In conclusion, interval breast cancer is associated with other tumors and common cancer variants are unlikely to be responsible for this association. These findings could have implications for future screening and prevention programs.Breast cancer (BC) patients diagnosed between two screenings  are more likely than screen-detected patients to carry rare deleterious mutations in cancer genes potentially leading to increased risk for other non-breast cancer (non-BC) tumors. In this study, we include 14,846 women diagnosed with BC of which 1,772 are interval and 13,074 screen-detected. Compared to women with screen-detected cancers, interval breast cancer patients are more likely to have a non-BC tumor before (Odds ratio (OR): 1.43 [1.19–1.70], P = 9.4 x 10 Interval cancer patients are more likely to carry rare gene mutations than screen-detected breast cancer patients. Here, the authors report that interval cancer patients are more likely cancer survivors and are at a greater risk of developing other non-breast tumors. However, in women regularly attending screening, between 20 and 30% of breast cancers are not detected by screening mammography but are diagnosed between screening intervals3. Failure of mammographic-screening protocols to detect cancer can be attributed to many different factors ranging from technical 4 to patient specific 5 to biological .Breast cancer screenings reduces breast cancer mortality by up to 35%6). Patients with interval breast cancer usually present with an average higher histological grade8, larger tumor size9, and more-metastatic local lymphnodes10, a higher proportion of estrogen receptor (ER)8/progesterone receptor (PR) negativity11, a higher frequency of HER2 positivity9, and are more often triple negative12.Compared with breast cancers detected at a screening, interval cancers are characterized by more-aggressive tumor characteristics and poorer prognosis  in most recent studies (summarized in ref. 13 and current hormone replacement therapy (HRT)14 use, a previous false-positive mammographic screening15 as well as family history of breast cancer within first degree family members16 have been implicated in interval cancer risk. Recently, we have shown that interval cancer cases (compared with screen-detected cancers) have a lower breast cancer genetic risk score (GRS), i.e., carry fewer breast cancer risk increasing alleles17. Conversely, rare loss of function mutations in 31 known and suspected breast cancer predisposition genes were found to be more common in interval cancer cases than in screen-detected cases18.Currently, few risk factors for interval breast cancers are known. Apart from high mammographic density19 and may increase the incidence of other tumors in those individuals or in close relatives. In this study, we find that interval cancer patients are more likely than screen-detected patients to be diagnosed with other tumors, either before or after breast cancer diagnosis and that rather rare and not common variants are responsible for the observed the association. Our results thus reveal insights into risk and consequence of interval breast cancer.Loss of function mutations in those genes are also frequently found to predispose individuals for other types of cancerPLR < 0.05, logistic regression, Wald test), are less likely to have a college degree  and are more likely to report the use of hormone replacement therapy  as well as a family history of breast cancer in close relatives  from three cohorts , colorectal  or skin  cancer was significantly associated with increased risk for interval breast cancer  as well as skin  cancer. However, here, the observed association of skin cancer with interval cancer risk was mostly restricted to non-melanoma skin cancers.Next, we investigated which specific type of cancer diagnosis is associated with interval breast cancer compared with screen-detected breast cancer. We only investigated tumors which were observed in at least 30 patients across the whole cohort and found that a diagnosis of most cancer types is more common in interval compared with screen-detected breast cancer Fig. . We founPLR < 0.05, Table PLR < 0.01) than in patients that were diagnosed with a tumor diagnosis after breast cancer .In order to elucidate potential genetic causes for the observed increased non-BC tumor prevalence in interval breast cancer patients, we investigated whether interval cancer patients are more likely to report a family history of other cancers compared with screen-detected patients. When we restricted the analyses to those individuals with any non-BC tumor diagnosis, we found that interval breast cancer patients compared with screen-detected patients are almost twice as likely to report a family history of non-BC tumors : 0.85, 95% CI: 0.76–0.95, PLR < 0.01, Fig. PLR < 0.01, Fig. PLR < 0.05). Similar to the results observed from the actual tumor diagnoses, we found that the risk score for non-melanoma skin cancer was more strongly associated with reduced disease risk than the melanoma-specific risk score. Importantly, most of the other genetic risk scores are also protective for interval compared with screen-detected cancer.The increased prevalence of non-BC tumor diagnoses in interval compared with screen-detected breast cancers can potentially be attributed to common genetic risk factors. In line with prior studiesPLR < 0.05), after adjustment for multiple testing (Bonferroni correction), none of the variants remains statistically significantly associated.Finally, we investigated the association of individual cancer risk variants with interval breast cancer risk  mutations in known or suspected cancer genes30. However, those genetic factors only explain a fraction of the breast cancer cases in ovarian cancer survivors31. Therefore, in order to find such an association, we would have to restrict our analysis to younger cases with multiple cancer diagnoses in their family, which is not possible owing to limited number of individuals that survived ovarian cancer in our data set. Alternatively, removal of both the uterus and the ovaries/fallopian tubes as part of ovarian cancer treatment may change sex hormone levels favorably for interval cancer risk, which is likely linked to hormonal changes owing to its risk association with hormone replacement therapy. The increased number of non-BC tumors in interval cancer cases compared with screen-detected cases can potentially be influenced by several factors. For instance, more-aggressive treatment of interval cancer patients33 could result in increased tumor incidence. However, as noted above, we found an increased occurrence of almost all particular cancers, thus, disqualifying treatment effects from being a major influence on the association35.In addition to the overall increased occurrence of other cancers in interval compared with screen-detected breast cancer patients, we found that virtually all types of cancer are more common in interval compared with screen-detected cancer patients. Although we only found a statistically significant increase of prior lung, skin, and colorectal as well as subsequent skin and colorectal tumors in interval compared with screen-detected cases, the lack of significance for the other cancers can be mainly attributed to the limited number of interval breast cancer patients with other tumor diagnoses in our data set. Interestingly, we did not find an increased number of ovarian cancer survivors in interval compared with screen-detected breast cancer patients, although both, breast and ovarian cancer, have well documented shared genetic risk factors18 and are also often aggregated in families with a (breast) cancer history. Accordingly, we found that family history of non-BC tumors is associated with increased risk for interval compared with screen-detected cancer (and consequently with worse prognosis) in patients with another tumor diagnosis. Importantly, the association of family history of non-BC tumors with increased interval cancer risk was found in patients with either a prior or a subsequent tumor. The association of non-BC family history with interval cancer was markedly stronger in patients with a prior compared with a subsequent non-BC tumor diagnosis. The reduced effect size can potentially be attributed to the increased incidence of secondary non-BC tumors that arise by chance with advanced age and are not linked to a strong rare genetic predisposition but rather shared environmental or behavioral factors, diluting the observed association37.Rare mutations that are associated with a strong increase in risk and a worse prognosis in breast cancer are more frequent in interval cancer patients than in screen-detected patients38 have ER-positive tumors and thus, a large proportion of the previously reported common variants may show a stronger association with ER-positive breast cancers, which are in turn associated with more benign tumors compared with ER-negative breast cancers. In addition, those genome-wide association studies were not designed to identify interval cancer-specific variants and thus the aggregate score would not be expected to reflect such an association. However, here, we show that other cancer risk increasing alleles are also less frequent in interval compared with screen-detected cancer patients, implicating that common cancer risk increasing alleles, in general, result less aggressive (screen-detected) breast cancer. Similar to the notion that the BCAC cohorts are more likely to include less-aggressive breast cancer cases, large case–control cohorts for other cancers may also be enriched for cancer survivors with less severe phenotypes. Therefore, genetic risk scores based on those association results may also predict milder phenotypes and more favorable prognosis. Alternatively, although we excluded variants in known BC loci in order to reduce the influence of known BC risk variation in the current analysis, it is possible that there is pleiotropy with BC present in the other cancer genetic risk scores, which is not captured by known BC risk variants and could potentially drive the association.The finding that both, common breast cancer and non-BC risk increasing alleles, are less frequent in interval compared with screen-detected cancer provides insights into the genetic basis of interval breast cancer. A possible explanation for common breast cancer alleles being associated with screen-detected (less aggressive) tumors may be attributed to the study population in which the common variants were identified. Most breast cancer