Leveraging Polygenic Risk Scores to Fight Breast Cancer

As a general rule, the earlier a cancer is detected, the more likely it is that physicians will be successful in treating it. Breast cancer, a condition that affects nearly 1 in 8 women, is no exception¹. Early breast cancer detection is largely done through routine mammography-based screening. However, this approach is imperfect, too often producing false-positive diagnoses that can result in subsequent patient harm ².
 
Genetic testing can help to reduce false positives by directing screening efforts towards patients with the greatest lifetime risk of cancer development. Currently, clinical genetic testing focuses on a small selection of high-impact mutations in genes such as BRCA1 and BRCA2. Yet, these mutations are present in less than 10% of breast cancer cases³. Risk assessment may be improved by considering the combined effect of low-impact variants as well—each of which may contribute a minuscule bump in disease risk, but when combined, add up to a clinically significant predisposition to cancer development.
 
Algorithms known as polygenic risk scores (PRS) help researchers integrate these low-impact variants into risk assessments, enabling the effect of both high- and low-impact variants to be condensed into a single readout. Such a comprehensive approach to risk assessment may help identify patients who are at an increased risk of breast cancer but would otherwise be missed by current genetic screening practices.
 
In supporting this point, a recent study published in the Journal of Medical Genetics used a Twist custom target enrichment panel to study the potential benefits of analyzing low-impact variants during patient risk assessments ⁴. The study, led by Daniel Eriksson, MD, Ph.D., a physician in the Department of Immunology, Genetics, and Pathology at Uppsala University, showed that the analysis of 313 relatively low-impact genetic variations could change the risk assessment for up to 45% of patients.
 
“We found that this tool, which is also being evaluated in larger cohorts, adds a lot of information to stratify risk between individuals,” said Claes Ladenvall, Ph.D., a bioinformatician in the Department of Immunology, Genetics and Pathology at Uppsala University. “We can actually change the risk class on a significant part of the population, and women who are more likely to develop breast cancer can be monitored more frequently.”
 
The study looked at 87 families affected by breast cancer whose genomes showed no obvious cancer-causing variants, and 1,000 controls (individuals with no recognized disease). In addition to sequencing information, the researchers collected detailed histories, clinical information and other data from participant records. Collectively, this data showed that women in the study group who went on to develop breast cancer were likely to have a high PRS.

The 313 low-impact variants used for this study had previously been used to build a validated PRS for breast cancer risk. In the current study, these variants were aggregated into a custom next-generation sequencing (NGS) panel, which the research team used to complement the traditional Mendelian variants associated with higher risk. To create that panel, the authors fell back on their long-standing relationship with Twist, having previously collaborated on targeted exome panels and other such tools.
 
“Working with Twist was an easy option,” said Ladenvall. “We started this project with a small Twist panel to assess inherited cancer genes. Then we created this add-on panel with the 313 SNPs to assess how they might affect risk.”
 
The group is now combining the two panels for possible use in the clinic. Notably, the team’s choice to forgo microarray technology, opting instead to use NGS for variant assessment, reflects a broader trend in the industry. Microarray technology has long served as a low-cost approach to genetic analyses in the clinical setting. However, the considerable challenge of updating assay content and the limited information density gained from microarrays have created a need for better, more flexible and informative tools.
 
As Ladenvall et al., demonstrate, such a need can be met through the development of optimized NGS workflows. The team’s use of the Twist Library Preparation and Standard Hybridization kits not only support optimal panel performance but also streamlines the assay workflow. And, unlike microarrays, NGS panels provide great flexibility to add informative variants and optimize for increased clinical precision as our understanding of breast cancer evolves.
 
“From a technical point of view,” explained Ladenvall in a recent press release, “the project was particularly interesting as it involved translating genetic results that originate from one analysis platform (array data) to another (sequencing). Now that we have these tools in place we are prepared when healthcare is ready for a clinical implementation.”

References

1. Siegel, Rebecca L., et al. “Cancer Statistics, 2022.” CA: A Cancer Journal for Clinicians, vol. 72, no. 1, 12 Jan. 2022, pp. 7–33, acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21708, https://doi.org/10.3322/caac.21708.
2. Independent UK Panel on Breast Cancer Screening. “The Benefits and Harms of Breast Cancer Screening: An Independent Review.” The Lancet, vol. 380, no. 9855, Nov. 2012, pp. 1778–1786, www.thelancet.com/journals/lancet/article/PIIS0140-6736(12)61611-0/fulltext, https://doi.org/10.1016/s0140-6736(12)61611-0.
3. Li, Na, et al. “Investigation of Monogenic Causes of Familial Breast Cancer: Data from the BEACCON Case-Control Study.” NPJ Breast Cancer, vol. 7, 11 June 2021, p. 76, www.ncbi.nlm.nih.gov/pmc/articles/PMC8196173/, https://doi.org/10.1038/s41523-021-00279-9.
4. Panagiotis Baliakas, et al. “Integrating a Polygenic Risk Score into a Clinical Setting Would Impact Risk Predictions in Familial Breast Cancer.” Journal of Medical Genetics, 14 Aug. 2023, p. jmg-109311, https://doi.org/10.1136/jmg-2023-109311.