In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts—an increased risk of developing an interval breast cancer.Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case–control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first‐degree family history by questionnaire, measured body mass index (BMI) and calculated age‐adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density , more so after adjusting for age and BMI , and for family history . When the latter two factors and their multiplicative interactions with age‐adjusted breast tissue aging ( What's new?Breast cancers that are detected between regular screening intervals are more aggressive and have poorer outcomes than screen‐detected tumors but no valid risk model exists to predict “interval” breast cancers. Here the authors combined breast density with other breast cancer risk factors such as body mass index, family history and a novel measure of age‐adjusted hormonal exposure. Applying this to more than 150 women with interval breast cancer, they find that risk discrimination can be doubled with the combination of factors as compared with just using breast density alone, thus improving risk predictions and clinical recommendations for breast screening procedures. AUCarea under the receiver operating characteristic curveBICBayesian Information CriterionBMIbody mass indexCCcraniocaudalCIconfidence interval.LLlog‐likelihoodOPERAodds per adjusted standard deviationInterval breast cancers are diagnosed after a negative screen but before the next recommended screen. Interval cancers are important for many reasons, not the least because women diagnosed with interval cancers have poor outcomes.A risk model for interval cancers would be important for breast cancer control. Women could be triaged according to their risk for tailored screening,et al.Dense breasts have become an important issue in the US with most states passing laws that require women with dense breasts to be notified and encouraged to discuss supplemental screening and concerns with their health care provider. More than 25 million US women aged 40–74 years (43%) have dense breasts defined by the Breast Imaging Reporting and Data System's (BI‐RADS) categories c and d.In this article, we define breast density as the proportion of the breast image that is mammographically dense, defined by the light or bright areas on a mammogram. Women with greater breast density are more likely to be diagnosed with interval cancers,The association of breast density with interval cancers could be due to, or amplified by, a combination of factors related to risk and masking. For example, having a mother or sister with breast cancer has perhaps an even stronger risk association with interval breast cancers than it does with screen‐detected cancers.et al.et al.To better inform women of the consequences of their breast density, we aimed to develop a risk model for interval breast cancer based on breast density and other breast cancer risk factors by conducting a matched case–control study nested within a prospective cohort. We also introduced a novel measure of hormonal exposure, age‐adjusted breast tissue aging. Pike We used the Melbourne Collaborative Cohort Study of adult residents of Melbourne, Australia aged 40–69 years when recruited between 1990 and 1994.All mammograms were screen‐film and were digitized using an Array 2905 Laser Film Digitizer at 12‐bit depth. Breast density was measured from the mammograms taken closest to baseline. For interval breast cancer patients, mammograms of the unaffected  breast taken at or before diagnosis were used.We used the CUMULUS computer‐assisted thresholding method to measure conventionally defined breast density.2. Family history of breast cancer was defined as having at least one first‐degree blood relative with breast cancer based on a questionnaire filled out by women at their time of screening by BSV.Subjects completed interviewer‐administered questionnaires at baseline that asked about age at menarche, number of full‐term live births, age at each birth, breastfeeding, menopausal status, age at menopause, hormonal replacement therapy use, oral contraceptive use and alcohol consumption. Height and weight were measured using standard protocols and used to calculate BMI = weight (kg)/height (m)et al.et al.,t, taken to the power of 4.5, fits the age‐specific incidence curve for breast cancer. We therefore defined breast tissue aging to be the area under the breast tissue age curve up to the woman's age at baseline questionnaire; see After Pike For both breast density and breast tissue aging, a cube‐root function gave the optimal Box–Cox power transformation to normality. Transformed breast density was adjusted for age and BMI, and transformed breast tissue aging was adjusted for age, using linear regression. The standardized adjusted breast density and standardized adjusted breast tissue aging were calculated by estimating the mean of the transformed measures for the controls (only) to establish the population norms. We then used these population norms to derive the residuals, for both the controls and the cases, and then divided these control‐based residuals by their respective standard deviations for the controls.We used conditional logistic regression to estimate the odds ratio (OR) for a binary factor and, for each continuous variable, the change in odds per standard deviation of that variable after it had been adjusted, as above (OPERA).2.5.Risk discrimination was assessed from considering log(OPERA), the risk gradient on the log‐odds scale, which is the difference between case patients and control subjects in the means of their standardized and normalized risk factor adjusted for covariates. The equivalent interquartile risk ratio is approximately OPERAp = 0.06 and p < 0.0001, respectively). Having at least one first‐degree relative with breast cancer was more common for case patients than control subjects (p = 0.00006).Table 2 = 43.10). There was very strong evidence that the latter gave an improved fit compared with the two other measures (both ΔBIC≥15). The OR for the upper versus lower 50% was 3.2 (95% CI 2.1–4.8). Compared with the lowest quartile, the ORs for the second, third and fourth quartiles were 1.9 (95% CI 1.0–3.8), 3.4 (95% CI 1.8–6.5) and 5.9 (95% CI 3.1–11.2), respectively.Figure p < 0.0001), and there were no associations with either age at mammogram (a matching variable) or BMI . Table p < 0.0001), and there was very strong evidence (2ΔLL = 16) that this gave a better fit than unadjusted breast density.Table Table p = 0.04, 0.07 and 0.003, respectively; see Models 1 and 2).Table p < 0.001) and both interaction terms were significant .Table p = 0.003) and greater than 0.66 (95% CI 0.62–0.71) when using unadjusted breast density as a continuum (p = 0.0001).Figure The AUCs of 0.73, 0.70 and 0.66 above correspond to log(OPERA)s of 0.86, 0.73 and 0.60, respectively, which are equivalent to IQRRs of 9.0, 6.2 and 4.5. Therefore, compared with using only unadjusted breast density, on the log(OPERA) scale the risk gradient increased by 43% from using adjusted breast density as a continuum, including family history, and allowing for interactions between age‐adjusted breast tissue aging and both adjusted breast density and family history. In terms of AUC compared to 0.5, the corresponding increase was from 0.66 to 0.73 (44%). The increase from considering dense breast as a binary construct was from 0.62 to 0.73 (190%).i) potentially detectable at screening but missed  or (ii) nonexistent, too small, or otherwise not detected at screening but grew rapidly thereafter. Possibility (i) is more likely the greater the proportion of the breast covered by light or bright areas.ii) is more likely the greater a woman's underlying risk of breast cancer risk, especially that of more aggressive disease.We found that the ability to predict interval breast cancers might be almost doubled by considering more than unadjusted breast density dichotomized about its median, similar to how dense breasts are defined using BI‐RADS. Interval cancers are either  risk deserves attention.Fifth, a novel breast cancer risk factor which combines absolute risk information from age, age at puberty, number and timing of any live births and age at menopause (if achieved) to represent the relative risk due to exposure to estrogen and progesterone for women of the same age,et al.Our finding of a more than multiplicative combination of breast tissue aging with breast density and family history, is not the only example of such interactions for interval cancers. Li Figure vs. c–d is similar to our classification based on the median for controls. The increased risk associated with having dense breasts, defined as being in the upper half of the breast density distribution, was about threefold. Given that the incidence of interval breast cancers is about 1–2 per 1,000 women undergoing 2 yearly screening,The BI‐RADS classification of dense breast based on a–b Our model almost doubled the risk gradient and could be used to derive more optimal cut‐offs for management strategies based on risk stratification. This empirical information could be useful for screening programs to conduct economic modeling and cost–benefit analyses. Decisions about how to use the model to inform future screening and use of other screening modalities should be based on absolute risk, not only on relative risk, so a model for the age‐specific incidence of interval cancer needs to be created specifically for the population being screened.Strengths of our study include the use of a novel risk measure based on Pike's model of breast tissue aging and fitting interactions. We did not presume the same risk factors, with same risk gradients, applied to interval cancers as they do to breast cancer overall and found evidence that contradicted this assumption.Weaknesses include the relatively small sample size so our risk discrimination is likely to be overestimated and there is a need for independent replication. Our findings were based on film mammograms and might not apply to digital mammograms. They were also based on only one mammogram; considering a woman's breast density history could improve risk prediction. We based our model on information collected at or around the baseline, on average 5–6 years prior to diagnosis,Risk prediction could also be substantially improved. We recently created Cirrus, a novel automated measure of breast cancer risk based on features of a mammogram, which has an OPERA of 1.7.In summary, our findings illustrate the potential for improving mammography screening. Women at increased risk of serious breast cancers that occur within regular screening intervals could be more accurately identified than by using BI‐RADS alone. Information routinely collected by some mammography services could be used to better classify women in terms of risk. Triaging women for breast screening might be substantially improved by considering breast density as a continuum, adjusting it for BMI and using other measures related to risk. This would also give women with dense breasts, and their physicians, more information about the consequences of having dense breasts—an increased risk of developing an interval breast cancer, a critically important outcome for screening programs and women.Appendix S1: Supporting informationClick here for additional data file.