patient cohorts are likely enriched for screen-detected cancers and breast cancer survivors. In addition, most of the patients in the cohorts which are part of the Breast Cancer Association Consortium (BCAC)In order to expand the number of available studies with interval breast cancer status, this, to our knowledge, is the first study to compute incident interval breast cancer status from questionnaire in the Women’s Health Study. Although the precise screening history and mammographic density estimates were not available in the WHI, compared with both Swedish studies, we observed that the interval cancer cases derived from questionnaire data have a comparable risk factor and tumor characteristic distribution as well as a similar prognosis. The effect sizes were, however, generally weaker in the Women’s Health Initiative (WHI) than in the Swedish cohorts and several factors may explain this observation. First, misclassification of interval cancers in the WHI is possible because we computed incident interval breast cancer status from questionnaire rather than using precise screening history. Second, the WHI did not report density measurements, thus preventing us from excluding intervals cancers missed at screening owing to masking effects of high mammographic density. Third, the increased age of the WHI participants may have resulted in increased mortality and thus lower incidence of other tumors after breast cancer diagnosis. However, these limitations are likely to dilute rather than inflate the observed results, as restricting our main analysis to individuals from LIBRO-1 and KARolinska MAmmography project for risk prediction of breast cancer (KARMA)  yielded consistent results  in order to further characterize tumors developing in women not currently benefiting from regular mammographic screenings. Thus, the current results are not intended to be directly relevant for clinical practice beyond raising awareness that some of these patients and family members are at increased risk for interval breast cancer and other tumors. Although the increased risk for interval cancer in cancer survivors may have potential implications for screening or prevention programs, other risk factors, co-morbidities as well as their potential impact on prognosis need to be considered jointly in disease management.41, which can be attributed to different periods of inclusion and differences in recruitment strategy. However, those differences should not affect the main conclusions of this study. Finally, in this study we have not identified the actual cause of the increased frequency of other tumors in interval compared with screen-detected patients and thus were only able to speculate on potential mechanisms. Among the strengths of this study are its large sample size with a broad range of available risk factor, tumor characteristics, and mortality data, precise ascertainment of other cancers before and after BC diagnosis, the inclusion of breast cancer patients from different study designs (population based and prospective study) as well as the inclusion of both, pre- and post-menopausal women.One of the limitations of our current study is that the types of tumor reported in close family members  in KARMA and WHI differed. In KARMA, participants were asked about ovarian and pancreatic cancer in close relatives while in WHI any type of cancer in close family members was ascertained. Furthermore, the variable was missing from the LIBRO-1 study. In addition, the frequency of estrogen receptor-positive tumors is somewhat higher in our IC cases compared with the frequencies observed in IC patients from other studiesIn conclusion, we show that either a previous or a subsequent non-BC tumor diagnosis was more common in interval compared with screen-detected breast cancer in three independent cohorts and that common cancer risk increasing variants are unlikely to be responsible for this association. The association of family history of other cancers with interval cancer risk suggests that rare penetrant cancer mutations predispose individuals for both, interval breast cancer and other cancers and further large-scale sequencing efforts  are necessary to uncover the underlying cause of the observed associations. Our results thus reveal insights into risk and consequence of interval breast cancer and highlight further genetic differences between interval and screen-detected breast cancer, which could have significant implications for future screening programs and the clinical management of cancer survivors.https://www.ncbi.nlm.nih.gov/gap) under the accession number phs000200.v11.p3, with approval from the data-access committee.Ethical approvals of the KARMA and LIBRO-1 studies were given by the ethical review board at Karolinska Institutet  and written informed consent was obtained from all participants. Study approval for data analysis in the Women’s Health Initiative  was granted by the Regional Ethical Review Board in Stockholm, Sweden. Data from the WHI were retrieved from the Database of Genotypes and Phenotypes , socio-economic factors as well as reproductive events were ascertained from mailed questionnaire. In addition, tumor characteristics were retrieved from the Swedish cancer registry and survival after breast cancer was ascertained from the Swedish causes of death registry, as described before43 with virtually no missing data.KARMA is a mammography screening-based study initiated in January 2011 and includes women who attended mammography screening or clinical mammography at four hospitals in SwedenLIBRO-1 is a breast cancer cohort with 5715 patients who gave informed consent and were diagnosed between 2001 and 2008 in the Stockholm/Gotland area. Similar to the KARMA study, breast cancer risk factors, socio-economic factors, and reproductive events were assessed by questionnaire and tumor characteristics as well as cause and date of death were retrieved from the respective Swedish registries. However, family history of tumors other than breast cancer was not available in LIBRO-1.45, with an average follow-up time of 17.20 (S.D. 4.17) years.In order to expand the number of patients for our analysis and to investigate our findings in a large collection of incident breast cancer patients, we included breast cancer patients from the Women’s Health Initiative. The WHI is a prospective national health study of >160,000 post-menopausal women aged 50–79 recruited between 1993 and 1998 at 40 clinical sites in the United StatesThe study consists of two major parts: (1) the observational study without intervention, and (2) the clinical trial arm which investigated the role of hormone replacement therapy, dietary modification, and vitamin supplementation on different outcomes such as cancer, cardiovascular disease, and osteoporosis. We identified 11,031 women with incident BC, whose BC status was ascertained by medical review (and excluded BC cases that were ascertained by death certificate). All demographic and socio-economic variables, breast cancer risk factors, and tumor characteristics were extracted from the respective aggregate outcome or questionnaire tables as deposited in dbGAP. The breast cancer patients in the WHI were comprised of different ethnicities: 87.7% were self-reported Caucasians, 6.8% were Blacks or African-Americans, 2.0% were Asians and 2.2% were Hispanics/Latinos, whereas 0.9% reported to belong to another ethnic group. We did not observe significant a difference in interval cancer incidence between ethnicities and thus analyzed all BC cases in WHI jointly while adjusting the statistical analyses for self-reported ethnicity.46. In brief, all women in the Stockholm area aged 50–69 have been invited to participate in regular mammographic screenings every 24 months since 1989, whereas women aged 40–49 were included from mid-2005 and screened at 18-month intervals. A BC diagnosed after a negative mammographic screening before the next scheduled screening visit was considered interval breast cancer. Conversely, cancers diagnosed at a regularly scheduled mammographic screening were deemed screen-detected cancer. Patients who did not attend the screening mammogram prior to breast cancer diagnosis  or with missing prior screening information were excluded from our data sets. We also determined the mammographic density of the prior screening mammogram  with STRATUS, a machine-learning algorithm capable of computing density from both, analog and digital mammograms47. Patients without data on mammographic density  were excluded from the KARMA and LIBRO-1 study. Furthermore, we also excluded interval cancer patients (but not screen-detected patients) with medium or high mammographic density (percent mammographic density > 25), as the tumor might have been masked by the high dense tissue and thus not been detected at the prior screening mammogram. Accordingly, this collection of interval breast cancer cases should represent ‘true’ interval cancer cases not confounded by missed screen-detected tumors.In both, the KARMA and LIBRO-1 study, interval cancer status was ascertained as recently describedIn KARMA, out of 3862 BC patients, we excluded 287 patients who did not attend their scheduled screening mammogram prior to the BC diagnosis, 680 patients with no history of screening prior to BC diagnosis and 216 patients with missing mammographic density estimates. We also excluded 427 out of 822 (52%) interval cancer patients with medium or high mammographic density. In total, 2252 BC patients  were included in the present study (Table N = 370) as well as those who did not report attending a mammographic screening more than 2 years prior to breast cancer diagnosis. (N = 259). In total, 10,672 post-menopausal breast cancer patients from both, the observational and clinical trial arms were included in the current study  and compared those screening dates with the date of breast cancer diagnosis. In order to account for imprecision in determining the accurate screening date, we defined a breast cancer diagnosed within 3 months of an estimated screening date as screen-detected cancer, whereas breast cancers diagnosed between three and 24 months after the last screening were considered interval cancers. We also excluded patients without information on their mammographic screening history (6. The average time between reported screenings was 581.69 days (S.D. 350.10), in line with a mixture of women screened annually and biennially. The average time between prior mammogram and interval cancer diagnosis was 243.89 days (S.D. 163.32). Thus, 