scholarly journals Validation of the Gail model for predicting individual breast cancer risk in a prospective nationwide study of 28,104 Singapore women

2012 ◽  
Vol 14 (1) ◽  
Author(s):  
Wen Yee Chay ◽  
Whee Sze Ong ◽  
Puay Hoon Tan ◽  
Nicholas Qi Jie Leo ◽  
Gay Hui Ho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Gail Model ◽  
Nationwide Study

scholarly journals A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245375
Author(s):  
Richard Allman ◽  
Erika Spaeth ◽  
John Lai ◽  
Susan J. Gross ◽  
John L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
African American ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Clinical Risk Factors ◽  
Polygenic Risk Score ◽  
Gail Model ◽  
Clinical Risk

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

scholarly journals Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study

2013 ◽  
Vol 138 (1) ◽  
pp. 249-259 ◽  
Author(s):  
Roberto Pastor-Barriuso ◽  
Nieves Ascunce ◽  
María Ederra ◽  
Nieves Erdozáin ◽  
Alberto Murillo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Population Based ◽  
Gail Model ◽  
Spanish Women

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.

Assessment of breast cancer risk in Libyan women using the gail model

2020 ◽  
Vol 4 (3) ◽  
pp. 115
Author(s):  
MounaM ElJilani ◽  
AfafA Shebani ◽  
AminaM Bishr ◽  
HamzaM Abdul Jalil ◽  
TarekM Dalyoum ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Gail Model

Breast cancer risk assessment in the Czech female population – an adjustment of the original Gail model

2005 ◽  
Vol 95 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Jan Novotny ◽  
Ladislav Pecen ◽  
Lubos Petruzelka ◽  
Adam Svobodnik ◽  
Ladislav Dusek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Risk Assessment ◽  
Gail Model ◽  
Female Population ◽  
Breast Cancer Risk Assessment

scholarly journals Novel Breast Tissue Feature Strongly Associated With Risk of Breast Cancer

2009 ◽  
Vol 27 (35) ◽  
pp. 5893-5898 ◽  
Author(s):  
Kevin P. McKian ◽  
Carol A. Reynolds ◽  
Daniel W. Visscher ◽  
Aziza Nassar ◽  
Derek C. Radisky ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Breast Biopsy ◽  
Breast Disease ◽  
Normal Breast ◽  
Gail Model ◽  
Risk Levels ◽  
C Statistic

Purpose Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women. Methods We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women. Results There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic. Conclusion Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.

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