scholarly journals Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results

2019 ◽  
Vol 112 (4) ◽  
pp. 418-422
Author(s):  
Robert J MacInnis ◽  
Yuyan Liao ◽  
Julia A Knight ◽  
Roger L Milne ◽  
Alice S Whittemore ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Disease Incidence ◽  
Brca2 Mutation ◽  
Estimation Algorithm ◽  
Breast Cancers ◽  
Risk Models ◽  
Replication Studies

Abstract The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%–56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models.

scholarly journals Use of systemic glucocorticoids and risk of breast cancer in a prospective cohort of postmenopausal women

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Manon Cairat ◽  
Marie Al Rahmoun ◽  
Marc J. Gunter ◽  
Pierre-Etienne Heudel ◽  
Gianluca Severi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Invasive Breast Cancer ◽  
Cox Regression ◽  
Breast Cancer Incidence ◽  
Breast Cancers ◽  
Systemic Glucocorticoid ◽  
Systemic Glucocorticoids

Abstract Background Glucocorticoids could theoretically decrease breast cancer risk through their anti-inflammatory effects or increase risk through immunosuppression. However, epidemiological evidence is limited regarding the associations between glucocorticoid use and breast cancer risk. Methods We investigated the association between systemic glucocorticoid use and breast cancer incidence in the E3N cohort, which includes 98,995 women with information on various characteristics collected from repeated questionnaires complemented with drug reimbursement data available from 2004. Women with at least two reimbursements of systemic glucocorticoids in any previous 3-month period since January 1, 2004, were defined as exposed. We considered exposure as a time-varying parameter, and we used multivariable Cox regression models to estimate hazard ratios (HRs) of breast cancer. We performed a competing risk analysis using a cause-specific hazard approach to study the heterogeneity by tumour subtype/stage/grade. Results Among 62,512 postmenopausal women (median age at inclusion of 63 years old), 2864 developed breast cancer during a median follow-up of 9 years (between years 2004 and 2014). Compared with non-exposure, glucocorticoid exposure was not associated with overall breast cancer risk [HR = 0.94 (0.85–1.05)]; however, it was associated with a higher risk of in situ breast cancer and a lower risk of invasive breast cancer [HRinsitu = 1.34 (1.01–1.78); HRinvasive = 0.86 (0.76–0.97); Phomogeneity = 0.01]. Regarding the risk of invasive breast cancer, glucocorticoid exposure was inversely associated with oestrogen receptor (ER)-positive breast cancer [HRER+ = 0.82 (0.72–0.94); HRER− = 1.21 (0.88–1.66); Phomogeneity = 0.03]; it was also inversely associated with the risk of stage 1 or stage 2 tumours but positively associated with the risk of stage 3/4 breast cancers [HRstage1 = 0.87 (0.75–1.01); HRstage2 = 0.67 (0.52–0.86); HRstage3/4 = 1.49 (1.02–2.20); Phomogeneity = 0.01]. Conclusion This study suggests that the association between systemic glucocorticoid use and breast cancer risk may differ by tumour subtype and stage.

scholarly journals Endogenous hormones and risk of invasive breast cancer in pre- and post-menopausal women: findings from the UK Biobank

2021 ◽  
Author(s):  
Sandar Tin Tin ◽  
Gillian K. Reeves ◽  
Timothy J. Key
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Cox Regression ◽  
Menopausal Status ◽  
Endogenous Hormones ◽  
Uk Biobank ◽  
Menopausal Women ◽  
Post Menopausal

Abstract Background Some endogenous hormones have been associated with breast cancer risk, but the nature of these relationships is not fully understood. Methods UK Biobank was used. Hormone concentrations were measured in serum collected in 2006–2010, and in a repeat subsample (N ~ 5000) in 2012–13. Incident cancers were identified through data linkage. Cox regression models were used, and hazard ratios (HRs) corrected for regression dilution bias. Results Among 30,565 pre-menopausal and 133,294 post-menopausal women, 527 and 2,997, respectively, were diagnosed with invasive breast cancer during a median follow-up of 7.1 years. Cancer risk was positively associated with testosterone in post-menopausal women (HR per 0.5 nmol/L increment: 1.18; 95% CI: 1.14, 1.23) but not in pre-menopausal women (pheterogeneity = 0.03), and with IGF-1 (insulin-like growth factor-1) (HR per 5 nmol/L increment: 1.18; 1.02, 1.35 (pre-menopausal) and 1.07; 1.01, 1.12 (post-menopausal); pheterogeneity = 0.2), and inversely associated with SHBG (sex hormone-binding globulin) (HR per 30 nmol/L increment: 0.96; 0.79, 1.15 (pre-menopausal) and 0.89; 0.84, 0.94 (post-menopausal); pheterogeneity = 0.4). Oestradiol, assessed only in pre-menopausal women, was not associated with risk, but there were study limitations for this hormone. Conclusions This study confirms associations of testosterone, IGF-1 and SHBG with breast cancer risk, with heterogeneity by menopausal status for testosterone.

