CHEK2:c.1100delC is 2–threefold but higher in carriers with a family history of breast cancer than without, suggesting that other genetic loci in combination with CHEK2:c.1100delC confer an increased risk in a polygenic model. Part of the excess familial risk has been associated with common low-penetrance variants. This study aimed to identify genetic loci that modify CHEK2:c.1100delC-associated breast cancer risk by searching for candidate risk alleles that are overrepresented in CHEK2:c.1100delC carriers with breast cancer compared with controls. We performed whole-exome sequencing in 28 breast cancer cases with germline CHEK2:c.1100delC, 28 familial breast cancer cases and 70 controls. Candidate alleles were selected for validation in larger cohorts. One recessive synonymous variant, rs16897117, was suggested, but no overrepresentation of homozygous CHEK2:c.1100delC carriers was found in the following validation. Furthermore, 11 non-synonymous candidate alleles were suggested for further testing, but no significant difference in allele frequency could be detected in the validation in CHEK2:c.1100delC cases compared with familial breast cancer, sporadic breast cancer and controls. With this method, we found no support for a CHEK2:c.1100delC-specific genetic modifier. Further studies of CHEK2:c.1100delC genetic modifiers are warranted to improve risk assessment in clinical practice.The risk of breast cancer associated with  BRCA1, BRCA2, TP53, STK11 and PTEN1. Other rare, intermediate-risk variants, such as PALB2, CHEK2 and ATM account for about 5% of the inherited risk3 and common low-risk variants for another 18–19%6.Breast cancer aggregates in families and has a considerable inherited component. Approximately 20% of the genetic risk for breast cancer is explained by pathogenic mutations in the high-penetrance genes CHEK2 gene that localizes to chromosome 22q12.1. It is part of the network that responds to DNA damage in order to maintain genomic integrity7. The protein-truncating variant CHEK2:c.1100delC is associated with a two-threefold risk of breast cancer9. In women with familial aggregation of breast cancer, the risk is even higher. An odds ratio of up to 4.8 has been seen in women with a family history of breast cancer, which is equivalent to a 37% cumulative risk of breast cancer by the age of 70 years10. In addition, the c.1100delC allele has been associated with younger age at onset, a threefold increased risk of a second breast cancer, as well as a worse prognosis among women with oestrogen receptor-positive cancer12.Checkpoint kinase 2 is a protein product of the 14. The fact that the model also can be applied to CHEK2:c.1100delC carriers is supported by a study of low-risk breast cancer variants in 34 000 women with and without a family history of breast cancer. A polygenic risk score (PRS) that was based on the combined risk of 74 low risk variants was calculated. The result suggested that the polygenic risk score could be used to stratify risk in c.1100delC carriers and that the low-risk variants explained a part of the familial risk. The authors estimated that 20% of CHEK2:c.1100delC carriers with the highest PRS had an estimated lifetime breast cancer risk of > 30%. Correspondingly, 20% of carriers with the lowest PRS had an estimated lifetime risk of 14% which is close to the average population risk15. A synergistic effect between low-risk variants and BRCA1 and BRCA2 mutations has also been shown16. The risk for mutation carriers being affected is thus modified by other genetic variants and family history in addition to lifestyle factors. A risk prediction model, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) has been developed to calculate the lifetime risk of breast cancer, including carriers of a moderate-penetrance allele such as CHEK2:c.1100delC. The BOADICEA model allows risk stratification for established genetic and non-genetic risk factors17. Still, other causative gene variants possibly remain to be identified, since the previously identified low-, intermediate-, and high-risk genes cover less than half of the estimated heritable component. Characterising factors that increase the risk in carriers of moderate-risk alleles is important, in order to identify the high-risk group that benefits most from preventive interventions. In this study, we used whole-exome sequencing of a CHEK2:c.1100delC positive cohort with familial breast cancer, to identify putative risk modifying alleles. In the first phase we aimed to find candidate risk alleles for further validation in the second phase with larger cohorts of CHEK2:c.1100delC positive cases and controls.The considerably higher risk in women with a family history of breast cancer is in accordance with the suggested polygenic model where several susceptibility loci together confer a multiplicative effect on breast cancer riskCHEK2:c.1100delC, 28 familial breast cancer cases and 70 controls. Candidate alleles were selected for validation in larger cohorts  and CHEK2:c.1100delC was 0.46 , encouraging further analysis.Next, we did another follow-up using 45 5. The availability of a good number of healthy c.1100delC carriers in the consortium data enabled a proper interaction analysis for c.1100delC, rs16897117, and breast cancer risk. In the BCAC data, there was no allelic imbalance between rs16897117 and c.1100delC  and also loci that differed by the molecular subtype, luminal or non-luminal, of breast cancer19. The observations imply that tumour features should be taken into account when searching for candidate variants in CHEK2:c.del1100C carriers. Several loci that specifically modify risk in BRCA1 and BRCA2 carriers have also been found29. These are all low-risk susceptibility alleles identified through testing of candidates from breast cancer genome-wide association studies in BRCA1/2 mutation carriers and through fine-mapping of candidate regions.We aimed to identify candidate risk variants that specifically modify risk in CHEK2:c.1100delC modifying candidates could be done with more loose criteria in the discovery phase to increase the probability of finding good candidates for further testing. In accordance with previous findings, gene-specific modifiers are likely to be common low-risk variants. CHEK2:c.1100delC-specific modifiers may then rather be identified through large-scale genome-wide association studies. With this method, we found no support for a CHEK2:c.1100delC-specific genetic modifier. More studies of CHEK2:c.1100delC genetic modifiers are therefore warranted to improve risk assessment in clinical practice.Future studies of CHEK2:c.1100delC carriers with familial breast cancer, another 28 familial breast cancer patients and 70 healthy controls  from the Swedish cohorts. Candidate variants were validated in larger cohorts . Cluster generation and 125 cycle paired-end sequencing was performed using the Illumina HiSeq 2500 system and v4 sequencing chemistry . Next-generation sequencing was performed at SciFiLab, University of Uppsala.CHEK2:c.1100delC carriers were evaluated. The cases of hereditary breast cancer and the healthy controls (spouses of cases with hereditary colon cancer) served as genotyping controls in the work of identifying candidate alleles. Only variants passing a set of criteria, described below, were selected for further evaluation. The criteria were as follows:After exome sequencing, all detected coding non-synonymous variants in the CHEK2:c.1100delC cases and healthy controls and/or a ratio of 1.5 or more between CHEK2:c.1100delC cases and familial breast cancer cases was required.Ratios of the allele frequencies of the variants were calculated. A ratio of 2.0 or more between Genes/variants that were selected should display a function of a putative cancer driver gene when evaluated by online genome browser databases  and scientific publications available on PubMed.CHEK2:c.1100delC carriers compared with regional reference databases was required .A more than 30% higher allele frequency in Only variants with a sequencing accuracy of 65%, or more, in all study groups were included. The variants passing the selection criteria were functionally annotated using the in silico tools SIFT, Polyphen2 HDIV/HVAR, LRT, MutationTaster, FATHMM, RadialSVM, LR, and MutationAssessor.CHEK2:c.1100delC, 328 cases of sporadic breast cancer, 408 cases of familial breast cancer and 284 controls from the Swedish cohorts.Eleven SNPs  were genotyped using TaqMan SNP genotyping assay . rs35932273 was genotyped by Sanger sequencing following PCR. The candidates were validated in 72 cases with CHEK2:c.1100delC carriers. One recessive variant, rs16897117, was suggested, as among the 28 CHEK2 carriers, there were 3 patients homozygous for rs16897117, whereas among the non-carrier breast cancer cases or healthy controls, there were no rs16897117 homozygotes. The rs16897117 was further evaluated in Swedish and Finnish cohorts and in data from the Breast Cancer Association Consortium, BCAC.Exome sequencing data were analysed in search of recessive candidate variants in CHEK2:c.1100delC carriers analysed in the discovery phase were collected from the Department of Clinical Genetics, Karolinska University Hospital. A total of 112 samples from CHEK2:c.1100delC carriers were collected from the SWEA-study, a national Swedish collaboration aiming to study the prevalence of established breast cancer genes as well as to validate candidate genes and single nucleotide polymorphisms (SNPs) in Swedish women with familial breast- and ovarian cancer . All CHEK2:c.1100delC carriers were previously affected by breast cancer except for two carriers who had been diagnosed with ovarian cancer. All cases of hereditary breast cancer were collected from the Department of Clinical Genetics, Karolinska University Hospital and had previously received counselling and screened negative for relevant high-risk genes . Cancer-free spouses of colorectal cancer patients served as controls . They were recruited through the Swedish Colorectal Cancer Low-Risk Study. All 775 cases of breast cancer used for evaluating the recessive variant were collected from the Department of Clinical Genetics, Karolinska University Hospital. The 328 cases of sporadic breast cancer samples used in validation of non-synonymous variants were collected from a population-based cohort from Södersjukhuset, Stockholm. Genomic DNA was extracted from peripheral blood samples. Samples were genotyped using TaqMan SNP genotyping assay . Only the study subjects with European ethnic background were included, and the Swedish and Finnish cohorts included in the discovery analyses were excluded. The selection yielded 13,767 breast cancer cases and 21,456 controls 37. For the case-only analysis of the Swedish and Finnish cohorts, a stratified Mantel–Haenszel odds ratio was estimated with R library epiDisplay38. The BCAC data analysis was performed with logistic regression. The interaction between c.1100delC and rs16897117 was assessed with likelihood-ratio test.Odds ratios, 95% confidence intervals and p-values were calculated to test the association with allele frequency using the DeFinetti programme provided as an online sourceThis study was approved by the Ethics Committee of Karolinska Institutet/Karolinska University Hospital. All individual studies, from which data was used, were approved by the appropriate medical ethical committees and/or institutional review boards. All methods were performed in accordance with the relevant guidelines and regulations. All study participants provided informed consent.


Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V , or PMD and V  had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses’ Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6–6.8 years). PMD (OR  Research identifying mechanisms for these associations, or how changes in density affect risk, are limited by our reliance on visual estimation ) or operator-assisted thresholding methods which require inputs by a trained user 4, that are labor intensive and prone to intra- and inter-reader variability5. To address this need we developed APD, an automated approach to estimate percent mammographic density (PMD). APD is moderately correlated with operator-assisted thresholding methods (r = 0.70), and in a Mayo Clinic case-control study yielded stronger risk estimates when comparing extreme quartiles 6 may help us to better understand how dense tissue is influencing breast cancer risk.Mammographic density is one of the strongest risk factors for breast cancer, with a four- to six-fold greater breast cancer risk in women with the highest vs. lowest levels of density8. In addition, several studies have demonstrated that texture features are associated with breast cancer risk, independent of PMD13. Using an automated image analysis system, Manduca et al. identified features within several texture classes whose association with breast cancer was of similar magnitude to PMD10. A texture summary measure called ‘V’ captures gray-scale variation in mammograms and was a significant predictor of breast cancer in three Mayo Clinic cohort studies14. Comparing extreme quartiles, V was more strongly associated with breast cancer  than was PMD . While studies have demonstrated independent associations of texture features and PMD, the interrelationship between texture, automated and manual density, with respect to breast cancer risk remains unclear.Comparing mammograms from women with similar PMD, there may be considerable heterogeneity in the appearance of dense tissue known as texture. This information is ignored in standard measurements of PMD, yet, emerging evidence demonstrates that it is related to breast cancer riskIn the current nested case-control study, we evaluated the independent and joint associations of PMD, APD, and V with breast cancer risk, using data on 1900 breast cancer cases and 3921 matched controls in the Nurses’ Health Studies. Given demonstrated positive associations between PMD and breast cancer risk, one of our primary goals was to determine whether APD performed similarly and could therefore be a more accessible and reproducible mammographic breast density measure for use in research. Another primary goal was to determine whether APD, PMD, and V were each independently associated with breast cancer risk. Finding independence between these measures would provide further information about the interrelationships between their underlying phenotypes.Table r = 0.83 vs. r = 0.61). Compared to the lowest quartile (Q1), individuals in the highest quartile (Q4) of PMD were almost three times more likely to develop breast cancer . PMD associations were stronger among premenopausal  compared to postmenopausal women . With adjustment for V, the association was somewhat attenuated, but remained significant overall , and among premenopausal  and postmenopausal women . For APD, compared to Q1, women in Q4 were twice as likely to develop breast cancer . When adjusted for V, comparing Q4 vs. Q1 was associated with a 36% increased breast cancer risk overall , 55% among premenopausal , and 25% among postmenopausal women . Lastly, for V, compared to women in Q1, those in Q4 were more than twice as likely to develop breast cancer . Adjustment for PMD or APD attenuated the associations, but V remained associated with breast cancer risk. Associations did not differ by tumor hormone receptor status . Women in Q4 of PMD, but Q1 of V had more than twice the risk of breast cancer compared to those who were low on both measures . High V (Q4) coupled with low PMD (Q1) was not associated with increased risk of breast cancer . Similarly, women in the highest APD and V quartiles, had two and a half times greater risk of breast cancer compared to those with the lowest APD and V . The patterns were similar, though the magnitude of association was stronger when analyses were restricted to high-resolution images only  and a texture measure V. We demonstrated that PMD, APD, and V were independently associated with breast cancer risk. However, PMD was more strongly associated with breast cancer risk than were APD or V. When PMD and APD were modeled together, the association of APD with breast cancer risk was more attenuated than PMD. Associations were generally stronger among premenopausal women and high-resolution images but did not vary by hormone receptor status.14. In that study the authors conclude that V and PMD are at least equivalent and our findings are consistent with an interpretation that these factors are at least equivalent in their association with breast cancer risk. Further study is required to determine their precise interrelationship. As reviewed in Gastounioti et al. (2016) parenchymal texture classifiers have been assessed with respect to breast cancer risk in at least 20 studies8. A prospective cohort study by Wanders et al. (2018) found that percent dense volume and texture, as assessed using an algorithm the authors previously developed15, were each associated with breast cancer risk12. While risk prediction was not the goal of our study, Winkel et al. (2016) found that combining measures of PMD (BI-RADS), parenchymal patterns (Tabar’s classification), and an automated texture measure, improved breast cancer risk prediction. The area under the curve (AUC) for either measure alone ranged from 0.63 to 0.65, while inclusion of all three yielded an AUC of 0.6916. Importantly, while these features may ultimately improve breast cancer risk prediction, they can also yield insights into breast cancer etiology by identifying specific breast structures implicated in cancer development.We observed independent and joint effects of V, APD, and PMD on breast cancer risk demonstrating that both the relative amount of fibroglandular tissue and its greyscale variation contribute to breast cancer risk. While all three measures were associated with breast cancer risk, PMD had the strongest magnitude of association. This differs from the Mayo Clinic Studies, where V yielded higher risk estimates than PMD in two of three included cohorts, and in one they were equivalent18. Manual measures are subject to reader differences19, measurement error20, and are time-intensive. An automated measure, such as APD, can provide a more reliable measure, suitable for use in risk assessment, mandatory breast density notification, and measuring changes in density. Inconsistent breast density measurement could bias study results toward the null21, lead to unreliable risk stratification if included in risk assessment models, or misinformed decision-making regarding screening intervals and modalities after legally mandated breast density notification23, and reduce reliability when assessing change in PMD24. We have demonstrated that including breast density in a breast cancer risk assessment model improves discriminatory accuracy26 and a recent paper shows that the Tyrer–Cuzick model with density can provide useful data at on risk for at least 10 years27. However, those estimates were based on a single baseline breast density assessment. Cuzick et al24. noted that the reliability for change in BI-RADS density was only moderate (r = 0.48–0.67) when evaluating mammograms 10 years apart. As changes in PMD over time are likely to be small28, a continuous measure, more sensitive than BI-RADS which has just four categories, is needed. In recent years, several automated measurement tools have been developed to assess volumetric or area-based breast density32. These new measurement approaches are essential for PMD use in breast cancer risk assessment and screening decision-making. Yet, our finding that PMD yielded stronger associations with breast cancer risk than did APD or V, two automated measures, suggests that there is information captured through operator-assisted thresholding that is not captured through automation. Continued effort is needed to compare the strengths and weaknesses of these approaches and develop standardized methods for clinical PMD assessment.Despite the somewhat stronger associations observed for PMD, validation of APD has important implications for future researchp < 0.05), several of which were only weakly correlated with PMD9. In future studies, we will assess the relative importance of multiple features, independently and in combination with PMD. There is potential measurement error in the exposure assessment, particularly given that mammograms in this study were collected from across the United States across many years and images had multiple image resolution levels. To address this, we conducted sensitivity analyses and found the strongest associations among high-resolution images. This study utilized digitized film mammograms. As of November 1, 2019, 99.9%  of all accredited mammography units in the US are digital33. However, Nielsen et al. found that while differences in population characteristics and imaging technology did affect texture feature measurement, these factors did not impact the association between texture and breast cancer risk7. Further, Vachon et al. showed that associations with breast cancer were similar between full field digital mammogram image types (raw or processed) and digitized film34, demonstrating that these measures are stable phenotypes across image acquisition approaches and our findings are valid despite the use of digitized film mammograms. The relative consistency of our results compared to other studies with different imaging modalities confirms this assertion. Lastly, the Nurses’ Health Studies consist of predominantly white women. Future studies should assess associations among more diverse populations.Study strengths include the use of prospective data from the Nurses Health Studies, two cohorts with validated disease ascertainment, and comprehensive data on breast cancer risk factors, tumor hormone receptor status, measures of PMD, APD, and texture features. This study has several important limitations. Our study focused on V, a summary texture measure, but there are many other features that have potential implications. For example, Malkov et al. (2016) examined 46 breast texture features and identified 15 that were significantly associated with breast cancer risk  and Nurses’ Health Study II (NHSII). NHS began in 1976 with 121,700 female registered nurses aged 30–55 from 11 states. NHSII began in 1989 with 116,429 female registered nurses, aged 25–42, from 14 states. Participants in each cohort completed a baseline questionnaire at enrollment and are followed by biennially mailed questionnaires to collect information on newly diagnosed diseases, exposures, and covariates.35. One or two controls were matched to breast cancer cases on age, menopausal status at blood draw and diagnosis, current hormone therapy use, month, time of day, fasting status at time of blood collection, and luteal day (NHSII timed samples only). We collected pre-diagnostic screening mammograms conducted as close as possible to the blood draw date, but before June 1, 2004 (NHS) or June 1, 2007 (NHSII). We collected additional mammograms conducted around 1997 from NHSII cases and controls who participated in the NHSII cheek cell collection. In total, mammograms were collected from 2062 breast cancer cases and 4196 matched controls. We excluded 162 cases and 275 controls due to missing data on V or BMI. Film mammogram cranio-caudal (CC) views of each breast were digitized with a Lumysis 85 laser film scanner or a VIDAR CAD PRO Advantage scanner . The correlation between percent density measures from the two scanners was 0.8836. Cohort participants provided written informed consent. The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required.Mammogram collection was conducted within the NHS and NHSII breast cancer case-control studies nested in the blood subcohorts37. PMD was calculated as dense breast area divided by the total breast area. All images were assessed by a single reader (within-person intra-class correlation coefficient > 0.90)38. Observed inter-batch variability was accounted for using methods described elsewhere40. PMD measures in the left and right breast were averaged.PMD was assessed using Cumulus, a computer-assisted thresholding software program , from digitized film mammograms 42. To avoid the chest wall, detection is performed in cranioclaudial views only. First, the breast area was detected creating a binary mask shown in Supplementary Fig. This APD method detects small dense regions on a mammogram after applying a wavelet high-pass filter and produces an output analogous to that of the operator-assisted PMD. The basic algorithm and its validation were described previously and details are provided in Supplementary Methods6 [and then processed with a high-pass wavelet filter. The density detection is performed in two stages in the wavelet filtered image, differing in thresholds based on predefined significance levels . To boost the noise signal, each transformed image was multiplied with a different realization of a zero mean unit variance Gaussian noise field and then scaled to 14 bit (integer) prior to applying the wavelet high-pass filter. Examples of this process are shown in Supplementary Fig. 42. In the first stage, a reference variance  is estimated using the entire breast area in the filtered image. A small search window (4 × 4 pixels) is scanned across the filtered image. At each window location, a chi-square statistical test is performed by comparing the local variance calculated within the window with the reference variance to decide if the region is dense or not as represented in the raw image . The connection between the local variation in the filtered image and the digitized (raw) image density characteristics was demonstrated previously43. This detection method is applied a second time in the filtered image by updating (refining) the reference variance. The reference variance is updated by calculating the variance across the breast area in the filtered image using regions not labeled as dense in the first detection stage. Because of the noise field multiplication