6.8 icipants . In each14 cases . No furtP<0.05) with breast cancer risk  and PABPC4L  for breast and ovarian cancer, respectively. Eight loci  were associated with risks for both breast cancer and ovarian cancer (P<0.05).Analysis of 5848 putative deletions delineated by gene regions identified a total of 52 loci associated  found to be associated with breast or ovarian cancer risk in the BRCA1 pathogenic variant carrier cohort. Eight of these 29 (28%) CNV loci were confirmed by qPCR and/or Nanostring analysis, including four loci that were associated with breast cancer  and two loci that were associated with ovarian cancer  in 48 study samples only found seven deletions not identified by the calling algorithms, suggesting a low false negative rate ; P=7 × 10−3) overlapping the CYP2A7 locus  that was not verified in our data was present in the CNV Map (CYP2A7 locus (P=0.007), all validated CNV regions returned a modest association  for ovarian or breast cancer risk. Validated CNVs ranged in allele frequency from 0.2 to 7.8%.Zarrei  CNV Map . Using t CNV Map , and 13  CNV Map . With thBRCA1 pathogenic variant carriers to identify CNVs that are associated with breast and/or ovarian cancer risk, and the first implementation of the retrospective likelihood to CNV data. Our study used multiple CNV calling algorithms with the aim of increasing the sensitivity and specificity of CNV detection. Initial assessment of known deletions overlapping the BRCA1 gene indicated 100% detection specificity and 64% detection sensitivity. This assessment also showed that all 14 CNVs identified at BRCA1 were called by two or more calling algorithms, setting the calling criteria for the remainder of the study. However, validation of 29 predicted CNVs throughout the genome confirmed <30% of predicted deletions, highlighting a large number of false variant calls. None of the nine rare variants  chosen for validation was verified by qPCR or Nanostring. However, CNV calling correctly predicted 40% (8/20) deletions we tested which ranged in allele frequency from 1.2 (OR2A1 locus) to 8.2% (ZNF385B locus). These results confirm other published reports that indicate array-based CNV data can be unreliable without further validation using ancillary technologies, such as qPCR.25 The accuracy may be increased by employing more stringent criteria but likely at the expense of detection sensitivity. For example, a larger number of probe markers could be used to generate a CNV call, but this approach will also reduce the spatial resolution of the array and sensitivity. PennCNV algorithm called ~90% of variants assessed in this study including all the deletions that were detected across BRCA1 in 14 cases. These data suggest that the combination of four algorithms for generating putative CNV information may not have been a vast improvement over using PennCNV alone.Compared with SNPs, the contribution of CNVs to genetic variability and breast and/or ovarian cancer risk is relatively unknown. This is the first genome-wide CNV association study of CYP2A7 locus (19q13.2) with decreased ovarian cancer risk . To our knowledge, this locus has not previously been associated with cancer risk from SNP-based or CNV-based genome-wide association studies and requires further investigation. CYP2A7 encodes a member of the cytochrome P450 superfamily of enzymes, although the substrate(s) for this gene have not yet been determined. The deletion variant in this region may also affect the regulation of a nearby gene CYP2A6,29 which is known to have a key role in the metabolism of a number of substrates including nicotine, coumarin and valproic acid.30 Interestingly, a deletion at the CYP2A6 locus has been found to be associated with decreased risk of lung cancer in Asian smokers,31 which is comparable to our finding that CYP2A7 deletions were more frequent in non-affected high-risk BRCA1 pathogenic variant carriers compared with those with ovarian cancer (MAF—3.8% versus 1.1%). Examining published data from The Cancer Genome Atlas showed that ~40% of high-grade serous ovarian tumors, including 6% BRCA1 pathogenic variant carriers, exhibited somatic hemizygous deletions overlapping CYP2A7.32 Moreover, these deletions correlated with a reduced expression level compared with copy neutral CY2A7  and sample size limits the power to detect association in this study, in particular no associations reported here are significant after controlling for a false discovery rate of 0.05.34 Replication of CNVs identified by this study using larger data sets will be required to verify these associations. Moreover, larger cohort sizes will facilitate more detailed analyses to be performed, such as competing risks analyses to evaluate the associations with breast and ovarian cancer risks simultaneously. Importantly, genotyping data currently being derived by the large Oncoarray Network containing DNA samples from ~20 000 BRCA1 pathogenic variant carriers (http://epi.grants.cancer.gov/oncoarray/) will enable additional genome-wide CNV analysis and further assessment of candidate gene regions identified by this study.Genetic associations identified by this study included rare (<1% MAF) and polymorphic (>1% MAF) deletions that occurred at relatively low frequency (<10%) within the study cohort. Notably, no deletion polymorphism was observed overlapping the