82% of the identified interval cancer cases were discovered within one year after the last screening in WHI compared with 42 and 33% in KARMA and LIBRO-1, respectively. However, previous research has shown that there is little difference in tumor characteristics between interval breast tumors detected within the first or second year of the screening regimes12. In general, the women in the WHI were diligent in answering their yearly questionnaire with an average time between yearly questionnaires of 383.92 days (S.D. 77.03)Utilizing our approach, we found that there were 87.26 breast cancer diagnoses  per 10,000 self-reported mammographic screens, in line with numbers typically observed in screened populations48 and thus be incorrectly detected as interval cancer. In addition, we calculated the occurrence of specific (common) cancers in women: lung (ICD10 code: C34), cervical (C53), endometrial (C54), colorectal (C18 and C20), skin (C43 and C44), non-melanoma skin cancer (C44), melanoma (C43), ovarian (C56), thyroid (C73), central nervous system (C69–72), pancreas (C25), bladder (C67), or kidney cancer (C64).For both, KARMA and LIBRO-1 participants, extensive registry data are available. Thus, for breast cancer patients in those studies, we extracted the number of (prior and subsequent) non-BC tumor diagnoses from the Swedish cancer registry. We only counted (solid) cancer diagnoses within 15 years prior to breast cancer diagnosis and available ICD10 code. We did not include women diagnosed with prior non-BC tumor diagnoses less than 1.5 years (540 days) before breast cancer diagnosis, as the breast cancer might be diagnosed in the diagnostic workup of the previous cancerAll 10,672 post-menopausal BC patients in WHI were incident breast cancer cases and we counted the number of  other, solid non-BC tumor diagnoses between study enrollment and the first breast cancer diagnosis and those that occurred after the first BC diagnosis. In the WHI, only melanoma diagnoses were available and thus skin cancer only included melanoma cases and no non-melanoma skin cancers. Similarly, we documented the diagnosis of the same specific tumors as above and did not consider women with non-BC tumor diagnoses that occurred within 1.5 years before BC diagnosis.38 for 398 individuals genotyped on the iCOGS array and in ref. 49 for 1292 women genotyped on the OncoArray platform. Similarly, 1784 women in LIBRO-1 (as part of the pKARMA study) were genotyped on the iCOGS array which was first described in ref. 38. Quality control and imputation of missing and un-genotyped variants was performed by the BCAC. In brief, missing genotypes were imputed to the 1000 Genomes Phase 3 reference haplotypes using ShapeIt50 and IMPUTE51 and variants with an imputation quality (R2) >  0.8 and a minor allele frequency > 0.01 were retained.Patients in KARMA and LIBRO-1 were genotyped through the BCAC on a custom Ilumina iSelect genotyping array as part of the Collaborative Oncological Gene-environment Study (iCOGS) or on the OncoArray. The genotyping data used for this study from the KARMA project was first described in ref. phs000746) for 971 out of 9103 self-identified Caucasian BC cases52 in the minimac53 dosage/matrix format and converted to variant call format (vcf) in R54. The genotypes were coded as the expected genotype dosage (DS) of each non-reference (ALT) allele. We excluded variants that were poorly imputed (R2 < 0.3) or which showed a significant deviation from Hardy–Weinberg–Equilibrium . We also extracted the un-imputed genotypes of an additional 614 BC cases genotyped on the Metabochip as part of the PAGE WHI study55 (accession: phs000227) and imputed missing genotypes using the 1000 Genomes Phase 3 (version 5) reference haplotypes. First, we excluded very rare and poorly performing genotyped variants , minor allele frequency < 0.001, missing rate per variant > 30%) and poorly genotyped samples . Next, the phase of each variant was determined with ShapeIt251 and missing genotypes were imputed with minimac356 with standard settings. We excluded poorly imputed variants with the same criteria as above. Finally, we merged all imputed genotype sets with bcftools57 to generate a single genotype file for all genotyped samples.Patients from the WHI were genotyped on different genotyping platforms as part of various sub-studies and were jointly imputed to the 1000 Genomes Phase 3 reference by the WHI investigators. Imputed genotype data were retrieved from dbGAP (accession: http://www.well.ox.ac.uk/~gav/qctool) for all studies.In order to account for potential population stratification and mixed ethnicities, we computed the first three principal components from the relatedness matrix derived from imputed genotypes with QCTOOL , as recently reported by BCAC  weighted sum of cancer risk increasing alleles, normalized to the average effect size of all variants61  with the genome-wide significant lead variant and an additional 500,000 base-pairs added up- and downstream. In particular, we computed a non-BC genetic risk score for colorectal cancer (35 variants), endometrial cancer (5 variants), lung cancer (78 variants), skin cancer (57 variants), melanoma (18 variants), non-melanoma skin cancer (39), ovarian cancer (29 variants), bladder cancer (11 variants), thyroid cancer (11 variants), central nervous systems (36 variants), renal cancer (15 variants), and pancreatic cancer (14 variants). We did not compute a genetic score for cervical cancer as there were less than five genome-wide significant variants in Europeans reported as of 25 June, 2018. For the all non-BC cancer genetic risk score, we computed the sum of the individual genetic risk scores, effectively generating a compound score of 304 variants associated with 12 different cancers, excluding variations in known breast cancer susceptibility loci  retrieved from the GWAS catalog54. We used logistic regression, adjusted for age at diagnosis to identify factors that were significantly different between screen-detected and interval cancers. In addition, in the joint/pooled analyses, we additionally adjusted for study. The reported P values of association from the logistic regression models were based on a Wald test and are denoted as PLR. All genetic analyses were restricted to individuals from European descent (Caucasians) and additionally adjusted for the first three principal components of ancestry computed from the imputed genotypes.All statistical analyses were performed in Rsurvival package62. We plotted the results with the ggadjustedcurves function (from the package survminer63 implemented in R).In order to estimate the effect of interval cancer diagnosis on overall  and/or breast cancer-specific survival, we fit Cox proportional hazard models (adjusted for age at diagnosis) as implemented in the Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Transparent Peer Review FileReporting Summary


TP53 gene and breast cancer risk in Iranian patients. Therefore, acase-control study was designed. A meta-analysis was also carried out to provide evidence of association between thisvariation and breast cancer in Iran, based on all available published data. Mutations of TP53 as a tumor suppressor gene are frequently observed in different types of cancer. Acodon 72 polymorphism located on exon 4 with two alleles encoding either Proline (CCC) or Arginine (CGC) has beenindicated as a common variation in association with cancers. Controversial results have been reported regarding theassociation of allelic polymorphism of codon 72 of TP53 polymorphism and the risk of breast cancer in Iran. Statistical analysis was performed using SPSS 16.0 andMetaGenyo. In this case-control study, blood sample of 622 participants, including 308 breast cancercases and 314 controls were collected. Genotyping for rs1042522 was conducted by Allele Specific polymerase chainreaction (AS-PCR). In order to set a meta-analysis study, PubMed, Scopus and ISI Web of Knowledge and Persiandatabases were searched to explore relevant studies, published up to September 2018, containing information on All retrieved available data as well as the results of our current study were consisted of 1965 breast cancercases and 1999 healthy controls. No significant difference was observed in allele frequencies between groups (P=0.90)in our study. The cumulative results did not also show any association between rs1042522 and breast cancer risk onthe dominant (P=0.61) and recessive (P=0.89) models.These findings cannot support contribution of rs1042522 polymorphism to breast cancer risk in an Iranianpopulation. Future larger studies may help confirm this finding with a greater power. . Breast malignancy is among the major types of cancerand the universal cause of cancer death in women. Theincidence of breast cancer in western populations issignificantly higher than other populations. However, 50%of new cases and approximately 60% of deaths caused bybreast cancer occur in developing countries . BreastBased on epidemiological studies, a number of factorshave been identified associating with increased risk ofbreast malignancies. These factors are not necessarilycauses of breast cancer and they can be subcategorizedin both genetic and environmental factors, increasingliability of the breast cancer. Association studies, wherebythe relations of certain markers with the disease areinvestigated prominently in case-control designs, are ofcrucial importance to dissect the genetic basis of commonmultifactorial disorders, such as breast cancer. Based onthis, several candidate genes have been analyzed so far incase-control studies.One of the highest involved genetic factors in the riskof breast cancer is the tumor suppressor gene, TP53.The corresponding protein, P53, has a role in cell cycleregulation including cell growth and division, apoptosis,DNA repair and the maintenance of genome stability andits mutations have been commonly observed in differenttypes of cancer . DysfuncTP53 codon 72 polymorphism andsusceptibility to breast cancer has been considered inseveral regions of Iran. These case-control studies didnot indicate consistent results, likely due to the smallsample sizes with limited power. A meta-analysis alongwith combining the different results and small data, canachieve a reasonable level of significance and an increasein power of results. In the present study, we examinedassociation of rs1042522 and breast cancer susceptibilityin the North-East of Iran and a meta-analysis wasperformed to quantitatively assess effect of the TP53codon 72 