Assessment of tumor suppressor gene methylation for breast cancer risk screening

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 1004-1004
Author(s):  
D. Euhus ◽  
R. Ashfaq ◽  
D. Bu ◽  
A. M. Leitch ◽  
C. Lewis
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Tumor Suppressor ◽  
Cancer Risk ◽  
Tumor Suppressor Gene ◽  
Lower Risk ◽  
Suppressor Gene ◽  
Gene Copies ◽  
Wide Range

1004 Background: Tumor suppressor gene (TSG) methylation is frequently detected in benign proliferative breast tissue suggesting that it occurs early in breast carcinogenesis. If it can be screen-detected and is associated with breast cancer risk it could be exploited for breast cancer prevention. Methods: Nipple duct lavage (NDL) samples, obtained from 150 women selected to represent a wide range of breast cancer risk, were evaluated by quantitative methylation-specific real time PCR. High risk breasts were defined as those contralateral to a breast cancer (N = 63) and those of women with a 5-year Gail risk ≥ twice the age- and race-matched general population risk (N = 64). The prevelence of TSG methylation and marked atypia was compared for high risk and lower risk breasts using Chi-square. Data for breasts ipsilateral to a breast cancer are shown for comparison, but not included in the calculations for the high risk category. Results: Samples with adequate cellularity were obtained for 219 breasts (76%). The proportion of healthy breasts with ≥ 1% of the gene copies methylated was 13% for Cyclin D2, 19% for APC, 19% for HIN-1, 16% for RASSF1A, and 9% for RAR-beta. RAR-beta provided the best risk discrimination as 15% of high risk breasts were methylated at a level that exceeded the 95th percentile of the lower risk breasts (0.9% of gene copies methylated, P = 0.05). For the table , methylation fractions for all five genes were summed and the threshold for classifying a breast as positive was set to the 95th percentile of the lower risk breasts (methylation sum = 25.0%). Both methylation and marked atypia provide some discrimination between high and lower risk breasts; the combination, however, provides the best discrimination (24% marker positive for high risk versus 9% for lower risk, P = 0.02). Conclusions: TSG methylation in NDL samples is a marker of breast cancer risk that is complementary to cytology. [Table: see text] [Table: see text]

Cell-based breast cancer risk stratification based on DNA methylation in fine needle aspiration samples

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 1508-1508
Author(s):  
D. Euhus ◽  
D. Bu ◽  
S. Milchgrub ◽  
A. M. Leitch ◽  
C. M. Lewis
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Lower Risk ◽  
Breast Tissue ◽  
Needle Aspiration ◽  
Gail Model ◽  
Rassf1a Methylation ◽  
Risk Patients

1508 Background: Tumor suppressor gene (TSG) methylation is identified in nearly all breast cancers, but rarely in histologically normal breast tissue from wonen unaffected with breast cancer. Its occurrence in high risk preneoplasia and in benign breast tissue adjacent to breast cancer suggests that it may represent a high risk field change that could be exploited for cell-based breast cancer risk stratification. Methods: TSG methylation was measured by quantitative methylation-specific real time PCR in 53 breast tumor fine needle aspiration (FNA) biopsies, 84 cellular random periareolar FNAs (RP-FNA) ipsilateral or contralateral to these cancers, 36 cellular RP- FNAs from unaffected women at high risk for breast cancer by the Gail model, and 95 cellular RP-FNAs from unaffected women at lower risk by the Gail model. Results: The breast tumors showed a high frequency of TSG methylation: RASSF1A 80%, HIN-1 65%, Cyclin D2 60%, RAR-β2 53%, and APC 47%. In general, RP-FNA samples from cancer patients and Gail high risk patients showed a greater frequency of methylation than samples from Gail lower risk patients: RASSF1A 43% vs. 21%, P = 0.001, HIN-1 32% vs. 20%, P = 0.05; Cyclin D2 18% vs. 9%, P = 0.10; RAR-β2 21% vs. 18%, P = 0.68; and APC 25% vs. 16%, P = 0.17. Twelve of 215 RP-FNA samples (5%) showed very high levels of methylation (>10% methylation for two or more genes). Only two of these samples were from women classified as lower risk by the Gail model. Methylation frequencies were entirely independent of cell yields but the frequency of RASSF1A methylation increased with increasing Masood scores (P = 0.05). Methylation of RASSF1A in one breast was highly predictive of RASSF1A methylation in the opposite breast (P < 0.0001). Conclusions: TSG methylation appears to be a breast cancer risk-associated field change that can be quantified in RP-FNA samples. RASSF1A methylation occurs frequently in benign breast epithelium, provides reasonable discrimination between high and lower risk breasts (O.R. = 2.0), is related to cytological atypia, and may be an early marker of a methylator phenotype. Quantification of TSG methylation in RP-FNA samples may provide a valuable surrogate endpoint biomarker for Phase II prevention trials. No significant financial relationships to disclose.

