Whitepaper: Validating Orchid’s Breast Cancer Genetic Risk Score

Introduction

Breast cancer is a disease in which malignant cells form in breast tissue. It most commonly presents with physical changes to the breast, such as a new lump or thickening, changes in breast size or shape, or skin changes involving the breast or nipple.¹

Risk for breast cancer is influenced by both genetic and non-genetic factors. Non-genetic risk factors include increasing age, reproductive history such as having a first menstrual period before age 12 or menopause after age 55, use of certain hormone therapies, alcohol consumption, being overweight or obese after menopause, lack of regular physical activity, and exposure to diethylstilbestrol (DES).² A small proportion of cases are associated with inherited high-risk genetic variants, including pathogenic variants in the BRCA1 and BRCA2 genes, which substantially increase lifetime risk of breast cancer.^{3, 4}

There are currently more than 4 million breast cancer survivors in the United States, and approximately 13% of women in the US will be diagnosed with the disease in their lifetime.^{5, 6} Prognosis for breast cancer varies considerably by stage at diagnosis. The 5-year relative survival rate is more than 99% for localized disease (cancer confined to the breast), about 87% for regional disease, and about 32% for metastatic disease.⁷ Mammography screening can help with earlier detection, and several treatments such as medications (including chemotherapy), surgery, and radiotherapy may be prescribed by an oncologist.⁸

Genetic Risk Score

Breast cancer is shaped by both environmental and genetic factors. Some rare monogenic variants such as BRCA1/2 are known to substantially increase breast cancer risk^{3, 4} but most cases arise from the combined effects of many genetic variants and environmental exposures. A minority of individuals carry a pathogenic monogenic BRCA1/2 variant; for these individuals, the single pathogenic variant is the most important factor in determining their breast cancer risk. For the large majority of individuals who do not carry a pathogenic BRCA variant, the risk of breast cancer can be modeled by building a genetic risk score (GRS) which combines the small effects of many variants into a single score. Although not diagnostic, a GRS can indicate how likely an individual is to develop the disease compared to the population baseline risk.

The breast cancer GRS was developed using data from multiple large studies, including the Breast Cancer Association Consortium (BCAC), UK Biobank (UKB), FinnGen, the Million Veteran Program (MVP), and All of Us, consisting of individuals of European ancestry.^9–13 For the UK Biobank, a subset of individuals was held out to use in the validation study. Summary statistics were generated for the UKB and All of Us using the Regenie method.¹⁴ Summary statistics across all studies were then meta-analyzed using METAL.¹⁵ The final GRS was trained using SBayesRC and includes over 7 million variants.¹⁶

Risk predictions are adjusted to each individual’s ancestry, with predictive power decaying as genetic distance from the predominantly European training data increases.¹⁷ Orchid considers a GRS meaningfully predictive if individuals at approximately the 97.7th percentile have an odds ratio (OR) of at least 2. The breast cancer GRS meets this criterion for all common ancestry groups.

Evaluation on UK Biobank Data

We evaluated the predictive accuracy of Orchid’s breast cancer GRS using the UK Biobank (UKB), a research database of roughly 500,000 genotyped individuals from the United Kingdom.¹⁰ Because some data from the UKB was used to train the GRS, our validation study included only the set of samples that were held out for testing and not included in the GRS training. We also restricted the analysis to females of British ancestry and defined breast cancer using the C50 ICD-10 code, yielding 4,159 cases and 62,085 controls (6.3% prevalence). We then grouped individuals by GRS percentile and compared the observed disease prevalence within each group to our model’s predictions (Figure 1). For additional technical details, see the Supplementary Information.

Table 1 shows the breast cancer observed prevalence for individuals in the UKB grouped by GRS percentile range (top 10%, 5%, and 1%), as well as how their risk compares to the baseline risk at the 50th GRS percentile. Those with higher GRS relative to the population baseline also had substantially higher observed prevalence of breast cancer, supporting the predictive accuracy of the GRS to identify individuals with elevated risk.

Estimating Lifetime Risk

The average observed prevalence of breast cancer in the UKB was 6.3%. This is considerably lower than the lifetime prevalence in the US general population, which has been estimated to be approximately 13%.⁵ This is likely due in part to the fact that UKB participants tend to be healthier than the general population, which leads to lower observed disease prevalence.¹⁸ Additionally, the observed prevalence in the UKB includes people still living who could develop the disease when they are older, and so does not capture the full lifetime risk of the disease.

Orchid’s clinical reports include predicted lifetime disease risk, which we calculate by first estimating how disease risk varies across GRS in the UKB and then rescaling that pattern so the average matches the known lifetime population risk (Figure 2).¹⁹ People at the high end of the GRS distribution are predicted to have an elevated lifetime risk of the disease relative to the population (Table 2).

Conclusion

In this study, we evaluated our breast cancer GRS, which was generated using summary statistics from BCAC, on data from the UKB. We found that it performed well, particularly for identifying individuals with elevated risk of the disease relative to the population. In our embryo and couple reports, we adjust the model to predict lifetime risk, which is generally higher than observed prevalence in the UKB. The breast cancer GRS model is available to individuals of all ancestry groups.

Acknowledgments

This research was conducted using the UK Biobank Resource under Application Number 80545.

References

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