modification, we used 300 images (100 images from each resolution) as a test set to adjust the algorithm’s parameters. Each training image had the corresponding density determined with Cumulus. The operating points were determined by finding the parameters that gave the optimal correlation with the Cumulus measures. These correspond to significance levels used in the first and second detection stages discussed above respectively: 0.001 and 0.0001. The entire dataset was then processed.The density detection is based on the signal dependent noise characteristic in mammograms, analyzed in the high-pass wavelet filtered image. Due to the low resolution, the noise characteristic was not strong enough to characterize the density in the filtered images, requiring algorithm modifications. These modifications are described here within the context of the algorithm flow described in the main report. In the unmodified algorithm, the digitized images were first transformed (a pixel mapping) described previouslyThis algorithm produced acceptable breast area detection in most samples. However, there were both total and partial failures that were corrected manually. In some instances, artifacts external to the breast area remained that induced failures in subsequent segmentation processing steps; these failures occurred ~3% of the images, which were corrected manually. Approximately 6% on the images had a digitizing process anomaly  noted as bright wedges on the vertical borders of the images. After the breast area detection, these over contrasted areas were removed manually.42. The breast is first segmented from the background. The breast area is then eroded by 25% along a radial direction to eliminate the region corresponding to where the breast was not in contact with the compression paddle (an approximation) during the image acquisition44. The erosion process is illustrated in Supplementary Fig. 45. Example images with high and low levels of PMD and V are shown in Supplementary Fig. V is an automated measure that captures gray-scale variation on a mammogram. The algorithm was described previously and is briefly outlined here2), menopausal status , hormone therapy use , mammogram read batch . APD, PMD, and V were categorized into quartiles based on their distribution in controls. We tested for linear trend using category medians as a continuous variable. We also evaluated each measure as a continuous variable and present effect estimates for a one SD increase to account for scale differences. To determine whether each measure was independently associated with breast cancer risk, we present models for PMD or APD adjusted for V, V adjusted for PMD or APD, and APD adjusted for PMD and vice versa. To assess potential interaction, we used likelihood ratio tests with nine degrees of freedom to compare a model with cross-classified quartiles of PMD (or APD) and V to a model with PMD (or APD) and V in quartiles. We conducted secondary analyses: (1) by estrogen (ER) and progesterone (PR) receptor status  and (2) restricted to high-resolution images. All analyses use α = 0.05 for statistical significance. All statistical tests were two sided. All analyses were performed using SAS software .We used unconditional logistic regression to determine the association between PMD measures, V, and breast cancer risk, while adjusting for matching factors and the following potential confounders: age at mammogram (years), body mass index (kg/mFurther information on research design is available in the Supplementary InformationReporting Summary


Plant-based diets are recommended for cancer survivors, but their relationship with breast cancer outcomes has not been examined. We evaluated whether long-term concordance with plant-based diets reduced the risk of recurrence and mortality among a prospective cohort of 3646 women diagnosed with breast cancer from 2005 to 2013. Participants completed food frequency questionnaires at diagnosis and 6-, 25-, and 72-month follow-up, from which we derived plant-based diet indices, including overall (PDI), healthful (hPDI), and unhealthful (uPDI). We observed 461 recurrences and 653 deaths over a median follow-up of 9.51 years. Using multivariable-adjusted Cox proportional hazards models, we estimated hazard ratios (HR) and 95% confidence intervals for breast cancer recurrence and all-cause, breast-cancer-specific, and non-breast-cancer mortality. Increased concordance with hPDI was associated with a reduced hazard of all-cause  and non-breast-cancer mortality , whereas increased concordance with uPDI was associated with increased hazards . No associations with recurrence or breast-cancer-specific mortality were observed. In conclusion, healthful vs. unhealthful plant-based dietary patterns had differing associations with mortality. To enhance overall survival, dietary recommendations for breast cancer patients should emphasize healthful plant foods. There are an estimated 3.8 million female breast cancer survivors in the United States . Due to Plant-based diets in which individuals consume low amounts of animal-based foods, rather than completely excluding animal-based foods, have been adopted in nutritional research to reflect patterns of eating common in the population. A popular method of assessing plant-based diets is using a dietary pattern index, which is a numerical score measuring concordance to an overall pattern of eating . Many plOur study examined the relationship between repeated measures of an a priori plant-based diet index and its healthful and unhealthful variations at the time of diagnosis and in the post-diagnosis period, with breast cancer recurrence, all-cause mortality, breast-cancer-specific mortality, and non-breast-cancer mortality in a large cohort of breast cancer survivors.This analysis used data from the Pathways Study, a prospective cohort of 4505 female invasive breast cancer survivors diagnosed at Kaiser Permanente Northern California (KPNC) between the years 2005 and 2013. The protocol for this cohort has been previously published . In brien = 782, 17.4%), reporting a daily total energy intake of less than 400 or greater than 4000 kcal . An additional 14 (0.3%) participants were excluded for missing data on covariates. This brought the final study analytic population to 3646 women with breast cancer. Of the 3646 participants who had a baseline dietary assessment, 66.1% (n = 2410) had a repeat dietary measurement at 6 months, 42.8% (n = 1561) at 24 months, and 6.8% (n = 247) at 72 months. Dietary data were collected at baseline with a 139-item modified version of the Block 2005 Food Frequency Questionnaire (FFQ) . During Using a methodology defined by Satija et al., three plant-based diet indices were created based on the FFQ: an overall plant-based diet index (PDI), a healthful plant-based diet index (hPDI), and an unhealthful plant-based diet index (uPDI) . Table SThe scores from the indices were operationalized in two approaches: a baseline score and a time-dependent cumulative average score. The baseline score used only the first dietary measurement. This approach allowed us to assess diet around the time of diagnosis. The cumulative average score used the time-updated average of all scores if a participant had repeat dietary measurements. Baseline dietary measurements were carried forward for participants missing follow-up questionnaires, thus assuming dietary intake to be constant over the study period. Using time-dependent cumulative average scores allowed us to account for long-term diet.Breast cancer recurrences were identified using a combination of follow-up health status questionnaires and KPNC electronic medical record searches . Breast Demographic and behavioral covariates collected at the baseline interview included age at diagnosis, race/ethnicity , education , menopausal status, smoking status , total energy in kcal/d, and physical activity as metabolic equivalent of task hours/week of moderate-vigorous activity (MET-hours/week). Breast cancer diagnosis characteristics including stage at diagnosis, estrogen receptor status (ER), and human epidermal growth factor receptor 2 status (HER2) were obtained from the VDW tumor file, which is based on data from the KPNC Cancer Registry . The KPNSpearman correlation coefficients were estimated to compare the scores of PDI, hPDI, and uPDI. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for recurrence, all-cause mortality, breast-cancer-specific mortality, and non-breast-cancer mortality. Separate models were fit for PDI, hPDI, and uPDI. Within the regression analyses, all scores were expressed as a 10-point continuous scale. Expressing the score as quintiles and restricted cubic splines was considered, but we found no evidence of non-linearity, and the 10-point continuous scale was the selected method using the Bayesian Information Criterion. Person-time was calculated as the years from baseline dietary assessment to the date of first confirmed recurrence or death, depending on the model. If no event occurred, participants were censored at the end of the study period, 31 December 2018. Two models for each plant-based index were fit. Model 1 is a minimally adjusted model that adjusted for age at diagnosis, total energy intake in kcal, and physical activity in MET-hours/week. Model 2 is a stratified multivariable-adjusted model that adjusted for covariates in Model 1, and additionally for education, race/ethnicity, smoking status, menopausal status, HER2 status, and stratified by tumor stage and ER status (Model 2). All statistical analyses were conducted using R . Specifip-value < 0.001 and uPDI r = −0.16; p-value < 0.001). A total of 461 breast cancer recurrences and 653 deaths occurred during a median follow-up period of 9.2 years for recurrence and 9.51 years for deaths (range: 0.05 to 12.9 years). A higher proportion of participants with repeated measurements were white, had a post-graduate education, and were postmenopausal. The mean age at diagnosis was 60 years . Baseline characteristics of the study population stratified by PDI quintiles are listed in In Neither PDI, hPDI, and uPDI were associated with recurrence or breast cancer mortality, regardless of exposure method (baseline vs. time-dependent cumulative average) or model (Model 1 vs. Model 2) .In this study of 3646 breast cancer survivors, we found that concordance with healthful plant-based eating patterns (hPDI) in the postdiagnosis period (time-dependent cumulative measurements) reduced the risk of non-breast-cancer mortality. In contrast, greater concordance with unhealthful plant-based eating patterns (uPDI) increased non-breast-cancer mortality risk. We observed no associations between plant-based eating patterns and breast cancer recurrence or breast-cancer-specific mortality. Few studies have addressed long-term dietary patterns and prognosis after breast cancer, and none have examined the hPDI, uPDI, or PDI. Even fewer studies have examined dietary patterns and breast cancer recurrence, and none emphasized the distinction between healthful and unhealthful plant foods or found associations with recurrence . SeveralDifferentiating between healthful and unhealthful plants in plant-based diet indices is a relatively new concept . Our finThough prior research has shown healthful dietary patterns to be important for breast cancer prevention, our study did not observe an association between hPDI, uPDI, and PDI and recurrence or mortality after a breast cancer diagnosis. Potential explanations include that the effect of plant-based diets on breast-cancer-specific outcomes, if they exist, are small, and overwhelmed by the strong influence of breast tumor characteristics  for which we controlled. Another possibility is that a precision nutrition approach with specific foods or nutrients, rather than overall concordance with a healthful, plant-based diet, would be influential in specific breast cancer subtypes . FurtherNo prior study has examined plant-based diets and breast-cancer-specific outcomes such as recurrence, making direct comparison difficult, and previous studies of breast cancer incidence have had inconsistent results ,9,10. InOur study assessed plant-based dietary indices using baseline and time-dependent cumulative average scores. We used both approaches to assess the effect of concordance with plant-based diets at diagnosis and in the postdiagnosis period. We observed that the cumulative average scores, as compared to the baseline scores, yielded stronger associations between hPDI and uPDI, and non-breast-cancer mortality. One explanation for these differences is that an individual’s long-term maintenance of a dietary pattern matters more with regard to breast cancer survival than their diet at the time of diagnosis. However, it is also possible that leveraging repeated measures of a diet (rather than a one-time dietary measurement at baseline) mitigates measurement error, resulting in stronger associations. In addition, since we were observing usual consumption patterns, we could not assess the impact of maximum adherence to any variation of a plant-based diet, as no participant in our study scored at the theoretical maximum. A greater contrast in extremes of exposure could be achieved through a dietary intervention that coaches or provides food to help participants achieve high levels of concordance.There are several strengths to this study, including its population being a large, representative prospective breast cancer survivor cohort recruited from a community setting. By combining active data collection via study questionnaires with KPNC electronic medical records and state and federal data sources, we were able to leverage a rich resource of covariates and outcomes data. With a long follow-up period and repeated measures, we were able to (1) have more robust measures of baseline and long-term dietary intake, (2) distinguish between the quality of different plant food patterns, and (3) assess, for the first time, the association between long-term concordance with healthful and unhealthful plant-based diets and breast cancer recurrence and survival. In summary, healthful plant-based dietary patterns may reduce the risk of non-breast-cancer mortality, whereas an unhealthful plant-based dietary pattern may increase the risk of this outcome. It is thus important to consider the quality of plant foods to achieve a healthful dietary pattern. Healthful plant-based dietary patterns may improve overall survival in breast cancer survivors.


