AmbryScore for Breast Cancer

Learn more about your patient’s breast cancer risk and better understand the impact of single nucleotide polymorphisms (SNPs) by opting into AmbryScore, a remaining lifetime breast cancer risk calculation.

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What is AmbryScore for Breast Cancer?

Why is this important?

  • Gather additional breast cancer risk information for unaffected women who test negative for a mutation in a breast cancer susceptibility gene
  • Contribute to longitudinal data to further the understanding of breast cancer heritability and the clinical impact of a PRS
  • Use combined clinical, family history, and genetic risk factors to better guide your patient’s medical management

Who is eligible for AmbryScore for Breast Cancer?

Patients must meet all of the following criteria to be eligible for AmbryScore:

  1. Female biological sex
  2. 18-84 years old
  3. Non-Ashkenazi Jewish, Northern European ancestry
  4. No personal history of cancer (excluding non-melanoma skin cancer)
  5. No personal history of atypical hyperplasia or lobular carcinoma in situ (LCIS)
  6. No personal or family history of a mutation in a breast cancer susceptibility gene*

**ATM, BARD1 (if tested), BLM (if tested), BRCA1, BRCA2, BRIP1, CDH1, CHEK2, FANCC (if tested), NBN, NF1, PALB2, PTEN, RAD51C, RAD51D, STK11 (if tested), TP53

AmbryScore for breast cancer can be added to any of the following multigene panels,
but is not available as a standalone test:

Test Description

The AmbryScore tool for breast cancer provides a personalized estimate of remaining lifetime breast cancer risk (up to age 85) based on the following patient-specific factors: age at testing, ethnicity, clinical and family history data, and results of single nucleotide polymorphism (SNP) profiling. A population-standardized polygenic risk score (PRS) is computed as the sum of the patient’s risk alleles across 100 SNPs, weighted by the SNP-specific effects reported in large breast cancer studies1-11, and ethnicity-specific allele frequencies12-14. A patient’s absolute risk of breast cancer is computed according to the Tyrer-Cuzick model (v.8),15 which is based on her age, family history and clinical information, and combined with her PRS to produce an estimate of her remaining lifetime risk. The AmbryScore calculation is highly-dependent on the accuracy of clinician-provided clinical data. Other factors not accounted for in the AmbryScore calculation may impact lifetime breast cancer risk including, but not limited to, germline mutations not analyzed by the ordered genetic test. The AmbryScore provided is patient-specific and cannot be used to infer risk to relatives.

Sequencing of the SNPs is carried out by a bait-capture methodology using long biotinylated oligonucleotide probes followed by polymerase chain reaction (PCR) and Next-Generation sequencing. A PRS score is generated when genotype data is available for at least 90/100 (90%) analyzed SNPs.

Remaining Lifetime Breast Cancer Risk by Age (General Population, Non-Hispanic White)

Non-Hispanic White Current Age Remaining Lifetime Risk (%)
20 14.49
25 14.51
30 14.50
35 14.41
40 14.14
45 13.51
50 12.57
55 11.50
60 10.27
65 8.71
70 6.70
75 4.48
80 2.23


  1. Michailidou, K., et al. 2017. Association analysis identifies 65 new breast cancer risk loci. Nature 551(7678): 92-94.6
  2. Easton DF., et al. 2007. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 447(7148):1087-93.
  3. Gold, B., et al. 2008. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci U S A 105(11): 4340-4345.
  4. Fletcher, O., et al. 2011. Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst 103(5): 425-435.
  5. Orr, N., et al. 2012. Genome-wide association study identifies a common variant in RAD51B associated with male breast cancer risk. Nat Genet 44(11): 1182-1184.
  6. Garcia-Closas, M., et al. 2013. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 45(4): 392-398, 398e391-392.
  7. Michailidou, K., et al. 2013. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 45(4): 353-361, 361e351-352.
  8. Michailidou, K., et al. 2015. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet 47(4): 373-380.
  9. Lindstrom, S., et al. 2014. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 5: 5303
  10. Couch, F. J., et al. 2016. "Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer. Nat Commun 7: 11375.
  11. Milne, R. L., et al. 2017. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49(12): 1767-1778.
  12. Mealiffe, M. E., et al. 2010. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst 102(21): 1618-1627.
  13. Allman, R., et al. 2015. SNPs and breast cancer risk prediction for African American and Hispanic women. Breast Cancer Res Treat 154(3): 583-589.
  14. Dite, G. S., et al. 2016. Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev 25(2): 359-365.
  15. Cuzick, J., et al. 2017. Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results from Two Randomized Tamoxifen Prevention Trials. J Clin Oncol 35(7): 743-750.

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