Aspirin use is associated with lower mammographic density in a large screening cohort

Aspirin Metabolites and Mammographic Breast Density in Premenopausal Women

BACKGROUND

Studies investigating the associations of self-reported aspirin use and mammographic breast density (MBD) have reported conflicting results. Therefore, we investigated the associations of aspirin metabolites with MBD in premenopausal women.

METHODS

We performed this study on 705 premenopausal women who had a fasting blood draw for metabolomic profiling. We performed covariate-adjusted linear regression models to calculate the least square means of volumetric measures of MBD [volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV)] by quartiles of aspirin metabolites [salicyluric glucuronide, 2-hydroxyhippurate (salicylurate), salicylate, and 2,6-dihydroxybenzoic acid].

RESULTS

Approximately 13% of participants reported taking aspirin in the past 12 months. Aspirin users had higher levels of 2-hydroxyhippurate (salicylurate), salicylate, and salicyluric glucuronide (peak area) than nonusers, but only the mean peak area of salicyluric glucuronide was increased by both dose (1-2 tablets per day = 1,140,663.7 and ≥3 tablets per day = 1,380,476.0) and frequency (days per week: 1 day = 888,129.3, 2-3 days = 1,199,897.9, and ≥4 days = 1,654,637.0). Aspirin metabolites were not monotonically associated with VPD, DV, or NDV.

CONCLUSIONS

Given the null results, additional research investigating the associations of aspirin metabolites in breast tissue and MBD is necessary. Impact: Elucidating the determinants of MBD, a strong risk factor for breast cancer, can play an important role in breast cancer prevention. Future studies should determine the associations of nonaspirin NSAID metabolites with MBD.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening

PURPOSE

Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort.

MATERIALS AND METHODS

We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman.

RESULTS

PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical.

CONCLUSIONS

Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Use of Nonsteroidal Anti-Inflammatory Drugs and Risk of Breast Cancer: Evidence from a General Female Population and a Mammographic Screening Cohort in Sweden

A link has been proposed between the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk of breast cancer. There is, however, insufficient data regarding the subtype and stage of breast cancer, and few studies have assessed the interaction between the use of NSAIDs and breast density or previous breast disorders. There is also a lack of data from population-based studies. We first conducted a nested case-control study within the general female population of Sweden, including 56,480 women with newly diagnosed breast cancer during 2006-2015 and five breast cancer-free women per case as controls, to assess the association of NSAID use with the risk of incident breast cancer, focusing on subtype and stage of breast cancer as well as the interaction between NSAID use and previous breast disorders. We then used the Karolinska Mammography Project for Risk Prediction of Breast Cancer (Karma) cohort to assess the interaction between NSAID use and breast density in relation to the risk of breast cancer. Conditional logistic regression was used to estimate the hazard ratio (HR) and a 95% confidence interval (CI) was used for breast cancer in relation to the use of aspirin and non-aspirin NSAIDs. In the nested case-control study of the general population, exclusive use of aspirin was not associated with the risk of breast cancer, whereas exclusive use of non-aspirin NSAIDs was associated with a modestly higher risk of stage 0-2 breast cancer (HR: 1.05; 95% CI: 1.02-1.08) but a lower risk of stage 3-4 breast cancer (HR 0.80; 95% CI: 0.73-0.88). There was also a statistically significant interaction between the exclusive use of NSAIDs and previous breast disorders (p for interaction: <0.001). In the analysis of Karma participants, the exclusive use of non-aspirin NSAIDs was associated with a lower risk of breast cancer among women with a breast dense area of >40 cm² (HR: 0.72; 95% CI: 0.59-0.89). However, the possibility of finding this by chance cannot be ruled out. Overall, we did not find strong evidence to support an association between the use of NSAIDs and the risk of breast cancer.

The association of metformin and aspirin intake with mammographic breast density: A cross-sectional study

Background

Our purpose was to investigate the association between Mammographic breast density (MBD), a known strong marker for breast cancer and metformin and aspirin use and duration of use alone or simultaneously, in a sample of Iranian women considering other confounding factors.

Methods

In a cross-sectional study, 712 individuals were selected out of women referred to two university hospitals for screening mammography. Participants' information was collected with a questionnaire. Four-category density scale (a = almost entirely fatty, b = scattered fibroglandular densities, c= heterogeneously dense, and d = extremely dense) was categorized as low (a&b) and high (c&d) density.