Obesity is an established risk factor for breast cancer growth and progression. A number of advances have been made in recent years revealing new insights into this link. Early events in breast cancer development involve the neoplastic transformation of breast epithelial cells to cancer cells. In obesity, breast adipose tissue undergoes significant hormonal and inflammatory changes that create a mitogenic microenvironment. Many factors that are produced in obesity have also been shown to promote tumorigenesis. Given that breast epithelial cells are surrounded by adipose tissue, the crosstalk between the adipose compartment and breast epithelial cells is hypothesized to be a significant player in the initiation and progression of breast cancer in individuals with excess adiposity. The present review examines this crosstalk with a focus on obese breast adipose-derived estrogen, inflammatory mediators and adipokines, and how they are mechanistically linked to breast cancer risk and growth through stimulation of oxidative stress, DNA damage, and pro-oncogenic transcriptional programs. Pharmacological and lifestyle strategies targeting these factors and their downstream effects are evaluated for feasibility and efficacy in decreasing the risk of obesity-induced breast epithelial cell transformation and consequently, breast cancer development. Breast cancer is the second most common cancer diagnosed in women in the U.S. and the most common cancer among women worldwide, accounting for an estimated 627,000 deaths in 2018 . There a2) greater than or equal to 30, has consistently been associated with breast cancer in postmenopausal women. In premenopausal women, an inverse association has been observed and attributed in part to higher rates of amenorrhea in obese women and consequently, decreased circulating estrogen levels  which was shown to increase expression and nuclear localization of hypoxia inducible factor-1α (HIF-1α) which in turn, stimulates promoter II-driven aromatase expression in ASCs , a negative regulator of aromatase, and by increasing expression and nuclear localization of transcription factors cAMP response element-binding protein (CREB), liver receptor homolog-1 (LRH-1), and transcription factor co-activator peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) which drive aromatase expression  in both adipocytes and macrophages , which is a chemokine that signals macrophages to dead adipocytes  is a byproduct of cellular metabolism with both endogenous and exogenous sources. A major intracellular source of ROS is the mitochondria as a result of increased oxidative phosphorylation , 102. MiA number of factors secreted by obese breast adipose tissue have the ability to stimulate ROS production. Inflammatory cytokines increase ROS production in both cancer and non-cancerous cells , 107. Fovia the process of β-oxidation in which fatty acids are broken down to produce energy (ATP). Fatty acid β-oxidation produces substrates that are used by the tricarboxylic acid (TCA) cycle and the mitochondrial ETC resulting in ATP production. Importantly, this process results in the release of ROS. Increased mitochondrial ROS production in several disease contexts has been demonstrated to be due to oxidation of fatty acids  when needed as source of fuel, such as during fasting. In obesity, FFA levels are often elevated due to enhanced lipolysis of expanding adipocytes and reduced clearance. Cells utilize fatty acids as a fuel source ty acids , 113. Inty acids . Since oty acids . One stuty acids .Abundant evidence demonstrates the ability of obese breast adipose-derived factors, including inflammatory mediators, fatty acids, and leptin, to elevate ROS and consequently oxidative stress. While it is conceivable that this elevation in breast microenvironment oxidative stress leads to DNA damage in pre-neoplastic breast epithelial cells, studies directly demonstrating this link are lacking. Further studies are warranted to determine whether an increase in ROS in obese breast adipose tissue represents a direct mechanistic link between obesity and breast cancer initiation.in vitro studies have demonstrated that the estrogen catechol metabolites 4-hydroxy-estradiol (4-OH-E2), 2-hydroxy-estradiol (2-OH-E2), and 2-methoxy-estradiol (2-MeO-E2) increase ROS and induce DNA damage in normal human mammary epithelial cells (Estrogens stimulate DNA damage in three ways: 1) ligand binding to ERα stimulates proliferation which can lead to replication stress and accumulation of errors when the volume of replication errors overwhelms DNA damage repair capacity. Stimulation of proliferation also increases cellular respiration, which produces ROS as a byproduct of oxidative phosphorylation , 118. 2)al cells –125. Gival cells , 127, fu.Targeting obesity-induced changes in breast adipose tissue has long been a proposed strategy to decrease the risk of breast cancer development. Given the genomic instability associated with obesity, it is also possible that interventions focused on decreasing obesity-induced DNA damage in breast epithelial cells could prevent chromosomal defects, including mutations, that leads to tumor formation. Interventions that target body weight and pharmacological approaches that target obesity-induced changes in hormones and signaling pathways have shown promise in decreasing the risk of breast cancer Weight loss, achieved through reduction of caloric intake, physical activity, or surgery, has been examined as a risk reduction strategy in both preclinical models and in human studies. The 2016 IARC working group’s review of body weight and cancer risk literature determined that weight loss, either through changes in lifestyle or by bariatric surgery, may reduce breast cancer risk, although the number and quality of the studies were deemed “insufficient for formal evaluation” . The mixMechanistically, beyond simply reducing the volume of dysfunctional adipose tissue, weight loss also mitigates many of the obesity-induced changes in adipose tissue that create a pro-carcinogenic microenvironment. In pre-clinical studies, weight loss after diet-induced obesity reduced mammary adipocyte size in association with decreases in adipose inflammation, aromatase expression and oxidative stress-related genes –137. Simin vitro and in vivo , competitively binds the estrogen receptor causing a conformational change that prevents binding by estrogen in the breast . HoweverA more tenable strategy to reduce estrogen in the obese breast adipose microenvironment may be through use of metformin, a low cost, well-tolerated antidiabetic drug that has emerged as an intriguing drug candidate for breast cancer prevention. Although primarily used to reduce gluconeogenesis and increase insulin sensitivity in patients with type 2 diabetes, some, but not all, observational studies have reported lower incidence of breast cancer among diabetic patients taking metformin –159. WhiAs reviewed above, many of the factors found to be reduced after weight loss or by pharmacological intervention, including estrogen, inflammatory mediators, and leptin have roles in promoting DNA damage and inhibiting repair processes. Therefore, it is possible that reduced DNA damage and improved DNA repair capacity represents an additional mechanistic link between the therapeutics that reduces these factors and lower risk of breast cancer. In a randomized controlled trial where participants were placed on a 6-month calorie restriction regimen, a significant decrease in DNA damage measured in blood by the comet assay was reported . SimilarAbundant evidence demonstrates the importance of the crosstalk between obese breast adipose tissue and neighboring normal and neoplastic breast epithelial cells. Excess caloric intake and subsequent weight gain initiates a cascade of effects in breast adipose tissue beginning with adipocyte hypertrophy and progressing to oxidative stress, chronic inflammation, altered adipokine secretion, and elevated estrogen production by ASCs. While these are major, well-established changes in the breast microenvironment, a wide variety of additional changes occur in the setting of obesity, which can also promote breast cancer and have been reviewed by others, for example extracellular matrix stiffening, immune cell dysfunction, and dysregulation of insulin. The current review highlights the effects of breast adipose-derived estrogen, inflammatory mediators, leptin and decreased adiponectin on two aspects of breast cancer: initial events leading to carcinogenesis and progression. These factors have the ability to initiate breast cancer through regulation of oxidative stress, DNA damage, and DNA repair response. Further large-scale studies are warranted to elucidate whether there is a direct mechanistic link between adipose derived factors and breast tumorigenesis through stimulation of genomic instability. Promotion of breast cancer growth occurs through regulation of pro-proliferative and anti-apoptotic transcriptional programs in cancer cells and through effects on microenvironment oxidative stress. Targeting obese breast adipose dysfunction with weight reduction or pharmacological approaches has so far shown promise at decreasing breast cancer risk. With obesity rates expected to reach over 50% in the next 10 years in the U.S., additional studies aimed at disrupting the crosstalk between obese breast adipose tissue and neighboring breast epithelial cells may elucidate novel, effective prevention strategies for obese women at elevated risk for breast cancer development.PB prepared first draft, KB reviewed and edited. All authors contributed to the article and approved the submitted version. This work was supported by NIH R01 CA215797, Anne Moore Breast Cancer Research Fund, and NIH 5 F31 CA236306-02.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


