BlueCross and BlueShield of Montana Medical Policy/Codes
Use of Common Genetic Variants to Predict Risk of Non-familial Breast Cancer
Chapter: Surgery: Procedures
Current Effective Date: October 25, 2013
Original Effective Date: August 01, 2012
Publish Date: October 25, 2013
Revised Dates: September 25, 2013

Breast cancer risk increases with age, environmental factors and genetics.  The modern era of breast cancer risk assessment began with the identification of highly penetrant mutations of BRCA1 and BRCA2 genes that is found in strong family histories.  BRCA1 and BRCA2 mutations, along with a few others, account for less than 25% of inherited breast cancers.  The majority of breast cancer occurs in women with little or no family history. 

Several common single nucleotide polymorphisms (SNPs), also known as a DNA (deoxyribonucleic acid) sequence difference in the gene, associated with breast cancer, have been identified primarily through genome-wide association studies of very large case-control populations.  The high-risk alleles (one or more forms of a gene that may express as a specific trait) occur with high frequency in the general population, although the increased breast cancer risk associated with each is very small relative to the general population risk.  Some have suggested that these common-risk SNPs could be combined to achieve an individualized risk prediction either alone or in combination with traditional predictors in order to personalize screening programs in which starting age and intensity would vary by risk.  In particular, the American Cancer Society has recommended that women at high risk (greater than a 20% lifetime risk) should undergo breast magnetic resonance imaging (BMRI) and a mammogram every year, while those at moderately increased risk (15% to 20% lifetime risk) should talk with their doctors about the benefits and limitations of adding BMRI screening to their yearly mammogram.

Several companies (see the grid below) currently offer internet-based testing for breast cancer risk profiles using SNPs.  Most of these companies offer testing direct-to-consumers (DTCs), although Navigenics (Forest City, CA) appears to now offer testing only through referred network physicians or providers.  The company does provide interested consumers with access to a network of physicians who are reported to be familiar with the company’s test profile and who utilize the test.



Test Offered DTCs

# of SNPs Used in Breast Cancer Risk Panel


Mt. View, CA, USA




Reykjavik, Iceland


deCODE BreastCancer™ – 16

deCODEme Complete Scan – 16


Elk, Grove, CA, USA


Not Described on Company Website


Dublin, Ireland




Nottingham, UK


Not Described on Company Website


Forest City, CA, USA

No – Only thru Providers

Not Described on Company Website

Pathway Genomics

San Diego, CA, USA


Not Described on Company Website

The algorithms or risk models used for all the tests identified, except for those offered by deCODE, are proprietary and not described on company websites.  In the three tests providing some information on the SNPs used for testing, these range from panels as small as six SNPs (GenePlanet) to as large as 16 SNPs (deCODE).

deCODE appears to offer two separate tests for breast cancer risk: one is the deCODE BreastCancer test and the other is part of the deCODEme Complete Scan for risk assessment of a broad assortment of 48 diseases (including heart disease, diabetes, multiple cancers, etc.).  Although in the past, these two tests appeared to use different SNP combinations for testing, both are now described as 16 SNP panels.  It is likely that the tests are the same, with the second of the two simply part of a larger assessment panel.  The deCODE BreastCancer test includes a list of SNPs used and an explanation for the simple multiplicative model applied to this list.

No test combining the results of SNPs to predict breast cancer risk has been approved or cleared by the U.S. Food and Drug Administration (FDA).  These are offered as laboratory-developed tests, that is, tests developed and used at a single testing site.  Laboratory developed tests, as a matter of enforcement discretion, have not been traditionally regulated by FDA in the past.  They do require oversight under the Clinical Laboratory Improvement Amendments of 1988 (CLIA) and the development, and use of laboratory developed tests is restricted to laboratories certified as high complexity under CLIA.