Pelvic inflammatory disease (PID) is characterized by infection and inflammation of the upper genital tract in women and is associated with health sequelae. We used a nationwide population-based retrospective cohort study to explore the relationship between PID and the subsequent development of gynecological cancers including ovarian, breast or uterine cancer.st, 2000 and December 31st, 2002 in the Taiwan National Health Insurance Research Database. A comparison cohort constructed for patients without PID were matched according to age and sex. All PID patients and control groups were observed until diagnosed with ovarian, breast or uterine cancer, or until death, withdrawal from the NHI system, or until December 31st, 2009.We identified subjects diagnosed with PID between January 1The PID cohort consisted of 32,268 patients, and an equal number of matched controls without PID. The adjusted hazard ratio (HR) of ovarian, breast or uterine cancer in subjects with PID were: HR 1.326 , HR: 1.039 , and HR: 1.439  respectively in comparison with controls during follow-up.This large nationwide population-based cohort study suggests that there is no increased risk for ovarian, breast or uterine cancer among women who have PID compared to a matching population.The online version of this article (doi:10.1186/s12885-016-2857-1) contains supplementary material, which is available to authorized users. Pelvic inflammatory disease (PID) is a polymicrobial infection and inflammatory disorder of the upper female genital tract, including the uterus, fallopian tubes, and adjacent pelvic structures in women , 2. It pWithout adequate treatment, PID may lead to major sequelae, including ectopic pregnancy, pelvic pain, abscesses, and infertility. Since infertility and low parity are common complications of PID and are risk factors for ovarian cancer, a few studies have demonstrated that PID may increase the risk of ovarian cancer. A case-control study in Canada with 450 histologically verified primary epithelial ovarian cancer cases and 564 randomly selected population controls done by Risch and Howe, first reported that PID increased the risk of epithelial ovarian cancer . Furtherhttp://nhird.nhri.org.tw/en/index.htm). Recently, using LHID2005, we have demonstrated an increased risk of uterine but not ovarian and breast cancer in women with polycystic ovary syndrome [Instituted in 1995, the National Health Insurance (NHI) program is a mandatory health insurance program that offers comprehensive medical care coverage, including outpatient, inpatient, emergency, and traditional Chinese medicine, to all residents of Taiwan, with a coverage rate of up to 98 % [syndrome .st, 2000 and December 31st, 2002. To ensure diagnostic validity and patient homogeneity, we selected only patients who had at least two consensus PID diagnoses for the study group. We excluded patients who were diagnosed with PID between January 1st, 1996, and December 31st, 1999. We also excluded patients who were diagnosed with malignancies (ICD-9-CM codes: 140-208) before they were diagnosed with PID. For every PID patient included in the final cohort, one age- and sex-matched control without PID, and any malignancy was randomly selected from LHID 2005 in the same time period as PID patient. Random assignment procedures were performed by SAS statistical software and were based on random numbers generated from the uniform distribution. All PID patients and controls were observed until diagnosed with breast cancer (ICD-9-CM code: 174-175), ovarian cancer (ICD-9-CM codes: 183), uterine cancer , or until death, withdrawal from the NHI system, or until December 31st, 2009. Our main dependent variable was the occurrence of breast cancer, ovarian cancer, or uterine cancer, as reported in the Registry for Catastrophic Illness. For a diagnosis of cancer to be reported in the Registry, histological confirmation is required. Common co-morbidities including hypertension, diabetes mellitus, dyslipidemia, congestive heart failure, chronic pulmonary diseases, coronary artery diseases, and cerebrovascular diseases were also compared between PID and controls. The study design and the criteria had been used in similar studies [Using data extracted from the LHID2005, we conducted a retrospective cohort study of patients who were newly diagnosed with PID by an obstetrician-gynecologist between January 1 studies –12.t tests and chi-squared tests were used to examine differences in demographic characteristics between the PID patients and controls. A Cox proportional-hazards regression model was constructed to calculate the hazard ratio (HR) of breast cancer, ovarian cancer, or uterine cancer of the PID and control cohorts, respectively. Control variables, such as age, urbanization, monthly income, common co-morbidities including hypertension, diabetes mellitus, dyslipidemia, congestive heart failure, chronic pulmonary diseases, coronary artery diseases, and cerebrovascular diseases were included as covariates in the multivariate model to calculate adjusted HR. SAS statistical software for Windows, version 9.3 , was used for data extraction, computation, data