polymorphism on risk of breast cancer in Iran.Association of the In this case-control study, 308 breast cancer cases and314 healthy controls with no sign of breast cancer or historyof malignant breast disease participated in this associationstudy. The important demographics and histopathologicaldata were obtained from a questionnaire. The EthicsCommittee of the Mashhad University of Medical Scienceapproved this study  and all of the participants signed thewritten informed consent.The salting out method was used to extract DNA fromperipheral blood samples. Genotyping was performed usingAllele specific polymerase chain reaction (AS-PCR) methods. We usedArginine-based (G) allele:F: 5´-TCCCCCTTGCCGTCCCAA-3´R: 5´-CTGGTGCAGGGGCCACGC-3´Proline-based (C) allele:F: 5´-GCCAGAGGCTGCTCCCCC-3´R: 5´-CGTGCAAGTCACAGACTT-3´PCR was done in a final volume of 10 μl reaction for eachallele containing: Taq DNA Polymerase 2x Master MixRED , 1 μl genomic DNA (200-300ng), 1 μl of each primer  and adequate DNase free water . Amplification temperature stages were performedfor 5 minutes at 95˚C and then 35 cycles including 30seconds at 94˚C, 30 seconds at 63˚C, 30 seconds at 72˚C,followed by 7 minutes at 72˚C in a Veriti 96 well PCRThermal Cycler (Thermo Fisher Scientific).TP53 codon 72 polymorphism and breastcancer risk before September 2018, were gathered bysearching Scopus, PubMed and ISI Web of Knowledgedatabases. The Persian articles and conference abstractswere also explored by searching SID, Iranmedex,Magiran, Medlib and Google. The references ofretrieved articles were also investigated to discoverother related studies. The terms of "TP53" and "geneticvariant", "genetic variation" or "polymorphism" and"breast cancer", "breast carcinoma", "breast tumour"or "breast tumor" and "Iran" or "Iranian" were used tosearch for the articles of interest. Case-control studieswere selected for the analysis.The original publications, reporting associationbetween A meta-analysis was designed according to PRISMAguidelines . FeatureChi-square test and logistic regression models wereperformed to find association between histopathologicalor demographic criteria and genotypes of rs1042522.2 statistic wasevaluated by analysis of the genotype frequenciesin the controls. Association was measured initiallyusing both random-effect and fixed-effect models.However, since the random-effect method proposesheterogeneity, this method was considered as the mainapproach. The strength of association between TP53codon 72 polymorphism and risk of breast cancer wasmeasured by odds ratios (ORs) with 95% confidenceintervals (CIs).In the meta-analysis study, frequency of two alleleswas calculated for the case and control groups ineach experiment. HWE test using the XThe risk for the genotypes GC and CC was comparedto the GG homozygote, as the wild-type genotype.Furthermore, according to the dominant and recessivemodels, the risk of Arg-carriers (GC+GG) versus CCgenotype and Pro-carriers (CC+GC) versus GG genotypewere evaluated, respectively.2 index.Probability of publication bias was checked by the funnelplot and Egger’s test for all genetic models. Randomeffects model was used to analyze the data.Heterogeneity was evaluated by the Q-test and IMeta-analysis was carried out using MetaGenyo . All othA total of 308 breast cancer women, out of 622 samples, and314 healthy controls were enrolled in this study. The averageage of case and control groups were 47.80 ± 10.90 and 44.15 ±12.07 years, respectively, with a significant difference betweenthe groups (P<0.001). Furthermore, menopausal status wasconsidered as peri- and pre-menopausal, compared to postmenopausalindividuals. The control group was younger thancases, and therefore most of the patients typically belonged tothe post-menopausal group. The difference between groupswas significant (P<0.001).Body mass index (BMI), as a continuous and categoricalvariable, was compared between these groups. MeanBMI was higher in the patient cases than in controls and asignificant difference was observed (P<0.001). Moreover,by categorizing patients, it was distinguished that a largenumber of cases were included in BMI ≥25 category.The history of lactation (P<0.01), history of other cancers(P<0.001) and family history of cancer (P<0.001) werealso significantly different between patients and healthycontrols. Genotypic distribution of the rs1042522 in ourstudy conforms HWE (P>0.05). The C allele (Pro)frequency, as the minor allele was 24% in the cases,compared to 23.7% in the controls. According to thisdata, no association was found between case and controlgroups for allele frequency (P=0.90). The most frequentgenotype for rs1042522 was GG (Arg/Arg) in both case(60.7%) and control (60.2%) groups. Genotype frequencydid not show any significant difference between these twogroups (P>0.05). Moreover, dominant and recessivemodels did not indicate any association with the riskof breast cancer. Results have been shown in Adjustment for confounding factors including age, BMI,history of cancer, family history of cancer and history oflactation did not change the results.133 articles were identified from different databasesby two authors, individually. After matching the data,discrepancies were reanalyzed by the third author. 42articles were similar between databases. Therefore, 91articles were assessed for eligibility. 73 studies were notrelated to the subject. Furthermore, two records werenot designed as the case-control study. After excludingduplicated, unrelated and improperly designed papers, 16case-control studies, concerning the association betweenTP53 codon 72 polymorphism and breast cancer, wereincluded in the meta-analysis. The selection process hasbeen demonstrated in Figure 1.Overall the data included 1965 breast cancer subjectsand 1999 healthy people. Diversity in the sample size,ranged from 42 to 314 individuals, was found betweenstudies. Moreover, genotyping methods were differentbetween projects. Nine studies used the AS-PCR methodand seven studies were performed by the PCR-restrictionfragment length polymorphism (PCR-RFLP) method.Although the main source of DNA template was blood inmany studies, one study had been performed on normaland cancerous tissues and five projects had used tissuesamples for patient’s genotyping and blood samples forhealthy groups.TP53 codon 72 genotypes in the control group.The results have been shown in Frequency of the 72 Pro allele varied in the controlparticipants, from 30 to 60%, depending on the geographicalregion. Apart from the four studies, HWE was confirmed forfrequency of Pooled allele frequencies did not significantly differbetween Iranian patients and healthy controls. Resultsconfirmed a lack of association between the TP53 codon72 and susceptibility to breast cancer in all geneticsmodels. This finding did not vary after removing studieswith no HWE data.Publication bias was evaluated by the analysis ofBegg’s funnel plots and Egger’s test for all geneticmodels. All plots indicated some evidence of publicationbias, however, this finding was not significant . According to the P value ofheterogeneity, we found high incidence of heterogeneitybetween the studies in different genetic models (P<0.01).Results have been shown in The mortality rate in comparison with the rate of breastcancer is higher in developing rather than developedcountries, due to the early detection of diseases usinggenetic biomarkers in the latter countries. SNPs are agood example of these biomarkers. The TP53 codon 72polymorphism (rs1042522) has been reported to have anassociation with the susceptibility to cancer in the differentpopulations. Due to the lack of association studies withsufficient sample sizes and strong conclusions to assesscorrelation between rs1042522 and risk of breast cancerdevelopment, we conducted the case-control study on 622breast cancer patients and controls in the northeast of Iran.A previous meta-analysis study without any ethnicityrestriction showed association of TP53 codon 72polymorphism with breast cancer risk in the recessivemodel. This study only confirmed a significant differencein the allelic model of an Asian population . HoweverOur pooled data from 17 case-control studies, meeting theinclusion criteria, confirmed lack of association betweenrisk of breast cancer and TP53 codon 72 polymorphismin Iran. The heterogeneity amongst the studies hasbeen pointed out in the analysis. Ethnicity, minor allelefrequency, sample size, genotyping methods, source ofDNA and disease phenotypes may be regarded as thesource of such heterogeneity. However, we acknowledgethat we focused on studies considering germline variantsas risk factors, there are some studies which may haveinvestigated the somatic variants to provide a catalogueof tumoral genetic variations , 16.www.celljournal.org).Our results indicated that pooled control samplesfollowed HWE. Four studies failed to show HWE,however, when they were excluded from the analysis, theoverall results were not significantly changed (data hasbeen shown in the Table S1) . This finding was also seenin recessive and dominant models. Statistical evidencefrom other meta-analysis studies have indicated thispolymorphism may have a potential effect on breast and ovarThe pooled data indicated that allele frequency difference between cases and controls was about 3%. Therefore, it isproposed to evaluate the association between TP53 codon72 polymorphism and risk of breast cancer with a powerof 80%; in other words 4129 individuals is proposed foreach group in future studies. In our study the overallpower was calculated as 50% which may not support theevidence of this association. Designing proper studieswith adequate sample sizes will provide more valuableevidence for this association.Our results showed that TP53 codon 72 polymorphismmay not influence the overall risk of breast cancer in anIranian population. By that means, there is no associationbetween TP53 codon 72 polymorphism and breast cancerrisk in the Iranian population of this meta-analysis. Severalincluded studies had limited sample size and they wereunderpowered. To assess this association more precisely,well-designed case-control studies with adequate samplesizes are still necessary.Molecular classification, as well as evaluating the effectof other polymorphisms and environmental factors, suchas alcohol consumption and tobacco smoke, should alsobe considered. Characteristics of different individuals,including premenopausal or postmenopausal status,metabolic index, family history, epistasis and clinicalcourse are also important.