With the increasing number of breast cancer (BC) diagnosed as a second primary malignancy after a first primary non-breast cancer (BCa-2), it is unclear about the familial risk of BC among women with a first-degree relative  affected by a BCa-2.In this Swedish nationwide cohort study, 5315 women with a FDR affected by BCa-2 and 115,048 women with a FDR affected by BC as the first primary cancer (BCa-1) were followed for the first primary invasive BC diagnosis. Relative risk (RR) of BC was estimated through Poisson regression by using 2,743,777 women without a family history of cancer as reference. The risk was stratified by the diagnostic age of BC in FDR, proband type, the time interval between the first primary cancer and BCa-2 in FDR as well as the site of first primary cancer diagnosed in FDR before BCa-2. We also calculated the cumulative incidence of BC from birth to a specific age for the three groups.early-onset, 1.72 vs. RR late-onset 1.67) had less influence on the risk compared to BCa-1 in FDR (1.89 vs. 1.63). In the analysis stratified by the time between the first primary cancer and BCa-2 in relatives, the risks were largely similar. For the site of first primary cancer diagnosed in FDR before BCa-2, the increased BC risk was found in women whose FDRs were diagnosed with first primary gastric, colorectal, endometrial, ovarian, nervous system and endocrine gland cancers, and non-Hodgkin lymphoma.The cumulative incidence from birth to age 70 was 10% among women with a family history of BCa-2. The RR of BC with a family history of BCa-2  was comparable to that with BCa-1 . The risk was largely consistent irrespective of proband type. The age of onset of BCa-2 in FDR (RR Women with a family history of BCa-2 have a similar overall BC risk as those with a family history of BCa-1. The risk varied according to the site of first primary cancer diagnosed in FDR before BCa-2.The online version contains supplementary material available at 10.1186/s12885-021-08925-y. Breast cancer (BC) is the leading cause of cancer death among women worldwide . Family The aim of the study was to estimate the familial risk of BC among women whose FDR was diagnosed with BCa-2 after a first primary non-breast cancer. The findings may bridge the knowledge gap of the family history of BCa-2, given the increasing number of cancer patients diagnosed with BCa-2. It will also be important for the realization of personalized cancer prevention and early detection.This study was conducted with linkage of several Swedish national registers using each person’s unique identification number. To preserve confidentiality, this ID number was replaced by a serial number. The Swedish Cancer Registry recorded all incident tumors from 1958 with more than 90% coverage . The SweThe design of the familial risk analysis is shown in Fig. N = 395). We additionally removed women who had more than one FDR affected by BCa-1 or BCa-2, or women who had FDR affected by higher order primary cancers (such as the third or fourth primary BC) to keep the analyses comparable among different categories. We excluded the latter group, as it is difficult to distinguish which primary cancer contributed to risk modification. Ultimately, a total of 5315 women with a family history of BCa-2 were retained for the main analysis together with 2,743,777 women who had relatives without any cancer, and 115,048 women with a family history of BCa-1. We also analyzed the risk keeping families with multiple affected FDRs or FDRs with higher order primary cancers as a supplementary result  by using women without FDR affected by cancer as reference group. In the model, age groups (5 years), periods (5 years), parity , socioeconomic status  and place of residence  were also adjusted for. Number of childbirth was considered as null parity is a risk factor for breast cancer . The twoThe median  age at diagnosis of the BCa-1 and BCa-2 in relatives were 58 (49–67) years and 68 (59–76) years, respectively. For women with family history of BCa-2, the median time between the first primary cancer and BCa-2 diagnoses in FDR was 8 (3-18) years. At the end of the follow-up, 5345 women, who had a FDR affected by BCa-1, were diagnosed with BC at the median age of 54 (47–62) years, and 298 women, who had a FDR affected by BCa-2, were diagnosed with BC at the median age of 54 (48–62) years.Table Women with a family history of BCa-2 were classified into four groups based on quartiles of the interval  between the first primary cancer and BCa-2 diagnosed in relatives .We present the results without removing women with more than one FDR affected by BCa-1 or BCa-2 and women with FDRs affected by cancer after BCa-1 and BCa-2 in Supplementary Table With the increasing number of the BCa-2, it is of great importance to investigate if familial risk differs between a family history of BCa-1 and BCa-2. Most of the studies involved with a family history of multiple primary cancers were set up in families with hereditary cancer syndrome . No studWe excluded women who had multiple FDRs affected by BCa-2 and women who had FDRs affected by BCa-2 then later developed with third, fourth or even fifth primary cancers, which could be the reason that we observed slightly lower familial risk (1.68). After removing those restrictions, the familial risk was comparable to those reported from other studies . The risThe development of the second primary cancers is linked to various factors which is partially indicated by the interval between first and second primary cancer diagnoses. The intensified medical surveillance increases the chance of second primary cancers diagnosed right after the first primary and those cancers are unlikely to be caused by the treatment from the first primary. Instead, treatment-related second primary cancers develop after several years from the first primary cancer diagnosis. In the analysis stratified by the time between the first primary cancer and BCa-2 in relatives, despite fluctuation, the familial risks were largely consistent, implying the influences of medical surveillance and cancer treatments on familial risk of BC is limited.CHD1 [The finding of the varied risk for a family history of BCa-2 after some specific cancers, indicates that the site of cancer before BCa-2 gives a better prediction of BC risk. For endometrial and ovarian cancers, the increased risk can be explained by shared reproductive factors, the germline mutation manifested in hereditary breast and ovarian cancer syndrome or the interaction between them. The elevated risk with a FDR affected by first primary stomach cancer may be related to a type of hereditary diffuse gastric cancer associated with germline mutation of CHD1 . For menCHD1 . As for CHD1 . For smoCHD1 . HoweverCHD1 . BC may Family history has been an important determinant for the assessment of BC risk in commonly used models including NCI-Gail, BCSC, IBIS and BOADICEA , 24. TheBy using the Swedish national registries, we were able to obtain accurate family relationships and order of multiple primary cancers without information bias. We implemented very strict analyses to control the possible influences (such as the number of relatives with cancers) on the risk estimation. Analyses without the restrictions were also performed to generalize the results. We conducted multiple stratification analyses to characterize BC risk with the family history of BCa-2. The family history can be defined in dynamic and static scale depending on if the presence of the disease in FDRs is considered as time-varying and time-fixed. The two methods generate a similar estimation of relative risk , 26, so As far as we are aware, we for the first time, provided information on BC risk assessment for women with family history of BCa-2. The similar BC risk indicates that women with FDR affected by BCa-2 can be managed similarly as those with FDR affected by BCa-1, regarding BC prevention and screening. In addition, for those with family history of BCa-2, the familial risk varies most according to the site of first primary cancer in FDR, which should be considered in the clinical practice.Additional file 1.Supplementary Figure 1. The flowchart of the population selection. Supplementary Table 1. Breast cancer risk in women when FDRs were diagnosed with BCa-1 or BCa-2.*


CCL2, VEGF-C, IL6, and aromatase. Collectively, these results help to explain why high levels of trunk fat are associated with an increased risk of breast cancer in normal BMI women.Increased trunk fat is associated with an elevated risk of breast cancer in normal-weight postmenopausal women. The main objective of this study was to determine whether levels of trunk fat are associated with changes in breast gene expression in normal-weight women. Non-tumorous breast tissue was collected from 32 normal BMI women who underwent mastectomy for breast cancer risk reduction or treatment. Body composition was measured by dual-energy x-ray absorptiometry. High levels of trunk fat were associated with a large number of differentially expressed genes and changes in multiple pathways and processes potentially linked to breast cancer pathogenesis. High levels of trunk fat were also associated with an elevated immune score and increased levels of leptin,  The link between excess body fat and malignancy has largely been established using anthropometric measurements such as body mass index (BMI), which takes into account weight and height but does not discriminate between muscle, bone, and fat. Some women deemed to be healthy because of a normal BMI have underlying cardiometabolic abnormalities and are commonly referred to as metabolically obese normal weight (MONW)4. Dual-energy x-ray absorptiometry (DXA) is used to obtain direct measures of body fat including trunk fat. In normal BMI women, high levels of body fat including trunk fat have been associated with enlarged breast adipocytes and breast white adipose tissue inflammation (B-WATi)5, as defined by the presence of crown-like structures (CLSs). B-WATi, in turn, has been associated with an elevated risk of breast cancer in women who have benign breast disease6. Given this background, we previously posited that normal BMI women with high levels of body fat would be at increased risk for postmenopausal breast cancer7. In a study of more than 3000 normal BMI women who had enrolled in the Women Health Initiative, we relied on DXA-derived measures of body fat and found that women in the top two quartiles of trunk fat had about a doubling in the risk of estrogen-dependent breast cancer compared to women in the lowest quartile of trunk fat8. The sample size was too small to determine whether the amount of trunk fat was also associated with altered risk of either HER-2/neu-overexpressing or triple-negative breast cancer. Circulating levels of C-reactive protein, insulin, leptin, and triglycerides were higher, whereas levels of sex hormone-binding globulin and high-density lipoprotein cholesterol were lower in those in the highest vs. lowest quartiles of trunk fat mass8. More recently, we found that high levels of blood C-reactive protein and testosterone or low blood levels of sex hormone-binding globulin were associated with an elevated risk of postmenopausal breast cancer among normal BMI women9.Obesity is a risk factor for numerous cancers, including postmenopausal hormone receptor-positive breast cancer10. Increased levels of aromatase, the rate-limiting enzyme for estrogen biosynthesis, have been reported in the breast tissue of normal BMI women with B-WATi and higher levels of trunk fat7. Despite these recent advances, very little is known about the molecular changes that occur in the breast itself in normal BMI women with high vs. low levels of trunk fat. Accordingly, the main objective of the current study was to use RNA-sequencing (RNA-seq) to define the changes in the breast transcriptome that occur in normal-weight women who have high vs. low levels of trunk fat.It has become increasingly clear that normal BMI as a category includes women with relatively high levels of body fat who are at increased risk for postmenopausal breast cancer and that abnormal levels of systemic factors may be contributory5. RNA-seq data were obtained from non-tumorous breast tissue of 32 normal-weight women, which defined the study cohort. To determine the potential association between levels of trunk fat and breast gene expression, the 32 women were divided into two groups based on percent trunk fat and are referred to as high and low groups, respectively. A comparison was done of 16 normal-weight women with high levels of trunk fat  vs. 16 normal-weight women with low levels of trunk fat (less than the median of 29.45%). Women with high levels of trunk fat were older (P = 0.01), had a higher BMI (P = 0.003), were more likely to have B-WATi (P < 0.001) and had larger breast adipocytes (P < 0.001) (Table Subjects were recruited from April 2016 through August 2018 and underwent DXA scans prior to mastectomy2FoldChange| > 0.6) were