linkage, processing, and sampling. All other statistical analyses were performed using SPSS statistical software for Windows, version 20 . Results for comparisons with a P value of less than .05 were considered as a statistically significant relationship.The incidences of newly diagnosed breast cancer, ovarian cancer, or uterine cancer in PID patients and controls were calculated, and independent For further confirmation of our study results, we also designed a case control study. All subjects who were aged 20 and older and were newly diagnosed with uterine, ovarian or breast cancer, respectively in 2009 were included as case group. The controls were matched to cases by age, sex, and index date with a ratio of 1:4. Diagnosis of PID before index events was recorded during period of 2000-2009. The percentage of PID diagnosis in patients with uterine, ovarian, breast cancer and three control cohorts were calculated respectively. Odds ratios and 95 % confidence intervals were also estimated by using multiple logistic regression.Our study included 32,268 PID patients and 32,268 controls without PID. Comparisons of demographic and clinical variables between PID patients and controls are presented in Table After adjusting for age, co-morbidities, urbanization, and monthly income, the adjusted HR of ovarian, breast and uterine cancer in subjects with PID were 1.33 , 1.04 (95 % CI 0.86–1.25), 1.44 (95 % CI 0.85–2.43) respectively in comparison with controls during follow-up  and compared above figures with our study. The results showed that incidence rate of ovarian, breast or uterine cancer was similar between general population of Taiwan and subjects of our study (Additional file Based on the relatively small number of patients diagnosed with cancer in our study, we collected annual number and incidence of ovarian, breast and uterine cancer of Taiwan from Health Promotion Administration, Ministry of Health and Welfare of Taiwan (The strength of our study is the use of a population-based data set with an enrollment of a large number of subjects, enabling us to trace the subsequent risk of gynecologic cancers in PID subjects and controls. Furthermore, diagnosis of cancer in our study must be confirmed by histological confirmation. In addition, our study design included an unbiased participant selection process. Because participation in NHI is mandatory and all residents of Taiwan can access health care with low copayments, referral biases were low with high follow-up compliance.Certain limitations to our findings should, however, be considered: First, LHID2005 did not contain some important information regarding the studied subjects. Some of the patient information which may influence the risk of gynecologic cancers, such as smoking, alcohol consumption, parity, infertility, age at menarche, and family history of gynecologic cancers, were not available for analysis . Thus, wIn summary, our study did not support the idea that the risk of ovarian cancer is higher in PID patients as shown in an earlier report with same nationally representative cohort database. We also demonstrated that PID diagnosis is not a risk factor for breast or uterine cancers.


A breast-risk score, published in 2016, was developed in white-American women using 92 genetic variants (GRS92), modifiable and non-modifiable risk factors. With the aim of validating the score in the Spanish population, 1,732 breast cancer cases and 1,910 controls were studied. The GRS92, modifiable and non-modifiable risk factor scores were estimated via logistic regression. SNPs without available genotyping were simulated as in the aforementioned 2016 study. The full model score was obtained by combining GRS92, modifiable and non-modifiable risk factor scores. Score performances were tested via the area under the ROC curve (AUROC), net reclassification index (NRI) and integrated discrimination improvement (IDI). Compared with non-modifiable and modifiable factor scores, GRS92 had higher discrimination power . Adding the non-modifiable factor score to GRS92 improved patient classification by 23.6% (NRI = 0.236), while the modifiable factor score only improved it by 7.2%. The full model AUROC reached 0.6244. A simulation study showed the ability of the full model for identifying women at high risk for breast cancer. In conclusion, a model combining genetic and risk factors can be used for stratifying women by their breast cancer risk, which can be applied to individualizing genetic counseling and screening recommendations. Epidemiological research has led to the identification of several risk factors , most of them associated with estrogen production3. A number of risk factors are related with lifestyle , although their importance seems to be smaller than the estrogen/reproductive life-associated risk factors4. Known risk factors can only explain about 40% of breast cancer risk.Breast cancer is the most frequent type of cancer in women worldwide and a main cause of cancer death in developed countries6; although their low prevalence hardly allows them to explain around 5% cases of breast cancer7, carrying any of these variants puts women in such a high risk that screening practices have been adapted in carrier women and even oophorectomy or early mastectomy