GATAD2A) associated with risks of breast cancer and schizophrenia. The epidemiological bidirectional association between breast cancer and schizophrenia may partly be explained by the genetic overlap between the two phenotypes and, hence, shared biological mechanisms.An association between schizophrenia and subsequent breast cancer has been suggested; however the risk of schizophrenia following a breast cancer is unknown. Moreover, the driving forces of the link are largely unclear. Here, we report the phenotypic and genetic positive associations of schizophrenia with breast cancer and vice versa, based on a Swedish population-based cohort and GWAS data from international consortia. We observe a genetic correlation of 0.14 (95% CI 0.09–0.19) and identify a shared locus at 19p13 ( Schizophrenia has been associated with increased risk of breast cancer, yet the risk of schizophrenia following breast cancer is unclear. Here, the authors show a bidirectional association between breast cancer and schizophrenia in Sweden and a shared genetic contribution to both diseases. Some evidence suggests that vulnerability to stress4, particularly indicated by the presence of psychiatric disorders including schizophrenia6, may be associated with increased risk of subsequent breast cancer. Conversely, recent studies by us and others support that patients with breast cancer are at higher risk of a range of psychiatric disorders subsequently, including depression, anxiety, and posttraumatic stress disorder8. The longitudinal bidirectional association raises the question of shared risk factors between breast cancer and psychiatric disorders.Breast cancer is the most commonly diagnosed cancer among women worldwide9. Affecting up to 1% of the population, schizophrenia is one of the most severe and costliest psychiatric disorders10. Several risk factors for breast cancer are commonly present in schizophrenia, including behavioral risk factors such as smoking, alcohol use, excess bodyweight, and physical inactivity16, as well as prenatal- and early-life exposure to stress18. Despite lower attendance to mammography screening in patients with schizophrenia19, the results from a recent meta-analysis suggest that schizophrenia is associated with increased risk of breast cancer6. Meanwhile, the subsequent risk of schizophrenia among breast cancer patients remains unclear. If confirmed, such longitudinal bidirectional association would lend further support to the shared risk factors between both diseases rather than one causing the other. Although recent studies suggest that genetic markers of schizophrenia are associated with higher risk of breast cancer21, a potential genetic overlap between the diseases has not been thoroughly assessed.A hypothesized link between schizophrenia and breast cancer dates several decades backHere we illustrate the longitudinal association of schizophrenia with subsequent risk of breast cancer and vice versa, by utilizing a Swedish population-based cohort. Leveraging data from international consortia, we further find evidence for a shared genetic basis between breast cancer and schizophrenia and identify a shared locus. Together with the pathways enriched in the functional annotation, our findings may shed light on the potential biological mechanisms that drive the susceptibility to both diseases.N = 94,626) diagnosed in Sweden during 1990–2009 . For each cancer case, at the time of cancer diagnosis, we randomly selected 30 cancer-free women as controls  who were individually matched on birth year. By cross-linkage to the Patient Register, we identified any first-ever inpatient or outpatient diagnoses of schizophrenia before or after the date of cancer diagnosis for cases and matching date for controls.Using nationwide population and health registers in Sweden, we identified all women with invasive breast cancer  and 11,036 (0.39%) inpatient diagnoses of schizophrenia were observed among women who later did vs. did not develop breast cancer, respectively. The nested case–control analysis , by design, is equivalent to the prospective analysis using full cohortN = 11,947). During the follow-up , we noted 100 (0.11%) and 2584 (0.09%) first-ever inpatient diagnoses of schizophrenia among women with and without breast cancer, respectively. We found that women with invasive breast cancer were at 31% increased risk of hospitalization for schizophrenia . An increased risk was also indicated when we included outpatient visits for schizophrenia and/or additionally controlling for parity and previous diagnoses of psychiatric disorders and obesity . We excluded women with inpatient or outpatient diagnosis of schizophrenia prior to the matching N = 1,947. DuTo shed light on the early-onset schizophrenia, we restricted the analysis to women aged 18–44 years and suggested a bidirectional, positive association between early-onset schizophrenia and breast cancer, although the numbers were small and estimates were not statistically significant  score regression. Genetic correlation is the proportion of variance that two traits share due to common genetic factors, as an estimate of the degree of pleiotropy or genetic overlap.23  and schizophrenia24  from the consortia’s meta-analyzed genome-wide association studies (GWASs) in populations of European ancestry, we estimated the genetic correlation between both traits at 0.14 , suggesting that around one-seventh of the genetic contribution to these two phenotypes is shared. A significant, albeit small, LD score regression intercept  suggests a minor sample overlap between both consortia, which is largely inevitable.Using summary statistics for breast cancerP value thresholds , the association between PRS for schizophrenia and breast cancer yielded the lowest P value , explaining 0.37% of the variance. At PT < 0.4492, the association between PRS for breast cancer and schizophrenia yielded the lowest P value , explaining 1.04% of the variance. Notably, the association between the breast cancer PRS and schizophrenia appears when large numbers of SNPs weakly associated with breast cancer are included in the analysis, in support of the polygenic architecture in schizophrenia.Based on the same GWAS summary statistics, we next calculated polygenic risk scores (PRSs) to further assess the genetic association between breast cancer and schizophrenia. Single-nucleotide polymorphisms (SNPs) in the extended major histocompatibility complex (MHC) region (chr6: 25–34 Mb) were removed due to the long-range LD and unusual genetic architecture. The PRS for schizophrenia was significantly associated with the risk of breast cancer and vice versa across most 25, whereas sex hormones may possibly play a role in schizophrenia development, particularly the differential risk between women and men26. To address potential differences in the genetic overlap between different subgroups of breast cancer and schizophrenia, we performed separate analyses among estrogen receptor (ER)-positive and ER-negative breast cancer as well as female and male schizophrenia. Using the GWAS summary statistics from consortia, similar findings were noted in the associations between schizophrenia and both ER-positive and ER-negative breast cancer . After correcting for multiple testing, six lead SNPs of schizophrenia were significantly associated with breast cancer, while nine lead SNPs of breast cancer were also associated with schizophrenia.To identify the strongest genetic loci shared between breast cancer and schizophrenia, we first visualized the associations of lead SNPs  for schizophrenia with the risk of breast cancer, and vice versa. When assessing 91 genome-wide significant SNPs for schizophrenia, we approximated the average effects of these alleles on breast cancer and observed a positive association (P = 0.024), although somewhat attenuated  and by removing SNPs iteratively until the heterogeneity test was no longer significant. The results remained largely similar. The association between lead SNPs for schizophrenia and risk of breast cancer was significant P = 0.04, althour2 = 0.93) for breast cancer and schizophrenia, respectively . Based on PhenoScanner database27, this locus is also associated with lipid levels , triglycerides (TG), and low-density lipoprotein [LDL]). However, the associations with lipid levels are in the opposite direction as seen for breast cancer and schizophrenia  in addition to breast cancer and schizophrenia (Supplementary Table r2 from 0.813 to 0.871), are less correlated with rs2965183/rs2905426 (r2 < 0.6). This suggests that the signal at 19p13 shared by breast cancer and schizophrenia is less likely explained by the genetic susceptibility to the aforementioned traits.The 15 lead SNPs come from 14 loci, which we examined individually by using regional association plots to assess colocalization of the signals . We identified a common locus at 19p13 anchored by the lead SNPs rs2965183 and rs2905426 (LD cis-expression quantitative trait locus (eQTL) analysis of these two SNPs (rs2965183 and rs2905426) for genes up to 1 Mb on either side of each variant. We used gene expression data from tissues of breast (N = 251), brain , or blood samples (N = 369) in the Genotype-Tissue Expression (GTEx) Project29. In blood samples, the risk alleles (A) of rs2965183 and (G) of rs2905426 were associated with elevated expression of nearby genes GATAD2A (corrected P = 3.34 × 10−15) and TSSK6 (corrected P = 4.15 × 10−5) but reduced expression of LPAR2 (corrected P = 1.83 × 10−18) and MAU2 . Moreover, the variants of rs2965183 and rs2905426 were related to higher expression of HAPLN4 in breast (corrected P = 0.001) and brain tissues (corrected P = 1.9 × 10−4), respectively. All the aforementioned genes were nearby locus 19p13.Next, we performed the P < 5 × 10−5, of which 897 SNPs (94.8%) were with effect sizes in the same direction for both traits. These SNPs came from 17 genomic loci .As evidenced by the PRS analysis and the significant genetic correlation between schizophrenia and breast cancer, it is unlikely that the association is only driven by the signal at 19p13. We, therefore, performed a pathway analysis to identify more common mechanisms. We first mapped the overall genetic architecture shared by breast cancer and schizophrenia using cross-trait meta-analysis with adjustment for overlapping controls. To avoid detecting signals unduly driven by association from one side, we restricted the analysis to 946 SNPs of To the best of our knowledge, this is the first in-depth dissection, from