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Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field. [ABSTRACT FROM AUTHOR]

Background: Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. Methods: We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants. Results: There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%). Conclusion: Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins. [ABSTRACT FROM AUTHOR]

Background: The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known. Methods: Using 750 cases and 405 controls from the population- based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent riskassociated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement. Results: The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score. Conclusions: By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. Impact: Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention. [ABSTRACT FROM AUTHOR]

The article discusses a study that estimated the proportions of variance in the computer-assisted Cumulus mammographic measures that predict breast cancer risk factors in female twin pairs and their sisters. The researchers used questionnaires to record demographic information and applied multilevel mixed-effects linear regression to estimate the associations. They predict that about 50% to 60% of residual variance would be due to genetic factors.

The article discusses a research study which applied inference about causation from examination of familial confounding to longitudinal twin data on mammographic density measures that predict breast cancer risk. Results show the correlation between mammographic density measures over time for the Australian female monozygous pairs and for the dizygous pairs. Data also indicate the regression coefficients for the past dense area of the co-twins and the estimates for percentage dense area.

The article reports on a study which measured mammographically dense areas for monozygotic and dizygotic Australian and North American twin pairs ages 40 to 70 years. According to the authors, results have shown that the associations between mammographic density measures and height are consistent with shared early life environmental factors.

The article discusses a study which replicated an association between a variant in the HSD3B1 gene and percentage mammographic density (PMD) suggesting the former to be a genetic determinant of mammographic density. In 457 women studied, six polymorphisms from selected genes, namely COMT, HSD3B1, IGFBP3, HER2, XPD and XRCC3 were measured. Within-pair regression estimates proved the association between the HSD3B1 Asnx367Thr variant allele with lower PMD.

The article discusses a case-control study on the roles of detoxification gene polymorphisms on risk of breast cancer. According to the proponents the associations between phase I microsomal epoxide hydrolase gene EPHX and phase II glutathione S-transferase genes GSTT1, GSTM1 and GSTP1, suggests that the risk of breast cancer from such gene-gene interactions may be higher than for many of the known epidemiologic risk factors of breast cancer.

Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field., (©2024 American Association for Cancer Research.)

Background: Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus., Methods: We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants., Results: There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%)., Conclusion: Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins., Impact: Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens., (©2023 American Association for Cancer Research.)

Prevention Relevance: In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions., (©2023 The Authors; Published by the American Association for Cancer Research.)

We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk., Prevention Relevance: Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum., (©2021 The Authors; Published by the American Association for Cancer Research.)

Although physical activity is associated with lower breast cancer risk for average-risk women, it is not known if this association applies to women at high familial/genetic risk. We examined the association of recreational physical activity (self-reported by questionnaire) with breast cancer risk using the Prospective Family Study Cohort, which is enriched with women who have a breast cancer family history ( N = 15,550). We examined associations of adult and adolescent recreational physical activity (quintiles of age-adjusted total metabolic equivalents per week) with breast cancer risk using multivariable Cox proportional hazards regression, adjusted for demographics, lifestyle factors, and body mass index. We tested for multiplicative interactions of physical activity with predicted absolute breast cancer familial risk based on pedigree data and with BRCA1 and BRCA2 mutation status. Baseline recreational physical activity level in the highest four quintiles compared with the lowest quintile was associated with a 20% lower breast cancer risk (HR, 0.80; 95% confidence interval, 0.68-0.93). The association was not modified by familial risk or BRCA mutation status ( P interactions >0.05). No overall association was found for adolescent recreational physical activity. Recreational physical activity in adulthood may lower breast cancer risk for women across the spectrum of familial risk. SIGNIFICANCE: These findings suggest that physical activity might reduce breast cancer risk by about 20% for women across the risk continuum, including women at higher-than-average risk due to their family history or genetic susceptibility. See related commentary by Niehoff et al., p. 23 ., (©2019 American Association for Cancer Research.)