Each benefit plan, summary plan description or contract defines which services are covered, which services are excluded, and which services are subject to dollar caps or other limitations, conditions or exclusions.  Members and their providers have the responsibility for consulting the member's benefit plan, summary plan description or contract to determine if there are any exclusions or other benefit limitations applicable to this service or supply.  If there is a discrepancy between a Medical Policy and a member's benefit plan, summary plan description or contract, the benefit plan, summary plan description or contract will govern.


Genetic testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of non-familial breast cancer is considered experimental, investigational and unproven.

Policy Guidelines

There are no specific CPT or HCPCS codes available for internet-based testing for breast cancer risk profiles using SNPs.  Companies offer testing direct-to-consumers (DTCs) or through a provider to the consumer or patient.


A search of peer reviewed literature through August 2011 was completed, with the following results, for (SNP) testing for breast cancer risk assessment.

Genome-wide association studies (GWAS) examine the entire genome of each of thousands of individuals for single nucleotide polymorphisms (SNPs), single base-pair variations in the DNA sequence at semi-regular intervals, and attempt to associate variant single nucleotide polymorphism (SNP) alleles with particular diseases.  Several case-control GWAS have been carried out, primarily in white women, to investigate common risk markers of breast cancer.  In recent years, a number of SNPs associated with breast cancer have been reported at a high level of statistical significance and validated in two or more large, independent studies.  Recently SNPs associated with breast cancer risk in Asian women have been the subject of more than a half-dozen articles, although these appear exploratory.

Estimates of breast cancer risk, based on SNPs derived from large GWAS and/or from SNPs in other genes known to be associated with breast cancer, are available as laboratory-developed test services from different companies.  There is growing literature on these associations although public information on the actual models being offered commercially is sparse.  Independent determination of clinical validity in an intended use population to demonstrate clinical validity has not been performed.  There are also no studies to suggest that use of SNP-based risk assessment has any impact on health care outcomes.

No peer-reviewed reports have been published in which these commercially available breast cancer risk estimators have been compared to each other to determine if they report similar results on the same individuals specifically for breast cancer.  In July 2008, deCODE, 23andme, and Navigenics agreed to work with the Personalized Medicine Coalition (PMC) on a set of standards regarding the scientific validity of their genotyping panels; in the process test individuals were genotyped for three disease associations, but the PMC provides actual information on only one (breast cancer) with very little detail.

Since there are no published studies of commercial SNP-based breast cancer risk predictors, other published studies of the clinical usefulness of other similar combinations of SNPs as risk predictors will be considered here.  In 2008, Pharoah et al. considered a combination of seven well-validated SNPs associated with breast cancer, five of which are included in the deCODE BreastCancer test.  A model that simply multiplies the individual risks of the seven common SNPs was assumed, and would explain approximately 5% of the total genetic risk of non-familial breast cancer.  Applying the model to the population of women in the United Kingdom, the authors concluded that the risk profile provided by the seven SNPs would not provide sufficient discrimination between those who would and would not experience future breast cancer to enable individualized preventive treatment such as tamoxifen.  However, the authors did consider the effect on a population screening program that could be personalized with the results of SNP panel testing.  They concluded that no women would be included in the high-risk category (currently defined as 20% risk within the next ten years at age 40–49, according to the National Institute for Health and Clinical Excellence), and therefore none would warrant the addition of MRI screening or the consideration of more aggressive intervention on the basis of the SNP panel results.

Wacholder et al. evaluated the performance of a panel of ten SNPs with established associations with breast cancer that had, at the time of the study, been validated in at least three published GWAS.  Cases (n=5,590) and controls (n=5,998) from the National Cancer Institute’s Cancer Genetic Markers of Susceptibility GWAS of breast cancer were included in the study (women of primarily European ancestry).  The panel contained five SNPs included in the deCODE BreastCancer test.  The SNP panel was examined as a risk predictor alone and in addition to readily available components of the Gail model (minus mammographic density and diagnosis of atypical hyperplasia).  The authors found that adding the SNP panel to the Gail model resulted in slightly better stratification of a women’s risk than either the SNP panel or the Gail model alone, but that this stratification was not adequate to inform clinical practice.  For example, only 34% of the women who actually had breast cancer were actually assigned to the top 20% risk group.  The area under the curve (AUC) for the combined SNP and Gail model was 61.8% (50% is random, 100% is perfect).