In this study, we report the findings of the genetic architecture of BRCAX with novel and redefined candidate loci and their potential impacts on preventive strategy. We performed a genome-wide association study involving 1,469 BRCAX cases from the Korean Hereditary Breast Cancer study, and high-risk breast cancer cases  from the Breast Cancer Association Consortium. We also evaluated the previously reported susceptibility loci for their roles in the high-risk breast cancers. We have identified three novel loci  associated with BRCAX, and replicated previously reported SNPs (24 of 92) and moderate/high-penetrance (seven of 23) genes for Korean BRCAX. For the novel candidate loci, evidence supported their roles in regulatory function. We estimated that the common low-penetrance loci might explain a substantial part of high-risk breast cancer (39.4% for Koreans and 24.0% for Europeans). Our study findings suggest that common genetic markers with lower penetrance constitute a part of susceptibility to high-risk breast cancers, with potential implications for a more comprehensive genetic screening test. The National Cancer Screening Program in Korea provides mammography tests for screening breast cancer for women aged 40–69 years3. The effectiveness of this screening program, however, is controversial mainly because the validity of mammography-based test is lower for Asian women than for Caucasians8 About 4.8% and 11.1% of breast cancers were diagnosed before age 40 and 45, respectively, in the United States between 2005 and 2014. The proportions of these early-onset cases almost tripled for Korean women during the same period (13.9% and 29.8%), although overall incidence rate is less than a half of that for Western women10. Early-onset breast cancer cases are generally more likely to have higher pathologic grade and to be receptor-negative tumors14. Given the low predictive value of mammography for Asian women, ultrasonographic breast examination might be introduced to improve the validity but at greater cost. An effective genetic screening test, if available, might be able to identify women at high-risk of breast cancers for personalized prevention programs. So far, to our knowledge, the genetic etiology of high-risk breast cancers is understudied particularly for Asian women with more burdens from high-risk breast cancers.Breast cancer is the most common female cancer worldwide, and its incidence rate is increasing rapidly in Asian populations including South KoreaBRCA1 and BRCA2 (hereafter referred to as BRCA1/2), the most important genetic susceptibility markers, account for up to 15% and 30% of the familial recurrence risk (FRR) of overall and high-risk breast cancers15. Clinical practice guidelines are well-established for BRCA1/2 mutation carriers16. Conversely, lack of proper knowledge has left the BRCA1/2 mutation non-carriers’ genetic counseling largely unattended, although these non-carriers with strong family history show similar clinical presentations predictive of BRCA1/2 mutation carriers (“BRCAX” cases), with four-fold or greater lifetime risk of breast cancer than the general population18. In Korea, breast cancer cases with early-onset (age < 40) or other indications suggesting a strong genetic burden are entitled to BRCA1/2 mutation testing covered by Korean National Health Insurance. The test has found clinically significant BRCA1/2 mutations for only 15.7% of eligible patients 19, to make the understanding of “BRCAX genes” a priority. Several studies have been conducted to elucidate the genetic architecture of BRCAX cancers22. Studies do not have replicated the novel loci from each other, suggesting that susceptibility loci for BRCAX might be population-specific, at least partly. Asian women might have specific genetic susceptibility for the high-risk breast cancers, but, to our knowledge, no studies have been conducted toward this end.Mutations in BRCAX genes involving 1,469 Korean female breast cancer cases who were eligible for the BRCA1/2 test but negative for causal mutations. Then, we replicated our findings using the Breast Cancer Association Consortium (BCAC) data. We also estimated the role of previously reported susceptibility loci on the risk of BRCAX occurrence. Finally, we evaluated the impacts and estimated risk of those associated markers on high-risk breast cancers for Asians and Europeans.We conducted a genome-wide search for BRCAX cases and 5,979 controls were selected with 3,378,933 markers  comprised the largest proportion of the case composite (70.6%), followed by having a family history of breast cancer (42.9%).GC) was 1.09 and p-values were corrected for the inflation factor. Figure p-values < 10−5. Two SNPs reached genome-wide significance level in the first phase analysis, and both were reported before: rs9383936  on the intron of CCDC170 (near ESR1) and rs4784227  on the intron of TOX3. Three previously reported loci  and nine new loci also comprised the initial 15 loci. One variant on CDKN2B-AS1 (rs1011970) was previously associated with breast cancer risk for European descendants24, but our study found another variant in the proximal regions of the CDKN2B-AS1 gene, rs78545330. These two SNPs constituted separate haplotype blocks (r2 = 0.026 for East Asians), and we considered rs78545330 as independent.From the genome-wide association study (GWAS), the genomic inflation factor  including two previously known  and four novel susceptibility loci. Among four novel loci, rs11154838, located on 6q23.3 (intron of PDE7B), showed associations at genome-wide significance level . Other two novel candidate loci , while replicated in Asian high-risk cases at nominal p-value, did not reach genome-wide significance in the meta-analysis . The other novel variant, rs4969001 on SLC39A11, was associated in the opposite direction; thus, it was not considered for further analysis. We summarize findings of these 15 candidate loci in Table p-values = 0.34, 0.24 and 0.50).The replication set consisted of 1,482 cases  and 3,612 control women without family history of breast cancer. We compared the characteristics and composite of high-risk breast cancer cases of different populations . It is noteworthy that markers at SIAH2, FGFR2, and PGAM1P5/NTN4 showed an increased level of significance in subgroup analyses despite smaller sample sizes. None of the variants identified from the GWAS were differentially associated according to HER2 receptor status or being TNBC. More details about the receptor-status-wise analyses were described in Supplementary Table When we conducted a meta-analysis by the receptor-status, four loci of the initial 15 candidate loci from the GWAS showed receptor-status specific associations with even smaller sample sizes. We only focused on findings where smaller subgroup resulted in the stronger association, to reduce the influence of sample size variation. rs60538652, on PDE7B, were annotated for regulatory elements . rs78545330 on the intron of CDKN2B-AS1 and correlated markers showed strong evidence of regulatory function from multiple tissues including breast tissues, while rs1011970 (previously reported SNP) was not annotated for regulatory elements. East Asians have a risk allele of rs78545330  three times more frequently than Europeans (0.08). We presented the haplotype structures of this region between Asian and Europeans , indicating that rs78545330 is a novel candidate variant. When we examined the genetic regions harboring candidate variant near UBL3, the region consisted of two independent LD blocks, and rs278050 and correlated SNPs showed enhancer-related histone marks in several tissues including breasts and mammary tissues. Also, rs73444211 in the other LD block near UBL3 participated in diverse regulatory functions.Three novel candidate regions were visualized using LD plots and regional association plots , but not with other tissues. rs78545330 on CDKN2B showed strong associations with gene expressions in multiple tissues. The risk allele substantially decreases the expression of CDKN2B, a tumor suppressor gene . rs278050 near UBL3 also altered cis-gene regulation in several tissues.Results from eQTL analyses for three candidate loci also showed evidence of associations with gene-expression levels of different tissues  of 92 loci from the GWAS catalog showed meaningful associations  of each replicated marker was largest for Korean BRCAX cases (29.8%), followed by European high-risk cases (24.0%) and Asian high-risk cases (10.6%). Three novel candidate variants accounted for additional 9.6% and 3.0% FRR for Koreans and Asians. It is noteworthy that three replicated variants on FGFR2, TOX3, and ESR1 have been reported in previous searches for BRCAX genes22. These variants were also associated with overall breast cancer risk in multiple studies25. Other candidate loci reported from previous BRCAX studies were not replicated in this study22. Finally, among the breast cancer predisposition genes with moderate/high-penetrance26, seven genes  were replicated for Korean BRCAX  and Asians (OR: from 1.60 to 1.71). Also, we estimated the genetic risk of BRCAX according to genotypes of “shared between populations ”. The estimated increases in the risk of BRCAX or high-risk breast cancers from the variants on FGFR2, TOX3, and ESR1 were quite similar across populations  “Asian-specific BRCAX” markers according to “shared between population” (FGFR2/TOX3/ESR1) genotype status, those with multiple risk alleles of FGFR2/TOX3/ESR1 showed steeper increases in risk for breast cancers, compared with those in none or one allele groups  for each population Fig. . Predictups Fig. .Figure 2BRCAX breast cancer development. PDE7B (phosphodiesterase 7B) encodes a cyclic adenosine monophosphate (cAMP)-specific phosphodiesterase and CDKN2B (cyclin-dependent kinase 4 inhibitor B) is a known tumor suppressor gene27. The risk allele of the top hit (rs78545330) on CDKN2B-AS1 showed strong evidence of reducing gene expression level of this tumor suppressor gene.From a genome-wide search, three candidate loci were newly identified with suggestive evidence of functional involvement. The three novel loci identified by this study might tag the closely-located functional variants based on the downstream analyses. The marker itself, or markers within the LD block, have marks for regulatory elements, suggesting these SNPs themselves or variants tagged by the SNPs might be associated with the pathophysiology of PDE7B) and rs78545330 (CDKN2B-AS1) showed stronger level of significance but with smaller effect sizes , but rs278050 (UBL3) was not associated (p = 0.2). For