phenotypic to genetic associations, of the link between breast cancer and schizophrenia. Leveraging the Swedish nationwide population-based cohort, we showed an epidemiological bidirectional association between breast cancer and schizophrenia. Consistently, we further illustrated the genetic association of both traits using GWAS summary statistics from international consortia and identified a distinct locus (19p13) shared between breast cancer and schizophrenia using regional association plots. The noteworthy genetic overlap between breast cancer and schizophrenia may partly explain the phenotypic association found in the epidemiological analysis, and the identified shared locus may provide leads to the potential common biology for both traits.19, it has recently been recognized that patients with schizophrenia are at increased risk of breast cancer6 but not of cancer overall31. Our results demonstrate that women with breast cancer are also at elevated risk of subsequent schizophrenia—largely representing a late-onset group documented as the second incidence peak following menopause26. It is plausible that many patients with schizophrenia who developed breast cancer later were early-onset ones, which might be phenotypically different from those developed after breast cancer. Our additional analysis restricting to women aged 18–44 years lends some support to a bidirectional association between early-onset schizophrenia and breast cancer, although the statistical power was very limited. The longitudinally bidirectional association in epidemiological analysis may point toward a partially shared etiology. Common behavioral or lifestyle factors (including nulliparity and obesity) have previously been suggested6, but our results remained robust in an effort to control for these factors. It is also hypothesized that some treatment for schizophrenia, for instance, antipsychotics, may induce higher level of prolactin and subsequently increased risk of breast cancer6. Nevertheless, use of antipsychotics may not explain the excess risk of schizophrenia among patients with breast cancer. Altogether, these evidences lend some support to a potential genetic overlap between both diseases.Despite lower screening attendance32. In the same study, based on a much smaller GWAS consortium of breast cancer (the Genetic Associations and Mechanisms in Oncology consortium (GAME-ON)), a weaker association between schizophrenia and breast cancer was detected by the stratified quantile–quantile plot and no further analysis was pursued32. Recently, both Byrne et al.20 and Shi et al.21 performed Mendelian randomization analyses based on similar GWAS summary statistics, which indicate that the genetic determinants of schizophrenia are associated with an increased risk of breast cancer. Another study based on GAME-ON data does, however, not support a causal genetic correlation between the two diseases33. Together with the longitudinal, bidirectional association observed in the epidemiological analysis and the weak pattern in the scatter plot of the lead SNPs, our findings also suggest a genetic overlap between the two traits rather than that one causing the other. We estimated that around one-seventh of the genetic contribution to breast cancer is shared with genetic contribution to schizophrenia, which is in line with previous reports34. In fact, the genetic correlation of 0.14 between schizophrenia and breast cancer (as a somatic disease) is noteworthy in comparison with genetic correlation between some psychiatric disorders, for example, between schizophrenia and autism (rg = 0.14)35. Of note, the genetic overlap is also noted between breast cancer and other psychiatric traits including depressive symptoms and neuroticism34. By contrast, some non-significant genetic correlations were detected between schizophrenia and other cancer types  but no correlation with colorectal cancer34.A recent study reporting shared genetic variants between schizophrenia and lung cancer highlighted a locus related to nicotinic acetylcholine receptors and smokingP = 0.0063), and prostate cancer (P = 0.01)36, although a precise biological pathway cannot be pinpointed by these data. The index cross-cancer variant rs1469713 is located between the lead SNPs of breast cancer  and schizophrenia . rs2965183 is located in enhancer elements reported to interact with GATAD2A in normal and tumor breast cells36. Our results also show that the locus 19p13 is associated with increased expression of GATAD2A in blood samples, while others observed a similar pattern in brain tissue38. GATAD2A is a subunit of the nucleosome remodeling and histone deacetylase (NuRD) complex. As a chromatin-level regulator of transcription, the NuRD complex may facilitate breast cancer development and progression by interacting with YAP/TAZ from the Hippo tumor-suppressor pathway39, modulating estrogen functions and the human epidermal growth factor receptor 2 pathway40, and by blocking p53-induced apoptosis40. Moreover, the locus 19p13 is associated with intellectual performance among broad psychosis cases38. GATAD2A is related to greater prefrontal activity41, while NuRD complex regulates gene expression with temporal precision42. The results of several studies indicate that GATAD2A may be one of the causal genes in the pathogenesis of schizophrenia, potentially through dopamine receptor D2 and nerve growth factor (NGF) pathways44. NGF signaling was also enriched in our pathway analysis.The novelty of our study further lies in the discovery of a distinct signal at 19p13 that is associated with both breast cancer and schizophrenia. A careful search of previously reported associations in this region support that this shared locus is not clearly explained by other traits. Importantly, this locus has also been identified as a shared locus associated with three hormone-related cancers, including breast, ovarian (P = 1.57 × 10−5), which regulates the processing of corticotropin (known as adrenocorticotropic hormone (ACTH)), a well-known player in hypothalamic–pituitary–adrenal axis and hence the stress response. ACTH and other stress hormones are closely related to many mental disorders, including schizophrenia46, and have a pivotal role in cancer development, potentially interacting through immune system and sex hormones48. The genetic association between schizophrenia and ER-positive breast cancer indeed appeared slightly stronger in our data, although it could be partially explained by the larger sample size of ER-positive breast cancer.As indicated by the PRS analysis and cross-trait meta-analysis, other weakly associated variants besides the signal at 19p13 may also pillar the shared genetic architectures between breast cancer and schizophrenia. In the pathway analysis accounting for weaker signals, we identified several pathways that are potentially underlying the two traits. The most significant one is the peptide hormone biosynthesis pathway , the residual confounding cannot be ruled out. For instance, we have no information whether the obesity was pre- or post-menopausal. However, many individuals had first episode of schizophrenia in their 20s/30s and were subsequently at risk for overweight/obesity due to antipsychotic treatment52 yielding decreased risk of breast cancer53, which suggests that our estimates are rather conservative. We also lack information on hormone levels, e.g., estradiol. However, women with schizophrenia often have a lower level of circulating estradiol54, which would have led to decreased risk of breast cancer56. Moreover, some treatments for schizophrenia  may induce higher levels of prolactin and thereby increasing the risk of breast cancer6, whereas breast cancer treatment  may effect on brain functions. It is plausible that genetic overlap, together with potential behavioral factors and treatment, contribute to the observed phenotypic association between breast cancer and schizophrenia. Replication of the PRS analysis in individually genotyped studies independent of these two international consortia would have been preferable. Nevertheless, the genetic association between breast cancer and schizophrenia has been observed in several studies using different data sources33. In addition, although we showed that the locus 19p13 shared by breast cancer and schizophrenia is independent of a number of traits including blood lipid levels, an association of this locus with ovarian and prostate cancer has also been suggested36. Thus future studies are needed to understand the potential genetic overlap between schizophrenia and other hormone-related cancers as well as the link between schizophrenia and male breast cancer.One major strength of the present study is the use of large-scale population data, with high validity data on the diagnoses of breast cancer and schizophreniaIn conclusion, our findings suggest that breast cancer and schizophrenia are both phenotypically and genetically correlated. The noteworthy genetic contribution to both traits, highlighted by a shared locus at 19p13 and among other loci, has the potential to shed light on the common biology underlying breast cancer and schizophrenia. Functional studies are needed to uncover how the identified genetic signals contribute to the pathogenesis of both diseases.N = 3,937,114). Using the unique national identification numbers, we followed all women through cross-linkages to the Swedish Cancer Register and Patient Register to identify occurring cases of breast cancer and schizophrenia, respectively. The Cancer Register has been available since 1958 and approaches 100% completeness. The Patient Register has information on ≥80% of hospital discharge records since 1980s, complete records since 1987, and ≥80% of hospital-based outpatient visits since 2001. The Cause of Death and Migration Registers were also cross-linked for follow-up. Demographic characteristics were obtained from the Census. Information on educational level was retrieved from the Education Register, while parity at matching was obtained from the Medical Birth Register and the Total Population Register. In addition, diagnoses of other psychiatric disorders and obesity were identified from the Patient Register. See Supplementary Table Based on the Swedish Population and Housing Census in 1990, we identified all women born and living in Sweden in 1990  and filtered for a minor allele frequency (MAF) ≥ 0.5% and imputation quality score ≥0.3, retaining 11.8 million variants. Association testing was conducted on these imputed markers with adjustment