can be considered in some cases in the absence of breast cancer diagnosis8. With the advent of next generation sequencing techniques, an increasing number of low-penetrant genetic variants are being identified as related to breast cancer7 and some polygenic tests have been marketed intending to identify women at high risk of breast cancer, although their clinical relevance is uncertain.A few highly penetrant genetic variants, like those in BRCA1 and BRCA2 genes, have been proved to increase breast cancer risket al. presented a breast cancer risk model among white women in the United States, which included modifiable and non-modifiable risk factors, as well as a genetic risk score with information from 92 genetic variants9. The goal of this article is to validate Maas et al.’s model in a Spanish case-control study.In this way, Maas 10. Cases and controls were recruited between September 1st, 2008 and December 31st, 2013.The Multi Case-Control (MCC-Spain) study is a population-based case-control study of common tumors in Spain and has been described elsewhere. It has been carried out in 23 hospitals and primary care centers in 12 Spanish provinces and assesses five types of cancer  using the same series of population controls for all casesWe included 1,732 incident cases of breast cancer in women and 1,910 controls in ten participating centers . Cases were aged 20–85 and with a new pathology-confirmed diagnosis of breast cancer living in the catchment area of each hospital at least 6 months prior to the diagnosis. Controls were women without history of breast cancer living in the same catchment area as cases; they were randomly selected from the rosters of General Practitioners at the Primary Health Centers. Controls were frequency-matched to cases by 5-year age groups and study area. Response rates were 72% among cases and 52% among controls.The study was carried out according to Spanish laws on biomedical research. All procedures were performed with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments.The Ethics Committees of the participating hospitals  approved the study protocols, and participants provided written informed consent at the time of enrollment.Participants were interviewed face-to-face by trained interviewers with a comprehensive epidemiological questionnaire that assessed socio-demographic information, personal and family history of cancer, anthropometric data, alcohol consumption, smoking habits, reproductive and medical history, and family history. Participant’s weight was recorded by self-report, as estimated one year before diagnosis for cases and for controls. Body mass index (BMI) was estimated from self-reported weight and height one year before the diagnosis for cases and one year prior to the interview for controls. Blood samples were obtained following the study protocol. Elapsed time between breast cancer diagnosis and interview was 117 days on average.The genotyping was performed for 1,138 cases and 1,239 controls using the Infinium Human Exome BeadChip  that includes >200,000 coding markers plus 5,000 additional custom SNPs selected from previous GWAS or in genes of interest.13. 17 of them were included in the performed genotyping; the remaining 7  were imputed using SNPSTATs15. The 24-SNP genetic score (GRS24) was estimated by the addition of the beta coefficients as obtained in BPC3 study13. Barrdahl et al.13 considered rs10483813 as surrogate for rs999737 as they are in strong linkage disequilibrium; therefore, we excluded rs10483813 from GRS24 in order to do not double the weight of this locus. As genotyping was not available for 594 cases and 671 controls, we simulated GRS24 in them using the method suggested by Chatterjee et al.16 as explained in the following paragraph.In order to construct the genetic score, we consider two SNP sets. The first set included 24 SNPs identified from Breast and Prostate Cancer Cohort Consortium (BPC3 study)  and conditional on case-control status and family history of breast cancer as:i refers to the participants included in the study and k refers to the SNPs included in the model, fk are the risk allele frequencies. fk were obtained from the BCAC18.The second set of SNPs included 68 identified in Breast Cancer Association Consortium (BCAC) and Collaborative Oncological Gene-Environment Study (COGS)  was obtained by adding GRS24 plus GRS68. Supplementary Table et al.9 (Supplementary Table The modifiable risk factor score (MRFS) included BMI, menopausal hormone therapy, level of alcohol consumption, and smoking status. Alcohol use and BMI were categorized as in Maas et al.9, age at first birth and parity were considered “non-modifiable” as it is unlikely that women modify these factors because of their breast cancer risk. Age at menarche, age at first birth, height and age at menopause were categorized as in Maas et al.9 (Supplementary Table The non-modifiable risk factor score (NMRFS) included family history, age at first birth, parity, age at menarche, height, menopausal status, and age at menopause. According to Maas The full model (FM) was obtained as the addition of GRS92 + MRFS + NMRFS.The independence among the scores was tested using the Pearson correlation coefficient. Differences in scoring between