found in high compared to low trunk fat women using DESeq2  (P = 0.002) but not high BMI      Fig. . In fact14. Given the observed increase in macrophages in the breast tissue of normal-weight women with high vs. low trunk fat, we evaluated the relationship between levels of trunk fat (trunk fat percent) and CCL2 expression. As shown in Fig. CCL2 expression . Notably, the level of CCL2 expression also positively correlated with CD68 expression  and the density of CLS  , a component of the complement pathway, attracts inflammatory cells including monocytes into adipose tissue contributing to CLS formation15. Consistent with the increased macrophage population found in the breast tissue of normal BMI women with high vs. low levels of trunk fat, levels of C3AR1 positively correlated with trunk fat percentage  and density of CLS  Fig. . In addi16. In contrast, little is known about the expression of select genes linked to carcinogenesis in the breast tissue of normal-weight women with high vs. low trunk fat. Hence, we evaluated the relationship between trunk fat percentage and the expression of several genes that potentially play a role in the pathogenesis of breast cancer. Some studies have suggested that leptin contributes to the pathogenesis of breast cancer17. Moreover, blood levels of leptin are elevated in normal-weight women with high levels of trunk fat and B-WATi8. Here we observed a strong correlation between trunk fat percentage and leptin expression  and HMOX1 , which may reflect an increase in reactive oxygen species . A nearly significant positive correlation was found between trunk fat percentage and the expression of CYP19A1 . Institutional Review Board approval was obtained from MSKCC (IRB Protocol Number 15-235) and Weill Cornell Medicine (IRB Protocol Number 1511016737)  to conduct this study. Trunk fat was measured by DXA using a Lunar Prodigy multiple detector fan-beam densitometer  before surgery. Following calibration, single-beam, whole-body scanning was performed in the supine position. Weight and height were recorded before surgery and used to calculate BMI. A standard definition was used to define normal BMI (18.5–<25 kg/m2). Clinicopathological data  were extracted from the electronic medical record. Menopause status was categorized as either postmenopausal or premenopausal based on National Comprehensive Cancer Network criteria25.The subjects in this study of normal BMI women represent a subgroup of a previously reported cohort study that included measurements of body composition, B-WATi, and breast adipocyte diameter in women of all sizesBreast WAT specimens were obtained prospectively under a standard tissue acquisition protocol. In specimens that contained tumors, tissue from an uninvolved quadrant of the breast was used for research purposes. For each subject, paraffin blocks were prepared from breast WAT not involved by a tumor on the day of surgery. Frozen samples were stored in the presence RNAlater (Ambion).27. Briefly, B-WATi was detected by the presence of CLS, which represents a dead or dying adipocyte surrounded by CD68-positive macrophages28. Five formalin-fixed paraffin-embedded (FFPE) blocks were prepared from each mastectomy specimen and one section per FFPE block  was generated such that five sections were stained for CD68, a macrophage marker . Light microscopy was used to examine immunostained tissue sections to detect the presence or absence of CLS and record the number of CLS per slide. Digital photographs were generated and the WAT area was measured with the Image J Software . The number of CLS per square centimeter of WAT (#CLS/cm2) also referred to as CLS density was calculated to determine the severity of B-WATi.The absence or presence of B-WATi was determined by histologic assessment as described before5. Adipocyte diameter is expressed in microns.Two hematoxylin & eosin-stained sections per case were prepared from FFPE breast tissue in order to measure adipocyte diameters as previously described29. Then, the transcript abundance was quantified to obtain raw counts.Qiagen’s RNeasy minikit was used to isolate total RNA from non-tumorous breast tissue. Then, using the Illumina TrueSeq Stranded Total RNA Library preparation protocol with rRNA depletion, the libraries were constructed. RNA-seq was carried out with paired-end 51 bp using the Illumina HiSeq4000 platform (Weill Cornell Medicine). Raw sequenced reads were pseudoaligned to the human reference genome (UCSC hg19) using Kallisto30. The genes with low expression values were filtered out (baseMean ≤ 15) first. Then, genes with adjusted P value < 0.05 and |Log2FoldChange| > 0.6 were defined as DEGs. Volcano plots were generated to visually display the most biologically significant genes using statistical significance vs. fold-change. Two vertical dashed lines correspond to −0.6 and 0.6, which represent one of the cutoffs for the DEG. The horizontal dashed line corresponds to P adjusted value of 0.05, which is another cutoff for the DEG.Raw counts acquired from Kallisto were used to characterize DEGs and DESeq2 R package was used32 and BioCarta33 gene sets from the Molecular signature database were used.To determine the molecular function of DEGs in various biological processes, KEGG11. Then, specific immune cell-type changes were determined using a webtool, xCell (https://xcell.ucsf.edu/)34. To confirm findings from xCell, immune cell expression markers from the study of Danaher et al. were used12.To determine whether the overall immune cell population in breast WAT changed in association with percent trunk fat, Estimate R package was used to generate an immune scorehttps://www.genepattern.org/modules/docs/ssGSEAProjection/4). Gene sets from Hallmark and Gene Ontology databases were used37.To determine whether a given pathway is coordinately upregulated or downregulated in a sample set, single-sample GSEA (ssGSEA) was used. ssGSEA calculates an enrichment score for each gene set and the score represents the activity level of a biological process. The analysis was carried out on the public server .Patient characteristics were summarized in terms of counts and proportions for categorical variables and median and interquartile range for continuous variables. Fisher’s exact test was used to examine the difference in the distribution of a categorical variable between two independent groups. Wilcoxon rank-sum test was used to examine the difference of a continuous variable between two independent groups. Correlation between two continuous variables was examined using Spearman’s method. For all analyses, statistical significance was set at two-tailed Further information on research design is available in the Supplementary InformationReporting Summary


This study aimed to evaluate the cost-effectiveness of a breast cancer screening programme that incorporates genetic testing using breast cancer associated single nucleotide polymorphisms (SNPs), against the current biennial mammogram-only screening programme to aid in its implementation into the current programme in Singapore.A Markov model was used to compare the costs and health outcomes of the current screening programme, against a polygenic risk-tailored screening programme, which can advise a long-term screening strategy depending on the individual’s polygenic risk. The model took the perspective of the healthcare system, with a time horizon of 40 years, following women from the age of 35 to 74. Epidemiological and cost data were taken from Asian studies, and an annual discount rate of 3% was used. The model outcome was the incremental cost-effectiveness ratio (ICER), calculated from the difference in costs per quality-adjusted life year (QALY). Scenarios with varying risk thresholds for each polygenic risk group were examined. One-way and probabilistic sensitivity analyses were performed to assess parameter uncertainty.The ICER for a polygenic risk-tailored breast cancer screening programme, compared with the current biennial mammogram-only screening programme, was − 3713.80 SGD/QALY, with incremental costs < 0 and incremental effects > 0. The scenario analysis of different polygenic risk cutoffs showed that the ICERs remain negative, with all ICERs falling within the south-east quadrant of the cost-effectiveness plane, indicating that tailored screening is more cost effective than mammogram-only screening, with lower costs and higher QALYs to be gained. This suggests that a polygenic risk-tailored breast cancer screening programme is cost effective, entailing lower cost than the current mammogram-only programme, while causing no additional harm to women.Results from this cost-effectiveness analysis show that polygenic risk-tailored screening is cost effective with an ICER of − 3713.80 SGD/QALY. Tailored screening remains cost effective even across varying percentile cutoffs for each risk group. While the results look promising for incorporating polygenic risk into the current breast cancer screening programme, further studies should be conducted to address various limitations.The online version contains supplementary material available at 10.1186/s12913-021-06396-2. Breast cancer is the most prevalent cancer among women globally. In 2018 alone, approximately two million new cases of breast cancer were diagnosed, accounting for 11.6% of all cancers, resulting in more than 626,000 deaths . FurtherGiven the critical importance of early detection in improving breast cancer outcome in Singapore, the BreastScreen Singapore programme was established in 2002 by the Health Promotion Board and has promoted the early detection of breast cancer to improve mortality rates , 5. SingClearly, improvements can be made to the Singaporean age-based mammogram screening programme. Polygenic risk scores (PRS) have been shown to predict an individual’s risk of diseases –13. UsinHence, we recognize the potential of a genetic risk approach to breast cancer screening, and through this study, we will present a cost-effectiveness analysis on a genetic risk-based screening programme in the context of Singapore. We will introduce a polygenic risk-tailored screening programme which aims to facilitate early detection by providing tailored screening recommendations based on an individual’s risk group. This may reduce unnecessary testing and false positives that are common in a one size fits all mammogram screening programme, while also acting as a platform to increase awareness in personal breast cancer risks. We compare the current age-based biennial mammogram screening programme with a strategy that incorporates genetic testing using breast cancer associated SNPs, thus evaluating the cost-effectiveness of the genetic risk prediction approach for breast cancer in Singapore to aid in its implementation within the current strategy.The target population of this cost-effectiveness analysis is Singaporean women aged 35 to 74, a time horizon of 40 years. The two strategies being compared are – the proposed tailored screening strategy, which advises a screening programme based on an individual’s PRS, and the current mammogram screening only strategy, where mammogram screening is done every 2 years. A Markov model Figs.  and 2 foIn the polygenic risk tailored screening strategy, individuals aged 35 to 74 will be genotyped by buccal swab and asked to complete a questionnaire on breast cancer risk factors, before being stratified into three risk groups based on