Reeves et al. evaluated the performance of a panel of seven SNPs with established associations with breast cancer in a study of 10,306 women with breast cancer and 10,383 without cancer in the United Kingdom.  The risk panel also contained five SNPs included in the deCODE BreastCancer test and used a similar multiplicative approach.  Sensitivity studies were performed using only four SNPs and using ten SNPs, both demonstrating no significant change in performance.  While use of the risk score was able to show marked differences in risk between the upper quintile of patients (8.8% cumulative risk to age 70) and the lower quintile of patients (4.4%) these changes were not viewed as clinically useful when compared to patients with an estimated overall background risk of 6.3%.  Of note, simple information on patient histories; for example presence of one or two first-degree relatives with breast cancer provided equivalent or superior risk discrimination (9.1% and 15.4% respectively).

Finally, Mealiffe et al. evaluated a seven SNP panel in a nested case-control cohort of 1664 case patients and 1636 controls.  Again a multiplicative model was used and, as in the study by Wacholder et al., the genetic risk score was reviewed as a potential replacement for or add-on test to the Gail clinical risk model.  These authors employed a relatively new statistic (the net reclassification improvement [NRI]) to evaluate performance.  While they concluded that statistically significant improvements could be observed by addition of the genomic risk assessment to the Gail clinical risk assessment, they were unable to posit or demonstrate the observed changes would lead to improved clinical outcomes.  They suggested further studies were needed and that benefit might be observed by careful selection of patients (e.g., those who on Gail score analysis exhibited intermediate risk) who might comprise a priori be candidates who would benefit from enhanced or improved risk assessment.

It is assumed that many more risk factors remain to be discovered as the majority of the genetic risk of breast cancer has not been explained by known gene variants and SNPs.  One reason more genetic associations have not been found is that even large GWAS are underpowered to detect uncommon genetic variants.  For example, known, uncommon variants in the ataxia telangiectasia mutated (ATM) and cell cycle checkpoint kinase2 (CHEK2) genes have not been detected by GWAS, even though they are strongly associated with breast cancer.  Additionally, pooled GWAS data have allowed the discovery of significant SNPs that were not significant in the individual studies.

Although there are no guidelines regarding the clinical use of SNP panels for estimating breast cancer risk, the published literature is in general agreement that their use in clinical or screening settings is premature due to a lack of a more complete set of explanatory gene variants and to insufficient discriminatory power at this time.  Whether or not additional SNP studies are likely to be informative is under debate, as the study size to detect more and more rare variants becomes prohibitively large.  As the cost of whole genome sequencing continues to decrease, some predict that this will become the preferred avenue for researching risk variants.  One challenge in sorting through the growing literature on this diagnostic approach is nonstandardization and nontransparency of studies.  Janssens et al. have recently published a methods paper providing a road map for optimal reporting and an accompanying detailed article describing good reporting practices.

Recently, Bloss et al. reported on the psychological, behavioral and clinical effects of risk scanning in 3,639 subjects followed for a short-term period (mean of 5.6 months; SD [standard deviation] of 2.4 months).  These investigators evaluated the patients’ anxiety, intake of dietary fat, and exercise based on information from genomic testing.  They concluded there were no significant changes before and after testing.  They also noted no increase in the number of screening tests obtained in enrolled patients.  While more than half of patients participating in the study indicated the intent to have screening tests performed in the future, during the course of the study itself, there was no actual increase observed.