cases and controls were tested with the Student-t test. In order to analyze the association among scores and breast cancer, scores were categorized into deciles; then, a logistic regression model was constructed for each score, with decile 1 as reference. ROC curves and their area under the curve (AUROC) were obtained for each logistic model.19 is the net sum of classifying cases and controls better using GRS92 rather than GRS24:For analyzing whether a score adds or not to the predictive ability of another score, we estimated the net reclassification improvement (NRI), the integrated discrimination improvement (IMI) and the improvement in the AUROC. The goal for a score is to classify breast cancer cases and controls adequately; when two scores -say GRS24 and GRS92- are compared, GRS92 would classify some cases better than GRS24 and some others worse; likewise, GRS92 would classify some controls better and some others worse. NRI20.It is noteworthy that NRI can only be applied when both scores are nested one in each other, as it is the case between GRS24 and GRS92, because GRS92 equals GRS24 plus GRS68. In this article, we use the continuous version of NRI19:While NRI measures the improvement in classification, IMI estimates the improvement in predicted probability. A better score is expected to predict higher probabilities in cases and lower probabilities in controls than a worse score. Therefore, IMI is defined asβ coefficient (=log (OR)). Second, incidence rates by 5-year age groups were obtained from Globocan21; these rates were attributed to patients with the average GRS92. Lastly, incidence rates for each patients were simulated by multiplying the average age-specific incidence rate times exp[β*(GRS92 – average GRS92)]. Similar simulations were performed for NMRFS, MRFS and full model. All statistical analyses were performed with the software Stata 14/SE .Finally, in order to explore the ability of the scores to identify women at high risk of breast cancer, we simulated the expected breast cancer incidence rate according to their scoring in GRS92, NMRFS, MRFS and full model. For instance, the simulation for GRS92 was built as follows: First, a logistic regression model was estimated using GRS92 as regressor and breast cancer as event, obtaining a 1,732 breast cancer cases and 1,910 controls were included in the analysis; their main characteristics are reported in Supplementary Table th decile had 3.8 the odds of breast cancer than patients in the first decile and double the odds than patients around the median of GRS92 distribution (i.e.: patients in 5th or 6th deciles). The area under the ROC curve (AUROC) was 0.6195; GRS24 and GRS68 -the two components of GRS92- had smaller prediction ability, with AUROC being equal to 0.5676 and 0.5936, respectively   (equation ) and impThe full model, thus, mainly reflects the combination of the genetic score GRS92 and the non-modifiable risk score (NMRFS). Its relationship with breast cancer stepped up by each decile Table ; the AURFigure 9 similarly performs in the Spanish population.In this case-control study, we have found that a model for breast cancer risk stratification developed in white women in the United StatesAccording to our results, modifiable risk factors do not discriminate well between breast cases and controls, which questions our ability to implement breast cancer primary prevention policies. Of note, several modifiable factors  have been identified as risk factors for many other frequent diseases ; thus, general recommendations for moderating alcohol consumption, avoiding tobacco smoking and maintaining BMI in its optimal range are considered among the most important health promotion recommendations, despite we do not observe that these factors can discriminate between breast cases and controls.22. GRS92, NMRFS and full model, however, could be useful in stratifying women according to their breast cancer risk. Women scoring in the 10th decile in GRS92 or full model could have double the risk of women with the average scoring and four- to six-fold the risk of women in the first decile, which makes it sensible to use these scores for individualizing the breast cancer screening recommendations. Genetic risk is already being the basis for changing screening periodicity; for instance, women carrying mutations in the gene BRCA1 have about 4-fold the risk of the average women, which supports performing screen mammography every six months23 and could even sustain recommendations for oophorectomy and/or mastectomy on an individual basis. In a similar way, a two-fold increase in risk over the average could lead to shorten the usual two-year interval for mammography in women scoring high in GRS92 or in the full model; alternatively, women scoring high in GRS92 or in the full model could benefit from initiating screening mammography before being 50; in this way, results from a simulation study24 suggest that women with two-fold to four-fold increased risk for breast cancer could be annually screened starting at age 40 with similar harm-to-benefit ratio than the biennial screening starting at age 50 for the average-risk women. Likewise, women in the first decile have about half the risk of the average women, which could induce to