Results

The mean age of the participants was 49.80 ± 7.26 years. Sixty-five percent of women belonged to the high and 35% to the low MBD category. Both aspirin and metformin had a significantly negative association with MBD, however, when confounding factors were entered into the models, only aspirin after adjustment for age and BMI had an inverse association with MBD (OR = 0.53, 95% CI: 0.35-0.94). Simultaneous use of metformin and aspirin (OR = 0.44, 95 %CI: 0.17-1.12) was associated with lower MBD. Furthermore, in women who used metformin (OR = 0.23, 95% CI: 0.09-0.62) and aspirin (OR= 0.35, 95% CI: 0.17-0.72) for 2 to 5 years, MBD was significantly lower. However, after the adjustment of confounding factors, these associations were not statistically significant.

Conclusion

It seems metformin and aspirin intakes are associated with MBD. However, further studies with more sample size are needed.

Associations of aspirin and other anti-inflammatory medications with breast cancer risk by the status of COX-2 expression

BACKGROUND

We investigated the associations of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) with breast cancer risk by the status of COX-2 protein expression.

METHODS

This study included 421 cases and 3,166 controls from a nested case-control study within the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts. Information on medication use was first collected in 1980 (NHS) and 1989 (NHSII) and was updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users using data collected from all biannual questionnaires preceding the reference date. Immunochemistry for COX-2 expression was performed using commercial antibody (Cayman Chemical and Thermo Fisher Scientific). We used polychotomous logistic regression to quantify associations of aspirin and NSAIDs with the risk of COX2+ and COX2- breast cancer tumors, while adjusting for known breast cancer risk factors. All tests of statistical significance were two-sided.

RESULTS

In multivariate analysis, we found no differences in associations of the aspirin exposures and NSAIDs with breast cancer risk by COX2 expression status. In stratified analyses by COX2 status, significant associations of these medications with breast cancer risk were observed for dosage of aspirin among current users in COX2- tumors (OR for > 5 tablets per week vs. none 1.71, 95% CI 1.01-2.88, p-trend 0.04). Regular aspirin use was marginally associated with the risk of COX2- tumors (p-trend = 0.06).

CONCLUSIONS

Our findings suggested no differences in associations of aspirin and other NSAIDs with COX2+ and COX2- tumors.

Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications

Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.

Air pollution exposure and mammographic breast density in Tehran, Iran: a cross-sectional study

BACKGROUND

Air pollution is one of the major public health challenges in many parts of the world possibly has an association with breast cancer. However, the mechanism is still unclear. This study aimed to find an association between exposure to six criteria ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) and mammographic breast density (MBD), as one of the strongest predictors for developing breast cancer, in women living in Tehran, Iran.

METHODS

Participants were selected from women attending two university hospitals for screening mammography from 2019 to 2021. Breast density was rated by two expert radiologists. Individual exposures to 3-year ambient air pollution levels at the residence were estimated.

RESULTS

The final analysis in 791 eligible women showed that low and high breast density was detected in 34.8 and 62.2 of participants, respectively. Logistic regression analysis after considering all possible confounding factors represented that an increase in each unit of NO₂ (ppb) exposure was associated with an increased risk of breast density with an OR equal to 1.04 (95CI: 1.01 to 1.07). Furthermore, CO level was associated with a decreasing breast density (OR = 0.40, 95CI = 0.19 to 0.86). None of the other pollutants were associated with breast density.

CONCLUSION

Higher MBD was associated with an increased level of NO₂, as a marker of traffic-related air pollution. Furthermore, CO concentration was associated with a lower MBD, while other criteria air pollutants were not related to MBD. Further studies are needed to evaluate the association between ambient air pollutants with MBD.

Inhibiting the Priming for Cancer in Li-Fraumeni Syndrome

Simple Summary

Li-Fraumeni Syndrome (LFS) is a rare cancer pre-disposition syndrome associated with a germline mutation in the TP53 tumour suppressor gene. People with LFS have a 90% chance of suffering one or more cancers in their lifetime. No treatments exist to reduce this cancer risk. This paper reviews the evidence for how cancers start in people with LFS and proposes that a series of commonly used non-cancer drugs, including metformin and aspirin, can help reduce that lifetime risk of cancer.

