Reading Room of the ASCO | Julie Palmer, ScD, on predicting breast cancer risk in black women

Most breast cancer risk prediction models have been developed and validated primarily in data from white women. These models underperformed in black women, researchers said in a study in Journal of Clinical Oncology.

“The lack of a breast cancer risk prediction model tailored to women of color represents a critical gap,” wrote Julie Palmer, ScD, director of the Slone Epidemiology Center at Boston University School of Medicine, and colleagues. “Black US women are, on average, earlier in age at diagnosis than white US women and are more likely to be diagnosed with poor prognosis breast cancers. Many young black women are diagnosed with and die of breast cancer before we even reach the age at which mammography screening is typically recommended.”

Palmer’s team developed and externally validated two absolute risk prediction models for breast cancer in black women: one that considered all breast cancers together and another that incorporated heterogeneity based on estrogen receptor status. (ER).

The overall model, derived from 3,468 cases and 3,578 controls in three studies and validated with prospective follow-up data from the Black Women’s Health Study [BWHS], had excellent calibration overall, by age, and in both low- and high-risk women, the researchers said. “The discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in white women, suggesting that effective risk stratification for black women is now possible.”

“It is based on variables that can be easily obtained from the women themselves and entered into an online risk calculator, available at the BWHS Breast Cancer Risk Calculator,” the researchers added. “The validation data indicated better performance among women younger than 40, for whom personalized referral for breast cancer screening may be more important.”

In the following interview, Palmer elaborates on the research.

How did currently available risk prediction models tend to underperform in black women?

Palmer: The discriminatory accuracy of currently available risk prediction models has been shown to be lower in black women than in white women.

What risk prediction models did you compare your new model to and how did it compare in terms of accuracy?

Palmer: We have directly evaluated the NCI BCRAT [Breast Cancer Risk Assessment Tool] model, also known as the Gail model, in the same data from the Black Women’s Health Study. The discriminatory precision was lower: 0.56 compared to 0.58 in our model. While not a huge difference, the magnitude of the improvement is exactly what’s being sought with advances in breast cancer risk prediction models.

Are there specific predictors or variables in your model that differ from other models or differ in terms of weight or importance?

Palmer: Our model included a family history of first-degree prostate cancer, a variable that had not been included in previous breast cancer risk prediction models. This may be a more important factor in predicting risk for black women because the incidence of prostate cancer is markedly higher in black men and therefore the prevalence of having a family history of prostate cancer will be higher in black women. .

The model also included breastfeeding, a variable that had not been included in most previous models. Breastfeeding has been associated with a reduced risk of breast cancer, and the prevalence of breastfeeding is historically lower in black women.

You hypothesized that a model that considers specific ER subtypes would improve breast cancer risk prediction in black women. Has your research confirmed this?

Palmer: no. We found that the risk prediction was equally good in the model that considered all breast cancers together.

What would be needed to further improve breast cancer risk prediction models in the future?

Palmer: Breast cancer risk prediction models can be improved by adding a polygenic risk score, which is genetic information beyond knowledge of family history of cancer. There are two barriers, however.

First of all, genetic data across the genome is not currently available for most patients, although this may change in the near future as the cost of genotyping decreases and the potential for knowledge increases.

Second, at this point, existing polygenic risk scores for breast cancer work well for most other populations, but have poor predictive ability for black women. This is due to the substantially greater genetic diversity in individuals of African ancestry and the relative paucity of genetic research in populations of African ancestry. We simply do not yet have the data to derive and validate useful polygenic risk scores for breast cancer in women of African descent.

Read the study here and expert commentary here.

The study was supported by the NIH, the Susan G. Komen Foundation and the Karin Grunebaum Foundation.

Palmer has disclosed no conflicts of interest.

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