for countries and ancestry-informative principle components (PCs). A fixed-effect meta-analysis was conducted combining each result and results from the Collaborative Oncological Gene–environment Study and other 11 independent GWASs of breast cancer.The multi-stage GWAS of breast cancer was recently conducted among 122,977 cases and 105,974 controls of women of European ancestry from 68 studies collected by the Breast Cancer Association Consortium and Discovery, Biology and Risk of Inherited Variants in Breast Cancer Consortium24. We used the summary statistics from 33,640 cases and 43,456 controls of European ancestry from 46 ancestry matched, non-overlapping case–control samples and 3 family-based samples (public data downloaded from the PGC website). Unified quality control (QC) procedures, Ricopilli pipeline, were used and genotype data were imputed using the 1KG (Phase I Integrated Release Version 3). In each sample, associations were assessed for imputed marker dosages by adjusting for PCs to control for population stratification. After filtering on MAF ≥ 0.01, imputation quality score ≥0.6, about 9.5 million variants were used for a combined inverse-variance weighted fixed-effect analysis.The multi-stage GWAS of schizophrenia were performed on up to 36,989 cases and 113,075 controls by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC)R2 from MACH59 or INFO from IMPUTE60), removing duplicates, insertion/deletion, and allele-mismatched variants. Six million seven hundred and twenty-five thousand eight hundred and eighteen SNPs in autosome shared between two GWAS were included for analysis.To harmonize the GWAS summary statistics of breast cancer and schizophrenia, we filtered the imputed markers by MAF ≥ 0.01, imputation quality score ≥0.6  and 2.69 million SNPs. To be consistent, we only used SNPs that were reported in GWASs of breast cancer and schizophrenia.In order to disentangle the potential confounding of genetic susceptibility to lipid levels, we also utilized the GWAS summary statistics of TC, TG, and LDL cholesterol as a negative control. We obtained the GWAS summary statistics of blood lipids from the Global Lipids Genetics Consortium24. Cases of this study were adults with two or more hospitalization due to schizophrenia or schizoaffective disorder, ascertained through the Swedish Patient Register. Controls were randomly selected from the Swedish population and had never been hospitalized for schizophrenia or bipolar disorder. The data genotyped on different arrays were processed using the PGC Ricopilli pipeline for QC and imputation24.The individual-genotype data for schizophrenia was based on the S3 as part of the Schizophrenia Working Group of PGCThe register-based study was approved by the Central Ethical Review Board in Stockholm, Sweden (Dnr 12–2013). The requirement of informed consent is waived in register-based studies in Sweden. The S3 were approved by the Regional Ethical Review Board in Stockholm, Sweden (Dnr 04/449/4), and informed consent was obtained from every participant. Data from international consortia (summary statistics) are publicly available.First, we examined schizophrenia-associated risk of invasive breast cancer using a nested case–control design. Among 3,937,114 women, we excluded 39,990 women with breast cancer, 81,692 with other malignancy, and 48,320 with emigration before January 1, 1990, or age 18 years, whichever came later, and 982 with erroneous records. We followed the remaining 3,766,130 women until the diagnosis of any cancer, death, emigration, December 31, 2009, whichever came first. The average duration of follow-up was 15.2 years.22. Briefly, within this nationwide study base, we identified 94,626 women with newly diagnosed invasive breast cancer as cases. For each case, 30 women (whenever possible) who were free of cancer at the date of the case’s cancer diagnosis  were randomly selected from the study base. In total, 2,838,765 controls were individually matched on year of birth and age at the reference date. We then ascertained the date of a first-ever inpatient diagnosis of schizophrenia before the reference date (since 1980). The median time from schizophrenia to breast cancer diagnosis was 12.74 years. Both primary and secondary diagnoses were used. To capture persons with schizophrenia who might not have been hospitalized, we performed an additional analysis including outpatient diagnoses of schizophrenia.To assess the schizophrenia-associated risk of breast cancer, we first conducted a nested case–control analysis, which preserves the validity of full prospective cohort analysis22. The estimates were conditioned on the matching sets  and adjusted for educational levels  and region of residence . To disentangle the environmental effect from potential genetic factors, in a second model, we further adjusted for characteristics shared between breast cancer and schizophrenia, including parity , pre-existing psychiatric disorder , and obesity (yes or no) at the reference date.We derived ORs and 95% CIs from the conditional logistic regression by comparing cases with controls. The estimates, however, should be interchangeably interpreted as relative risk of breast cancer among patients with schizophreniaSecond, we assessed invasive breast cancer-associated risk of schizophrenia using a matched cohort design. We prospectively followed these cases and controls from the reference date until diagnosis of schizophrenia, cancer, death, emigration, December 31, 2010, whichever came first. We excluded 548 cases and 11,399 controls with inpatient or outpatient diagnosis of schizophrenia before the reference date. During the follow-up , we further ascertained newly diagnosed schizophrenia in inpatient (and outpatient) care. We calculated the hazard ratio of schizophrenia and 95% CI by using the stratified Cox proportional hazards regression comparing cases and controls. The approaches of adjustment in models were the same as the nested case–control study.In an additional analysis, we restricted the analysis to women of age at follow-up during 18–44 years. Namely, we included women whose age at the reference date was 18–44 years for both analysis; in the analysis of breast cancer-associated schizophrenia risk, we censored the follow-up when the individual turned age 45 years.P < 0.05 indicated statistical significance.These analyses were conducted in STATA (version 14.2). All tests were two sided and 62. LD clumping (r2 < 0.1 in 500 kb window) used 1000 Genomes Project European samples as LD reference (after removing MHC region). Similarly, subgroup analyses on ER-positive/negative breast cancer were also performed.First, we tested the association between PRS for schizophrenia and risk of breast cancer and vice versa, using the GWAS summary statistics from breast cancer and schizophrenia. The SNPs reported in both GWAS were included to generate the PRS using the software packages PRSice (version 1.25)r2 < 0.1 in 500 kb window) using the same LD reference as above, 71,121 SNPs with relative independence remained for PRS calculation. We generated PRS for breast cancer in the target sample as the sum of the imputed SNP dosages weighted by the allele effect (logistic regression coefficients) from the discovery set across all SNPs under a PT of 5 × 10−8, 1 × 10−6, 1 × 10−4, 1 × 10−3, 0.01, 0.05, 0.1, 0.2, 0.5, and 1, respectively. PLINK (version 1.9) was used for the PRS profiling. All scores were standardized within each target set to account for variation in SNP numbers used for PRS calculation. Subsequently, we examined the associations using logistic regression in R (version 3.4.3). The variance explained by PRS was calculated as the difference in variance from the full model including the PRS and variance from the baseline model without PRS using Nagelkerke’s R-squared63. We adjusted for first ten PCs and genotyping platform.Second, to examine the association of breast cancer with schizophrenia in women and men, we derived PRS for breast cancer for all participants in S3. The harmonized GWAS summary statistics of breast cancer was considered as discovery set for SNP selection and risk allele weighting. We further excluded variants with MAF < 0.05, imputation quality score <0.8, or with ambiguous strand. After LD clumping . It is implemented in the free open source LDSC (version 1.0.1) software package. Briefly, this method quantifies the separate contributions of polygenic effects by examining the relationship between LD scores and test statistics of SNPs from GWAS summary statistics and produces genetic correlation based on the deviation of chi-square statistics from the null hypothesis. The genetic correlation was estimated by using unconstrained-intercept LD score regression accounting for potential sample overlap between GWASs of breast cancer and schizophrenia.We estimated the genetic correlation, i.e., the extent to which breast cancer and schizophrenia share genetic etiology, by using LD score correlationr2 < 0.1 in 500 kb window) with breast cancer, by using the GWAS summary statistics of both traits. A global test that approximates the average effects of a set of selected SNPs on the other trait was carried out to highlight the overall effect of lead SNPs for schizophrenia on breast cancer65. Moreover, the individual effect of lead SNPs for schizophrenia on breast cancer was corrected for multiple comparison using the false discovery rate method66. The remained significant SNPs, regardless of effect direction, stand as potential regions shared with breast cancer. In addition, to assess the effect of influential SNPs, we calculated the Cook’s distance for each SNP and those with Cook’s distance >N/4 were removed. We further plotted the association by removing SNPs iteratively until the heterogeneity test was no longer significant. This procedure was done by using grs.filter.Qrs function in the gtx package in R. Identical analyses were performed for breast cancer lead SNPs and their corresponding associations with schizophrenia.To identify the most significant loci shared between breast cancer and schizophrenia, we first plotted the associations of schizophrenia lead SNPs 27. Based on the established links of both SNPs to lipids, we further plot the regional associations of locus 19p13 with TC, TG, and LDL using the GWAS summary statistics. We also sought reported associations of this region from GWAS Catalog (https://www.ebi.ac.uk/gwas/home) and calculated R2 to rs2965183/rs2905426 in European population using LDmatrix (https://ldlink.nci.nih.gov/?tab=ldmatrix).Next, we visualized the regional associations (500 kb on either side of the lead SNPs that remained significant after correction for multiple comparison) with both breast cancer and schizophrenia by integrating 1000 Genomes LD data with gene annotation tracks using LocusZoom (version 1.4)N = 251), brain (N = 80–154 depending on brain regions), or blood samples (N = 369) were used, through FUMA, from the GTEx Project29.To capture potentially altered gene expression due to lead SNPs rs2965183 and rs2905426, the eQTL analyses were then performed for genes up to 1 Mb on either side of each variant. Data on genetic variant and expression in breast  from multiple studies. It provides an approximate asymptotic P value for association and appropriately accounts for sample overlap between studies by estimating the correlation matrix of summary statistics. To avoid detecting signals unduly driven by association from one side, we only included SNPs with P value <5 × 10−5 reported in both GWAS summary statistics for meta-analyses.To better capture the genetic architecture shared by both traits, we performed cross-trait meta-analysis by combining GWAS summary statistics for breast cancer and schizophrenia. We applied metaUSAT (implemented in the R software package), a data-adaptive statistical approach for multivariate meta-analysis69, a web-based platform (http://fuma.ctglab.nl/), to perform functional mapping and annotation for the shared genetic components associated with both breast cancer and schizophrenia. To functionally annotate GWAS findings, FUMA uses information from 18 biological data repositories and tools to run two core processes. Based on the uploaded results of cross-trait meta-analysis, 32 independent significant SNPs were identified by FUMA , and 84 genes were mapped in either positional  or eQTL mapping . Pathways were then enriched from these genes using knowledge-based databases, including Reactome, KEGG, GO Biological Process, and BioCarta. The P values in eQTL and pathway analyses were corrected for multiple comparison using the false discovery rate method66.We used FUMAFurther information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Reporting Summary


This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences. But the issue is much larger than mammographic density, at least as it has been defined conventionally as the ‘white or bright areas on a mammogram’. The last few decades had seen many efforts to measure this historical concept, including automated measures now that digital mammography is widespread, building on the seminal work of Boyd and Yaffe who created a semi-automated method . But thoThis commentary is about finding ways to predict a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on what has become known as mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional measure of mammographic density in predicting breast cancer risk for interval cancers and screen-detected cancers, as well as across different ages at diagnosis and for both Caucasian and Asian women. We also discuss more recent attempts by others to pursue this line of investigation, the Measurement Challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.Before we do so, we discuss the pivotal concept of OPERA (odds ratio per adjusted standard deviation) which should not be relegated to an Appendix or Supplementary Material. It is fundamental to understanding, and putting into proper perspective, the relative strengths of risk factors with one another, and with other risk factors. It is particularly relevant to mammographic density due to its negative confounding with the major breast cancer risk factor of age and, for some, another breast cancer risk factor in body mass index. The reader might want to first skip this section and go straight to the section on Wolfe and beyond, but it is essential to understanding the area. Consider the issue of how to rank risk factors in terms of their ability to predict disease risk on a population-basis; namely the extent to which they differentiate cases from controls. For simplicity, we consider risk factors measured on a continuous scale, though this is not an essential assumption. The key issue is to recognise that disease risk almost invariably depends on age, and often sex , as well as many other factors. Some of these factors are measured and adjusted for in analysis while others can be controlled for, at least to some extent, by design. Consequently, studies of breast cancer and mammography measures typically consider women only, and use cases and controls who are similar, or even matched for, age. Some family-based designs control for familial factors—genetic and environmental. The result is an estimate of relative, not absolute, risk—the latter can be determined by appropriate use of population age-specific incidences. while holding all other factors constant. Therefore, the risk scale is not that of the unadjusted risk factor. Expressing the risk gradient in terms of units of the unadjusted risk factor is inappropriate, yet standard practice throughout epidemiology despite it giving misleading results.The point that is often not appreciated is that the resulting risk estimate applies to a change in the risk factor of concern For mammogram-based risk measures in particular, these considerations are particularly important. Conventional mammographic density declines with age in the decades around the menopausal years. Percent mammographic density has a mean of around 25–35% before age 40 years, then decreases over the next two decades to plateau at about 15–25% in later life for Caucasian women [The commonly used concept of percent mammographic density  is also moderately, and negatively, correlated with body mass index. But breast cancer risk increases substantially with age, and post-menopause, with body mass index. Intriguingly, body mass index has a negative  association with breast cancer risk prior to menopause . ConsequMajor problems arise when the estimated risk gradient is presented in terms of the so-called ‘change in risk per standard deviation’ specifically as the increment in risk for a one-unit change of the standard deviation of the unadjusted, raw measures . These ‘adjusted for other factors being taken into consideration by the design and analysis. The considerations above when applied to mammogram-based risk factors were the driving force in us developing the concept of OPERA (odds ratio per adjusted standard deviation) . The worOPERA is the correct way to interpret risk estimates in terms of risk discrimination on a population basis because it recognises that a risk factor is in effect the risk measure adjusted for covariates, not the raw measure itself. In addition, if it is assumed that the risk measure is approximately normally distributed  and that risk increases multiplicatively across the risk spectrum , then the log(OPERA) is approximately equal to the difference in means (in terms of standard deviation for the population) between cases and controls, and so is easily interpreted. 2.5 , therefore, is an appropriate scale on which to discuss and compare risk discrimination. There is a mathematical relationship between log(OPERA) and the area under the receiver operating characteristic curve (AUC) which is approximately linear at least for AUC between 0.5 to 0.7 (OPERA between 1 and 2). Under these assumptions, the inter-quartile risk ratio (IQRR) is approximately OPERAMaterial ). When OPERA is used, it emerges that conventional mammographic density is one of the stronger risk factors for female breast cancer. The OPERA is typically around 1.4, substantially more than for the classic epidemiological risk factors, as discussed previously . The risIn the 1970s, John Wolfe demonstrated that certain aspects of a mammogram were predicting, for women deemed at a mammographic screen to not have a breast cancer, which of them were more likely to be diagnosed with breast cancer at subsequent screens . An advop for difference = 0.002). Moreover, percent mammographic density was the best predictor of whether a woman was to develop interval versus screen-detected cancer, with an OPERA of 1.51 .The seminal work of Boyd and Yaffe, who created a semi-automated way to measure mammographic density called CUMULUS  and applTherefore, percent mammographic density was clearly predicting something specific to interval cancers, presumably masking, and was also associated with intrinsic breast cancer risk, but to a far lesser extent. While its association with interval cancer was by its nature likely to be causal, in the sense that changing percent density could lead to a reduction in risk, it was still unclear if the conventional measure of density, or even density per se, is causal for breast cancer. It is possible that the conventional concept of density as a risk factors is correlated with other mammogram-or lifestyle-related causal risk factors. To examine these hypotheses, we took two approaches to discovering new mammogram-based measures of risk, and importantly, considered their risk predictions concurrently with those arising from conventional mammographic density when fitted together.Cumulus in honour of the method and its creators, and because it aligns with the meteorological use of this word to describe clouds often described as puffy, cotton-like or fluffy and based on the Latin word “cumulo”, meaning heap or pile. Initially, mammographic density was defined as the light or bright area and calculated using the CUMULUS software . We callAltocumulus) and brightest (Cirrocumulus) regions. We used the CUMULUS software but chose the bright regions within the white areas to define Altocumulus, and the brighter regions among the bright areas to define Cirrocumulus. These words also align with their meteorological use by matching pixel intensity to height . Altocumulus are middle altitude clouds typically higher than cumulus clouds and have elements that are larger than those of cirrocumulus clouds which typically appear at an even higher altitude—the Latin word “cirro” means curl and is used to describe a high cloud.We challenged this convention by defining mammographic density at, in effect, two substantially higher pixel-brightness thresholds as the brighter . The risk estimates for Altocumulus were often little different to those for Cumulus, but the risk estimates for Cirrocumulus in particular were in general stronger, especially for Korean women  [A = 1.6; ) and oftn women)  and for n women) . These fn women) .Wolfe