Abstract

The concept of the pre-cancerous niche applies the ‘seed and soil’ theory of metastasis to the initial process of carcinogenesis. TP53 is at the nexus of this process and, in the context of Li-Fraumeni Syndrome (LFS), is a key determinant of the conditions in which cancers are formed and progress. Important factors in the creation of the pre-cancerous niche include disrupted tissue homeostasis, cellular metabolism and chronic inflammation. While druggability of TP53 remains a challenge, there is evidence that drug re-purposing may be able to address aspects of pre-cancerous niche formation and thereby reduce the risk of cancer in individuals with LFS.

Current regular aspirin use and mammographic breast density: a cross-sectional analysis considering concurrent statin and metformin use

PURPOSE

The nonsteroidal anti-inflammatory drug aspirin is an agent of interest for breast cancer prevention. However, it is unclear if aspirin affects mammographic breast density (MBD), a marker of elevated breast cancer risk, particularly in the context of concurrent use of medications indicated for common cardiometabolic conditions, which may also be associated with MBD.

METHODS

We used data from the New York Mammographic Density Study for 770 women age 40-60 years old with no history of breast cancer. We evaluated the association between current regular aspirin use and MBD, using linear regression for continuous measures of absolute and percent dense areas and absolute non-dense area, adjusted for body mass index (BMI), sociodemographic and reproductive factors, and use of statins and metformin. We assessed effect modification by BMI and reproductive factors.

RESULTS

After adjustment for co-medication, current regular aspirin use was only positively associated with non-dense area (β = 18.1, 95% CI: 6.7, 29.5). Effect modification by BMI and parity showed current aspirin use to only be associated with larger non-dense area among women with a BMI ≥ 30 (β = 28.2, 95% CI: 10.8, 45.7), and with lower percent density among parous women (β = -3.3, 95% CI: -6.4, -0.3).

CONCLUSIONS

Independent of co-medication use, current regular aspirin users had greater non-dense area with stronger estimates for women with higher BMI. We found limited support for an association between current aspirin use and mammographically dense breast tissue among parous women.

Biological Mechanisms and Therapeutic Opportunities in Mammographic Density and Breast Cancer Risk

Mammographic density is an important risk factor for breast cancer; women with extremely dense breasts have a four to six fold increased risk of breast cancer compared to women with mostly fatty breasts, when matched with age and body mass index. High mammographic density is characterised by high proportions of stroma, containing fibroblasts, collagen and immune cells that suggest a pro-tumour inflammatory microenvironment. However, the biological mechanisms that drive increased mammographic density and the associated increased risk of breast cancer are not yet understood. Inflammatory factors such as monocyte chemotactic protein 1, peroxidase enzymes, transforming growth factor beta, and tumour necrosis factor alpha have been implicated in breast development as well as breast cancer risk, and also influence functions of stromal fibroblasts. Here, the current knowledge and understanding of the underlying biological mechanisms that lead to high mammographic density and the associated increased risk of breast cancer are reviewed, with particular consideration to potential immune factors that may contribute to this process.

Sulindac, a Nonselective NSAID, Reduces Breast Density in Postmenopausal Women with Breast Cancer Treated with Aromatase Inhibitors

PURPOSE

To evaluate the effect of sulindac, a nonselective anti-inflammatory drug (NSAID), for activity to reduce breast density (BD), a risk factor for breast cancer.

EXPERIMENTAL DESIGN

An open-label phase II study was conducted to test the effect of 12 months' daily sulindac at 150 mg twice daily on change in percent BD in postmenopausal hormone receptor-positive breast cancer patients on aromatase inhibitor (AI) therapy. Change in percent BD in the contralateral, unaffected breast was measured by noncontrast magnetic resonance imaging (MRI) and reported as change in MRI percent BD (MRPD). A nonrandomized patient population on AI therapy (observation group) with comparable baseline BD was also followed for 12 months. Changes in tissue collagen after 6 months of sulindac treatment were explored using second-harmonic generated microscopy in a subset of women in the sulindac group who agreed to repeat breast biopsy.

RESULTS

In 43 women who completed 1 year of sulindac (86% of those accrued), relative MRPD significantly decreased by 9.8% [95% confidence interval (CI), -14.6 to -4.7] at 12 months, an absolute decrease of -1.4% (95% CI, -2.5 to -0.3). A significant decrease in mean breast tissue collagen fiber straightness (P = 0.032), an investigational biomarker of tissue inflammation, was also observed. MRPD (relative or absolute) did not change in the AI-only observation group (N = 40).