Background: The single-nucleotide polymorphism (SNP) 5p12-rs10941679 has been found to be associated with risk of breast cancer, particularly estrogen receptor (ER)-positive disease. We aimed to further explore this association overall, and by tumor histopathology, in the Breast Cancer Association Consortium., Methods: Data were combined from 37 studies, including 40,972 invasive cases, 1,398 cases of ductal carcinoma in situ (DCIS), and 46,334 controls, all of white European ancestry, as well as 3,007 invasive cases and 2,337 controls of Asian ancestry. Associations overall and by tumor invasiveness and histopathology were assessed using logistic regression., Results: For white Europeans, the per-allele OR associated with 5p12-rs10941679 was 1.11 (95% CI = 1.08-1.14, P = 7 × 10(-18)) for invasive breast cancer and 1.10 (95% CI = 1.01-1.21, P = 0.03) for DCIS. For Asian women, the estimated OR for invasive disease was similar (OR = 1.07, 95%CI = 0.99-1.15, P = 0.09). Further analyses suggested that the association in white Europeans was largely limited to progesterone receptor (PR)-positive disease (per-allele OR = 1.16, 95% CI = 1.12-1.20, P = 1 × 10(-18) vs. OR = 1.03, 95% CI = 0.99-1.07, P = 0.2 for PR-negative disease; P(heterogeneity) = 2 × 10(-7)); heterogeneity by ER status was not observed (P = 0.2) once PR status was accounted for. The association was also stronger for lower grade tumors [per-allele OR (95% CI) = 1.20 (1.14-1.25), 1.13 (1.09-1.16), and 1.04 (0.99-1.08) for grade 1, 2, and 3/4, respectively; P(trend) = 5 × 10(-7)]., Conclusion: 5p12 is a breast cancer susceptibility locus for PR-positive, lower grade breast cancer., Impact: Multicenter fine-mapping studies of this region are needed as a first step to identifying the causal variant or variants., (©2011 AACR)

Mammographic density for age and body mass index (BMI) is a heritable risk factor for breast cancer. We aimed to determine if recently identified common variants associated with small gradients in breast cancer risk are associated with mammographic density. We genotyped 497 monozygotic and 330 dizygotic twin pairs and 634 of their sisters from 903 families for 12 independent variants. Mammographic dense area, percent dense area, and nondense area were measured by three observers using a computer-thresholding technique. Associations with mammographic density measures adjusted for age, BMI, and other determinants were estimated (a) cross-sectionally using a multivariate normal model for pedigree analysis (P(x)), (b) between sibships, and (c) within sibships using orthogonal transformations of outcomes and exposures. A combined test of association (P(c)) was derived using the independent estimates from b and c. We tested if the distributions of P values across variants differed from the uniform distribution (P(u)). For dense area and percent dense area, the distributions of P(c) values were not uniform (both P(u) <0.007). Consistent with their breast cancer associations, rs3817198 (LSP1) and rs13281615 (8q) were associated with dense area and percent dense area (all P(x) and P(c) <0.05), and rs889312 (MAP3K1), rs2107425 (H19), and rs17468277 (CASP8) were marginally associated with dense area (some P(x) or P(c) <0.05). All associations were independent of menopausal status. At least two common breast cancer susceptibility variants are associated with mammographic density measures that predict breast cancer. These findings could help elucidate how those variants and mammographic density measures are associated with breast cancer susceptibility.

Background: Percent mammographic density (PMD) is a risk factor for breast cancer. Our previous twin study showed that the heritability of PMD was 63%. This study determined the heritabilities of the components of PMD, the areas of dense and nondense tissue in the mammogram., Methods: We combined two twin studies comprising 571 monozygous and 380 dizygous twin pairs recruited from Australia and North America. Dense and nondense areas were measured using a computer-assisted method, and information about potential determinants was obtained by questionnaire. Under the assumptions of the classic twin model, we estimated the heritability of the log dense area and log nondense area and the genetic and environmental contributions to the covariance between the two traits, using maximum likelihood theory and the statistical package FISHER., Results: After adjusting for measured determinants, for each of the log dense area and the log nondense area, the monozygous correlations were greater than the dizygous correlations. Heritability was estimated to be 65% (95% confidence interval, 60-70%) for dense area and 66% (95% confidence interval, 61-71%) for nondense area. The correlations (SE) between the two adjusted traits were -0.35 (0.023) in the same individual, -0.26 (0.026) across monozygous pairs, and -0.14 (0.034) across dizygous pairs., Conclusion: Genetic factors may play a large role in explaining variation in the mammographic areas of both dense and nondense tissue. About two thirds of the negative correlation between dense and nondense area is explained by the same genetic factors influencing both traits, but in opposite directions.