reconsider the balance of harms and benefits they have when being screened and, thus, to discuss the possibility of widening their mammography interval, delaying their starting age or even reconsidering the utility the screening program has for them.The US Preventive Services Task Force recommends biennial breast cancer screening with mammography for all women aged 50 to 74 “who are not at high risk of breast cancer because of a known underlying genetic mutation”25. New sequencing technologies make polygenic panel tests accessible27; applying them to risk assessment would allow genetic counseling to go further than a few highly-penetrant variants, which opens new ways for advising women before breast cancer can develop. Moreover, genetic scores on breast cancer and non-modifiable risk factors seem to be independent from each other; according to our results, stratifying breast cancer risk combining both genetic score (GRS92) and non-modifiable risk factors (NMRFS) improves patient classification by 28%. From a woman’s point of view, it is of no relevance whether her [possible] breast cancer would be caused by a sum of genetic variant or non-modifiable risk factors or a combination of both; in this way, it could be time for moving from genetic counseling towards risk counseling, irrespective of the factors related to that risk. Maas et al.’s model9, which we have here validated in a Spanish population, could be a well-founded instrument for doing it.Genetic counseling has been available since testing for BRCA1 and BRCA2 was developed about 20 years ago9. Second, when constructing GRS92, rs10483813 was excluded because of its almost perfect correlation with rs999737; apart from it, only two other SNPs (rs6678914 and rs4245739) displayed R2 with each other greater than 0.3 (Supplementary Table 28, which makes it difficult to find a relevant impact on breast cancer risk. Fourth, we limited our analysis to validate Maas et al.’s model in our case-control study; therefore, we did not go further than their article13 in exploring interactions between genetic and non-genetic risk factors, which -on the other hand- could be limited given the sample size our study has. Fifth, other risk factor models for BC have been published; for instance, Rosner and Colditz developed29 and refined30 a cumulative risk model based on the Nurses’ Health Study. Rosner – Coldtiz model, however, does not include genetic variants, which is a main point in Maas et al.’s score; therefore, we have not conducted a specific comparison between them.Our study has some limitations. First, genotyping was available only for 65% of our patients; therefore, GRS24 had to be imputed in the remaining 35%. In order to validate this imputation, we carried out a sensitivity analysis on the GRS24 – breast cancer relationship in participants with and without genotyping data (Supplementary Table In conclusion, when validating a breast cancer model, we have found that adding a non-modifiable risk factor score to a polygenic score can largely improve patient’s risk stratification. Its potential utilities would include risk-based screening programs or changes in genetic/risk counseling on breast cancer.Supplementary material


Several frequently used prescription drugs may affect bleeding risk. We investigated use of aspirin, nonsteroidal anti-inflammatory drugs (NSAIDs), selective serotonin reuptake inhibitors (SSRIs), and statins and risk of postoperative red blood cell transfusion in breast cancer patients.n = 22,238) during 2005–2012 and ascertained their use of aspirin, NSAIDs, SSRIs, and statins. For each drug, patients were categorized as users if they filled ≥1 prescription in the 60 days prior to surgery. We calculated the 14-day risk of red blood cell transfusion and relative risks (RRs) with 95% confidence intervals (CIs), comparing users with nonusers for each drug and adjusting for age, cancer stage, and Charlson Comorbidity Index score.Using Danish population-based registries, we identified a cohort of women who underwent surgery for primary breast cancer  women were aspirin users, 1794 (8.0%) were NSAID users, 1110 (4.9%) were SSRI users, and 2053 (9.1%) were statin users. The overall risk of red blood cell transfusion was 1.3%. The 14-day risk of RBC transfusion was 3.5% among aspirin users versus 1.1% among aspirin nonusers , and 1.8% among SSRI users versus 1.2% among SSRI nonusers . Red blood cell transfusion risk was increased among NSAID users, but not in a sensitivity analysis with a 30-day exposure window. Red blood cell transfusion risk was not increased among SSRI and statin users.Primary breast cancer surgery confers a low risk of RBC transfusion. Still, use of aspirin and possibly NSAIDs, but not SSRIs and statins, is associated with increased red blood cell transfusion. This increased risk is not explained by differences in age, stage, or comorbidity level.The online version of this article (doi:10.1186/s13058-017-0926-2) contains supplementary material, which is available to authorized users. Breast cancer is the most common cancer in women and surgery is the primary treatment . BleedinWe have previously studied the association of prescriptions for SSRIs and glucocorticoids and the risk of re-operation due to