Androgen receptor expression in normal breast tissue and subsequent breast cancer risk

Kensler, Kevin H.; Beça, Francisco; Baker, Gabrielle M.; Heng, Yujing J.; Beck, Andrew H.; Schnitt, Stuart J.; Hazra, Aditi; Rosner, Bernard; Eliassen, A. Heather; Hankinson, Susan E.; Brown, Myles; Tamimi, Rulla M.

doi:10.1038/s41523-018-0085-3

Cited by 26 publications

(17 citation statements)

References 43 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Details about the study design methods for the NHS and NHSII have been published previously ( 34 ). Eligible women with biopsy-confirmed BBD were placed into 2 substudies—the BBD Incidence study ( 35‐38 ) and/or the BBD BC nested case-control study ( 2 , 3 , 5 , 24 , 30 , 32 , 33 , 39‐41 ). BC diagnosis was confirmed verbally by the participant, via medical record review, or via the cancer registry.…”

Section: Methodsmentioning

confidence: 99%

“…Among women diagnosed with BBD, the subsequent BC risk varies with the subtype of BBD in this order: nonproliferative, proliferative without atypia, and proliferative with atypia ( 2–4 ). Researchers continue to identify new biomarkers of risk ( 2 , 3 , 5–8 ) as well as update risk assessment models ( 9–13 ) to improve BC risk prediction. For example, the well-validated Rosner–Colditz model includes age at menarche, age at first birth, age at subsequent births, age at menopause, family history of BC, body mass index (BMI), alcohol intake, and postmenopausal hormone therapy use ( 14 ).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

Self Cite

View full text Add to dashboard Cite

Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, Ptrend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

Self Cite

View full text Add to dashboard Cite

show abstract

“…In ER positive BC cell, the AR effect appeared to be dual. AR expression is an independent prognostic factor of better outcome in patients with ER-positive BC, but there are also publications that conclude there is no correlation between the presence of these receptors and neoplastic development [35,71,72].…”

Section: Discussionmentioning

confidence: 99%

The importance of androgen receptors in breast cancer

Niţă

Nițipir

Toma

et al. 2021

Medicine and Pharmacy Reports

View full text Add to dashboard Cite

Background and aim. Breast cancer (BC) is the most common malignancy among women worldwide, and one of the leading causes of cancer-related deaths in females. For the breast malignant tumors there are numerous targeted therapies, depending on the receptors expressed. Regulating the process of epithelial-mesenchyme transcription, the steroid nuclear receptors are important in invasion and progression of BC cells. Till now, it is known that androgen receptor (AR) is present in about 60-80% of BC cells but, unfortunately, there is no targeted therapy available yet. Methods. We revised the recent literature that included the AR mechanism of action in patients diagnosed with breast cancer, the preclinical, retrospective and clinical studies and the aspects related to the prognosis of these patients, depending on the molecular subtype. Results. A total of 12 articles were eligible for this review. AR positivity was assessed using immunohistochemistry. Herein, neither 1 nor 10% cut-points were robustly prognostic. AR was an independent prognostic marker of BC outcome, especially in triple negative BC group. Conclusion. AR is a potential targeted pathway which can improve the prognostic of AR positive patients with BC. Further preclinical and clinical studies are necessary to clarify the mechanism of action and to establish the drugs which can be used, either alone or in combination.

show abstract

“…Participants who reported breast cancer were confirmed verbally by the participant, via medical record review, or via the cancer registry. Eligible women with biopsy-confirmed BBD were placed into two sub-studies within the NHS/NHSII—the BBD Incidence study 28–31 and/or the BBD breast cancer nested case-control (NCC) study 2–4,24,26,27,32–35 . whole slide images (WSIs) from women in the BBD Incidence study were used in the development phase to engineer the deep-learning networks.…”

Section: Methodsmentioning

confidence: 99%

“…Breast cancer risk factors may be non-modifiable (e.g., genetics, dense breast tissue, and benign breast disease (BBD)) or modifiable (e.g., adiposity and alcohol consumption). Researchers continue to identify new biomarkers of risk 2–7 as well as update risk assessment models 8–12 to improve breast cancer risk prediction.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattana

Kensler

et al. 2020

Preprint

View full text Add to dashboard Cite

Background New biomarkers of risk may improve breast cancer risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images (WSIs) into epithelium, fibrous stroma, and fat. We applied our method to the BBD breast cancer nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of breast cancer. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 WSIs from 293 cases who developed breast cancer and 1132 controls who did not. Percentages of each tissue region were calculated and 615 morphometric features were extracted. Elastic net regression was used to create a breast cancer morphometric signature. Associations between breast cancer risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and BMI evaluated the relationship between tissue composition and breast cancer risk. Results Among controls, BBD subtypes, parity, and number of births were differentially associated with all three tissue regions (p<0.05); select regions were associated with childhood body size, BMI, age of menarche, and menopausal status (p<0.05). Higher proportion of epithelial tissue was associated with increased breast cancer risk (OR=1.39, 95% CI 0.91-2.14 comparing highest and lowest quartiles; p-trend<0.05). No morphometric signature was associated with breast cancer. Conclusion The amount of epithelial tissue may be incorporated into risk assessment models to improve breast cancer risk prediction.

show abstract

Androgen receptor expression in normal breast tissue and subsequent breast cancer risk

Cited by 26 publications

References 43 publications

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

The importance of androgen receptors in breast cancer

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Contact Info

Product

Resources

About