Incorporating Polygenic Risk Scores Enhances Breast Cancer Screening Strategies

Belangrijkste concepten

Combining polygenic risk scores with family history and pathogenic variants improves breast cancer risk stratification, enabling more personalized screening approaches.

Samenvatting

The content discusses the potential of using polygenic risk scores (PRS) alongside family histories and pathogenic gene variants to enhance breast cancer risk assessment and screening strategies.

Key highlights:

• Researchers in Finland used a nationwide genetic database to calculate PRS for 117,252 women and linked the scores to their breast cancer outcomes.
• A high PRS (top 10%) was associated with over a 2-fold higher risk of breast cancer compared to a lower PRS (below 90%), similar to the risk conferred by pathogenic variants and positive family histories.
• Combining a high PRS with positive family history or pathogenic variants increased the positive predictive value for a breast cancer diagnosis after a positive screening mammogram to 44.6% and 50.6%, respectively.
• High PRS was also linked to a 2-fold higher risk of interval breast cancers and higher risk of bilateral breast cancers during screening ages, suggesting the need for shorter screening intervals or earlier screening for these individuals.
• Women with low PRS (bottom 10%) had very low risk of interval and screen-detected cancers, potentially allowing for less frequent screening.
• The study demonstrates the value of incorporating PRS into breast cancer risk stratification, optimizing screening strategies by combining genetic, family history, and other risk factors.

Statistieken

A high polygenic risk score (top 10%) was associated with over a 2-fold higher risk of any breast cancer before, during, and after screening age (HR 2.50, 2.38, and 2.11, respectively).
Pathogenic variants led to a 3.13, 2.30, and 1.95-fold higher risk at the same timepoints.
Positive family history led to a 1.97, 1.96, and 1.68-fold higher risk at the same timepoints.
A high polygenic risk score had a 39.5% positive predictive value for a breast cancer diagnosis after a positive screening mammogram.
Combining a high polygenic risk score with positive family history increased the positive predictive value to 44.6%.
Combining a high polygenic risk score with pathogenic variants increased the positive predictive value to 50.6%.
A high polygenic risk score was associated with a 4.71-fold higher risk of bilateral breast cancer during screening ages.

Citaten

"Compared with a lower polygenic risk score (below 90%), a high polygenic risk score — a score in the top 10% — was associated with more than a twofold higher risk for any breast cancer before, during, and after screening age."
"A high polygenic risk score had a positive predictive value of 39.5% for a breast cancer diagnosis after a positive screening mammography, about the same as positive family history (35.5%) and pathogenic variants (35.9%)."
"Combining a high polygenic risk score with a positive family history increased the positive predictive value to 44.6% and with pathogenic variant carriers increased the positive predictive value to 50.6%."

Belangrijkste Inzichten Gedestilleerd Uit

Polygenic Risk Scores Improve Breast Cancer Screening

by M. Alexander... om www.medscape.com 04-03-2024

https://www.medscape.com/viewarticle/polygenic-risk-scores-improve-breast-cancer-screening-2024a10006ag

Diepere vragen

How can the findings of this study be applied to improve breast cancer screening guidelines and policies?

The findings of this study suggest that incorporating polygenic risk scores alongside family histories and pathogenic variants can significantly enhance risk stratification for breast cancer screening. This information can be utilized to develop more personalized and effective screening guidelines and policies. By identifying individuals with high polygenic risk scores, healthcare providers can recommend tailored screening strategies such as more frequent screenings or starting screenings at an earlier age. This targeted approach can lead to earlier detection of breast cancer in high-risk individuals, potentially improving outcomes and reducing mortality rates.

What are the potential barriers to the widespread adoption of polygenic risk scores in clinical practice, and how can they be addressed?

One potential barrier to the widespread adoption of polygenic risk scores in clinical practice is the need for healthcare providers to be educated and trained on how to interpret and utilize these scores effectively. Understanding the complexities of genetic data and integrating it into clinical decision-making can be challenging. Additionally, there may be concerns about the cost-effectiveness of incorporating polygenic risk scores into routine screening programs and the accessibility of genetic testing for all individuals.
To address these barriers, healthcare systems can invest in training programs to educate providers on the interpretation of polygenic risk scores and their implications for patient care. Efforts can also be made to streamline the process of genetic testing and make it more affordable and accessible to a broader population. Collaborations between genetic counselors, primary care providers, and specialists can help ensure that polygenic risk scores are integrated seamlessly into clinical practice.

What other types of genetic and genomic data, beyond polygenic risk scores, could be integrated with family history and clinical factors to further refine breast cancer risk assessment and screening strategies?

In addition to polygenic risk scores, other types of genetic and genomic data that could be integrated with family history and clinical factors to enhance breast cancer risk assessment include somatic mutations, epigenetic markers, and gene expression profiles. Somatic mutations, such as those in the tumor DNA, can provide valuable information about the specific genetic alterations driving the development of breast cancer in an individual. Epigenetic markers, which regulate gene expression without altering the DNA sequence, can offer insights into how genes are being turned on or off in cancer cells.
Gene expression profiles, which measure the activity of genes in a tissue sample, can help identify specific molecular subtypes of breast cancer and predict response to treatment. Integrating these additional layers of genetic and genomic data with polygenic risk scores, family history, and clinical factors can further refine risk assessment models and screening strategies, leading to more precise and personalized approaches to breast cancer prevention and early detection.