European cases, rs78545330 was replicated for overall cases only , but other two novel candidate loci did not show significant associations. One possible explanation for this population-specific manner of association is that genetic susceptibility to BRCAX might comprise multiple genetic loci which are only partly shared between populations. The newly identified candidate loci might be Asian-specific and also more associated with the early-onset type of high-risk cancers. When we estimated OR according to GRS, the addition of novel variants to known markers increased the risk for Korean and Asian, but not for European high-risk breast cancers.When we examined the association of newly identified markers with overall Asian cases including late-onset and sporadic breast cancers of BCAC, both rs11154838 , compared with OR = 1.17 for overall breast cancers. Also, rs10069690 on the intron of TERT was replicated for BRCAX with higher risk estimate (OR = 1.24) than the OR (1.10) from the original studies. These findings are in line with the findings from the colorectal cancer study28: some SNPs show stronger associations with high-risk cases with larger impacts. Our findings suggest that role of some variants might not be same between in high-risk and in general breast cancers, although they are associated with both types. It might be logical to infer that a substantial part of the genetic risk of high-risk breast cancers comprises common variants overlapping with general types of breast cancers but at augmented risks.It is interesting that some of the markers shared between high-risk and general breast cancers showed augmented impacts on the FGFR2 (rs2912774), TOX3 (rs4784227), and ESR1 (rs9383936) deserve a special remark. All three previous GWAS for BRCAX replicated FGFR2, and multiple studies replicated TOX3 and ESR122. Our study also replicated these three variants for high-risk breast cancers in all population data. When we compared the estimated risk of these three variants, the estimated risk was quite similar between three populations , partly because of its strict case definitions  might not be attributed to so-called “winner’s curse” bias, an inflated impact estimation in the discovery set itself, because the Korean BRCAX served as a replication set. Three novel loci accounted for 9.6% of the FRR for Korean BRCAX. The impact of three novel loci on Korean BRCAX cases, however, might be inflated for having used the discovery set on itself. Novel candidate loci added 3.0% to Asian, but none to European high-risk cancers. When the %FRR of three novel loci was estimated from all types of Asian breast cancers, the estimate decreased to 0.75%, suggesting that these loci mainly account for the genetic risk of high-risk breast cancers.We also evaluated the impact of known and novel susceptibility loci for high-risk breast cancers. The estimated Σ%FRR from replicated markers was 29.8% for Korean et al.’s study replicated 44 of 78 previously reported loci involving 11,760 overall breast cancer cases of East Asians29. When we performed an analysis including late-onset breast cancer cases , we could have replicated 24 loci . Whether the difference is attributable to the power or the differences in the genetic susceptibility between high-risk and late-onset breast cancers is not conclusive. The six additional loci, however, did not show any clues of possible association with high-risk breast cancers (p > 0.3), and we believe the differences in the genetic architecture between sporadic/low-risk and familial/high-risk breast cancers might explain a part of the discrepancy. Korean data might have shown compatible Σ%FRR of high-risk breast cancers with Europeans, partly due to their size and stricter definition for cases. We believe it might be reasonable to interpret the findings that common variants explain as much Σ%FRR of high-risk cancers as BRCA1/2 mutations. We also examined the associations and their impacts on breast cancer predisposition genes with moderate/high-penetrance. Although imputed markers covered most of the known variants, our approach might have materially underestimated their impacts, and cannot replace the findings from the sequence-based approach.Sum of FRR (Σ%FRR) from common variants are considerably compatible between populations: 29.8–39.4% for Koreans, and 24.0% for Europeans, and 10.6–13.6% for other Asians. One reason why Σ%FRR is smaller for Asians might be explained their heterogenic ethnic compositions . If associated markers have not been identical between ethnic groups, some variants more specifically associated with certain populations might be missed. Moreover, replication was conducted using 92 markers, most of them were reported from studies of European ancestry. Also, a smaller sample size could be another reason for fewer replicated markers in Asians. Wen BRCA/BRCAX, if implemented, might contribute to detecting high-risk women for providing personalized screening program such as ultrasonography. Additionally, it will provide information for consulting the relatives of BRCAX cases. Findings from our study support the necessity and possibility of developing a new genetic screening for BRCAX. However, our study alone does not provide other important information necessary for effective genetic screening, in particular, estimates of penetrance and predictive values of the test. Estimating sensitivity and specificity of BRCAX-associated markers are required first in larger data. Also, this study considered the genetic markers associated with the occurrence of high-risk breast cancer, and their potential implications for detecting high-risk populations. To provide evidence that support a new genetic screening program, further data about a reduced mortality rate from breast cancer might be required, but these are beyond scope of ours.A proper genetic screening program for BRCAX studies. First, the number of test-proven BRCA-negative cases was not modest , and more than 11,300 high-risk breast cancer cases were included, larger than previous studies22. Second, all the BRCA1/2 mutation status was more accurately identified by standard sequencing analysis of each BRCA1 and BRCA2 exon. Third, we selected high-risk cases with genetic burdens using the similar indications as BRCA1/2 mutation test: age of onset, family history, history of other malignancy, and bilateral cancers. Applying these strict indications might have enabled us to select cases with high genetic burdens. Fourth, we attempted to re-evaluate the findings from our own GWAS study and previous studies jointly including both common variants in the GWAS catalog, and moderate/high-penetrance genes. This approach might have provided more comprehensive views on the genetic architecture of BRCAX and high-risk breast cancers. Similarly, this approach enabled us to estimate the impacts of associated markers on the occurrence of BRCAX for different populations and context. The impact estimates from our study will confer implications for genetic screening test of high-risk women. Finally, to our knowledge, this is the first Asian study focused on BRCAX cases, where epidemiologic features suggest unique susceptibility might exist.We believe that this study confers some advantages over previous BRCA1/2 mutation status was scarce, and not available for most studies of the BCAC. Applying the criteria for high-risk cancers only which were not confirmed to be BRCA1/2-negative might have decreased the power of detecting associated markers. Nevertheless, this limitation might not have influenced toward increasing false-positive findings in the study. Second, Asian cases in the BCAC data were limited so that the criteria for early-onset age were relaxed to the age 45, while the same criteria for having family history was applied. These relaxed criteria in the replication data might have reduced the power to detect susceptibility loci more specific to the BRCAX cases. When we estimated the associations for replicated and novel loci using different age criteria for Asian high-risk cases (938 cases for replication set when age 40 was used), direction and strength of associations were largely same with slightly less statistical power: the number of replicated markers and Σ%FRR of them declined from 14 (10.6%) to eight (9.0%). However, ORs of three novel markers increased and Σ%FRR increased from 3.0% to 3.8% accordingly. Third, even with the sample size of 1,469 confirmed BRCAX cases, our study has limitations in power to newly detect relatively rare alleles (0.01 < MAF < 0.05) because of the scarcity of high-risk cancer cases. We estimated that the relative risk should exceed 2.5 to be detected as novel with our sample size30. Fourth, although this not being the main purpose of our study, when we attempted to replicate the “moderate/high-penetrance” variants, the effects might have been underestimated because some of the variants were not exactly identified in our imputed genotype data. We believe the estimated impacts from these genes should have been underestimated. The estimated impact of the common variants, however, is not likely affected by this limitations, because the frequency of the common risk alleles is independent of the rare alleles. Fifth, the replication analysis of previously reported loci might have inflated as the replication set could include data of the discovery set. Finally, as the mean age was different by affection status in the discovery set , the age was not used as a covariate, because we did not observe any difference in allele frequencies between controls (older) and those in the population with same age with cases (younger)  who met the following criteria were included in this study:(i)BRCA1/2 gene mutation Breast cancer patients who were tested to be free of clinically significant AND at least one of the following(ii)Diagnosed as breast cancer before or at age 40; OR having one or more relative(s) with breast/ovarian cancer; OR bilateral cases; OR previously diagnosed as other cancer.We conducted a case-control GWAS. Breast cancer patients were selected from the Korean Hereditary Breast Cancer (KOHBRA) study. Breast cancer patients with one of following five conditions and family members of 32 and in Supplementary Notes. Genetically unrelated (estimated identity by descent <0.125) Korean women without family history of breast or ovarian cancer were included. All participants gave informed written/oral consent, and this study was approved by the Institutional Review Board of Asan Medical Center, Seoul, Republic of Korea. This study was conducted in accordance with the World Medical Association