postsurgical bleeding after breast cancer primary surgery , 15. PreWe conducted this cohort study in Denmark using population-based registries. Denmark’s National Health Service provides tax-funded health care to all Danish residents, including access to hospital care and partial reimbursement for prescribed medications . The uniThe Danish Cancer Registry (DCR) has recorded data on incident cancers since 1943. Voluntary registration became mandatory in 1987. The DCR contains information on the civil registration number, age, gender, cancer diagnosis, method of verification, extent of spread of the tumor at the time of diagnosis, stage, treatment and vital status (date of death and emigration) . The Dann = 2653). In addition, we excluded patients who redeemed ≥ 1 prescription for antithrombotic drugs  codes in Additional file n = 783), as these patients are likely to have an increased risk of bleeding. The final study population included 22,238 women.As shown in Additional file All pharmacies in Denmark use computerized accounting systems to record patients’ CPR-number and type and quantity of medication dispensed . After adding the medication’s ATC system code and defined daily dose, the data is transferred electronically to the National Health Service and Danish National Health Service Prescription Database (DNHSPD) . The DNHWe used the Charlson Comorbidity Index (CCI) to estimate the extent of comorbid disease diagnosed up to 10 years before breast cancer diagnosis. The CCI scores were categorized into three levels: a score of 0 ; a score of 1–2 (moderate comorbidity); and a score of 3 or more (high comorbidity) . We obtaThe Danish Transfusion Database (DTDB) was used to ascertain data on the receipt of a RBC transfusion up to 14 days after surgery . The 14-p < 0.2 was considered statistically significant. All data analyses were performed using SAS version 9.4 .We tabulated characteristics of the study population according to aspirin, NSAID, SSRI, and statin use. We calculated the 14-day risk of RBC transfusion among users and nonusers of each drug and used logistic regression models to estimate crude and adjusted odds ratios (ORs) of receipt of postoperative RBC transfusions, with associated 95% confidence intervals (CIs), controlling for confounding by age, cancer stage, and CCI score. We expected RBC transfusion to be a rare event in breast cancer patients, so the ORs provide an estimate of the relative risks (RRs) . We evalOur study included 22,238 women aged ≥ 35 years with incident breast cancer who underwent surgery for their cancer between 2005 and 2012. Table Overall, 279 (1.3%) women received at least one RBC transfusion within 14 days of surgery. Risk of receiving a RBC transfusion, according to prescriptions redeemed for aspirin, NSAIDs, SSRIs, and statins, are presented in Table Table Primary breast cancer surgery among women with nonmetastatic breast cancer was associated with low risk of RBC transfusion. Our findings suggest that use of aspirin correlates with an increased risk of RBC transfusion. For every 42 women who filled ≥ 1 prescription in the 60 days prior to surgery, one patient underwent RBC transfusion within 14 days after surgery. We also found a potentially higher risk of RBC transfusion among NSAID users, compared with nonusers of these drugs. The risk of RBC transfusion did not differ among SSRI and statin users compared with nonusers.Several factors should be considered when interpreting our results. Study strengths include its population-based registry setting, with availability of complete prescription and follow-up data. This reduced the risk of misclassification due to differential loss to follow-up. The unique CPR number facilitated accurate individual-level linkage across the Danish registry network. Use of prospectively collected data from prescription records, whose completeness approaches 100%, ensured an unbiased assessment of exposure before breast cancer diagnosis and eliminated recall bias . We had We had no information on prescription compliance. Still, noncompliance would result in misclassification of nonusers as users and therefore could not account for any increased risk of RBC transfusion related to drug use. Some women defined as users may not have taken their medication right up to the day of surgery, however, misclassification would bias our findings to the null. Another concern is that our information on prescription drug exposure relied entirely on registration of dispensed prescriptions rather than actual consumption of the pills. However, as patients have to pay a portion of the cost of their redeemed prescriptions, our estimates are likely to reflect actual drug use. Over-the-counter use of aspirin and NSAIDs is an additional concern, because this is not covered by registry data. However, over-the-counter drug use would result in misclassification of users as nonusers and again bias our findings to the null. Research has suggested that nonprescription use of low-dose aspirin accounts for only 8% of all low-dose aspirin use in Denmark . As patiWe used a “prevalent user design”, comparing current users with nonusers of the exposure drugs. This design may have introduced selection bias via a “healthy user effect”. Individuals who