originally described ‘parenchymal patterns’ using verbal descriptions and subjective measures of textural features . We instCirrus. These features were highly correlated with one another, and the best predicting individual feature was ‘homogeneity’, a measure of the degree of “scatteredness” of the texture within an image; images with large areas of similar intensity pixels have a higher degree of homogeneity than those composed of a large number of small dissimilar regions.We call the risk measure based on fitting these 11 features to the combined sample vice versa. This allows inference to be made about the extent to which associations are due to confounding with one another from examining what happens to the estimates.We now consider what happens when the new measures are fitted with and without taking into account the conventional measure, and Cirrocumulus and Altocumulus were fitted with the conventional measure Cumulus, the Cumulus risk estimate attenuated towards the null and was often no longer statistically significant. In contrast, the new measures generally remained significant. This happened consistently, despite these measures all being correlated with one another . These general findings have been replicated [Except for interval cancer, when the new measures plicated . Altocumulus and Cirrocumulus measures both attenuated to be about 1.4 but remained highly significant. (The average of these two measures had an OPERA of about 1.8). The Cumulus measure, however, went to the null and was no longer significant. This happened for each combination that included Cumulus. The dense area measures gave a better fit than the percent mammographic density areas for which a similar pattern of results were observed ).Cirrocumulus being 24% greater on the log(OPERA) scale. When Cirrocumulus was fitted, the Cumulus measure went to the null and was no longer significant whether or not Altocumulus was also fitted.Cumulus being the greatest. Contrary to the previous examples, it was the Cumulus measure that remained significant in the multivariable analyses whereas both Altocumulus and Cirrocumulus were no longer significant once Cumulus was also fitted.Cirrus, Cirrus was fitted together with absolute and percentage Cumulus measures [Cirrus risk estimate remained high and attenuated by only 16% on the log(OPERA) scale, while the Cumulus risk estimate attenuated by more than 50%. Most of the data used these analyses came from a prospective cohort study that involved about 70% screen-detected and 30% interval cancers. Note that the approach used to create Cirrus only used relative brightness between pixels, not the absolute brightness of pixels as does the CUMULUS-based measures above.For measures . The CirOther researchers have also been trying to find aspects within a mammogram, other than mammographic density, that contain information about risk of breast cancer. But rarely have these researchers considered their new mammogram-based measure together with the conventional measure of mammographic density as we have done. One recent example is by Dembrower and colleagues  who deveBecause there is no convincing evidence that conventional mammographic density predicts screen-detected breast cancer once the other mammogram-based risk factors are taken into account, it appears to be a predictor of interval cancers due only (or mostly) to its role in masking. The new mammogram-based risk measures appear to be predicting interval cancer through their association with conventional mammographic density, particularly the other mammographic density measures defined by higher pixel brightness thresholds which attenuate once the conventional measure is fitted see . But theFollowing the concept of OPERA, Conventional mammographic density, adjusted for age and body mass index, is at most weakly associated with the other known lifestyle-based breast cancer risk factors , as mentWolfe was right, and wrong. There are aspects of a mammographic image that predict risk of breast cancer—but it isn’t all about parenchymal patterns, breast density, or the percentage of the breast covered by white areas. Wolfe’s initial strong predictions were, on face value correct, but they were mostly due to the strong role of conventional mammographic density in predicting masking, not so much as risk per se. per se.Risk of interval cancers is an important issue, but it is not all due to density and masking. We have found that it can be greatly improved by treating density as a continuum  and by including other risk factors such as family history, and a new measure based on breast tissue ageing . It willOur work has successfully identified new insights into breast cancer risk, but we would not have been able to do this without the enormous contributions of others. In particular, Professor Norman Boyd kept alive the ideas of Wolfe by developing the technology and resources, and inspiring the workforce, to pursue this line of investigation. The CUMULUS software has been an incisive tool for understanding breast cancer risk. We trained observers, not necessarily radiologists or physicians, to use CUMULUS in novel ways and achieved high repeatability and better prediction of breast cancer than the latest polygenic risk scores found from a massive expenditure on genetics applied to very large-scale epidemiological studies. These new mammogram-based risk factors are also much better than the other known epidemiological risk factors see  and compWhat differentiates our work over the last few years has been its focus on the aspects of a mammographic image that predict risk, rather than on mammographic density alone. We have not been alone in this pursuit, at least not recently, and now that digital mammography is widespread, the prospect of finding even stronger risk predictors using agnostic computer-learning approaches is a reality. Cirrus, this time using digital mammograms but with a much smaller dataset. They recently reported that OpenBreast had an OPERA of 2.5, equivalent to an extraordinarily high IQRR of almost 10 but with a wide 95% CI  for which the lower bound was 4 [Cirrocumulus (the amount of brightest areas). The next most prominent feature was ‘high gray-level run’ followed by ‘run-percentage’ and then both ‘short-run emphasis’ and ‘long-run emphasis’, separated by ‘gray-level range’. Therefore, OpenBreast could be utilising aspects of ‘homogeneity’ and the major principal component on which Cirrus is based, combined with measures of mean and range of brightness implicated in Cirrocumulus.For example, Perutz and colleagues created OpenBreast , by applnd was 4 . From thnd was 4 , the mosIt might be that, by applying machine learning to digital mammograms, essentially the same risk-associated aspects of a mammogram are being discovered, but with greater power and more precision. A similar approach to identify texture-based risk predictors agnostically also performed well with an IQQR of about 6, with a lower bound of the 95% CI < 4  . Nevertheless, these two recent studies, combined with our discoveries using mostly film mammograms, are very encouraging for finding very strong, and easily automated, breast cancer risk prediction from applying machine leaning to digital mammograms. Our studies were detecting signals despite considerable noise from digitising film mammograms across different machines. Despite this, we appear to have been detecting true signals. Discovery of more and better signals might be much easier using digital mammography.Another attempt to achieve this aim using ‘deep learning’ applied to a large data set  failed tTo help achieve the aim of determining the optimal measures of the different aspect of breast cancer risk from different mammographic images, we and others have created the Measurement Challenge . This isCumulus), or vice versa [These new discoveries have important implications for understanding the causes of breast cancer. We have applied a new method for making inference about causation by examining familial confounding, called ICE FALCON , to datace versa . We willThat conventional mammographic density might not necessarily be having a direct causal effect on breast cancer risk has serious implications for biological research. At this stage it is very difficult to identify which components of breast tissue are implicated in mammogram-based risk, but this will be an area of likely productive aetiological research once the new generation of risk factors emerging from agnostic approaches to digital mammograms are established and automated.There are also major public health implications, especially given the magnitude of breast cancer over-diagnosis associated with mammographic screening . It is iBRCA1 and BRCA2 mutation could be readily identified [If screening was to be tailored to a woman’s risk using new mammogram-based measures, family history and polygenic risk scores, large proportions of the population would be classified as well below ‘average’ risk while small proportions  of women with risks as high as entified . New autCirrus, and be validated for use in racially and ethnically diverse populations. These advances in breast cancer risk prediction based on aspects of mammograms could also be used to help alleviate racial and ethnic disparities. For example, in the U.S., black women are more likely to be diagnosed with more aggressive interval breast cancer. Asian women also have on average higher percent mammographic density and are at increased risk of this type of breast cancer. Better risk prediction for interval breast cancer using multiple risk factors as in  is, therIn summary, our research has directly challenged the historical paradigm of mammographic density research that there is one concept of mammographic density, defined as the light of bright regions on a mammogram, and that this measure is predictive of both masking of existing tumours and risk. We have drastically changed the conventional view by finding evidence that the conventional density is implicated in breast cancer risk—specifically interval cancers—by masking, not directly with risk per se. Other aspects of a mammogram, based on brightness and texture, appear to be more directly implicated in risk, and this applies to Caucasian as well as Asian women. The prospect of automated mammogram-based risk measures, combined with other major risk factors, could change population screening to be based more on relative risk than just age, make screening more effective, reduce its negative aspects, and hopefully save more lives.