Common, SNPs have been shown to be significantly associated with breast cancer and to individually convey slightly elevated risk of breast cancer compared to the general population risk.  Panels of well-documented and validated SNPs are commercially available, with results synthesized into breast cancer risk estimate that have not been clinically validated and for which clinical utility (in spite of potential statistical predictive ability) is questionable.  The majority of these tests are commercially available as direct-to-consumer (DTC) tests.  The application of such risk panels to individual patient management or to population screening programs is premature due to the uncertain performance of these profiles in the intended use populations and the expectation that the majority of the genetic risk of breast cancer has yet to be explained by undiscovered gene variants and SNPs.  The discrimination offered by the limited genetic factors currently known is insufficient to inform clinical practice.  Therefore, the use of this testing is considered experimental, investigational and unproven.


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Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy.  They may not be all-inclusive.         

The presence or absence of procedure, service, supply, device or diagnosis codes in a Medical Policy document has no relevance for determination of benefit coverage for members or reimbursement for providers. Only the written coverage position in a medical policy should be used for such determinations.          

Benefit coverage determinations based on written Medical Policy coverage positions must include review of the member’s benefit contract or Summary Plan Description (SPD) for defined coverage vs. non-coverage, benefit exclusions, and benefit limitations such as dollar or duration caps. 

ICD-9 Codes

174.0, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.8, 174.9, 175.0, 175.9, 233.0, 238.3, 239.3, V10.3, V16.3, V82.71, V82.79, V84.01, V84.09

ICD-10 Codes

C50.011, C50.012, C50.019, C50.111, C50.112, C50.119, C50.211, C50.212, C50.219, C50.311, C50.312, C50.319, C50.411, C50.412, C50.419, C50.511, C50.512, C50.519, C50.611, C50.612, C50.619, C50.811, C50.812, C50.819, C50.911, C50.912, C50.919, C50.021, C50.022, C50.029, C50.121, C50.122, C50.129, C50.221, C50.222, C50.229, C50.321, C50.322, C50.329, C50.421, C50.422, C50.429, C50.521, C50.522, C50.529, C50.621, C50.622, C50.629, C50.821, C50.822, C50.829, C50.921, C50.922, C50.929, D05.00, D05.01, D05.02, D05.10, D05.11, D05.12, D05.80, D05.81, D05.82, D05.90, D05.91, D05.92, D48.60, D48.61, D48.62, D49.3, Z12.39, Z13.71, Z13.79, Z13.89, Z15.01, Z80.3, Z85.3

