We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case ...patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval CI = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.
Obesity and elevated breast density are common risk factors for breast cancer, and their effects may vary by estrogen receptor (ER) subtype. However, their joint effects on ER subtype-specific risk ...are unknown. Understanding this relationship could enhance risk stratification for screening and prevention. Thus, we assessed the association between breast density and ER subtype according to body mass index (BMI) and menopausal status.
We conducted a case-control study nested within two mammography screening cohorts, the Mayo Mammography Health Study and the San Francisco Bay Area Breast Cancer SPORE/San Francisco Mammography Registry. Our pooled analysis contained 1538 ER-positive and 285 ER-negative invasive breast cancer cases and 4720 controls matched on age, menopausal status at time of mammogram, and year of mammogram. Percent density was measured on digitized film mammograms using computer-assisted techniques. We used polytomous logistic regression to evaluate the association between percent density and ER subtype by BMI subgroup (normal/underweight, < 25 kg/m
versus overweight/obese, ≥ 25 kg/m
). We used Wald chi-squared tests to assess for interactions between percent density and BMI. Our analysis was stratified by menopausal status and hormone therapy usage at the time of index mammogram.
Percent density was associated with increased risk of overall breast cancer regardless of menopausal status or BMI. However, when analyzing breast cancer across ER subtype, we found a statistically significant (p = 0.008) interaction between percent density and BMI in premenopausal women only. Specifically, elevated percent density was associated with a higher risk of ER-negative than ER-positive cancer in overweight/obese premenopausal women OR per standard deviation increment 2.17 (95% CI 1.50-3.16) vs 1.33 (95% CI 1.11-1.61) respectively, P
= 0.01. In postmenopausal women, elevated percent density was associated with similar risk of ER-positive and ER-negative cancers, and no substantive differences were seen after accounting for BMI or hormone therapy usage.
The combination of overweight/obesity and elevated breast density in premenopausal women is associated with a higher risk of ER-negative compared with ER-positive cancer. Eighteen percent of premenopausal women in the USA have elevated BMI and breast density and may benefit from lifestyle modifications involving weight loss and exercise.
The ectodysplasin receptor (EDAR) is a tumor necrosis factor receptor (TNF) superfamily member. A substitution in an exon of EDAR at position 370 (EDARV370A) creates a gain of function mutant present ...at high frequencies in Asian and Indigenous American populations but absent in others. Its frequency is intermediate in populations of Mexican ancestry. EDAR regulates the development of ectodermal tissues, including mammary ducts. Obesity and type 2 diabetes mellitus are prevalent in people with Indigenous and Latino ancestry. Latino patients also have altered prevalence and presentation of breast cancer. It is unknown whether EDARV370A might connect these phenomena. The goals of this study were to determine 1) whether EDARV370A is associated with metabolic phenotypes and 2) if there is altered breast anatomy in women carrying EDARV370A. Participants were from two Latino cohorts, the Arizona Insulin Resistance (AIR) registry and Sangre por Salud (SPS) biobank. The frequency of EDARV370A was 47% in the Latino cohorts. In the AIR registry, carriers of EDARV370A (GG homozygous) had significantly (p < 0.05) higher plasma triglycerides, VLDL, ALT, 2-hour post-challenge glucose, and a higher prevalence of prediabetes/diabetes. In a subset of the AIR registry, serum levels of ectodysplasin A2 (EDA-A2) also were associated with HbA1c and prediabetes (p < 0.05). For the SPS biobank, participants that were carriers of EDARV370A had lower breast density and higher HbA1c (both p < 0.05). The significant associations with measures of glycemia remained when the cohorts were combined. We conclude that EDARV370A is associated with characteristics of the metabolic syndrome and breast density in Latinos.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown.
We identified ...2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance.
On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (
values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065,
= .01) but did not reach statistical significance for interval cancer.
AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
Background
Breast density and body mass index (BMI) are used for breast cancer risk stratification. We evaluate whether the positive association between volumetric breast density and breast cancer ...risk is strengthened with increasing BMI.
Methods
The San Francisco Mammography Registry and Mayo Clinic Rochester identified 781 premenopausal and 1850 postmenopausal women with breast cancer diagnosed between 2007 and 2015 that had a screening digital mammogram at least 6 months prior to diagnosis. Up to three controls (
N
= 3535) were matched per case on age, race, date, mammography machine, and state. Volumetric percent density (VPD) and dense volume (DV) were measured with Volpara™. Breast cancer risk was assessed with logistic regression stratified by menopause status. Multiplicative interaction tests assessed whether the association of density measures was differential by BMI categories.
Results
The increased risk of breast cancer associated with VPD was strengthened with higher BMI for both premenopausal (
p
interaction
= 0.01) and postmenopausal (
p
interaction
= 0.0003) women. For BMI < 25, 25–30, and ≥ 30 kg/m
2
, ORs for breast cancer for a 1 SD increase in VPD were 1.24, 1.65, and 1.97 for premenopausal, and 1.20, 1.55, and 2.25 for postmenopausal women, respectively. ORs for breast cancer for a 1 SD increase in DV were 1.39, 1.33, and 1.51 for premenopausal (
p
interaction
= 0.58), and 1.31, 1.34, and 1.65 (
p
interaction
= 0.03) for postmenopausal women for BMI < 25, 25–30 and ≥ 30 kg/m
2
, respectively.
Conclusions
The effect of volumetric percent density on breast cancer risk is strongest in overweight and obese women. These associations have clinical relevance for informing prevention strategies.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Implementation of an optically active material on silicon has been a persistent technological challenge. For tandem photovoltaics using a Si bottom cell, as well as for other optoelectronic ...applications, there has been a longstanding need for optically active, wide band gap materials that can be integrated with Si. ZnSiP2 is a stable, wide band gap (2.1 eV) material that is lattice matched with silicon and comprised of inexpensive elements. As we show in this paper, it is also a defect-tolerant material. Here, we report the first ZnSiP2 photovoltaic device. We show that ZnSiP2 has excellent photoresponse and high open circuit voltage of 1.3 V, as measured in a photoelectrochemical configuration. The high voltage and low band gap-voltage offset are on par with much more mature wide band gap III-V materials. Photoluminescence data combined with theoretical defect calculations illuminate the defect physics underlying this high voltage, showing that the intrinsic defects in ZnSiP2 are shallow and the minority carrier lifetime is 7 ns. These favorable results encourage the development of ZnSiP2 and related materials as photovoltaic absorber materials.
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