adhere to preventive medication may be more likely to see their doctor on a regular basis, stop smoking, exercise, eat a healthy diet, and have more engagement with the health-care system resulting in better outcomes. This has been seen in studies of statin use with large protective effects . To addrWhile we adjusted for comorbidities, we had no information on the severity of conditions included in the CCI. We also lacked information on comorbid conditions diagnosed in primary care, but which may not be sufficiently severe to warrant a hospital diagnosis. These limitations may have resulted in residual confounding. Our findings may also be prone to confounding by indication. Unknown or unmeasured factors related to clinical characteristics or medical conditions may have triggered aspirin use, and at the same time, increased the risk of RBC transfusion. We note that aspirin users had a higher frequency of specific comorbidities including myocardial infarction, congestive heart failure, and diabetes. Cardiovascular disease (CVD) is a major indication for low-dose aspirin use, and the threshold for RBC transfusion is lower in patients with cardiovascular disease . HoweverWe had no data on the extent of postoperative bleeding and preoperative hemoglobin levels in over 40% of patients due to missing data in the DTDB. However, we note similar characteristics of patients with and without information on preoperative hemoglobin levels (data not presented). We could have imputed missing hemoglobin data, but hemoglobin may be an intermediate factor in the association between use of prescription drugs and the risk of postoperative RBC transfusion. We excluded 2653 patients who did not undergo surgery within 1 month of diagnosis. Thus patients who underwent neo-adjuvant chemotherapy were also excluded. Therefore, our findings may not be generalizable to all nonmetastatic breast cancer patients.To the best of our knowledge, this is the first population-based study to investigate the use of common prescription drugs and the risk of postoperative RBC transfusion after primary breast cancer surgery. Studies have shown that aspirin use increases the risk of postoperative bleeding and possibly the need for blood transfusion, and that it depends on extent of surgery in addition to patient factors , 35. StuAlthough our overall analyses suggested an elevated risk of postoperative RBC transfusion among users of NSAIDs, this was not evident in sensitivity analyses. This finding concurs with two previous meta-analyses – one investigating the association of NSAIDs with the risk of postoperative bleeding in patients undergoing plastic surgery , and theWe observed little evidence of an association between SSRI use and postoperative RBC transfusion. This agrees with previous studies on patients undergoing coronary artery bypass surgery (CABG), which reported that use of preoperative SSRIs was not associated with any substantial risk of blood transfusion and bleeding , 42. NonWe did not find an association between statin use and postoperative RBC transfusion. To our knowledge, no previous studies have investigated this association. Our findings agree with those observed among patients with gastrointestinal hemorrhage or bleeding in general, although we note that statin therapy has been associated with increased risk of spontaneous intracerebral hemorrhage –46.Breast cancer surgery is soft tissue surgery, and often characterized by extensive dissection, increasing the risk of blood loss. Although RBC transfusion is rare, it induces substantial morbidity and can delay further cancer-directed treatment , 47. ThePrimary breast cancer surgery confers a low risk of RBC transfusion. Still, use of aspirin, and possibly NSAIDs, but not SSRIs and statins, is associated with increased transfusion risk. The increased risk is not explained by differences in age, cancer stage, or comorbidity level.Additional file 1: Appendix.Diagnostic codes, drug list with ATC codes and surgery codes. (DOCX 16 kb)Additional file 2:Figure S1. Flow diagram. Table S1. Specific comorbid conditions included in the Charlson Comorbidity Index, according to use of selected prescription drugs. Table S2. Risk and crude and adjusted odds ratios for blood transfusion within 7 days of surgery among 22,238 breast cancer patients, according to use of selected prescription drugs. Table S3. Risk and crude and adjusted odds ratios for postoperative blood transfusion within 14 days of surgery among 22,238 breast cancer patients, according to use of selected prescription drugs and with the exposure window defined as 1–30 days before surgery. Table S4. Risk and crude and adjusted odds ratios for postoperative blood transfusion within 14 days of surgery among 22,238 breast cancer patients, according to use of selected prescription drugs and adjusted for selected comorbidities . Table S5. Risk and crude and adjusted odds ratios for postoperative blood transfusion within 14 days of surgery among 21,578 breast cancer patients according to use of selected prescription drugs, with the exposure window defined as 1–30 days before surgery and after excluding patients with anemia [< 12 g/dL (7.4 mmol/L)]. (ZIP 346 kb)Supplementary Figure S1 and Supplementary Tables S1–S5.