their initial PRS – low, intermediate, and high. The PRSs are stratified by setting cutoffs at below 60th percentile for the low-risk group, 60th to 95th percentile for the intermediate-risk group and above 95th percentile for the high-risk group. In the Asian population, these cutoffs correspond to expected proportions of 51, 41 and 8% for low-, intermediate- and high-risk groups respectively [study team unpublished data]. The starting age of 35 for this strategy was chosen based on the findings that the risk for women with a high genetic risk for developing breast cancer may be as high as the 10-year risk for an average 50-year old woman, already at 35 years of age . There iIndividuals in each risk group will receive their initial PRSs within three to 6 months a buccal swab. Depending on their age group, these individuals will be required to undergo a subsequent follow-up screening test comprising of self-examination, ultrasound or mammogram Fig. . ResultsParameters were extracted from Singaporean studies or closely related Asian studies if Singaporean equivalents were not available. Age-specific incidence rates (ASIR) for breast cancer were provided by the Demographic Epidemiological Model of Singapore (DEMOS), a published local micro-simulation disease model that synthesizes evidence from multiple data sources . ScreeneThe attendance rate of 100% of all women was assumed. The model also assumes that all women can die from natural causes in between screening cycles. Those who are diagnosed with breast cancer do not go into remission and instead remain in the diseased states. Risk group multipliers were selected based on the assumption that high-risk women for instance, are approximately twice as likely as the average population to develop breast cancer. Low-risk women, conversely, would be half as likely as the average population to develop breast cancer , 26. TheProbabilistic sensitivity analysis (PSA) and one-way sensitivity analysis (OSA) were carried out to assess parameter uncertainty. For OSA, the lower and upper limits of each parameter were calculated using 80 and 120% respectively of the original value. PSA was conducted on the cost parameters  and utilities . Ten thousand runs of Monte Carlo simulations were carried out for the PSA.Given that the cut-offs are somewhat arbitrary, we evaluated the impact of adopting different cutoffs for the risk groups in a scenario analysis. For the high-risk group, cut-offs at 5th to 10th percentile was explored, while for the low-risk group, cut-offs at 40th to 60th percentile was explored. These ranges were covered by three separate scenarios – 1) 60th percentile low−30th percentile intermediate-10th percentile high (60 L-30I-10H), 2) 40th percentile low-55th percentile intermediate-5th percentile high (40 L-55I-5H), and 3) 40th percentile low-50th percentile intermediate-10th percentile high (40 L-50I-10H), all in comparison with the base-case scenario of 60th percentile low-35th percentile intermediate-5th percentile high (60 L-35I-5H)  showed that only 39.6% of women aged 50–69 years old have attended screening in the previous 2 years . MammogrWe are aware that demonstrating cost-effectiveness is only the first step. There are many other barriers when it comes to implementing such a risk-based cancer screening programme, as we have seen in other examples . SuccessWe carried out a cost-effectiveness analysis comparing the current age-based mammogram breast cancer screening programme in Singapore, against a genetic risk-tailored programme. Our results show that tailored screening is cost-effective with an ICER of − 3713.80 SGD/QALY, while being less costly with no additional harm. Tailored screening remains cost-effective even when varying percentile cutoffs for each risk group. These results are crucial for policymakers in demonstrating the feasibility of a risk-based approach in Singapore. However, while the results may serve as important foundations for a risk-based approach to be implemented, further studies should be conducted to address the limitations related to data availability and modeling.Additional file 1: Supplementary Figure 1. Cost effectiveness acceptability curve for the baseline scenario (60L-35I-5H).Additional file 2: Supplementary Figure 2. Cost effectiveness acceptability curve for the 60L-30I-10H scenario.Additional file 3: Supplementary Figure 3. Cost effectiveness acceptability curve for the 40L-55I-5H scenario.Additional file 4. Excel model file for the 40L-50I-10H scenario.Additional file 5. Excel model file for the 40L-55I-5H scenario.Additional file 6.Excel model file for the 60L-30I-10H scenario.Additional file 7. Excel model file for the 60L-35I-5H scenario.


Meanwhile, long-term SERM treatment was effective at preventing metastatic cancer  and death . SERM treatment, albeit generally safe and effective, may increase diabetes risk with its long-term use in women with breast cancer. Further studies are required to verify the association between toremifene treatment and incident diabetes.Accumulating evidence indicates a link between diabetes and cancer. Selective estrogen receptor modulators (SERMs) may increase diabetes risk via antiestrogen effects. This study investigated incident diabetes risk of SERM treatment and its effects on metastatic cancer and death prevention in breast cancer survivors. This retrospective cohort study included female patients with early-stage breast cancer, treated with or without SERMs, between 2008 and 2020 in a tertiary care hospital in Korea. Four propensity score-matched comparison pairs were designed: SERM use versus non-use, long-term use (≥1500 days) versus non-use, tamoxifen use versus non-use, and toremifene use versus non-use; then, logistic regression analysis was performed for risk analysis. SERMs in general were not associated with an elevated risk of diabetes; however, when used for ≥1500 days, SERMs—especially toremifene—substantially increased diabetes risk in breast cancer patients (OR 1.63,  A pathophysiological link between diabetes and cancer has been suggested in several studies; diabetic patients are predisposed to cancer and vice versa when compared with their healthy counterparts, mediated by shared risk factors between the two disease states, metabolic alterations, and complications over disease progression, along with adverse effects from prolonged exposure to pharmacologic therapies ,3,4,5,6.Among various treatment modalities for breast cancer management—including surgery, radiation, and chemotherapy—endocrine therapy is reserved for patients diagnosed with hormone receptor (HR) (either estrogen receptor (ER) or progesterone receptor (PR)) positive breast cancer . SelectiA previous study reported a possible association between type 2 diabetes mellitus (T2DM) and tamoxifen . Evidencn = 148), metastatic cancer (n = 15), and unstable glycemic control (n = 4). Resultantly, 7471 patients were screened for study inclusion: 2531 SERM users and 4940 non-users. Baseline characteristics of the initial cohort patients are summarized in There were 7638 adult female patients who had hospital encounters associated with breast cancer diagnoses over the 13-year study period. Of those, a total of 167 patients were excluded due to the following reasons at study entry: preexisting T2DM (p = 0.53). To account for differential effects of SERMs per patient risk factors, the outcome analyses were then stratified by age and Charlson Comorbidity Index (CCI) category as well as comedication patterns with diabetes promoting drugs and oral glucocorticoid over the follow-up period. Overall, no signal of increased risk of T2DM was detected in any of the above strata. There was also no significant difference in glycemic changes observed between groups: the mean change in fasting glucose and glycated hemoglobin (HbA1c) was 4.5 ± 38.9 mg/dL versus 7.2 ± 44.0 mg/dL (p = 0.14) and 0.0 ± 0.4% versus 0.1 ± 0.7% (p = 0.39) in SERM users and non-users, respectively. Metastatic cancer was newly diagnosed in a comparable number of patients in both groups, while the risk of death was significantly lower in SERM users compared with non-users . No signal of incident diabetes risk was detected with SERMs use in general as compared to non-use.The incidence and risk of new-onset T2DM in SERM users as compared to non-users were assessed in the 1:1 PS-matched groups, and the results are summarized in p = 0.048). When SERMs per individual agent were compared separately against PS-matched control patients, none of the agents were associated with an increased risk of T2DM compared to non-users. However, when treatment duration was considered for each agent, an elevated risk of T2DM was detected with toremifene when used for ≥1500 days .A series of additional analyses were performed to assess differential effects of SERM agents and treatment durations on the risk of incident T2DM . Here, ap = 0.049). The incidence of T2DM with long-term use of SERMs was higher in both diabetes promoting drugs exposure and oral glucocorticoid exposure strata, but risk analyses were not performed due to the scarcity of outcome events and limited number of patients belonging to each stratum. When assessed per individual agent, we found elevated risk of T2DM with long-term use of toremifene only . Glycemic control over the follow-up was comparable between groups as newly diagnosed patients are likely initiated with lifestyle modification measures along with hypoglycemic treatments as needed: the mean change in fasting glucose and HbA1c was 5.4 ± 43.5 versus 4.9 ± 33.3 mg/dL ( p= 0.88) and 0.1 ± 0.7 versus 0.3 ± 0.6% (p = 0.40) in long-term users and non-users, respectively. The risk of metastatic cancer and death was substantially decreased with long-term use of SERMs as compared with non-use: the OR was 0.20 (p < 0.001) and 0.13 (p < 0.001), respectively. Here, an increased risk of incident diabetes was assessed when SERM therapy lasted for ≥1500 days as compared to non-use, especially in those aged 40 years to 65 years, with CCI of 2, and who received toremifene. Detailed results are available in To further investigate risk factors associated with an increased risk of T2DM among those who exposed to SERM for 1500 days or longer, subgroup analyses, stratified by age and CCI category and comedication patterns with diabetes promoting drugs and oral glucocorticoid, were performed between 1:1 PS-matched long-term users versus non-users . Here, wp = 0.16) and 0.0 ± 0.5% versus 0.1 ± 0.7% (p = 0.66) in long-term users and non-users, respectively. Tamoxifen-receiving patients had a significantly lower risk of death than non-users , although metastatic cancer risk was not significantly different between groups. There was no significant risk of incident diabetes with tamoxifen use as compared to non-use. Detailed results are provided in To explore potential risk factors associated with tamoxifen, the most frequently prescribed SERM agent, subgroup analyses were conducted using similar stratification methods as above by comparing 1:1 PS-matched tamoxifen users, irrespective of treatment duration, against non-users . No signp = 0.045). Although the rate of new-onset diabetes was 1.8 times higher among long-term toremifene users than in non-users (8.3% versus 4.7%), it did not reach statistical significance (p = 0.06) in this PS-matched subgroup analysis. Glycemic changes over the follow-up were not significantly different