NTHL1 gene are causative for the recently described, recessively inherited NTHL1 tumor syndrome that is characterized by an increased lifetime risk for colorectal cancer, colorectal polyposis, and breast cancer. Although there is strong evidence for breast cancer being a part of the cancer spectrum in these families, the role of pathogenic NTHL1 variants in breast cancer susceptibility in general population remains unclear.Rare protein truncating variants of NTHL1 nonsense variant c.268C>T, p.Q90*, which is the major allele in NTHL1 families and also shows enrichment in the Finnish population, in a total of 1333 breast cancer patients. Genotyping was performed for DNA samples extracted from peripheral blood by using high‐resolution melt analysis.We tested the prevalence of NTHL1 p.Q90* heterozygous carriers were identified : 5 in hereditary cohort  and 11 in unselected cohort . This frequency is equal to that in the general population . No NTHL1 p.Q90* homozygotes were identified.Sixteen NTHL1 p.Q90* heterozygous carriers do not have an increased risk for breast cancer and that the variant is unlikely to be a significant contributor to breast cancer risk at the population level.Our results indicate that  NTHL1 variants in breast cancer susceptibility in the general population remains unclear. Here, we have tested the prevalence of NTHL1 nonsense variant c.268C>T, p.Q90*, which is the major allele in NTHL1 families and also shows enrichment in the Finnish population, in a total of 1333 breast cancer patients. Our results indicate that NTHL1 p.Q90* heterozygous carriers do not have an increased risk for breast cancer and that the variant is unlikely to be a significant contributor to breast cancer risk at the population level, a notion that is particularly important for genetic counseling.Although there is strong evidence for breast cancer being a part of the cancer spectrum of recessively inherited NTHL1 tumor syndrome, the role of pathogenic  While the increased cancer risk is established for individuals with biallelic NTHL1 pathogenic variants, the risk estimates for heterozygous carriers are unclear  is the main mechanism for repairing endogenous DNA damage that results from oxidation, deamination, depurination, and alkylation, thereby protecting the genome from mutations  with a carrier frequency of 19/1324  in North Ostrobothnia . This geographical enrichment provides an excellent opportunity to test the association of NTHL1 p.Q90* with breast cancer susceptibility at the population level, along with the potential to establish risk estimates for the allele at heterozygous state. For this purpose, here, we have tested the prevalence of NTHL1 p.Q90* in breast cancer patients with an indication of hereditary disease susceptibility and those unselected for the family history of cancer and age at disease onset, all collected from the North Ostrobothnia area.According to public databases, the 22.1This study included informed consent from all participating individuals, and it was approved by the Ethical Board of the North Ostrobothnia Health Care District.2.2BRCA1/BRCA2/PALB2 mutation‐negative breast cancer cases with the indication of an inherited predisposition to the disease. Cases were selected using the following criteria: 1) index cases from families with three or more breast and/or ovarian cancer cases in first‐ or second‐degree relatives (n = 125), and 2) index cases from families with two cases of breast, or breast and ovarian cancer in first‐ or second‐degree relatives, of which at least one with early disease onset (<35 years), bilateral disease or multiple primary tumors (n = 29), and 3) breast cancer cases diagnosed at or below the age of 40 (n = 80). The young breast cancer cases were included based on the assumption that when a woman below the age of 40 years develops breast cancer, a hereditary predisposition can be suspected regardless of the family history , collected from the North Ostrobothnia area , included  Brunet, . The uns2.3NTHL1 p.Q90* (NM_002528.5) genomic DNA samples as positive controls. Verification of all detected p.Q90* variants were confirmed with Sanger sequencing .Genotyping was performed for DNA samples extracted from peripheral blood by using high‐resolution melt analysis  with Type‐It HRM reagents (Qiagen). All assays included heterozygous and homozygous 2.4U test to compare the mean age at diagnosis between carrier and noncarrier cases in unselected cohort . All p values were two‐sided and values <0.05 were considered statistically significant.Fisher's exact test was used to compare the carrier frequency between cases and controls, and Mann–Whitney 3NTHL1 p.Q90* carriers  were available for testing and one of them (breast cancer at the age of 62 years) was identified to carry NTHL1 p.Q90* . Of these, five had additional breast cancer cases in their first‐ and/or second‐degree relatives (Unsel 1–5, Table In the unselected breast cancer cohort, 11 NTHL1 p.Q90* in the studied breast cancer cohorts (16/1333, 1.2%, p = 0.61, OR = 0.8, 95% CI = 0.4–1.6, Table In total, the frequency of 4STK11 and PTEN gene mutations that are causative for dominantly inherited hamartomatous polyposis syndromes, Peutz‐Jeghers and Cowden syndrome, respectively (Couch, Nathanson, & Offit, MLH3 gene were reported to have extracolonic tumors, including breast cancer (Olkinuora et al., NTHL1 families (Grolleman et al., NTHL1 tumor syndrome is strong (Grolleman et al., NTHL1 p.Q90* heterozygosity, or even homozygosity, to breast cancer incidence in general population requires further investigation.Germline loss‐of‐function variants in genes behind familial colorectal cancer and polyposis syndromes have also been reported to increase the risk for breast cancer. High lifetime breast cancer risk has been established for NTHL1 p.Q90* carriers were identified in 2.1% cases with indication of hereditary predisposition to disease and in 1.0% of the breast cancer cases unselected for family history or age at disease onset. This did not significantly differ from the 1.4% carriers in the healthy population controls. Similar carrier frequencies between studied cases and the general population argue against association of NTHL1 p.Q90* in a heterozygous state with increased breast cancer risk, although we acknowledge that the sample size of hereditary cohort is limited to 234 cases. The absence of homozygotes can also be explained by sample size of the current cohorts, but nevertheless NTHL1 p.Q90* homozygosity appears to be an extremely rare event in cases unselected for the family history of adenomatous polyposis and colorectal cancer. It is noted that current study is limited to investigating only the germline DNA of the patients, and the presence of somatic inactivation of the NTHL1 wild‐type allele, and the mutational signature 30 associated with biallelic loss of NTHL1 (Grolleman et al., In the currently analyzed cohorts, heterozygous NTHL1 p.Q90* allele is unlikely to be a significant contributor to breast cancer risk at the population level and that the risk is not increased in heterozygous carriers, which is in line with results obtained from other cancer types (Belhadj et al., In conclusion, the current results indicate that The authors declare that there is no conflict of interest.Katri Pylkäs, Tuomo Mantere, Anna Tervasmäki, and Timo Kumpula conceived the study. Laura Huilaja, Kaisa Tasanen, Robert Winqvist, Richarda M. de Voer, and Katri Pylkäs provided the study samples. Timo Kumpula, Anna Tervasmäki, Tuomo Mantere, and Susanna Koivuluoma performed the experiments and data analysis, supervised by Katri Pylkäs. Timo Kumpula, Anna Tervasmäki, Tuomo Mantere, and Katri Pylkäs wrote the manuscript, and all the authors read and approved the final manuscript.


BRCA1/2 breast cancer families in which at least three siblings were affected, while no first‐degree relatives of the previous or following generation had breast cancer. Germline DNA from one of the siblings was subjected to exome sequencing, while all affected siblings were genotyped using SNP arrays to assess haplotype sharing and to calculate a polygenic risk score (PRS) based on 160 low‐risk variants. We found no convincing candidate recessive alleles among exome sequencing variants in genomic regions for which all three siblings shared two haplotypes. However, we found two families in which all affected siblings carried the CHEK2*1100delC. In addition, the average normalized PRS of the “recessive” family probands (0.81) was significantly higher than that in both general population cases  and controls (P = .0004). These findings suggest that the familial aggregation is, at least in part, explained by a polygenic effect of common low‐risk variants and rarer intermediate‐risk variants, while we did not find evidence of a role for novel recessive risk alleles.Breast cancer risk is approximately twice as high in first‐degree relatives of female breast cancer cases than in women in the general population. Less than half of this risk can be attributed to the currently known genetic risk factors. Recessive risk alleles represent a relatively underexplored explanation for the remainder of familial risk. To address this, we selected 19 non‐ What's new?To find new breast cancer susceptibility alleles, these authors tested families in which at least three affected siblings had non‐BRCA1/2 breast cancer. No new susceptibility alleles emerged, but the analysis did reveal that on average, women from these families who had cancer had significantly higher polygenic risk scores than either sporadic cases or controls. This result highlights the importance of moderate risk alleles acting together in familial breast cancer. BCACBreast Cancer Association ConsortiumBEDbrowser extensible dataCADDcombined annotation dependent depletionGATKgenome analysis toolkitGoNLgenome of the NetherlandsIBDidentical by descentIGVintegrative genome viewerLUMCLeiden University Medical CenterNKI‐AvLNetherlands Cancer Institute‐Antoni van Leeuwenhoek ZiekenhuisORodds ratioPRSpolygenic risk scorePTVprotein‐truncating variantSNPsingle nucleotide polymorphismVUSvariant of uncertain significance1BRCA1 and BRCA2, explain approximately 17% of the familial relative risk.TP53) and a number of genes in which pathogenic variants are associated with a more moderately increased risk  together explain another 5%. Moreover, approximately 160 common polymorphisms have been associated with small increases in risk, which jointly explain about 18% of the excess familial risk.Breast cancer is the most common cancer in females in the Western world and has a complex etiology in which both genetic and environmental factors affect disease risk. Having a family member affected by the disease is one of the most important risk factors.BRCA1 and BRCA2, several segregation studies have concluded that a polygenic