CONCLUSIONS

This is the first study to indicate that the NSAID sulindac may reduce BD. Additional studies are needed to verify these findings and determine if prostaglandin E₂ inhibition by NSAIDs is important for BD or collagen modulation.

Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.

Medication use and mammographic breast density

PURPOSE

A dense breast on mammogram is a strong risk factor for breast cancer. Identifying factors that reduce mammographic breast density could thus provide insight into breast cancer prevention. Due to the limited number of studies and conflicting findings, we investigated the associations of medication use (specifically statins, aspirin, and ibuprofen) with mammographic breast density.

METHODS

We evaluated these associations in 775 women who were recruited during an annual screening mammogram at Washington University School of Medicine, St. Louis. We measured mammographic breast density using Volpara. We used multivariable-adjusted linear regressions to determine the associations of medication use (statins, aspirin, and ibuprofen) with mammographic breast density. Least squared means were generated and back-transformed for easier interpretation.

RESULTS

The mean age of study participants was 52.9 years. Statin use in the prior 12 months was not associated with volumetric percent density or dense volume, but was positively associated with non-dense volume. The mean volumetric percent density was 8.6% among statin non-users, 7.2% among women who used statins 1-3 days/week, and 7.3% among women who used statins ≥ 4 days/week (p trend = 0.07). The non-dense volume was 1297.1 cm³ among statin non-users, 1368.7 cm³ among women who used statins 1-3 days/week, and 1408.4 cm³ among those who used statins ≥ 4 days/week (p trend = 0.02). We did not observe statistically significant differences in mammographic breast density by aspirin or ibuprofen use.

CONCLUSION

Statin, aspirin, and ibuprofen use was not associated with volumetric percent density and dense volume, but statin use was positively associated with non-dense volume. Any potential associations of these medications with breast cancer risk are unlikely to be mediated through an effect on volumetric percent density.

Associations of aspirin and other anti-inflammatory medications with mammographic breast density and breast cancer risk

PURPOSE

We investigated the associations of aspirin and other non-steroid anti-inflammatory drugs with mammographic breast density (MBD) and their interactions in relation to breast cancer risk.

METHODS

This study included 3,675 cancer-free women within the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts. Percent breast density (PD), absolute dense area (DA), and non-dense area (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root-transformed. Information on medication use was collected in 1980 (NHS) and 1989 (NHSII) and updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users from all bi-annual questionnaires preceding the mammogram date. We used generalized linear regression to quantify associations of medications with MBD. Two-way interactions were examined in logistic regression models.

RESULTS

In multivariate analysis, none of the anti-inflammatory medications were associated with PD, DA, and NDA. We found no interactions of any of the medications with PD with respect to breast cancer risk (all p-interactions > 0.05). However, some of the aspirin variables appeared to have positive associations with breast cancer risk limited only to women with PD 10-24% (past aspirin OR 1.56, 95% CI 1.03-2.35; current aspirin with < 5 years of use OR 1.82, 95% CI 1.01-3.28; current aspirin with ≥ 5 years of use OR 1.89, 95% CI 1.26-2.82).

CONCLUSIONS

Aspirin and NSAIDs are not associated with breast density measures. We found no interactions of aspirin with MBD in relation to breast cancer risk.

Complex roles of the old drug aspirin in cancer chemoprevention and therapy

The nonsteroidal anti-inflammatory agent aspirin is widely used for preventing and treating cardiovascular and cerebrovascular diseases. In addition, epidemiologic evidences reveal that aspirin may prevent a variety of human cancers, while data on the association between aspirin and some kinds of cancer are conflicting. Preclinical studies and clinical trials also reveal the therapeutic effect of aspirin on cancer. Although cyclooxygenase is a well-known target of aspirin, recent studies uncover other targets of aspirin and its metabolites, such as AMP-activated protein kinase, cyclin-dependent kinase, heparanase, and histone. Accumulating evidence demonstrate that aspirin may act in different cell types, such as epithelial cell, tumor cell, endothelial cell, platelet, and immune cell. Therefore, aspirin acts on diverse hallmarks of cancer, such as sustained tumor growth, metastasis, angiogenesis, inflammation, and immune evasion. In this review, we focus on recent progress in the use of aspirin for cancer chemoprevention and therapy, and integratively analyze the mechanisms underlying the anticancer effects of aspirin and its metabolites. We also discuss mechanisms of aspirin resistance and describe some derivatives of aspirin, which aim to overcome the adverse effects of aspirin.