between groups: the mean change in fasting glucose and HbA1c was 9.6 ± 64.8 mg/dL versus 5.2 ± 38.6 mg/dL (p = 0.50) and 0.1 ± 0.3% versus 0.1 ± 0.4% ( p = 0.10) in toremifene users and non-users, respectively. No substantial difference was observed in both the risk of metastatic cancer and death between groups. Here, as compared to non-users, a statistically significant risk of incident diabetes was detected in toremifene users who did not receive oral glucocorticoid concurrently. Detailed results are provided in Subgroup analyses were repeated to explore potential risk factors associated with toremifene’s effects on T2DM risk, based on the similar stratification methods by comparing 1:4 PS-matched toremifene users, irrespective of treatment duration, against non-users . ToremifIn this study, we assessed the risk of new-onset T2DM along with metastatic cancer and death risks in SERM-treated women with early-stage breast cancer as compared to those never exposed to SERMs. SERMs in general were not associated with elevated risk of T2DM. However, long-term (≥1500 days) use of SERMs—particularly toremifene—substantially elevated T2DM risks in women with breast cancer as compared to non-use. Unlike the findings in a previous study , tamoxifMeanwhile, beneficial effects were detected with long-term use of SERMs on both metastatic cancer and death prevention in study patients, although the type of breast cancer may also have played a role in these findings. Breast cancers can be characterized as HR positive (either single or double HR positive), human epidermal growth factor receptor 2 (HER2) positive, or triple negative, and the type of cancer determines the anticancer treatment modalities and also predicts tumor sensitivity to pharmacological therapies as well as patient prognosis. Of these, HR positive breast cancer is the major type—accounting for more than 70% of all breast cancers—and considered more benign than dual or triple negative cancers . In the p = 0.048). The incidence of T2DM was relatively higher in those patients concurrently receiving other medications known for diabetes risk, such as oral glucocorticoid and diabetes promoting drugs, implying that SERM treatment may place women with breast cancer, already predisposed to metabolic complications, at higher risk of new-onset diabetes in the long term.Diabetes increases the risk of site-specific cancers, including breast cancer ,22. Accop = 0.049) and those with a CCI score of 2 . Increasing age, usually above 45 years, is a major risk factor for T2DM, and menopause also disturbs glucose homeostasis and insulin sensitivity, further elevating diabetes risks [In the PS-matched risk analysis where long-term use of SERMs was compared against non-use, the majority of patients belonged to the age group of 40–65 years (72.4%) and CCI scores of 2 (94.2%), indicating that study patients here were mostly not elderly and had no comorbidity other than breast cancer at baseline. These characteristics likely correlate with the study inclusion criteria: only those with early-stage breast cancer were eligible for study entry. Long-term use of SERMs showed a substantially higher risk of T2DM than non-use, especially among patients aged 40–65 years  but not with tamoxifen . Toremifene has a similar drug structure to tamoxifen with the addition of a chloride, but possesses more favorable adverse event profiles than the latter: reduced risks of endometrial cancer and thromboembolism [Antiestrogen activity of SERMs increases T2DM risk in breast cancer patients as insulin homeostasis is regulated in an estrogen-dependent manner ,14,15. Hembolism ,28,29. Nembolism . TherefoIt is essential to continuously monitor and assess adverse events associated with SERMs reflecting new safety signals, considering the long treatment duration—5-years in most cases—in breast cancer patients. However, there have been insufficient data pertaining to post-marketing surveillance of both tamoxifen and toremifene with respect to their potential impact on metabolic complications among breast cancer survivors. Prevention of adverse effects and complications from long-term exposure to cancer treatment is crucial in order to improve the morbidity and mortality of these patient populations. This study confirmed that SERMs were effective at preventing metastatic progression of cancer and death, consistent with previous studies . HoweverThis study has several limitations. First, it was a retrospective cohort study based on patient data from single-center electronic medical records (EMRs). Thus, our findings may not be generally applicable to other clinical settings. Any missing or incorrect documentation of diagnosis, medication use, and laboratory values may have influenced the assessment of baseline comorbidities, comedication patterns, and study outcomes. Second, because of the inherent heterogeneity between SERM users and non-users in clinical settings, breast cancer types could not be balanced via PS matching or multiple logistic regression analysis. Third, due to the small sample size and rarity of outcome events, further studies are required to confirm the association between toremifene and new-onset diabetes risk. Additionally, although raloxifene and bazedoxifene—SERM agents prescribed for osteoporosis treatment or breast cancer prophylaxis in postmenopausal women—were not included as a study drug, its potential effects on incident diabetes development also need to be investigated to have a better understanding in SERMs’ class effects versus agent-specific effects. Despite the limitations, the findings of the current study suggested clinically important risks associated with SERMs use in women with early-stage breast cancer. Further research with a larger sample size representing each SERM agent is warranted to provide risk-based, personalized therapeutic optimization in long-term management of breast cancer.This retrospective cohort study included adult women with breast cancer, identified per International Classification of Disease, Tenth Revision (ICD-10) diagnosis code and who received an inpatient and/or outpatient care in a tertiary care hospital in Korea during the patient recruitment period between 1 January 2008 and 30 June 2020. Eligible patients were categorized into two cohorts (SERM users versus non-users), and only those with a follow-up period greater than one year were eligible for study inclusion. SERM agents in this study included tamoxifen and toremifene, but not raloxifene and bazedoxifene because they are not approved to treat breast cancer. Predetermined exclusion criteria were underlying T2DM or metastatic cancer at baseline. We also excluded those patients diagnosed with advanced-stage breast cancer (stages 3 and 4) prior to study entry, identified via surgery history and EMRs reviews. Hence, only those with early-stage breast cancer were screened for study entry. For outcome analysis, study patients were followed up until December 2020. This research was a retrospective study approved by the Institutional Review Board (IRB) and the requirement of informed consent was waived by the Ajou University Hospital IRB (AJIRB-MED-MDB-19-309).Breast cancer patients treated with SERMs for more than 90 consecutive days were categorized as SERM users and those not exposed to SERMs as non-users. To assess differential effects of the hormonal therapy, users were further classified into three cohorts in accordance with their treatment duration: <600, 600 to <1500, and ≥1500 days. Prespecified variables included patient demographics, comorbidity status along with CCI, use of other medications, such as aromatase inhibitors, diabetes promoting drugs , oral glucocorticoid, estrogen, progesterone, and adjuvant chemotherapy. The following comorbid conditions were identified per ICD-10 code: cardiovascular disease, end-stage renal disease, stroke, and venous thrombosis.The primary outcome was the incidence and risk of new-onset T2DM in female patients with breast cancer receiving SERM therapy versus those not exposed to such treatment. The outcome analysis was stratified by age category, and exposure to diabetes promoting drugs or oral glucocorticoid, in order to account for differential effects of SERMs per different patient factors. The secondary outcomes were newly-diagnosed metastatic cancer and all-cause death. These outcomes were identified via hospital visit episodes associated with the respective events over the follow-up period. The index date for each study participant was defined as the first hospital encounter date associated with a breast cancer diagnosis. A series of additional cohort risk analyses were performed to better evaluate differential effects of SERMs per individual agent and treatment duration on the risk of incident T2DM, by repeating PS matching against the non-user control group to design each comparison pair. Patient follow-up began on the index date and continued until the earliest occurrence of any of the following censoring events: study endpoint events, follow-up discontinuation prior to the study end date or 31 December 2020, allowing a follow-up period up to 13 years. To improve the reliability and quality of this retrospective study, when it comes to SERM users, only those cases that occurred at least 180 days after therapy initiation were counted as an eligible event and included in risk analysis.p-values were two-sided and considered statistically significant if <0.05. Statistical analyses were performed using SAS 9.4 software .To balance potential confounding factors between SERM users and non-users, eligible patients were matched in a 1:1 or 1:4 ratio to each of the two comparison groups, based on PS matching per relevant pretreatment variables in terms of age category, comorbidity at study entry, and comedication patterns during the follow-up. The PS matching method was then repeated to identify control group patients for each of the following seven comparison pairs: user group patients belonging to four different treatment duration cohorts  versus the corresponding number of non-users, plus two user group patients per SERM agent versus the corresponding number of non-users for each comparison pair, respectively. The multinomial PS for individual patients was estimated by fitting a logistic regression model accounting for the aforementioned baseline characteristics as covariates. The caliper matching method was utilized to improve the quality of matching due to heterogeneity with baseline glycemic control and comorbidity status between cohorts. The multiple logistic regression analysis was then used to further adjust for potential confounders among PS-matched patients. Between-group differences in the risk of each endpoint were then evaluated by computing ORs along with 95% confidence intervals (CIs). The This study found that SERM treatment in general was not associated with elevated risk of incident T2DM in women with early-stage breast cancer. However, a higher risk of new-onset diabetes was detected in those exposed to SERMs for ≥1500 days, especially to toremifene, as compared to non-users. Although long-term SERM treatment was effective in preventing metastatic cancer and death in breast cancer survivors, risk-based personalized therapeutic approach needs to be considered since long-term exposure to SERMs may worsen a preexisting risk of diabetes in these patients, already predisposed to metabolic complications.