scholarly journals European Breast Cancer Council manifesto 2018: Genetic risk prediction testing in breast cancer

2019 ◽  
Vol 106 ◽  
pp. 45-53 ◽  
Author(s):  
Emiel Rutgers ◽  
Judith Balmana ◽  
Marc Beishon ◽  
Karen Benn ◽  
D. Gareth Evans ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction ◽  
Cancer Council

Genetic Risk Prediction in Breast Cancer

2017 ◽  
pp. 217-232
Author(s):  
Anvy Nguyen ◽  
Jennifer K. Plichta ◽  
Jessica Cintolo-Gonzalez ◽  
Kinyas Kartal ◽  
Molly Elizabeth Griffin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction

Assessing the added value of breast tumor markers in breast cancer genetic risk prediction model BRCAPRO.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 164-164
Author(s):  
B. Arun ◽  
S. Biswas ◽  
N. Tankhiwale ◽  
A. L. Blackford ◽  
A. M. Gutierrez-Barrera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Tumor Markers ◽  
Risk Prediction ◽  
Validation Study ◽  
Genetic Risk ◽  
Breast Tumor ◽  
Model Building ◽  
Risk Prediction Model ◽  
Genetic Risk Prediction

164 Background: BRCAPRO is a widely used genetic risk prediction model for estimating the carrier probabilities of mutations in BRCA1/2 genes. BRCAPRO has been enhanced to utilize information on molecular markers ER, PR, CK5/6, and CK14. However, no independent validation study on the utility of these markers in risk prediction exists to support using these in actual clinical settings. Further, an important predictive and prognostic marker for breast cancer, Her-2/neu (Her2) is not utilized in BRCAPRO. Therefore, the aim of our study was to: 1) incorporate Her2 in BRCAPRO; 2) conduct a validation study of the markers. Methods: Patients with breast cancer at the UT M. D. Anderson Cancer Center’s breast clinic who were referred for genetic evaluation were included. Separate sets of cohort were used for model building with Her2 and validation to avoid bias. This study was approved by the IRB. For the model building, we estimated the joint probabilities of ER and Her2 status for carriers and non-carriers of BRCA1/2 mutations. For the validation, BRCAPRO was run at two settings: 1) no marker data used and 2) ER/PR used. We calculated the Area Under the receiving operator characteristic Curve (AUC) using the probabilities of carrying any of BRCA1 or BRCA2 and conditional probabilities of carrying BRCA1 (CondBRCA1) and BRCA2 (CondBRCA2) given a proband is carrier. Results: The model-building set for Her2 was based on 409 probands and validation set on 796 probands wherein 23% of the probands were carriers. In the model-building step, we found that joint consideration of Her2 and ER/PR is useful in discriminating between carriers and non-carriers in some subgroups, e.g., a proband with ER-, Her2+ is much more likely to be a non-carrier than a carrier. In the validation step, the AUC for CondBRCA1 and CondBRCA2 improved substantially when ER/PR was used. We are in the process of coding Her2 in BRCAPRO and then validating its utility. Conclusions: Breast tumor markers are useful for prediction of BRCA1/2 mutation status in the BRCAPRO model. ER/PR helps discriminate between BRCA1 and BRCA2 mutation carriers. In our ongoing validation study Her2 is expected to improve discrimination between carriers and non-carriers in certain sub-groups.

scholarly journals Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS Statement

2011 ◽  
Vol 4 (2) ◽  
pp. 206-209 ◽  
Author(s):  
A. Cecile J.W. Janssens ◽  
John P.A. Ioannidis ◽  
Cornelia M. van Duijn ◽  
Julian Little ◽  
Muin J. Khoury
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction

scholarly journals Moving towards population-based genetic risk prediction for ovarian cancer

2017 ◽  
Vol 124 (6) ◽  
pp. 855-858 ◽  
Author(s):  
B Rahman ◽  
L Side ◽  
S Gibbon ◽  
SF Meisel ◽  
L Fraser ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Population Based ◽  
Genetic Risk Prediction

scholarly journals The use of genetic risk prediction to study prodromal Alzheimer’s disease in the PISA study

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Michelle K. Lupton ◽  
Amir Fazlollahi ◽  
Amelia Ceslis ◽  
Jurgen Fripp ◽  
Stephen Rose ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction ◽  
Prodromal Alzheimer’S Disease
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