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Population Genomics: Personalizing Genetic Breast Cancer Screening

Discover how University of Nevada, Reno, Renown Health, and Helix are revolutionizing breast cancer screening by combining genetic screening and EHR data to identify high-risk individuals more accurately.

What we know about how to best use genetic screening for breast cancer prevention is continuously evolving. Our latest research, “Combining rare and common genetic variants improves population risk stratification for breast cancer,” recently published in Genetics in Medicine Open, adds to this collective knowledge by advocating for a tailored approach that combines screening well-established pathogenic variants and polygenic risk scores to provide a more individualized assessment of genetic risk that can not only improve patient outcomes, but optimize screening resources.

Central to this paper was data collected from the Healthy Nevada Project, a collaborative effort between Helix, the University of Nevada, Reno, and Renown Health that leverages the Helix Exome+® assay and allowed for the analysis of 25,591 female participants to evaluate the performance of different genetic screening approaches to identify women at high risk of breast cancer.

Here are the five fast facts of what we found:

1. It is difficult for people to know their family history and it is not generally ascertained when it would be the most useful.

In our study, we confirmed that family history is not an effective tool for screening the population for breast cancer. Many have speculated that it may be because individuals do not have a large enough family to provide an informative history or are not aware of their family's medical history. Regardless, this study provides real-world evidence of its limitations. In addition, family history was mainly ascertained at or after the diagnosis of breast cancer, rendering it ineffective for early screening.

2. Including predicted loss-of-function variants from the additional genes PALB2, ATM and CHEK2 is high impact.

Our research supports the inclusion of predicted loss-of-function variants in PALB2, ATM, and CHEK2, in addition to BRCA1 and BRCA2, for assessing breast cancer risk.These five genes led to a significant increase in an individual’s risk of breast cancer with PALB2 in particular having an impact similar to that of BRCA1 or BRCA2.

3. Screening for these additional variants is also efficient and feasible.

Our study also demonstrates a cost- and time-efficient process for variant classification in these important genes for screening purposes and determining breast cancer risk in a large population. The approach is more practical than using ACMG guidelines for variant interpretation, which may not be suitable for large-scale screening.

4. In addition, a well-validated polygenic risk score can be calculated and used for everyone.

We also show how a polygenic risk score model can be used effectively to identify the top 10% of individuals that may be at a significantly higher risk of breast cancer than others. However, it is important to note that having a high polygenic risk score is less indicative of a patient developing cancer than if the patient had a loss-of-function variant in the five genes identified earlier. More discussion would be needed to help guide physicians on how to best treat otherwise healthy patients that may have a high polygenic risk score but no predicted loss-of-function variants in one of the five genes.

5. The most effective screening strategy would be a combination of rare variants and polygenic risk.

We suggest that combining the identification of rare loss-of-function variants in BRCA1, BRCA2, PALB2, ATM, and CHEK2 with polygenic risk assessment may be the most effective screening strategy available. This combined approach, focused on the assessment of individual genetic risk, would not only elevate patient outcomes but improve efficiency and equity in healthcare overall.

The Importance of Population Genomics

The patient population in our study is a cohort comprising 25,591 adult women residing in Nevada. This population is part of a larger all-comers population genetic screening initiative to understand the genetic factors influencing health and disease in this region. The HNP endeavors to reflect the demographics of the Northern Nevada area, with efforts made to increase the diversity of participants to ensure they represent the overall patient population of Renown Health, the healthcare system partner in the project.

Our study leverages the power of large-scale genetic screening to identify variants associated with breast cancer risk. By analyzing rare and common genetic variants, we aim to improve risk stratification for breast cancer in the general population. The use of a well-validated polygenic risk score (PRS) model allows us to quantify the contribution of common variants to breast cancer risk, providing a more comprehensive assessment when combined with the analysis of rare pathogenic variants in critical genes such as BRCA1, BRCA2, PALB2, ATM, and CHEK2.

The approach exemplifies the application of population genomics in a real-world setting, where integrating genetic information into public health initiatives can lead to more personalized and effective screening strategies. By incorporating genomic data in to breast cancer risk assessment, we can more precisely tailor screening and preventions efforts to those who would benefit most, ultimately improving outcomes and reducing the burden of this disease.


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