Mammographic density and bone mineral density, risk factors for breast cancer and osteoporotic fractures, respectively, are both thought to reflect cumulative exposure to estrogen and are highly heritable. We asked if there was overlap between the genes that explain their variances. We studied 63 monozygous and 71 dizygous female twin pairs ages 38 to 71 years (mean, 50 years). Absolute and percent mammographic densities were measured by a computer-assisted method, and bone mineral density was measured at the lumbar spine, femoral neck, and forearm by dual energy X-ray absorptiometry. After adjusting for age, height, and weight, the within-person and cross-trait cross-twin correlations between the mammographic density and bone mineral density measures were between -0.09 and 0.16 (SEs, 0.07-0.09) and independent of zygosity (all P > 0.05). We conclude that there is little, if any, overlap between the genetic or environmental determinants of disease risk associated with these traits.

Background: Recent oral contraceptive use has been associated with a small increase in breast cancer risk and a substantial decrease in ovarian cancer risk. The effects on risks for women with germ line mutations in BRCA1 or BRCA2 are unclear., Methods: Subjects were population-based samples of Caucasian women that comprised 1,156 incident cases of invasive breast cancer diagnosed before age 40 (including 47 BRCA1 and 36 BRCA2 mutation carriers) and 815 controls from the San Francisco Bay area, California, Ontario, Canada, and Melbourne and Sydney, Australia. Relative risks by carrier status were estimated using unconditional logistic regression, comparing oral contraceptive use in case groups defined by mutation status with that in controls., Results: After adjustment for potential confounders, oral contraceptive use for at least 12 months was associated with decreased breast cancer risk for BRCA1 mutation carriers [odds ratio (OR), 0.22; 95% confidence interval (CI), 0.10-0.49; P < 0.001], but not for BRCA2 mutation carriers (OR, 1.02; 95% CI, 0.34-3.09) or noncarriers (OR, 0.93; 95% CI, 0.69-1.24). First use during or before 1975 was associated with increased risk for noncarriers (OR, 1.52 per year of use before 1976; 95% CI, 1.22-1.91; P < 0.001)., Conclusions: There was no evidence that use of current low-dose oral contraceptive formulations increases risk of early-onset breast cancer for mutation carriers, and there may be a reduced risk for BRCA1 mutation carriers. Because current formulations of oral contraceptives may reduce, or at least not exacerbate, ovarian cancer risk for mutation carriers, they should not be contraindicated for a woman with a germ line mutation in BRCA1 or BRCA2.

The HER2 gene controls cellular function and has prognostic significance in breast cancer. The I655V polymorphism was associated with increased risk of breast cancer in Chinese women under the age of 45 years and in women with a first-degree family history of the disease. These associations, however, have not been confirmed in several studies of older women. We conducted a population-based case-control-family study of the I655V polymorphism using 409 Australian women with breast cancer diagnosed before the age of 40 years and 299 controls frequency matched for age. The I655V polymorphism was more common in cases (P = 0.01). A recessive model, in which homozygotes were associated with an adjusted odds ratio of 2.8 (95% confidence interval 1.3-6.2; P = 0.005), gave the best fit under parsimony. Although the biological role of the I655V polymorphism is not known, large independent studies of early onset breast cancer are warranted to attempt to replicate this finding.

The BRCA2 N372H nonconservative amino acid substitution polymorphism appears to affect fetal survival in a sex-dependent manner, and the HH genotype was found to be associated with a 1.3-fold risk of breast cancer from pooling five case-control studies of Northern European women. We investigated whether the BRCA2 N372H polymorphism was associated with breast cancer in Australian women using a population-based case-control design. The BRCA2 372 genotype was determined in 1397 cases under the age of 60 years at diagnosis of a first primary breast cancer and in 775 population-sampled controls frequency matched for age. Case-control analyses and comparisons of genotype distributions were conducted using logistic regression. All of the statistical tests were two-tailed. The HH genotype was independent of age and family history of breast cancer within cases and controls, and was more common in cases (9.2% versus 6.5%). It was associated with an increased risk of breast cancer, 1.47-fold unadjusted (95% confidence interval, 1.05-2.07; P = 0.02), and 1.42-fold (95% confidence interval, 1.00-2.02; P = 0.05) after adjusting for measured risk factors. This effect was still evident after excluding women with any non-Caucasian ancestry or the 33 cases known to have inherited a mutation in BRCA1 or BRCA2, and would explain approximately 3% of breast cancer. The BRCA2 N372H polymorphism appears to be associated with a modest recessively inherited risk of breast cancer in Australian women. This result is consistent with the findings for Northern European women.