BACKGROUND: The lifetime risk of breast cancer (BC) in patients with hereditary breast cancer syndromes is as high as 80%. The Pedigree Assessment Tool (PAT) is a scoring system to aid in identifying these patients. This validation study compares the PAT to BRCA gene mutation probability models in predicting suitability for genetic referral. METHODS: Retrospective review identified subjects undergoing genetic counseling and BRCA testing from 2001 to 2008 at two institutions. PAT score and BRCA mutation probabilities were calculated using Myriad II and Penn II models. Comparisons were made between models in ability to discriminate patients appropriate for genetic evaluation based on accuracy in predicting a positive test result. RESULTS: Records evaluated represent 520 families. BRCA testing revealed 146 mutation-positive and 374 mutation-negative families. c-Statistic analysis was used to compare the discriminating ability of the models to correctly assign families as mutation (+) and (-). Both the PAT and Penn II model outperformed the Myriad II model. Using a threshold PAT score >or=8 and mutation probability >or=10% to assign families as mutation (+) versus (-), sensitivity, specificity, and positive and negative predictive values were calculated for each model. The PAT was more sensitive than the Myriad II model and more specific than the Penn II model. CONCLUSIONS: In overall performance, the PAT is at least comparable to the Myriad II and Penn II models in screening women appropriate for genetic referral. Simplicity and identification of families with non-BRCA hereditary BC syndromes suggest that the PAT is better suited for BC risk screening.
BACKGROUND: The lifetime risk of breast cancer (BC) in patients with hereditary breast cancer syndromes is as high as 80%. The Pedigree Assessment Tool (PAT) is a scoring system to aid in identifying these patients. This validation study compares the PAT to BRCA gene mutation probability models in predicting suitability for genetic referral. METHODS: Retrospective review identified subjects undergoing genetic counseling and BRCA testing from 2001 to 2008 at two institutions. PAT score and BRCA mutation probabilities were calculated using Myriad II and Penn II models. Comparisons were made between models in ability to discriminate patients appropriate for genetic evaluation based on accuracy in predicting a positive test result. RESULTS: Records evaluated represent 520 families. BRCA testing revealed 146 mutation-positive and 374 mutation-negative families. c-Statistic analysis was used to compare the discriminating ability of the models to correctly assign families as mutation (+) and (-). Both the PAT and Penn II model outperformed the Myriad II model. Using a threshold PAT score >or=8 and mutation probability >or=10% to assign families as mutation (+) versus (-), sensitivity, specificity, and positive and negative predictive values were calculated for each model. The PAT was more sensitive than the Myriad II model and more specific than the Penn II model. CONCLUSIONS: In overall performance, the PAT is at least comparable to the Myriad II and Penn II models in screening women appropriate for genetic referral. Simplicity and identification of families with non-BRCA hereditary BC syndromes suggest that the PAT is better suited for BC risk screening.
Authors: Emily E Anderson; Silvia Tejeda; Kimberly Childers; Melinda R Stolley; Richard B Warnecke; Kent F Hoskins Journal: J Oncol Pract Date: 2015-06-02 Impact factor: 3.840
Authors: Kent F Hoskins; Silvia Tejeda; Ganga Vijayasiri; Ifeanyi Beverly Chukwudozie; Mylene H Remo; Hiral A Shah; Ivy E Abraham; Lara E Balay; Tara K Maga; Ericka R Searles; Veena J Korah; Alana Biggers; Melinda R Stolley; Richard B Warnecke Journal: Cancer Date: 2018-10-15 Impact factor: 6.860
Authors: Gretchen L Gierach; Hui Li; Jennifer T Loud; Mark H Greene; Catherine K Chow; Li Lan; Sheila A Prindiville; Jennifer Eng-Wong; Peter W Soballe; Claudia Giambartolomei; Phuong L Mai; Claudia E Galbo; Kathryn Nichols; Kathleen A Calzone; Olufunmilayo I Olopade; Mitchell H Gail; Maryellen L Giger Journal: Breast Cancer Res Date: 2014-08-23 Impact factor: 6.466