Procedural Codes: [Deleted 1/2013: 83891, 83892, 83894, 83898, 83900, 83901, 83909, 83912, 83914]
  1. Easton, D.F., Pooley, K.A., et al.  Genome-wide association study identifies novel breast cancer susceptibility loci.  Nature (2007 June 28) 447(7148):1087-93
  2. Stacey, S.N., Manolescu, A., et al.  Common variants on chromesomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer.  Nature Genetics (2007 July) 39(7):865-9.
  3. Hunter, D.J., Kraft, P., et al.  A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.  Nature Genetics (2007 July) 39(7):870-4.
  4. Gold, B., Kirchhoff, T., et al.  Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33.  Proceedings of the National Academy of Sciences of the United States (2008 March 18) 105(11):4340-5.
  5. Garcia-Closas, M., Hall, P., et al.  Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics.  PLoS Genetics (2008 April) 4(4):e1000054.
  6. Stacey, S.N., Manolescu, A., et al.  Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer.  Nature Genetics (2008 June) 40(6):703-6.
  7. Pharoah, P.D., Antoniou, A.C., et al.  Polygenes, risk prediction, and targeted prevention of breast cancer.  New England Journal of Medicine (2008 June 26) 358(26):2796-803.
  8. Mavaddat, N., Dunning, A.M., et al.  Common genetic variation in candidate genes and susceptibility to subtypes of breast cancer.  Cancer Epidemiology, Biomarkers and Prevention (2009 January) 18(1):255-9.
  9. Zheng, W., Long, J., et al.  Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1.  Nature Genetics (2009 March) 41(3):324-8.
  10. Thomas, G., Jacobs, K.B., et al.  A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1).  Nature Genetics (2009 May) 41(5):579-84.
  11. Ahmed, S., Thomas, G., et al.  Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2  Nature Genetics (2009 May) 41(5):585-90.
  12. Offit, K.  Breast cancer single-nucleotide polymorphisms: statistical significance and clinical validity.  Journal of the National Cancer Institute (2009 July 15) 101(14):973-5.
  13. Wacholder, S., Hartge, P., et al.  Performance of common genetic variants in breast-cancer risk models.  New England Journal of Medicine (2010 March 18) 362 (11):986-93.
  14. Reeves, G.K., Travis, R.C., et al.  Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci.  Journal of the American Medical Association (2010 July 28) 304(4):426-34.
  15. Mealiffe, M.E., Stokowski, R.P., et al.  Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information.  (2010 November 3) 102 (21):1618-27.
  16. Ota, I., Sakurai, A., et al.  Association between breast cancer risk and the wild-type allele of human ABC transporter ABCC11.  Anticancer Research (2010 December) 30(12):5189-94.
  17. Mong, F.Y., Liu, C.W., et al.  Association of gene polymorphisms in prolactin and its receptors with breast cancer risk in Taiwanese women.  Molecular Biology Report (2010 December 2): [epub ahead of print]. 
  18. Yu, J.C., Hsiung, C.N., et al.  Genetic variation in the genome-wide predicted response element-related suquences is associated with breast cancer development.  Breast Cancer Research (2011 January 31) 13(1):R13 [epub ahead of print].
  19. Mukherjee, N., Bhattacharya, N., et al.  Association of APC and MCC polymorphisms with increased breast cancer risk in an Indian population.  Internal Journal of Biological Markers (2011 January-March) 26 (1):43-9.
  20. Beeghly-Fadiel, A., Shu, X.O., et al.  Genetic variation in VEGF family genes and breast cancer risk: a report from the Shanghai Breast Cancer Genetics Study.  Cancer Epidemiology, Biomarkers and Prevention (2011 January) 20 (1):33-41.
  21. Ren, J., Wu, X., et al.  Lysyl oxidase 473 G>A polymorphism and breast cancer susceptibility in Chinese Han population.  DNA and Cell Biology (2011 February) 30(2):111-6.
  22. Bloss, C.S., Schork, N.J., et al.  Effects of direct-to-consumer genomewide profiling to assess disease risk.  New England Journal of Medicine (2011 February 10) 364(6):564-34.
  23. Cai, Q., Wen, W., et al.  Replication and functional genomic analyses of the breast cancer susceptibility locus at 6q25.1 generalizes its importance in women of Chinese, Japanese, and European ancestry.  Cancer Research (2011 February 15) 71 (4):1344-55.
  24. Use of Common Genetic Variants to Predict Risk of Nonfamilial Breast Cancer.  Chicago, Illinois: Blue Cross Blue Shield Association Medical Policy Reference Manual (2011 May) Medicine 2.04.63.
  25. Han, W., Woo, J.H., et al.  Common genetic variants associated with breast cancer in Korean women and differential susceptibility according to intrinsic subtype.  Cancer Epidemiology, Biomarkers and Prevention (2011 May) 20(5):793-8.
  26. Jiang, Y., Han, J., et al.  Risk of genome-wide association study newly identified genetic variants for breast cancer in Chinese women of Heilongjiang Province.  Breast Cancer Research and Treatment (2011 July) 128(1):251-7.
  27. Janssens, A.C., Ioannidis, J.P., et al.  Strengthening the reporting of genetic risk prediction studies (GRIP).  European Journal of Clinical Investigations (2011 September 4) 41(9):10004-9 [epub ahead of print].
  28. Janssens, A.C., Ioannidis, J.P., et al.  Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration.  European Journal of Clinical Investigations (2011 September 4) 41(9):1010-35 [epub ahead of print].
August 2012  New Policy. Policy created with literature search through March 2012; considered investigational
October 2013 Policy formatting and language revised.  Policy statement unchanged.
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Use of Common Genetic Variants to Predict Risk of Non-familial Breast Cancer