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William J. Gradishar, MD, FACP, FASCO


Development of an Asian Population–Specific BRCA Mutation Prediction Model

By: Julia Fiederlein
Posted: Friday, August 19, 2022

Most BRCA mutation prediction tools have been developed using data from women of European descent. Thus, Soo Hwang Teo, PhD, of Cancer Research Malaysia, Subang Jaya, and colleagues developed a logistic regression model based on Southeast Asian patients with breast cancer. Their findings, which were published in the Journal of Clinical Oncology, suggested that population-specific customization of mutation prediction models and clinical genetic testing criteria may improve the accuracy of BRCA mutation prediction.

“With the advent of germline genetic testing for treatment selection, more women may consider genetic testing as part of their treatment plans,” the investigators commented. “Given that Asian women have a younger age of diagnosis for breast cancer and different distribution of breast cancer subtypes compared with women of European descent, population-specific customization of BRCA carrier prediction tools is important to enable more accurate BRCA mutation prediction in diverse populations.”

Using germline BRCA genetic testing results from a cross-sectional population-based study of 8,162 Asian patients with breast cancer, the investigators developed the Asian Risk Calculator. This model included age at diagnosis, ethnicity, bilateral breast cancer, tumor biomarkers, and family history of breast cancer or ovarian cancer as predictors of the likelihood of carrying a pathogenic variant in the BRCA1 or BRCA2 gene.

A significant improvement in model performance was observed with the inclusion of tumor grade. According to the investigators, the full model was calibrated and discriminated well between BRCA and non-BRCA pathogenic variant carriers. Adding grade to the existing clinical genetic testing criteria targeting patients with breast cancer younger than age 45 seemed to reduce the proportion of those referred for genetic counseling and testing from 37% to 33% (P = .003), thereby improving the overall efficacy.

Disclosure: For full disclosures of the study authors, visit

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