Declaration of Helsinki.We used data from Korean Genome Epidemiology Study (KoGES) as control. The profiles of KoGES consortium was described elsewherep-value < 10−9, or poor cluster plots. Genotype data were phased and imputed using SHAPEIT and IMPUTE2 software respectively, and East Asian data from the 1000 Genomes Project (1 kG) Phase 3 were used as reference36. From imputed markers, SNPs with info score greater than 0.95 were included. We genotyped cases and controls with four different platforms of dense genome-wide SNP arrays and conducted standard quality control and imputation process. And the batch effect detected through association analysis of control data genotyped from different platforms was removed (p < 0.01). For post-imputation quality control, markers with high missing rate >10%, minor allele frequency (MAF)<1%, evidence for deviation from Hardy-Weinberg equilibrium at p-value < 10−6 were excluded. More detailed information was described in Supplementary Notes.Cases were genotyped using Illumina OncoArray-500K Beadchip at the University of Southern California Epigenome Center, CA, USA. Peripheral blood samples (20 ml) were collected at baseline, and they were divided, treated and stored within 24 hours of sampling. We excluded individuals with low call rate (<95%) and SNPs with genotype call rate <95%, minor allele frequency (MAF)<1%, evidence for deviation from Hardy-Weinberg equilibrium at BRCA1/2 mutation testing in cases, 22 coding exons of BRCA1 and 26 coding exons of BRCA2 were scanned through fluorescence-based conformation sensitive gel electrophoresis (F-CSGE) and denaturing high-performance liquid chromatography (DHPLC). For a subset of PCR products with aberrant patterns, direct sequencing was performed on an ABI3100 or ABI3700  or a MegaBACE500  genetic analyzer. In this study, the definition of a genetic mutation is restricted to the protein-truncating mutation and the missense mutation, which are known to be associated with the disease. This study also includes the participants with a clinically unverified mutation in BRCA1/2 genes. More detailed information about BRCA1/2 mutation analysis has been described before37. Since this study also includes the cases with unverified mutation on BRCA1/2, we examined whether non-pathogenic variants on these genes are more frequent in the cases compared to controls at p-value of 0.05. And we searched them at the ClinVar database.Genomic DNA was extracted from the participants’ peripheral blood samples. For 38. For visualization of the results, Manhattan plot and quantile-quantile (QQ) plot were generated using R package ‘qqman’39. Principal component analysis (PCA) was conducted using EIGENSTRAT, and the first three principal components were used for adjustment40. Since most cases were early-onset breast cancer and cohorts for control recruited adults with a limited age range, mean age was much younger in cases than controls. Thus, allele frequencies of significant markers were presented by affection status and age groups, rather than adjusting for age.GWAS was carried out using logistic regression by trends test of PLINK version 1.0741 and in Supplementary Notes. Since most of the cases in the BCAC lack BRCA1/2 mutation information, we included high-risk breast cancers defined as: breast cancer cases diagnosed before or at age 45 or cases with family history of breast cancer in first degree relatives. For controls, women without family history of breast cancer were included among the BCAC control data.For the replication of the initial candidate loci, imputed data of Asian population from the BCAC were used. The overall description about the BCAC was previously reportedp-value for heterogeneity exceeds 0.05). Also, we conducted subgroup analyses by three receptor status . P-values for heterogeneity by receptor status were calculated using a Cochran’s Q test, and a p-value of 0.05 was used to determine heterogeneity.For newly identified loci, we conducted meta-analyses using fixed or random effect model according to evidence of heterogeneity  plots and regional association plots. LD plots were created using control data with ‘LDheatmap’ package in R45. Novel SNPs and correlated markers with r2 > 0.8 from East Asian population were explored. Results of expression quantitative trait loci (eQTL) analyses were found from the Genotype Tissue Expression (GTEx) portal46. Associations between novel markers and expression of the nearest gene of each SNP for all available tissues and associations between these markers and nearby genes with a window size of 1 Mb for the tissue showing the most significant associations were retrieved.Functional information of novel loci was annotated using Encyclopedia of DNA elements (ENCODE) project and HaploReg version 4.1BRCAX cases and how the significance of associations and their effect sizes differ. First, we selected markers associated with breast cancer at p-value < 10−5 from the NHGRI-EBI GWAS catalog25. We also examined the candidate loci from three previous BRCA1/2-negative cancer GWAS22. Finally, the variants on genes associated with increased risk of breast or ovarian cancer listed in the guideline for genetic screening of high-risk breast cancers  or breast cancer predisposition genes used in a recent article were evaluated in this study26. Seventeen genes listed on the NCCN guideline were ATM, CDH1, CHEK2, MSH2, MLH1, MSH6, PMS2, EPCAM, NBN, NF1, PALB2, PTEN, RAD51C, RAD51D, STK11, and TP53, except for BRCA1/2; six additional genes suggested from the study by Wong et al. were BARD1, BMPR1A, FANCC, SMAD4, VHL, and XRCC2. Variants in gene regions ± 10 kB were tested for association. In this analysis, the markers with MAF < 1%, which were excluded during quality control, were also included. Independent (r2 > 0.3) SNPs with p-value < 0.01 were included for impact estimation.We estimated associations between previously reported breast cancer susceptibility loci and BRCAX cases, we estimated the FRRs from Korean BRCAX women, Asian and European high-risk women using the BCAC data . The fraction of FRR (%FRR) explained by markers was calculated as ln(λ)/ln(λ0) where lambda (λ) is the FRR to a relative of an affected individual and λ0 is the overall FRR. λ is calculated as (pr2 × q)/(pr + q)2 where p is the frequency of the risk allele in the general population, q equals 1 − p, and r is the per-allele OR. And λ0 is assumed to be 1.8 as reported from the previous article48. For comparison of FRR explained by each SNP, we applied the same allele frequencies from the East Asian or European data from the 1 kG Phase 3 and different odds ratios (ORs) from each analysis.To assess the impact of reported loci in the occurrence of BRCAX by the different set of markers for different populations .To compare predictive power among different groups, we estimated ORs according to GRSs. GRSs were calculated as a weighted sum of the number of risk alleles, in which the weight was taken as the natural log of the OR from association analyses. ORs by quintiles of GRS were estimated using the first quintile as the reference. We compared the predictive power of BRCAX in multiple studies22. GRSs were calculated using markers replicated for Korean BRCAX among previously reported markers in breast cancer from the GWAS catalog (“BRCAX-known”), and with novel candidate markers from the GWAS added to replicated markers (“BRCAX-known + novel”).We re-evaluated the ORs from polygenic GRS, by stratifying subjects according to their risk alleles associated with Supplementary Materials


Ptrend=0.043). These results indicated that, in the GC gene, genetic mutations might be related to breast cancer susceptibility in Chinese women.The group-specific component (GC) gene, one of the vitamin D pathway genes, seems to play an important role in cancer development. A population-based breast cancer study including 818 cases and 935 controls in a Chinese population was carried out to evaluate the potential associations of four polymorphisms  in the GC gene with risk of breast cancer. We detected three SNPs with statistically significant effects on breast cancer development after adjusting for age, menopausal status, body mass index (BMI), family history of breast cancer, income, waist circumference, and education . Stratified analysis found that when an individual had a waist circumference <80 cm, rs17467825, rs2298850, and rs3755967 could markedly reduce the risk of breast cancer. Significant interactions between polymorphisms of rs2298850 and rs3755967 and waist circumference were also observed for breast cancer risk. Combined analysis revealed a significant association among the allele numbers of protective effects with decreased breast cancer risk ( Breast cancer is the common therioma in women around the world, with an estimation of 2.1 million new cases and 626,679 deaths [2D by 1α-hydroxylase in the kidney [A growing number of epidemiological researches have reported that vitamin D could lower the breast cancer risk in humans –5. Vitame kidney . Vitamine kidney . The fune kidney . DBP is e kidney . GC1F, Ge kidney , which me kidney –13.Previous studies on the GC gene polymorphism mainly focused on rs7041 and rs4588 and have demonstrated their associations with the risk of different cancers , 15. ElkThe Ethics Committee of Jiangsu Provincial Center for Disease Control and Prevention approved this work. All study subjects were voluntary and have completed written informed consent.The subjects including 1,410 cases and 1,072 cancer-free controls were recruited between 2013 and 2014 in the largest Wuxi Maternal and Child Health Hospital. All breast cancer cases were newly diagnosed. The exclusion criteria were as follows: (1) a metastatic carcinoma from other sites, (2) a recent history of severe acute diseases, and (3) failed to complete questionnaires. As a result, 818 cases were included. Controls resided in the same areas and were frequency matched with cases by age (within 5 years old); a total of 935 controls were finally included in the present analysis. Standard questionnaires were used to interview each participant to acquire information about demographic data, family history of breast cancer, reproductive and menstrual history, and relevant factors. Several anthropometric measurements, such as height, weight, and waist circumference, were collected during interview stages. Each participant provided approximately 5 ml venous blood sample for subsequent laboratory measurements.Total DNA was isolated from the blood sample with the use of QIAamp DNA Blood Mini Kit  and stored in a −80°C refrigerator. The Sequenom MassARRAY