scholarly journals Copy number variant in the candidate tumor suppressor gene MTUS1 and familial breast cancer risk

2007 ◽  
Vol 28 (7) ◽  
pp. 1442-1445 ◽  
Author(s):  
Bernd Frank ◽  
Justo Lorenzo Bermejo ◽  
Kari Hemminki ◽  
Christian Sutter ◽  
Barbara Wappenschmidt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Tumor Suppressor ◽  
Cancer Risk ◽  
Copy Number ◽  
Familial Breast Cancer ◽  
Copy Number Variant ◽  
Suppressor Gene ◽  
Candidate Tumor Suppressor Gene ◽  
Familial Breast Cancer Risk

scholarly journals Addressing Barriers to Uptake of Breast Cancer Chemoprevention for Patients and Providers

2015 ◽  
pp. e50-e58 ◽  
Author(s):  
Katherine D. Crew
Keyword(s):  
Breast Cancer ◽  
Primary Prevention ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Chemoprevention ◽  
The United States ◽  
Cancer Risk Assessment ◽  
Breast Cancer Risk Assessment ◽  
Breast Cancer Chemoprevention

Breast cancer is the most common malignancy among women in the United States, and the primary prevention of this disease is a major public health issue. Because there are relatively few modifiable breast cancer risk factors, pharmacologic interventions with antiestrogens have the potential to significantly affect the primary prevention setting. Breast cancer chemoprevention with selective estrogen receptor modulators (SERMs) tamoxifen and raloxifene, and with aromatase inhibitors (AIs) exemestane and anastrozole, is underutilized despite several randomized controlled trials demonstrating up to a 50% to 65% relative risk reduction in breast cancer incidence among women at high risk. An estimated 10 million women in the United States meet high-risk criteria for breast cancer and are potentially eligible for chemoprevention, but less than 5% of women at high risk who are offered antiestrogens for primary prevention agree to take it. Reasons for low chemoprevention uptake include lack of routine breast cancer risk assessment in primary care, inadequate time for counseling, insufficient knowledge about antiestrogens among patients and providers, and concerns about side effects. Interventions designed to increase chemoprevention uptake, such as decision aids and incorporating breast cancer risk assessment into clinical practice, have met with limited success. Clinicians can help women make informed decisions about chemoprevention by effectively communicating breast cancer risk and enhancing knowledge about the risks and benefits of antiestrogens. Widespread adoption of chemoprevention will require a major paradigm shift in clinical practice for primary care providers (PCPs). However, enhancing uptake and adherence to breast cancer chemoprevention holds promise for reducing the public health burden of this disease.

scholarly journals Induction of PDCD4 tumor suppressor gene expression by RAR agonists, antiestrogen and HER-2/neu antagonist in breast cancer cells. Evidence for a role in apoptosis

Oncogene ◽  
2004 ◽  
Vol 23 (49) ◽  
pp. 8135-8145 ◽  
Author(s):  
Olubunmi Afonja ◽  
Dominique Juste ◽  
Sharmistha Das ◽  
Sachiko Matsuhashi ◽  
Herbert H Samuels
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Tumor Suppressor ◽  
Cancer Cells ◽  
Tumor Suppressor Gene ◽  
Breast Cancer Cells ◽  
Suppressor Gene ◽  
Her 2

scholarly journals Perceptions of Breast Cancer Risk, Psychological Adjustment and Behaviors in Adolescent Girls at High-risk and Population-risk for Breast Cancer

2014 ◽  
Vol 54 (2) ◽  
pp. S87
Author(s):  
Angela R. Bradbury ◽  
Linda Patrick-Miller ◽  
Brian Egleston ◽  
Lisa Schwartz ◽  
Lisa Tuchman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Psychological Adjustment ◽  
Adolescent Girls ◽  
And Behaviors

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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