model, or a model with a recessive allele would best explain the remaining familial risk.Since the discovery of We performed a small‐scale search for recessive breast cancer risk alleles in families with at least three affected siblings and no other first or second‐degree relatives with early‐onset breast cancer. The regions in which all affected siblings shared two haplotypes, as determined by low‐density SNP arrays, were identified and used to filter the exome sequence data that was generated for one of the siblings. This approach significantly reduces the number of potentially interesting variants, allowing for less stringent filters on allele frequency and hence fewer assumptions about the characteristics of a novel breast cancer risk‐associated variant. In addition, we calculated a polygenic risk score based on 160 known breast cancer risk‐associated polymorphisms and assessed the contribution of exonic variants in known breast cancer susceptibility genes that were predicted to be damaging by in silico prediction algorithms.22.1BRCA1 and BRCA2, and all families with a pathogenic variant or variant of uncertain significance in BRCA1 or BRCA2 were excluded.Families were ascertained through the clinical genetics centers of two Dutch hospitals, the Leiden University Medical Center (LUMC) and the Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital (NKI‐AvL) and from a previously described set of breast cancer families collected throughout the Netherlands.2.2P > .05). We then selected all positions in which all siblings shared two alleles IBD or, for the analysis allowing for one phenocopy, all positions in which all but one sib shared two alleles IBD. These positions were converted into a BED file describing the regions IBD for both haplotypes. Each of these regions started one base pair after the last upstream position for which the affected siblings did not share two alleles IBD and ran until one base pair before the first downstream position for which they did not share two alleles IBD.We genotyped all available DNA samples from the affected siblings using the HumanLinkage V Panel from Illumina. Sample preparation was done according to the manufacturer's protocol (Rev. B October 2010). Samples were hybridized to GoldenGate Universal‐32 BeadChip (Illumina) and chips were scanned using a Bead Array Reader (Illumina). The GenomeStudio software  was used to call genotypes. We used Merlin (v1.12) to calculate, for each sib pair and marker position, the probability that at this position the sib pair shared zero, one or two alleles identical by decent (IBD).2.3From each family, one affected individual was selected for exome sequencing of germline DNA. In most instances, this was the individual with the youngest age of diagnosis; however, in two families, another individual was selected due to limited availability of DNA. Samples were prepared using Illumina's Paired‐End Library Preparation Kit, after which the coding regions of the genome were captured using SeqCap EZ Exome v3.0 (Nimblegen). Sequencing was done on a HiSeq 2000 (Illumina), generating 2×100 base pair reads. We used GATK for indel realignment, base recalibration and finally variant calling using Haplotypecaller.2.4Figure We also considered a scenario in which two missense changes in a gene on two haplotypes could cause a recessive inheritance . For this, we selected all missense changes in the regions specified by the BED files, with allele frequencies <1% and in silico annotations suggestive of deleteriousness .2.5TP53, CDH1, PTEN). PTVs in level 1/2 genes were filtered on allele frequency in the general population  with a cut‐off of 0.1% for the high‐risk genes  and 2% for the moderate risk genes , allowing for the observation that some PTVs in moderate‐risk genes (such as the c.1100delC in CHEK2) occur at >0.5% allele frequencies in some populations. All missense variants in the 35 genes were selected if their allele frequency in the general population was <2% and they had either a CADDWe examined a set of 35 known and suspected breast cancer susceptibility genes  associated with overall breast cancer by the Breast Cancer Association Consortium (BCAC), the largest case‐control study to date.nij is the number of risk alleles  SNP i carried by individual j and ORi is the per‐allele odds ratio associated with SNP i , 3222 (11.2%) and 734 (2.6%) variants remained in families with two, three or four individuals genotyped respectively. We first focused on variants that were predicted to result in a truncated protein. When a heterozygous protein‐truncating variant (PTV) was found, we assessed the gene for a second hit which could also be a missense variant . The allele frequency of 1.3% in the Genome of the Netherlands, also suggests that this variant is not associated with breast cancer.We originally set the PTV allele frequency cut‐off relatively high (<10%) to allow for the possibility of a single variant that was homozygous in multiple families. No such variants were detected in our dataset, but we did find six genes with two or more heterozygous positions in six different families. For compound heterozygotes, we assumed that the allele frequency of a potentially causal variant was lower (<2%), rendering the variants in SERINC2 p.R126W in family RF4 and ZNF717 p.H63L in family RF7  and c.1247T>G (p.L416R) found in family RF2. Both variants were shared by two of the three affected sisters. However, in GoNL, both variants were present in the same seven individuals and predicted to be on the same haplotype, excluding the possibility of compound heterozygosity. SCL26A10 has no known function and has been suggested to be an imprinted, maternally expressed, pseudogene.Since breast cancer is a common disease, there is a high probability that a case in a family is not genetic . Therefore, we assessed the regions of the genome where only two out of three (or three out of four) affected sisters share two haplotypes. PTVs obtained in this way were then filtered as in the previous analysis Table . Again, 3.4PALB2 and one in BRCA2. ClinVar lists the variant in PALB2 as benign, the one in BRCA2 as variant of uncertain significance (VUS). Family RF17 was included in our study as being non‐BRCA1/2 because the sister not carrying the missense variant was the one tested in the clinical setting. No studies on the functional effects of this variant have been published to date, but the CADD score of 35 indicates that it might affect protein function. Therefore, it is possible that this family harbors a pathogenic BRCA2 variant.We next examined 35 genes in which PTVs have been demonstrated or suspected to be associated with breast cancer risk Tables . We founCHEK2, associated with an odds ratio (OR) of approximately 2.3,ATM, CHEK2 and RAD51C. The effect of missense changes in ATM and CHEK2 on breast cancer risk is, besides a few specific examples, largely uncertain.ATM c.146C>G (p.S49C) was detected in families RF6 and RF20; its associated breast cancer risk is unlikely to be larger than 1.5.RAD51C c.790G>A (p.G264S) in families RF8 and RF19.The c.1100delC pathogenic variant in 3.5P = .026) and controls (P = .0004).Over 160 independent common SNPs have been found to be convincingly associated with breast cancer and can be combined into a PRS4CHEK2*1100delC. Furthermore, we showed that on average, the affected women in these families had significantly higher PRS than both sporadic cases and population controls. Together, these results indicate that our selection criteria enrich for these factors and suggest that, rather than being caused by a single highly penetrant variant, increased breast cancer risk in some of these families may be due to the combined effect of multiple rare and common genetic variants with varying effect‐sizes, and perhaps other nongenetic risk factors as well.In our study, we assessed whether breast cancer in families with at least three affected siblings, can be explained by a susceptibility gene with a recessive mode of inheritance. After a haplotype‐guided exome analysis, we identified no homozygous or compound‐heterozygous variants that were likely to explain the clustering of breast cancers in the selected families. We did identify two families in which all affected individuals carry the known moderate risk variant PDIA2 p.R148*) are so rare in the general population that they would require very large case‐control populations to assess their association with breast cancer. As they grow in size, publicly available reference datasets and databases in which variants in potential disease‐associated genes can be reported are becoming very valuable for this purpose. Second, a recessive risk allele might be located outside the protein‐coding regions of the genome and thus not be captured by an exome sequencing approach. Moreover, structural variation, affecting more than a few base pairs, is mostly undetectable with the methods used in our study. Whole‐genome sequencing would identify these, but their mostly poor genomic annotation will make their filtering for follow‐up analyses very hard.Due to a few limitations of our study, we cannot completely rule out that some of our families are nonetheless explained by recessive risk alleles. First, some of the variants we identified , and four possibly pathogenic variants in other susceptibility genes. At least for CHEK2*1100delC it has been shown that the risk associated with this pathogenic variant and the risk associated with a PRS combine multiplicatively.BRCA1/2 familial breast cancer cases have a higher PRS than both cases from the general population and cases who carry a BRCA1 or BRCA2 pathogenic variant.Nonetheless, our results are in agreement with previous exome sequencing studies in non‐The enrichment of moderate and low‐risk alleles among the cases of at least part of the families in our study adds to a growing body of evidence on the importance of this type of risk alleles in causing familial breast cancer. Multigene panel sequencing has rendered the detection of rare variation in known risk genes standard clinical genetic practice, but the genotyping of the many common low‐risk alleles is not yet routinely performed in this setting. Nonetheless, the risks associated with the PRS and the likely multiplicative way in which it combines with those of pathogenic variants in moderate‐risk genes argue for a more comprehensive approach to genetic testing and counseling. This calls for the development of integrative risk prediction models, including the effect of mammographic density, lifestyle and environmental risk factors.The authors declare that they have no conflicts of interest.All individuals provided informed consent and approval of the medical ethical committee at the LUMC was obtained (protocols P49/99 and P09.203).Data S1 Supporting Information.Click here for additional data file.