Platform was applied to perform genotyping without knowing the status of subjects. Around 5% of samples were randomly selected for quality control and regenotyped by this platform, and the two results were consistent. Haploview 4.2 was used to examine the linkage disequilibrium of four SNPs. The detailed information of four SNPs in the GC gene is displayed in t-test (for continuous variables) or Welch's t-test  and χ2 test . Logistic regression was applied to evaluate the associations between four SNPs in GC gene and risk of breast cancer. Odds ratios (ORs) and 95% confidence intervals (CIs) were presented with adjusting for age, menopausal status, body mass index (BMI), family history of breast cancer, income, waist circumference, and education. The effect heterogeneity between corresponding subgroups was measured by using the χ2-based Q-test. Three additive interaction measures [y is the case or control logit status, g represents the SNP, e indicates the environment factor: waist circumference, b0 is the constant, b1 and b2 are main effects for g and e, respectively, and b3 is the multiplicative interaction term. P < 0.05 was as a significant level with two-sided among all statistical tests. Combined analysis was applicable to evaluate the cumulative effect of protective alleles from significant SNPs . Statistical analyses were carried out using R software (version: 3.5.0).Differences between cases and controls in the demographic characteristics and selected variables were tested using student's measures  were calmeasures :(1)y=b0+The demographic characteristics of 818 cases and 935 controls are shown in r2 > 0.8 . Among the subgroups of age, menopause, and family history, there were no statistically significant associations observed.Further subgroup analyses stratified by age, menopausal status, family history, and waist circumference were conducted, and results are shown in P for multiplicative interaction = 0.031 and 0.039, respectively). However, no interaction was detected between rs17467825 and waist circumference on risk of breast cancer (P for multiplicative interaction = 0.057). For rs2298850, compared with the waist circumference ≥80 cm with the GG genotype, a significant decreased risk of breast cancer was observed for those with GC or CC genotypes with the waist circumference <80 cm, whereas a nonsignificant increased breast cancer risk was observed for those with the CC genotype with the waist circumference ≥80 cm. The additive interactions between waist circumferences and the SNPs  were also examined. However, significant additive interactions were not measured between these three SNPs and waist circumferences . Compared with subjects carrying “0” protective alleles, individuals with “1–2” alleles of protective effects could possess a nonsignificant 10%  reduced cancer risk, whereas “3–6” protective alleles could decrease a 19%  breast cancer risk.Previous results were mainly focused on the investigation of the association of single SNP with breast cancer risk. Combined analysis was also performed to estimate accumulative effects of these polymorphisms. In In this work, we estimated the correlations between four genetic variants in GC gene  and risk of breast cancer in Chinese population and found that heterozygotes of rs17467825, rs2298850, and rs3755967 but not rs16847024 were in significant relation to the breast cancer risk. Also, in the dominant model, three SNPs  were observed with an approximately 20% decreased breast cancer risk in our study population. Combined analysis also implied that subjects carrying more alleles of the protective effect could decrease the breast carcinogenesis risk.2D, has an effect not only on cell proliferation and apoptosis but also on the estrogen signal pathway [2D, which regulated cell proliferation and apoptosis [Epidemiological studies supported that 25(OH)D was related with breast cancer risk , 22. The pathway . When th pathway . In addipoptosis . Furtherpoptosis .2D, could inhibit breast cancer cell growth by reducing synthesis of estrogen.A breast cancer research in Shanghai showed that about 23% of women involved in study were vitamin D deficiency and 48.4% were vitamin D insufficiency . One stuApart from transport function, DBP may also take part in the anticancer process through non-vitamin D-related functions, including roles in chemotaxis and macrophage activation . DBP is In genome-wide association studies, GC rs2282679 was reported to correlate with serum 25(OH)D levels . FurtherA cohort including 28,965 postmenopausal women found that a larger waist circumference was connected to higher breast cancer risk, especially the risk was elevated 13% when the waist circumference increased by 10 cm . We alsoIt should be noted that there were several limitations in this study. First, we did not achieve the data about serum 25(OH)D concentration from the study population, so we could not evaluate the influence of 25(OH)D concentration on risk of breast cancer among our study population. Second, only the GC gene was evaluated, and we could not exclude the fact that other genetic variants of vitamin D pathway genes might also have impacts on risk of breast cancer. Finally, this study was carried out among Chinese women. The associations between breast cancer susceptibility and GC genetic polymorphisms in other ethnic populations require further investigation.In summary, our study identified some genetic variants of the GC gene associated with risk of breast cancer in Chinese women. Furthermore, we, for the first time, found that there were potential significant interactions between GC gene polymorphisms and waist circumference in susceptibility to breast cancer, thus supporting the insight that genetic variants in conjunction with environmental factors are able to influence individual's cancer risk.


The data presented herein represents the raw genotype data of a recently conducted larger study which investigated the association of single nucleotide polymorphisms (SNPs) in breast cancer related genes with the risk and clinicopathological profiles of sporadic breast cancer among Sri Lankan women. A case–control study design was adopted to conduct SNP marker disease association testing in an existing blood resource obtained from a cohort of Sri Lankan postmenopausal women with clinically phenotyped sporadic breast cancer and healthy postmenopausal women. The list of haplotype-tagging SNP markers for genotyping was selected based on information available in the published literature and use of bioinformatics tools and databases. Genotyping of 57 selected SNPs in 36 breast cancer related genes was performed using the iPLEX Sequenom Mass-Array platform.The raw genotype data for the 57 SNPs genotyped in 350 women with breast cancer and 350 healthy women are presented in this article. This data might be relevant to other researchers involved in investigating the role of SNPs in breast cancer related genes with the risk of sporadic breast cancer in South Asian populations. XRCC2:rs3218550 and PHB:rs6917 were associated with increased risk. CDH1:rs13689 and ATM:rs1801516 were found to be protective [AKT1, BRCA1, BRCA2, CCND1, CDH1 and NQO2 genes were associated with different clinicopathological profiles of breast cancer [XRCC2:rs3218550 and PHB:rs6917 were further investigated using the dual-luciferase assays [Breast cancer accounts for approximately 23% of all cancers in females and 12% of all cancers among Sri Lankans. Notably, 62.1% of breast cancers are diagnosed in Sri Lankan women aged above 50 years [e assays .The raw genotype data might be relevant to other researchers involved in investigating the association of SNPs in breast cancer related genes with sporadic breast cancer risk in South Asian populations.® Genomic DNA purification kit and quantified using the Quantus fluorometer with QuantiFluor® double stranded DNA system according to the manufacturer’s protocol . Each sample was diluted in distilled water and normalized to a DNA concentration of 10.0 ng/μl.DNA was extracted using the Promega WizardGenotyping was done using the Agena Bioscience MassArray technology on a Compact Spectrometer, iPLEX GOLD chemistry  . Primers2, 500 µM deoxynucleotide triphosphates (dNTPs), 0.1 µM each PCR primer, 0.5 U of HotStarTaq enzyme, and 1 µl DNA. The thermal cycling conditions included a first denaturation step at 95 °C for 2 min, followed by 45 cycles of denaturation at 95 °C for 30 s, annealing at 56 °C for 30 s, and extension at 72 °C for 1 min, with a final extension step at 72 °C for 5 min. To neutralize unincorporated dNTPs, PCR products were treated with 0.5 U shrimp alkaline phosphatase by incubation at 37 °C for 40 min, followed by enzyme inactivation by heating at 85 °C for 5 min. By adding 2 µl of an iPLEX Gold extension reaction cocktail to the purified PCR products, the extension reaction was carried out in a final volume of 9 µl containing 0.222 × iPLEX buffer, 1 × iPLEX termination mix, 1 × iPLEX enzyme, and the SBE primer mix of extension primers. The iPLEX extension reaction was performed as follows: initial denaturation step at 94 °C for 30 s, followed by 40 cycles of a denaturation step at 94 °C for 5 s, 5 cycles of annealing at 52 °C for 5 s and extension at 80 °C for 5 s and a final extension step at 72 °C for 3 min. After desalting of the products by using SpectroCLEAN resins following the manufacturer’s protocol, cleaned extension products were dispensed onto a 384 SpectroCHIP array using an RS1000 Nanodispenser, and the array was introduced into a MassARRAY Compact mass spectrometer. Spectra were acquired using SpectroAcquire software and data analysis, including automated allele calling, was done using MassARRAY Typer software, version 4.0.5. Fifty-seven SNPs were successfully genotyped, and the average SNP call rate was 99.87% in both cases and controls.Samples were amplified from a 5 µl final PCR volume composed of 1 × PCR buffer, 2 mM MgClThe raw genotype data for the 57 SNPs genotyped in the 350 cases and 350 controls are shown in data files 1 and 2 respectively and the primer sequences are included in data file 3 in Table The selected set of SNPs may not give as comprehensive a view of genetic variation as genomic sequencing does.It is possible that SNPs which show a null association either do not modify the susceptibility to breast cancer or their effects are minimal and can be detected only with larger study samples.These SNPs are mainly low-penetrant alleles that probably exert their effects through complex gene–gene and/or gene-environment interactions. Such interactions were not investigated in this study.