scholarly journals The frequency and outcome of breast cancer risk-reducing surgery in finnish BRCA1 and BRCA2 mutation carriers

2013 ◽  
Vol 103 (1) ◽  
pp. 34-40 ◽  
Author(s):  
L. Koskenvuo ◽  
C. Svarvar ◽  
S. Suominen ◽  
K. Aittomäki ◽  
T. Jahkola
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Brca2 Mutation ◽  
Brca1 And Brca2 ◽  
Mutation Carriers ◽  
Risk Reducing

scholarly journals Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics with Invasive Breast Cancer Risk

2021 ◽  
Author(s):  
Mustapha Abubakar ◽  
Shaoqi Fan ◽  
Erin Aiello Bowles ◽  
Lea Widemann ◽  
Máire A Duggan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Statistical Tests ◽  
Predictive Biomarkers ◽  
Breast Cancer Diagnosis ◽  
Breast Cancer Risk Factor ◽  
Computer Assisted ◽  
Tissue Composition

Abstract Background Benign breast disease (BBD) is a strong breast cancer risk factor but identifying patients that might develop invasive breast cancer remains a challenge. Methods By applying machine-learning to digitized H&E-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15,395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Cases (n = 514) who developed incident invasive breast cancer and controls (n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided. Results Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (Odds ratio [OR]Q4 vs Q1=1.85, 95% confidence interval [CI] = 1.13-3.04; Ptrend=0.02). Conversely, increasing stroma was associated with decreased risk in non-proliferative, but not proliferative, BBD (Pheterogeneity=0.002). Increasing epithelium-to-stroma proportion [ORQ4 vs Q1=2.06, 95% CI = 1.28-3.33; Ptrend=0.002) and percent mammographic density (MBD) (ORQ4 vs Q1=2.20, 95% CI = 1.20-4.03; Ptrend=0.01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion/high MBD had substantially higher risk than those with low epithelium-to-stroma proportion/low MBD [OR = 2.27, 95% CI = 1.27-4.06; Ptrend=0.005), particularly among women with non-proliferative (Ptrend=0.01) versus proliferative (Ptrend=0.33) BBD. Conclusion Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with non-proliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

scholarly journals Breast Cancer Risk for Noncarriers of Family-Specific BRCA1 and BRCA2 Mutations: Findings From the Breast Cancer Family Registry

2011 ◽  
Vol 29 (34) ◽  
pp. 4505-4509 ◽  
Author(s):  
Allison W. Kurian ◽  
Gail D. Gong ◽  
Esther M. John ◽  
David A. Johnston ◽  
Anna Felberg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Brca2 Mutation ◽  
Population Based ◽  
Brca1 And Brca2 ◽  
Breast Cancer Family ◽  
Cancer Family ◽  
Brca2 Mutations ◽  
Brca1 And Brca2 Mutations

Purpose Women with germline BRCA1 and BRCA2 mutations have five- to 20-fold increased risks of developing breast and ovarian cancer. A recent study claimed that women testing negative for their family-specific BRCA1 or BRCA2 mutation (noncarriers) have a five-fold increased risk of breast cancer. We estimated breast cancer risks for noncarriers by using a population-based sample of patients with breast cancer and their female first-degree relatives (FDRs). Patients and Methods Patients were women with breast cancer and their FDRs enrolled in the population-based component of the Breast Cancer Family Registry; patients with breast cancer were tested for BRCA1 and BRCA2 mutations, as were FDRs of identified mutation carriers. We used segregation analysis to fit a model that accommodates familial correlation in breast cancer risk due to unobserved shared risk factors. Results We studied 3,047 families; 160 had BRCA1 and 132 had BRCA2 mutations. There was no evidence of increased breast cancer risk for noncarriers of identified mutations compared with FDRs from families without BRCA1 or BRCA2 mutations: relative risk was 0.39 (95% CI, 0.04 to 3.81). Residual breast cancer correlation within families was strong, suggesting substantial risk heterogeneity in women without BRCA1 or BRCA2 mutations, with some 3.4% of them accounting for roughly one third of breast cancer cases. Conclusion These results support the practice of advising noncarriers that they do not have any increase in breast cancer risk attributable to the family-specific BRCA1 or BRCA2 mutation.

The CYP17A1 −34T > C polymorphism and breast cancer risk in BRCA1 and BRCA2 mutation carriers

2010 ◽  
Vol 126 (2) ◽  
pp. 521-527 ◽  
Author(s):  
Bella Kaufman ◽  
Yael Laitman ◽  
Elad Ziv ◽  
Ute Hamann ◽  
Diana Torres ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Brca2 Mutation ◽  
Brca1 And Brca2 ◽  
Mutation Carriers

Toward robust mammography-based models for breast cancer risk

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Massachusetts General Hospital ◽  
The United States ◽  
University Hospital ◽  
Chang Gung Memorial Hospital ◽  
Risk Models

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

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